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Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


1

Total Sky Imager (TSI) Handbook  

SciTech Connect (OSTI)

The total sky imager (TSI) provides time series of hemispheric sky images during daylight hours and retrievals of fractional sky cover for periods when the solar elevation is greater than 10 degrees.

Morris, VR

2005-06-01T23:59:59.000Z

2

Accounting for Circumsolar and Horizon Cloud Determination Errors in Sky Image Inferral of Sky Cover  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Accounting for Circumsolar and Horizon Cloud Determination Errors in Sky Accounting for Circumsolar and Horizon Cloud Determination Errors in Sky Image Inferral of Sky Cover. C. N. Long, Pacific Northwest National Laboratory 1) Introduction In observing the cloudless sky, one can often notice that the area near the sun is whiter and brighter than the rest of the hemisphere. Additionally, even a slight haze will make a large angular area of the horizon whiter and brighter when the sun is low on the horizon. The human eye has an amazing ability to handle a range of light intensity spanning orders of magnitude. But one of the persistent problems in using sky images to infer fractional sky cover is the intensity range limitations of the camera detector. It is desirable to have bright enough images to be able to detect thin clouds, yet this often means the part of the image near the

3

Deep Sky Astronomical Image Database Project at NERSC  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Deep Sky Astronomical Image Deep Sky Astronomical Image Database Deep Sky Astronomical Image Database Key Challenges: Develop, store, analyze, and make available an astronomical image database of unprecedented depth, temporal breadth, and sky coverage, consisting of images from the seven-year span of the Palomar-Quest and Near-Earth Astroid Tracking (NEAT) transient surveys and the current Palomar Transient Factory (PTF). The database currently has over 13 million images stored on the NERSC Global Filesystem but data from the PTF are accumulating at the rate of about 105TB per year. The challenge is not only archiving the data but processing it in near-real time to observe rare and fleeting cosmic events as they happen so that experimental astronomers can be alerted. Why it Matters: The PTF will probe gaps in the transient phase space and

4

Source Catalog Data from FIRST (Faint Images of the Radio Sky at Twenty-Centimeters)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

FIRST, Faint Images of the Radio Sky at Twenty-Centimeters, is a project designed to produce the radio equivalent of the Palomar Observatory Sky Survey over 10,000 square degrees of the North Galactic Cap. Using the National Radio Astronomy Observatory's (NRAO) Very Large Array (VLA) in its B-configuration, the Survey acquired 3-minute snapshots covering a hexagonal grid using 2?7 3-MHz frequency channels centered at 1365 and 1435 MHz. The data were edited, self-calibrated, mapped, and CLEANed using an automated pipeline based largely on routines in the Astronomical Image Processing System (AIPS). A final atlas of maps is produced by coadding the twelve images adjacent to each pointing center. Source catalogs with flux densities and size information are generated from the coadded images also. The 2011 catalog is the latest version and has been tested to ensure reliability and completness. The catalog, generated from the 1993 through 2004 images, contains 816,000 sources and covers more than 9000 square degrees. A specialized search interface for the catalog resides at this website, and the catalog is also available as a compressed ASCII file. The user may also view earlier versions of the source catalog. The FIRST survey area was chosen to coincide with that of the Sloan Digital Sky Survey (SDSS); at the m(v)~24 limit of SDSS, ~50% of the optical counterparts to FIRST sources will be detected.

Becker, Robert H.; Helfand, David J.; White, Richard L.; Gregg, Michael D.; Laurent-Muehleisen, Sally A.

5

EXIST A High Sensitivity Hard X-ray Imaging Sky Survey Mission for ISS  

E-Print Network [OSTI]

A deep all-sky imaging hard x-ray survey and wide-field monitor is needed to extend soft (ROSAT) and medium (ABRIXAS2) x-ray surveys into the 10-100 keV band (and beyond) at comparable sensitivity (~0.05 mCrab). This would enable discovery and study of >3000 obscured AGN, which probably dominate the hard x-ray background; detailed study of spectra and variability of accreting black holes and a census of BHs in the Galaxy; Gamma-ray bursts and associated massive star formation (PopIII) at very high redshift and Soft Gamma-ray Repeaters throughout the Local Group; and a full galactic survey for obscured supernova remnants. The Energetic X-ray Imaging Survey Telescope (EXIST) is a proposed array of 8 x 1m^2 coded aperture telescopes fixed on the International Space Station (ISS) with 160deg x 40deg field of view which images the full sky each 90 min orbit. EXIST has been included in the most recent NASA Strategic Plan as a candidate mission for the next decade. An overview of the science goals and mission concep...

Grindlay, J; Chakraborty, D; Elvis, M; Fabian, A C; Fiore, F; Gehrels, N; Hailey, C J; Harrison, F; Hartmann, D; Prince, T A; Ramsey, B; Rothschild, R; Skinner, G K; Woosley, S

1999-01-01T23:59:59.000Z

6

Astronomical Images from the Very Large Array (VLA) FIRST Survey Images from the STScI Archive (Faint Images of the Radio Sky at Twenty-cm)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

FIRST, Faint Images of the Radio Sky at Twenty-Centimeters was a project designed to produce the radio equivalent of the Palomar Observatory Sky Survey over 10,000 square degrees of the North Galactic Cap. Using the National Radio Astronomy Observatory (NRAO) Very Large Array (VLA) in its B-configuration, the Survey acquired 3-minute snapshots covering a hexagonal grid. The binary data are available in detailed source catalogs, but the full images themselves, developed through special techniques, are also available for public access. Note that the images are fairly large, typically 1150x1550 pixels. Access to the images is simple through the search interface; the images are also available via anonymous ftp at ftp://archive.stsci.edu/pub/vla_first/data. Another convenient way to obtain images is through the FIRST Cutout Server, which allows an image section to be extracted from the coadded image database at a user-specified position. The cutout server is also linked to the FIRST Search Engine, so that the catalog can be searched for sources of interest and then images can be obtained for those objects. All images taken through 2011 are available through the cutout server at http://third.ucllnl.org/cgi-bin/firstcutout.

7

Discovery of Four Doubly Imaged Quasar Lenses from the Sloan Digital Sky Survey  

E-Print Network [OSTI]

We report the discovery of four doubly imaged quasar lenses. All the four systems are selected as lensed quasar candidates from the Sloan Digital Sky Survey data. We confirm their lensing hypothesis with additional imaging and spectroscopic follow-up observations. The discovered lenses are SDSS J0743+2457 with the source redshift z_s=2.165, the lens redshift z_l=0.381, and the image separation theta=1.034", SDSS J1128+2402 with z_s=1.608 and theta=0.844", SDSS J1405+0959 with z_s=1.810, z_l~0.66, and theta=1.978", and SDSS J1515+1511 with z_s=2.054, z_l=0.742, and theta=1.989". It is difficult to estimate the lens redshift of SDSS J1128+2402 from the current data. Two of the four systems (SDSS J1405+0959 and SDSS J1515+1511) are included in our final statistical lens sample to derive constraints on dark energy and the evolution of massive galaxies.

Inada, Naohisa; Rusu, Cristian E; Kayo, Issha; Morokuma, Tomoki

2014-01-01T23:59:59.000Z

8

Data Catalogs based on Images from FIRST, Faint Images of the Radio Sky at Twenty-Centimeters, from the Very Large Array (VLA) First Survey  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

FIRST, Faint Images of the Radio Sky at Twenty-cm, is a project designed to produce the radio equivalent of the Palomar Observatory Sky Survey over 10,000 square degrees of the North Galactic Cap. Using the National Radio Astronomy Observatory (NRAO) Very Large Array (VLA) in its B-configuration, the Survey acquired 3-minute snapshots covering a hexagonal grid using 27 3-MHz frequency channels centered at 1365 and 1435 MHz. The data were edited, self-calibrated, mapped, and cleaned using an automated pipeline based largely on routines in the Astronomical Image Processing System (AIPS). Data were collected from 1993 through 2002, with enhanced images produced up through 2011. The Data Catalogs have been cleaned and reissued over time, with the latest version coming out in March, 2014. They contain maps, images, and binary data. The FIRST survey area was chosen to coincide with that of the Sloan Digital Sky Survey (SDSS); at the m(v)~24 limit of SDSS, ~50% of the optical counterparts to FIRST sources will be detected.

Becker, Robert H.; Helfand, David J.; White, Richard L.; Gregg, Michael D.; Laurent-Muehleisen, Sally A.

9

Validation of MODIS-Retrieved Cloud Fractions Using Whole Sky Imager Measurements at the Three ARM Sites  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

MODIS-Retrieved Cloud Fractions Using MODIS-Retrieved Cloud Fractions Using Whole Sky Imager Measurements at the Three ARM Sites Z. Li, M. C. Cribb, and F.-L. Chang Earth System Science Interdisciplinary Center University of Maryland College Park, Maryland A. P. Trishchenko Canada Centre for Remote Sensing Ottawa, Ontario, Canada Introduction Given the importance of clouds in modulating the surface energy budget, it is critical to obtain accurate estimates of their fractional amount in the atmospheric column for use in modeling studies. Satellite remote sensing of cloud properties such as cloud amount has the advantage of providing global coverage on a regular basis. Ground-based surveys of cloud fraction offer a practical database for use in determining the accuracy of these remotely sensed estimates of cloud fraction on a regional scale.

10

COBE Sky Map  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

COBE sky map COBE sky map This map of the ancient sky shows the minute variations in the microwave background discovered by the team led by Lawrence Berkeley Laboratory astrophysicist George Smoot. As seen in the map, vast regions of space have minute variations in temperature. Over billions of years, gravity magnified these small differences into the clusters of galaxies we observe today. Displayed horizontally across the middle of the map is the Milky Way galaxy. The image, a 360-degree map of the whole sky, shows the relic radiation from the Big Bang. The map was derived from one year of data taken by the Differential Microwave Radiometers onboard NASA's Cosmic Background Explorer satellite. Using Galactic coordinates, the map shows the plane of the Milky Way galaxy horizontally and the center of our galaxy at its

11

Total  

Gasoline and Diesel Fuel Update (EIA)

Total Total .............. 16,164,874 5,967,376 22,132,249 2,972,552 280,370 167,519 18,711,808 1993 Total .............. 16,691,139 6,034,504 22,725,642 3,103,014 413,971 226,743 18,981,915 1994 Total .............. 17,351,060 6,229,645 23,580,706 3,230,667 412,178 228,336 19,709,525 1995 Total .............. 17,282,032 6,461,596 23,743,628 3,565,023 388,392 283,739 19,506,474 1996 Total .............. 17,680,777 6,370,888 24,051,665 3,510,330 518,425 272,117 19,750,793 Alabama Total......... 570,907 11,394 582,301 22,601 27,006 1,853 530,841 Onshore ................ 209,839 11,394 221,233 22,601 16,762 1,593 180,277 State Offshore....... 209,013 0 209,013 0 10,244 260 198,509 Federal Offshore... 152,055 0 152,055 0 0 0 152,055 Alaska Total ............ 183,747 3,189,837 3,373,584 2,885,686 0 7,070 480,828 Onshore ................ 64,751 3,182,782

12

Automated Alignment and On-Sky Performance of the Gemini Planet Imager Coronagraph  

E-Print Network [OSTI]

The Gemini Planet Imager (GPI) is a next-generation, facility instrument currently being commissioned at the Gemini South observatory. GPI combines an extreme adaptive optics system and integral field spectrograph (IFS) with an apodized-pupil Lyot coronagraph (APLC) producing an unprecedented capability for directly imaging and spectroscopically characterizing extrasolar planets. GPI's operating goal of $10^{-7}$ contrast requires very precise alignments between the various elements of the coronagraph (two pupil masks and one focal plane mask) and active control of the beam path throughout the instrument. Here, we describe the techniques used to automatically align GPI and maintain the alignment throughout the course of science observations. We discuss the particular challenges of maintaining precision alignments on a Cassegrain mounted instrument and strategies that we have developed that allow GPI to achieve high contrast even in poor seeing conditions.

Savransky, Dmitry; Poyneer, Lisa A; Dunn, Jennifer; Macintosh, Bruce A; Sadakuni, Naru; Dillon, Daren; Goodsell, Stephen J; Hartung, Markus; Hibon, Pascale; Rantakyrö, Fredrik; Cardwell, Andrew; Serio, Andrew

2014-01-01T23:59:59.000Z

13

Total............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Total................................................................... Total................................................................... 111.1 2,033 1,618 1,031 791 630 401 Total Floorspace (Square Feet) Fewer than 500............................................... 3.2 357 336 113 188 177 59 500 to 999....................................................... 23.8 733 667 308 343 312 144 1,000 to 1,499................................................. 20.8 1,157 1,086 625 435 409 235 1,500 to 1,999................................................. 15.4 1,592 1,441 906 595 539 339 2,000 to 2,499................................................. 12.2 2,052 1,733 1,072 765 646 400 2,500 to 2,999................................................. 10.3 2,523 2,010 1,346 939 748 501 3,000 to 3,499................................................. 6.7 3,020 2,185 1,401 1,177 851 546

14

Total...................  

Gasoline and Diesel Fuel Update (EIA)

4,690,065 52,331,397 2,802,751 4,409,699 7,526,898 209,616 1993 Total................... 4,956,445 52,535,411 2,861,569 4,464,906 7,981,433 209,666 1994 Total................... 4,847,702 53,392,557 2,895,013 4,533,905 8,167,033 202,940 1995 Total................... 4,850,318 54,322,179 3,031,077 4,636,500 8,579,585 209,398 1996 Total................... 5,241,414 55,263,673 3,158,244 4,720,227 8,870,422 206,049 Alabama ...................... 56,522 766,322 29,000 62,064 201,414 2,512 Alaska.......................... 16,179 81,348 27,315 12,732 75,616 202 Arizona ........................ 27,709 689,597 28,987 49,693 26,979 534 Arkansas ..................... 46,289 539,952 31,006 67,293 141,300 1,488 California ..................... 473,310 8,969,308 235,068 408,294 693,539 36,613 Colorado...................... 110,924 1,147,743

15

BME BIOMEDICAL IMAGING CONCENTRATION F12 MS: 30 total credit hours minimum  

E-Print Network [OSTI]

BME BIOMEDICAL IMAGING CONCENTRATION ­ F12 MS: 30 total credit hours minimum Advisor: Luis Hernandez-Garcia, Ph.D. (hernan@umich.edu) Biomedical Imaging: BIOMEDE 5161 Medical Imaging Systems (3) (I)2 General: BIOMEDE 500 Biomedical Engineering Seminar (1) (I,II) BIOMEDE 550 Ethics and Enterprise (1) (I

Kamat, Vineet R.

16

BME BIOMEDICAL IMAGING CONCENTRATION F11 MS: 30 total credit hours minimum  

E-Print Network [OSTI]

BME BIOMEDICAL IMAGING CONCENTRATION ­ F11 MS: 30 total credit hours minimum Advisor: Luis Hernandez-Garcia, Ph.D. (hernan@umich.edu) Biomedical Imaging: BIOMEDE 5161 Medical Imaging Systems (3) (I)2 General: BIOMEDE 500 Biomedical Engineering Seminar (1) (I,II) BIOMEDE 550 Ethics and Enterprise (1) (I

Eustice, Ryan

17

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

7.1 7.1 19.0 22.7 22.3 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 2.1 0.6 Q 0.4 500 to 999........................................................... 23.8 13.6 3.7 3.2 3.2 1,000 to 1,499..................................................... 20.8 9.5 3.7 3.4 4.2 1,500 to 1,999..................................................... 15.4 6.6 2.7 2.5 3.6 2,000 to 2,499..................................................... 12.2 5.0 2.1 2.8 2.4 2,500 to 2,999..................................................... 10.3 3.7 1.8 2.8 2.1 3,000 to 3,499..................................................... 6.7 2.0 1.4 1.7 1.6 3,500 to 3,999..................................................... 5.2 1.6 0.8 1.5 1.4 4,000 or More.....................................................

18

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

0.7 0.7 21.7 6.9 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.6 Q Q 500 to 999........................................................... 23.8 9.0 4.2 1.5 3.2 1,000 to 1,499..................................................... 20.8 8.6 4.7 1.5 2.5 1,500 to 1,999..................................................... 15.4 6.0 2.9 1.2 1.9 2,000 to 2,499..................................................... 12.2 4.1 2.1 0.7 1.3 2,500 to 2,999..................................................... 10.3 3.0 1.8 0.5 0.7 3,000 to 3,499..................................................... 6.7 2.1 1.2 0.5 0.4 3,500 to 3,999..................................................... 5.2 1.5 0.8 0.3 0.4 4,000 or More.....................................................

19

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

25.6 25.6 40.7 24.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.9 1.0 500 to 999........................................................... 23.8 4.6 3.9 9.0 6.3 1,000 to 1,499..................................................... 20.8 2.8 4.4 8.6 5.0 1,500 to 1,999..................................................... 15.4 1.9 3.5 6.0 4.0 2,000 to 2,499..................................................... 12.2 2.3 3.2 4.1 2.6 2,500 to 2,999..................................................... 10.3 2.2 2.7 3.0 2.4 3,000 to 3,499..................................................... 6.7 1.6 2.1 2.1 0.9 3,500 to 3,999..................................................... 5.2 1.1 1.7 1.5 0.9 4,000 or More.....................................................

20

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

4.2 4.2 7.6 16.6 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 1.0 0.2 0.8 500 to 999........................................................... 23.8 6.3 1.4 4.9 1,000 to 1,499..................................................... 20.8 5.0 1.6 3.4 1,500 to 1,999..................................................... 15.4 4.0 1.4 2.6 2,000 to 2,499..................................................... 12.2 2.6 0.9 1.7 2,500 to 2,999..................................................... 10.3 2.4 0.9 1.4 3,000 to 3,499..................................................... 6.7 0.9 0.3 0.6 3,500 to 3,999..................................................... 5.2 0.9 0.4 0.5 4,000 or More.....................................................

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Total.........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Floorspace (Square Feet) Floorspace (Square Feet) Total Floorspace 2 Fewer than 500.................................................. 3.2 Q 0.8 0.9 0.8 0.5 500 to 999.......................................................... 23.8 1.5 5.4 5.5 6.1 5.3 1,000 to 1,499.................................................... 20.8 1.4 4.0 5.2 5.0 5.2 1,500 to 1,999.................................................... 15.4 1.4 3.1 3.5 3.6 3.8 2,000 to 2,499.................................................... 12.2 1.4 3.2 3.0 2.3 2.3 2,500 to 2,999.................................................... 10.3 1.5 2.3 2.7 2.1 1.7 3,000 to 3,499.................................................... 6.7 1.0 2.0 1.7 1.0 1.0 3,500 to 3,999.................................................... 5.2 0.8 1.5 1.5 0.7 0.7 4,000 or More.....................................................

22

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

. . 111.1 20.6 15.1 5.5 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.4 500 to 999........................................................... 23.8 4.6 3.6 1.1 1,000 to 1,499..................................................... 20.8 2.8 2.2 0.6 1,500 to 1,999..................................................... 15.4 1.9 1.4 0.5 2,000 to 2,499..................................................... 12.2 2.3 1.7 0.5 2,500 to 2,999..................................................... 10.3 2.2 1.7 0.6 3,000 to 3,499..................................................... 6.7 1.6 1.0 0.6 3,500 to 3,999..................................................... 5.2 1.1 0.9 0.3 4,000 or More.....................................................

23

Total..........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

7.1 7.1 7.0 8.0 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.4 Q Q 0.5 500 to 999........................................................... 23.8 2.5 1.5 2.1 3.7 1,000 to 1,499..................................................... 20.8 1.1 2.0 1.5 2.5 1,500 to 1,999..................................................... 15.4 0.5 1.2 1.2 1.9 2,000 to 2,499..................................................... 12.2 0.7 0.5 0.8 1.4 2,500 to 2,999..................................................... 10.3 0.5 0.5 0.4 1.1 3,000 to 3,499..................................................... 6.7 0.3 Q 0.4 0.3 3,500 to 3,999..................................................... 5.2 Q Q Q Q 4,000 or More.....................................................

24

Total..........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

.. .. 111.1 24.5 1,090 902 341 872 780 441 Total Floorspace (Square Feet) Fewer than 500...................................... 3.1 2.3 403 360 165 366 348 93 500 to 999.............................................. 22.2 14.4 763 660 277 730 646 303 1,000 to 1,499........................................ 19.1 5.8 1,223 1,130 496 1,187 1,086 696 1,500 to 1,999........................................ 14.4 1.0 1,700 1,422 412 1,698 1,544 1,348 2,000 to 2,499........................................ 12.7 0.4 2,139 1,598 Q Q Q Q 2,500 to 2,999........................................ 10.1 Q Q Q Q Q Q Q 3,000 or More......................................... 29.6 0.3 Q Q Q Q Q Q Heated Floorspace (Square Feet) None...................................................... 3.6 1.8 1,048 0 Q 827 0 407 Fewer than 500......................................

25

Total...................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

2,033 2,033 1,618 1,031 791 630 401 Total Floorspace (Square Feet) Fewer than 500............................................... 3.2 357 336 113 188 177 59 500 to 999....................................................... 23.8 733 667 308 343 312 144 1,000 to 1,499................................................. 20.8 1,157 1,086 625 435 409 235 1,500 to 1,999................................................. 15.4 1,592 1,441 906 595 539 339 2,000 to 2,499................................................. 12.2 2,052 1,733 1,072 765 646 400 2,500 to 2,999................................................. 10.3 2,523 2,010 1,346 939 748 501 3,000 to 3,499................................................. 6.7 3,020 2,185 1,401 1,177 851 546 3,500 to 3,999................................................. 5.2 3,549 2,509 1,508

26

Total...........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................... 3.2 1.9 0.9 Q Q Q 1.3 2.3 500 to 999........................................... 23.8 10.5 7.3 3.3 1.4 1.2 6.6 12.9 1,000 to 1,499..................................... 20.8 5.8 7.0 3.8 2.2 2.0 3.9 8.9 1,500 to 1,999..................................... 15.4 3.1 4.2 3.4 2.0 2.7 1.9 5.0 2,000 to 2,499..................................... 12.2 1.7 2.7 2.9 1.8 3.2 1.1 2.8 2,500 to 2,999..................................... 10.3 1.2 2.2 2.3 1.7 2.9 0.6 2.0 3,000 to 3,499..................................... 6.7 0.9 1.4 1.5 1.0 1.9 0.4 1.4 3,500 to 3,999..................................... 5.2 0.8 1.2 1.0 0.8 1.5 0.4 1.3 4,000 or More...................................... 13.3 0.9 1.9 2.2 2.0 6.4 0.6 1.9 Heated Floorspace

27

Total...........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

14.7 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500.................................... 3.2 0.7 Q 0.3 0.3 0.7 0.6 0.3 Q 500 to 999........................................... 23.8 2.7 1.4 2.2 2.8 5.5 5.1 3.0 1.1 1,000 to 1,499..................................... 20.8 2.3 1.4 2.4 2.5 3.5 3.5 3.6 1.6 1,500 to 1,999..................................... 15.4 1.8 1.4 2.2 2.0 2.4 2.4 2.1 1.2 2,000 to 2,499..................................... 12.2 1.4 0.9 1.8 1.4 2.2 2.1 1.6 0.8 2,500 to 2,999..................................... 10.3 1.6 0.9 1.1 1.1 1.5 1.5 1.7 0.8 3,000 to 3,499..................................... 6.7 1.0 0.5 0.8 0.8 1.2 0.8 0.9 0.8 3,500 to 3,999..................................... 5.2 1.1 0.3 0.7 0.7 0.4 0.5 1.0 0.5 4,000 or More...................................... 13.3

28

Total................................................  

U.S. Energy Information Administration (EIA) Indexed Site

.. .. 111.1 86.6 2,522 1,970 1,310 1,812 1,475 821 1,055 944 554 Total Floorspace (Square Feet) Fewer than 500............................. 3.2 0.9 261 336 162 Q Q Q 334 260 Q 500 to 999.................................... 23.8 9.4 670 683 320 705 666 274 811 721 363 1,000 to 1,499.............................. 20.8 15.0 1,121 1,083 622 1,129 1,052 535 1,228 1,090 676 1,500 to 1,999.............................. 15.4 14.4 1,574 1,450 945 1,628 1,327 629 1,712 1,489 808 2,000 to 2,499.............................. 12.2 11.9 2,039 1,731 1,055 2,143 1,813 1,152 Q Q Q 2,500 to 2,999.............................. 10.3 10.1 2,519 2,004 1,357 2,492 2,103 1,096 Q Q Q 3,000 or 3,499.............................. 6.7 6.6 3,014 2,175 1,438 3,047 2,079 1,108 N N N 3,500 to 3,999.............................. 5.2 5.1 3,549 2,505 1,518 Q Q Q N N N 4,000 or More...............................

29

Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed  

E-Print Network [OSTI]

using numerical weather prediction (NWP) and satellite cloudimpossible with satellites or numerical weather prediction.

2011-01-01T23:59:59.000Z

30

Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed  

E-Print Network [OSTI]

solener.2011.02.014, Solar Energy. Lave, M. , Kleissl, J. ,smoothing. Submitted to Solar Energy. Linke, F. , 1922.24th European Photovoltaic Solar Energy Conference, Hamburg,

2011-01-01T23:59:59.000Z

31

Total electron and proton energy input during auroral substorms: Remote sensing with IMAGE-FUV  

E-Print Network [OSTI]

, it is found that the most critical factor is the assumption made on the energy of the auroral protonsTotal electron and proton energy input during auroral substorms: Remote sensing with IMAGE-FUV B and proton energy fluxes. The proton energy flux is derived from the Lyman a measurements on the basis

California at Berkeley, University of

32

Cloudy Skies  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

J. linn J. linn Space Science and Technology Division Los Alamos National Laboratory iLos Alamos, NM 87545 The earth's weather and climate are influenced strongly by phenomena associated with clouds. Therefore, a general circulation model (GCM) that models the evolution of weather and climate must include an accurate physical model of the clouds. This paper describes our efforts to develop a suitable cloud model. It concentrates on the microphysical processes that determine the evolution of droplet and ice crystal size distributions, precipitation rates, total and condensed water content, and radiative extinction coefficients. We assume a fixed temperature, acloud vertical thickness, and concentrations and size distributions of cloud condensation nuclei (CCN) and ice condensation nuclei

33

Ground Water Ground Sky Sky Water Vegetation Ground Vegetation Water  

E-Print Network [OSTI]

Bear Snow Vegetation RhinoWater Vegetation Ground Water Ground Sky Sky Rhino Water Vegetation Ground Vegetation Water Rhino Water Vegetation Ground Rhino Water Rhino Water Ground Ground Vegetation Water Rhino Vegetation Rhino Vegetation Ground Rhino Vegetation Ground Sky Rhino Vegetation Ground Sky

Chen, Tsuhan

34

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization  

SciTech Connect (OSTI)

Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical properties of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ?14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.

Dong, Xue; Niu, Tianye; Zhu, Lei, E-mail: leizhu@gatech.edu [Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States)] [Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States)

2014-05-15T23:59:59.000Z

35

Hacking the Sky  

E-Print Network [OSTI]

In this article I present some special astronomical scripts created for Google Earth, Google Sky and Twitter. These 'hacks' are examples of the ways in which such tools can be used either alone, in on conjunction with online services. The result of a combination of multiple, online services to form a new facility is called a mash-up. Some of what follows falls into that definition. As we move into an era of online data and tools, it is the network as a whole that becomes important. Tools emerging from this network can be capable of more than the sum of their parts.

Simpson, R J

2009-01-01T23:59:59.000Z

36

Global horizontal irradiance clear sky models : implementation and analysis.  

SciTech Connect (OSTI)

Clear sky models estimate the terrestrial solar radiation under a cloudless sky as a function of the solar elevation angle, site altitude, aerosol concentration, water vapor, and various atmospheric conditions. This report provides an overview of a number of global horizontal irradiance (GHI) clear sky models from very simple to complex. Validation of clear-sky models requires comparison of model results to measured irradiance during clear-sky periods. To facilitate validation, we present a new algorithm for automatically identifying clear-sky periods in a time series of GHI measurements. We evaluate the performance of selected clear-sky models using measured data from 30 different sites, totaling about 300 site-years of data. We analyze the variation of these errors across time and location. In terms of error averaged over all locations and times, we found that complex models that correctly account for all the atmospheric parameters are slightly more accurate than other models, but, primarily at low elevations, comparable accuracy can be obtained from some simpler models. However, simpler models often exhibit errors that vary with time of day and season, whereas the errors for complex models vary less over time.

Stein, Joshua S.; Hansen, Clifford W.; Reno, Matthew J.

2012-03-01T23:59:59.000Z

37

Sky Train Corp | Open Energy Information  

Open Energy Info (EERE)

Train Corp Jump to: navigation, search Name: Sky Train Corp. Place: Palm Harbor, Florida Zip: 34684 Sector: Services Product: Sky Train Corporation is a consultant company...

38

The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III  

SciTech Connect (OSTI)

The Sloan Digital Sky Survey (SDSS) started a new phase in August 2008, with new instrumentation and new surveys focused on Galactic structure and chemical evolution, measurements of the baryon oscillation feature in the clustering of galaxies and the quasar Ly{alpha} forest, and a radial velocity search for planets around {approx}8000 stars. This paper describes the first data release of SDSS-III (and the eighth counting from the beginning of the SDSS). The release includes 5-band imaging of roughly 5200 deg{sup 2} in the Southern Galactic Cap, bringing the total footprint of the SDSS imaging to 14,555 deg{sup 2}, or over a third of the Celestial Sphere. All the imaging data have been reprocessed with an improved sky-subtraction algorithm and a final, self-consistent recalibration and flat-field determination. This release also includes all data from the second phase of the Sloan Extension for Galactic Understanding and Evolution (SEGUE-2), consisting of spectroscopy of approximately 118,000 stars at both high and low Galactic latitudes. All the more than half a million stellar spectra obtained with the SDSS spectrograph have been reprocessed through an improved stellar parameters pipeline, which has better determination of metallicity for high metallicity stars.

Aihara, Hiroaki; /Tokyo U.; Prieto, Carlos Allende; /Laguna U., Tenerife; An, Deokkeun; /Ewha Women's U., Seoul; Anderson, Scott F.; /Washington U., Seattle, Astron. Dept.; Aubourg, Eric; /APC, Paris /DAPNIA, Saclay; Balbinot, Eduardo; /Rio Grande do Sul U. /Rio de Janeiro Observ.; Beers, Timothy C.; /Michigan State U.; Berlind, Andreas A.; /Vanderbilt U.; Bickerton, Steven J.; /Princeton U.; Bizyaev, Dmitry; /Apache Point Observ.; Blanton, Michael R.; /New York U., CCPP /Penn State U.

2011-01-01T23:59:59.000Z

39

Sky Cover from MFRSR Observations  

SciTech Connect (OSTI)

The diffuse all-sky surface irradiances measured at two nearby wavelengths in the visible spectral range and their model clear-sky counterparts are two main components of a new method for estimating the fractional sky cover of different cloud types, including cumulus clouds. The performance of this method is illustrated using 1-min resolution data from ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR). The MFRSR data are collected at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site during the summer of 2007 and represent 13 days with cumulus clouds. Good agreement is obtained between estimated values of the fractional sky cover and those provided by a well-established independent method based on broadband observations.

Kassianov, Evgueni I.; Barnard, James C.; Berg, Larry K.; Flynn, Connor J.; Long, Charles N.

2011-07-01T23:59:59.000Z

40

GLAST Observatory Renamed for Fermi, Reveals Entire Gamma-Ray Sky |  

Broader source: Energy.gov (indexed) [DOE]

GLAST Observatory Renamed for Fermi, Reveals Entire Gamma-Ray Sky GLAST Observatory Renamed for Fermi, Reveals Entire Gamma-Ray Sky GLAST Observatory Renamed for Fermi, Reveals Entire Gamma-Ray Sky August 26, 2008 - 3:20pm Addthis WASHINGTON, D.C. - The U.S. Department of Energy (DOE) and NASA announced today that the Gamma-Ray Large Area Space Telescope (GLAST) has revealed its first all-sky map in gamma rays. The onboard Large Area Telescope's (LAT) all-sky image-which shows the glowing gas of the Milky Way, blinking pulsars and a flaring galaxy billions of light-years away-was created using only 95 hours of "first light" observations, compared with past missions which took years to produce a similar image. Scientists expect the telescope will discover many new pulsars in our own galaxy, reveal powerful

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

RECIPIENT:SkyFuel, Inc.  

Broader source: Energy.gov (indexed) [DOE]

SkyFuel, Inc. SkyFuel, Inc. u.s. DEPARTl.\IIEN T OF ENER qY EERE PROJECT MANAGEMENT CENTER NEPA DETERlvIINATION Page 1 of2 STATE: CO PROJECT TITL E: SkyFuel 8aseload Parabolic Trough Funding Opportunity Announcement Number Procurement I.nstrument N mber NEPA Control Number CID Number Baseload DE-EEOO03584 GFO-OOO3584-002 G03584 Based on my review oftbe information concerning the proposed achon, as NEP] Compliance Officer (authorized under DOE Order 4Sl.tA), I have made the followmg determmatlOn: ex, EA, EIS APPENDIX AND NUMBER: Descnptlon : 83.6 Small-scale Sltmg, construction, modification, operation, and de mmlSSlonlng of faCilities for smaliscale research research and and development projects; conventionallaboralory 0 rations (such as preparation of chemical

42

Sky Volt | Open Energy Information  

Open Energy Info (EERE)

Volt Volt Jump to: navigation, search Name Sky Volt Facility Sky Volt Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Sky Volt LLC (community owned) Energy Purchaser City of Greenfield - excess to Central Iowa Power Cooperative Location Greenfield IA Coordinates 41.29038343°, -94.48851585° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.29038343,"lon":-94.48851585,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

43

NERSC's Deep Sky Project Provides a Portal into Data Universe - NERSC  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Deep Sky Project Deep Sky Project Provides a Portal into Data Universe Deep Sky Project Provides a Portal into Data Universe March 30, 2009 STARLIGHT: This image of the Coma cluster was made by combining over 500 images collected between 2001 and 2007. Every night approximately 3,000 astronomical files flow to the National Energy Research Scientific Computing (NERSC) Center from automated sky scanning systems all over the world for archiving. After a decade of collecting, the center currently holds over 8 million images, making this one of the largest troves of ground-based celestial images available. Now, a multidisciplinary team of astronomers, computer scientists, and engineers from NERSC are collaborating to develop a user-friendly database system and interface to instantly serve up high-resolution cosmic reference

44

Red Sky with Red Mesa  

ScienceCinema (OSTI)

The Red Sky/Red Mesa supercomputing platform dramatically reduces the time required to simulate complex fuel models, from 4-6 months to just 4 weeks, allowing researchers to accelerate the pace at which they can address these complex problems. Its speed also reduces the need for laboratory and field testing, allowing for energy reduction far beyond data center walls.

None

2014-06-23T23:59:59.000Z

45

One Sky Homes | Open Energy Information  

Open Energy Info (EERE)

Homes Jump to: navigation, search Name: One Sky Homes Place: Los Gatos, CA Website: http:www.oneskyhomes.com References: One Sky Homes1 Information About Partnership with NREL...

46

Green Skies of Brazil |GE Global Research  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Green Skies of Brazil Green Skies of Brazil Lucas Malta 2014.08.28 Not every professional gets to see on a daily basis the impact of herhis work on other people's lives. If you...

47

ARM: Fractional cloud cover, clear-sky and all-sky shortwave flux for each of 25 individual SGP facilities.  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Fractional cloud cover, clear-sky and all-sky shortwave flux for each of 25 individual SGP facilities.

Gaustad, Krista; Gaustad, Krista; McFarlane, Sally; McFarlane, Sally

48

Blue Sky Batteries Inc | Open Energy Information  

Open Energy Info (EERE)

Batteries Inc Jump to: navigation, search Name: Blue Sky Batteries Inc Place: Laramie, Wyoming Zip: 82072-3 Product: Nanoengineers materials for rechargeable lithium batteries....

49

The Cosmic Lens All-Sky Survey: I. Source selection and observations  

E-Print Network [OSTI]

The Cosmic Lens All-Sky Survey (CLASS) is an international collaborative program which has obtained high-resolution radio images of over 10000 flat-spectrum radio sources in order to create the largest and best studied statistical sample of radio-loud gravitationally lensed systems. With this survey, combined with detailed studies of the lenses found therein, constraints can be placed on the expansion rate, matter density, and dark energy (e.g. cosmological constant, quintessence) content of the Universe that are complementary to and independent of those obtained through other methods. CLASS is aimed at identifying lenses where multiple images are formed from compact flat-spectrum radio sources, which should be easily identifiable in the radio maps. Because CLASS is radio-based, dust obscuration in lensing galaxies is not a factor, and the relative insensitivity of the instrument to environmental conditions leads to nearly uniform sensitivity and resolution over the entire survey. In four observing seasons from 1994-1999, CLASS has observed 13783 radio sources with the VLA at 8.4 GHz at 0.2 arcsecond resolution. When combined with the JVAS survey, the CLASS sample contains over 16,000 images. A complete sample of 11685 flat-spectrum sources was observed, selected from GB6 catalogue at 4.85 GHz and the NVSS at 1.4 GHz. So far, CLASS has found 16 new gravitational lens systems, and the JVAS/CLASS survey contains a total of 22 lenses. (Abridged)

S. T. Myers; N. J. Jackson; I. W. A. Browne; A. G. de Bruyn; T. J. Pearson; A. C. S. Readhead; P. N. Wilkinson; A. D. Biggs; R. D. Blandford; C. D. Fassnacht; L. V. E. Koopmans; D. R. Marlow; J. P. McKean; M. A. Norbury; P. M. Phillips; D. Rusin; M. C. Shepherd; C. M. Sykes

2002-11-05T23:59:59.000Z

50

Sky Vegetables | Open Energy Information  

Open Energy Info (EERE)

Vegetables Vegetables Jump to: navigation, search Name Sky Vegetables Address 45 Rosemary Street, Suite F Place Needham, MA Zip 02494 Sector Solar Website http://www.skyvegetables.com/i Coordinates 42.2882945°, -71.2335259° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.2882945,"lon":-71.2335259,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

51

Einstein and the Daytime Sky - D  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

D. Fun with polarizers D. Fun with polarizers In one respect, Einstein's mathematical analysis (like Rayleigh's earlier one) proves quite accurate, in a way that's easy to demonstrate. This has to do with how the sky's scattered light is polarized. Try looking at a patch of clear sky through one lens of a pair of polarizing sunglasses while you rotate the lens. You'll notice that the sky looks brighter as you look through the lens in some positions, and darker when the lens is in other positions. If the sun is not far from the patch of sky you're looking at, you'll find that the sky looks brightest when the sun is to the left or right of the lens, and darkest when the sun is "above the top" or "below the bottom" of the lens. Why is this? Any kind of wave-whether sound wave, water wave, light wave-is associated

52

Image Resources  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Mosaic of earth and sky images Mosaic of earth and sky images Image Resources Free image resources covering energy, environment, and general science. Here are some links to energy- and environment-related photographic databases. Berkeley Lab Photo Archive Berkeley Lab's online digital image collection. National Science Digital Library (NSDL) NSDL is the Nation's online library for education and research in science, technology, engineering, and mathematics. The World Bank Group Photo Library A distinctive collection of over 11,000 images that illustrate development through topics such as Agriculture, Education, Environment, Health, Trade and more. Calisphere Compiles the digital collections of libraries, museums, and cultural heritage organizations across California, and organizes them by theme, such

53

Carolina Blue Skies & Green Jobs Initiative | Department of Energy  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

arravt064tiboyer2012o.pdf More Documents & Publications Carolinas Blue Skies & Green Jobs Initiative Carolina Blue Skies & Green Jobs Initiative New York State-wide...

54

Carolinas Blue Skies & Green Jobs Initiative | Department of...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

Carolinas Blue Skies & Green Jobs Initiative Carolinas Blue Skies & Green Jobs Initiative 2010 DOE Vehicle Technologies and Hydrogen Programs Annual Merit Review and Peer...

55

Carolina Blue Skies & Green Jobs Initiative | Department of Energy  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

arravt064tiboyer2011p.pdf More Documents & Publications Carolinas Blue Skies & Green Jobs Initiative Carolina Blue Skies & Green Jobs Initiative Puget Sound Clean Cities...

56

BIG SKY CARBON SEQUESTRATION PARTNERSHIP  

SciTech Connect (OSTI)

The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I fall into four areas: evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; development of GIS-based reporting framework that links with national networks; designing an integrated suite of monitoring, measuring, and verification technologies and assessment frameworks; and initiating a comprehensive education and outreach program. The groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that would complement the ongoing DOE research. Efforts are underway to showcase the architecture of the GIS framework and initial results for sources and sinks. The region has a diverse array of geological formations that could provide storage options for carbon in one or more of its three states. Likewise, initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil C on forested, agricultural, and reclaimed lands. Both options include the potential for offsetting economic benefits to industry and society. Steps have been taken to assure that the GIS-based framework is consistent among types of sinks within the Big Sky Partnership area and with the efforts of other western DOE partnerships. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts in developing and implementing MMV technologies for geological sequestration reflect this concern. Research is also underway to identify and validate best management practices for soil C in the Partnership region, and to design a risk/cost effectiveness framework to make comparative assessments of each viable sink, taking into account economic costs, offsetting benefits, scale of sequestration opportunities, spatial and time dimensions, environmental risks, and long-term viability. Scientifically sound information on MMV is critical for public acceptance of these technologies.

Susan M. Capalbo

2005-01-31T23:59:59.000Z

57

MAXI: all-sky observation from the International Space Station  

E-Print Network [OSTI]

Monitor of All-sky X-ray Image (MAXI) is mounted on the International Space Station (ISS). Since 2009 it has been scanning the whole sky in every 92 minutes with ISS rotation. Due to high particle background at high latitude regions the carbon anodes of three GSC cameras were broken. We limit the GSC operation to low-latitude region around equator. GSC is suffering a double high background from Gamma-ray altimeter of Soyuz spacecraft. MAXI issued the 37-month catalog with 500 sources above ~0.6 mCrab in 4-10 keV. MAXI issued 133 to Astronomers Telegram and 44 to Gammaray burst Coordinated Network so far. One GSC camera had a small gas leak by a micrometeorite. Since 2013 June, the 1.4 atm Xe pressure went down to 0.6 atm in 2014 May 23. By gradually reducing the high voltage we keep using the proportional counter. SSC with X-ray CCD has detected diffuse soft X-rays in the all-sky, such as Cygnus super bubble and north polar spur, as well as it found a fast soft X-ray nova MAXI J0158-744. Although we operate C...

Mihara, Tatehiro; Matsuoka, Masaru; Tomida, Hiroshi; Ueno, Shiro; Negoro, Hitoshi; Yoshida, Atsumasa; Tsunemi, Hiroshi; Nakajima, Motoki; Ueda, Yoshihiro; Yamauchi, Makoto

2014-01-01T23:59:59.000Z

58

The Ultraviolet Sky: An Overview from the GALEX Surveys  

E-Print Network [OSTI]

The Galaxy Evolution Explorer (GALEX) has performed the first surveys of the sky in the Ultraviolet (UV). Its legacy is an unprecedented database with more than 200 million source measurements in far-UV (FUV) and near-UV (NUV), as well as wide-field imaging of extended objects, filling an important gap in our view of the sky across the electromagnetic spectrum. The UV surveys offer unique sensitivity for identifying and studying selected classes of astrophysical objects, both stellar and extra-galactic. We examine the overall content and distribution of UV sources over the sky, and with magnitude and color. For this purpose, we have constructed final catalogs of UV sources with homogeneous quality, eliminating duplicate measurements of the same source. Such catalogs can facilitate a variety of investigations on UV-selected samples, as well as planning of observations with future missions. We describe the criteria used to build the catalogs, their coverage and completeness. We included observations in which bo...

Bianchi, Luciana; Shiao, Bernie

2013-01-01T23:59:59.000Z

59

Sloan Digital Sky Survey (SDSS): Data Release 4  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy.Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. The SDSS used a dedicated 2.5-meter telescope at Apache Point Observatory, New Mexico, equipped with two powerful special-purpose instruments. SDSS data have supported fundamental work across an extraordinary range of astronomical disciplines, including the properties of galaxies, the evolution of quasars, the structure and stellar populations of the Milky Way, the dwarf galaxy companions of the Milky Way and M31, asteroids and other small bodies in the solar system, and the large scale structure and matter and energy contents of the universe. (Taken from home page of www.sdss.org). DR4 provides provides images, imaging catalogs, spectra, and redshifts for download.

60

Sloan Digital Sky Survey (SDSS): Data Release 3  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy.Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. The SDSS used a dedicated 2.5-meter telescope at Apache Point Observatory, New Mexico, equipped with two powerful special-purpose instruments. SDSS data have supported fundamental work across an extraordinary range of astronomical disciplines, including the properties of galaxies, the evolution of quasars, the structure and stellar populations of the Milky Way, the dwarf galaxy companions of the Milky Way and M31, asteroids and other small bodies in the solar system, and the large scale structure and matter and energy contents of the universe. (Taken from home page of www.sdss.org). DR3 provides provides images, imaging catalogs, spectra, and redshifts for download.

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Sloan Digital Sky Survey (SDSS): Data Release 1  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy.Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. The SDSS used a dedicated 2.5-meter telescope at Apache Point Observatory, New Mexico, equipped with two powerful special-purpose instruments. SDSS data have supported fundamental work across an extraordinary range of astronomical disciplines, including the properties of galaxies, the evolution of quasars, the structure and stellar populations of the Milky Way, the dwarf galaxy companions of the Milky Way and M31, asteroids and other small bodies in the solar system, and the large scale structure and matter and energy contents of the universe. (Taken from home page of www.sdss.org). DR1 was the first major data release, providing images, imaging catalogs, spectra, and redshifts for download.

62

Sloan Digital Sky Survey (SDSS): Data Release 2  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy.Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. The SDSS used a dedicated 2.5-meter telescope at Apache Point Observatory, New Mexico, equipped with two powerful special-purpose instruments. SDSS data have supported fundamental work across an extraordinary range of astronomical disciplines, including the properties of galaxies, the evolution of quasars, the structure and stellar populations of the Milky Way, the dwarf galaxy companions of the Milky Way and M31, asteroids and other small bodies in the solar system, and the large scale structure and matter and energy contents of the universe. (Taken from home page of www.sdss.org). DR2 provides provides images, imaging catalogs, spectra, and redshifts for download.

63

Sloan Digital Sky Survey (SDSS): Data Release 5  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy.Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. The SDSS used a dedicated 2.5-meter telescope at Apache Point Observatory, New Mexico, equipped with two powerful special-purpose instruments. SDSS data have supported fundamental work across an extraordinary range of astronomical disciplines, including the properties of galaxies, the evolution of quasars, the structure and stellar populations of the Milky Way, the dwarf galaxy companions of the Milky Way and M31, asteroids and other small bodies in the solar system, and the large scale structure and matter and energy contents of the universe. (Taken from home page of www.sdss.org). DR5 provides provides images, imaging catalogs, spectra, and redshifts for download.

64

SkyFuel Inc | Open Energy Information  

Open Energy Info (EERE)

SkyFuel Inc SkyFuel Inc Jump to: navigation, search Logo: SkyFuel Inc Name SkyFuel Inc Address 18300 W Highway 72 Place Arvada, Colorado Zip 80007 Sector Solar Product Solar thermal power Website http://www.skyfuel.com/ Coordinates 39.862942°, -105.206509° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.862942,"lon":-105.206509,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

65

North Sky River | Open Energy Information  

Open Energy Info (EERE)

Sky River Sky River Jump to: navigation, search Name North Sky River Facility North Sky River Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser Pacific Gas & Electric Location Tehachapi CA Coordinates 35.335578°, -118.186347° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.335578,"lon":-118.186347,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

66

Big Sky Wind Facility | Open Energy Information  

Open Energy Info (EERE)

Sky Wind Facility Sky Wind Facility Jump to: navigation, search Name Big Sky Wind Facility Facility Big Sky Wind Facility Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Edison Mission Energy Developer Edison Mission Energy Energy Purchaser PJM Market Location Bureau County IL Coordinates 41.579967°, -89.46177° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.579967,"lon":-89.46177,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

67

Sky coverage of orbital detectors. Analytical approach  

E-Print Network [OSTI]

Orbital detectors without pointing capability have to keep their field of view axis laying on their orbital plane, to observe the largest sky fraction. A general approach to estimate the exposure of each sky element for such detectors is a valuable tool in the R&D phase of a project, when the detector characteristics are still to be fixed. An analytical method to estimate the sky exposure is developed, which makes only few very reasonable approximations. The formulae obtained with this method are used to compute the histogram of the sky exposure of a hypothetical gamma-ray detector installed on the ISS. The C++ code used in this example is freely available on the http://cern.ch/casadei/software.html web page.

Diego Casadei

2005-11-23T23:59:59.000Z

68

Einstein and the Daytime Sky - C  

Office of Scientific and Technical Information (OSTI)

Einstein "Einstein and the Daytime Sky" (continued) A B C D C. Imitation of opal Since Einstein was addressing a more general question than the color of the atmosphere, his results...

69

Sky River Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Sky River Wind Farm Sky River Wind Farm Jump to: navigation, search Name Sky River Wind Farm Facility Sky River Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer Zond Systems Energy Purchaser Southern California Edison Co Location Tehachapi CA Coordinates 35.07665°, -118.25529° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.07665,"lon":-118.25529,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

70

Sky Power LLC | Open Energy Information  

Open Energy Info (EERE)

Oregon Zip: 97204 Sector: Wind energy Product: Developer of a high-altitude wind turbine technology. References: Sky Power LLC1 This article is a stub. You can help...

71

BIG SKY CARBON SEQUESTRATION PARTNERSHIP  

SciTech Connect (OSTI)

The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership fall into four areas: evaluation of sources and carbon sequestration sinks; development of GIS-based reporting framework; designing an integrated suite of monitoring, measuring, and verification technologies; and initiating a comprehensive education and outreach program. At the first two Partnership meetings the groundwork was put in place to provide an assessment of capture and storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that would complement the ongoing DOE research. During the third quarter, planning efforts are underway for the next Partnership meeting which will showcase the architecture of the GIS framework and initial results for sources and sinks, discuss the methods and analysis underway for assessing geological and terrestrial sequestration potentials. The meeting will conclude with an ASME workshop. The region has a diverse array of geological formations that could provide storage options for carbon in one or more of its three states. Likewise, initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil C on forested, agricultural, and reclaimed lands. Both options include the potential for offsetting economic benefits to industry and society. Steps have been taken to assure that the GIS-based framework is consistent among types of sinks within the Big Sky Partnership area and with the efforts of other western DOE partnerships. Efforts are also being made to find funding to include Wyoming in the coverage areas for both geological and terrestrial sinks and sources. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts begun in developing and implementing MMV technologies for geological sequestration reflect this concern. Research is also underway to identify and validate best management practices for soil C in the Partnership region, and to design a risk/cost effectiveness framework to make comparative assessments of each viable sink, taking into account economic costs, offsetting benefits, scale of sequestration opportunities, spatial and time dimensions, environmental risks, and long-term viability. Scientifically sound information on MMV is critical for public acceptance of these technologies. Two key deliverables were completed in the second quarter--a literature review/database to assess the soil carbon on rangelands, and the draft protocols, contracting options for soil carbon trading. The protocols developed for soil carbon trading are unique and provide a key component of the mechanisms that might be used to efficiently sequester GHG and reduce CO{sub 2} concentrations. While no key deliverables were due during the third quarter, progress on other deliverables is noted in the PowerPoint presentations and in this report. A series of meetings held during the second and third quarters have laid the foundations for assessing the issues surrounding carbon sequestration in this region, the need for a holistic approach to meeting energy demands and economic development potential, and the implementation of government programs or a market-based setting for soil C credits. These meetings provide a connection to stakeholders in the region and a basis on which to draw for the DOE PEIS hearings. In the fourth quarter, three deliverables have been completed, some in draft form to be revised and updated to include Wyoming. This is due primarily to some delays in funding to LANL and INEEL and the approval of a supplemental proposal to include Wyoming in much of the GIS data sets, analysis, and related materials. The de

Susan M. Capalbo

2004-10-31T23:59:59.000Z

72

Big Sky Carbon Sequestration Partnership  

SciTech Connect (OSTI)

The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I are organized into four areas: (1) Evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; (2) Development of GIS-based reporting framework that links with national networks; (3) Design of an integrated suite of monitoring, measuring, and verification technologies, market-based opportunities for carbon management, and an economic/risk assessment framework; (referred to below as the Advanced Concepts component of the Phase I efforts) and (4) Initiation of a comprehensive education and outreach program. As a result of the Phase I activities, the groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that complements the ongoing DOE research agenda in Carbon Sequestration. The geology of the Big Sky Carbon Sequestration Partnership Region is favorable for the potential sequestration of enormous volume of CO{sub 2}. The United States Geological Survey (USGS 1995) identified 10 geologic provinces and 111 plays in the region. These provinces and plays include both sedimentary rock types characteristic of oil, gas, and coal productions as well as large areas of mafic volcanic rocks. Of the 10 provinces and 111 plays, 1 province and 4 plays are located within Idaho. The remaining 9 provinces and 107 plays are dominated by sedimentary rocks and located in the states of Montana and Wyoming. The potential sequestration capacity of the 9 sedimentary provinces within the region ranges from 25,000 to almost 900,000 million metric tons of CO{sub 2}. Overall every sedimentary formation investigated has significant potential to sequester large amounts of CO{sub 2}. Simulations conducted to evaluate mineral trapping potential of mafic volcanic rock formations located in the Idaho province suggest that supercritical CO{sub 2} is converted to solid carbonate mineral within a few hundred years and permanently entombs the carbon. Although MMV for this rock type may be challenging, a carefully chosen combination of geophysical and geochemical techniques should allow assessment of the fate of CO{sub 2} in deep basalt hosted aquifers. Terrestrial carbon sequestration relies on land management practices and technologies to remove atmospheric CO{sub 2} where it is stored in trees, plants, and soil. This indirect sequestration can be implemented today and is on the front line of voluntary, market-based approaches to reduce CO{sub 2} emissions. Initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil Carbon (C) on rangelands, and forested, agricultural, and reclaimed lands. Rangelands can store up to an additional 0.05 mt C/ha/yr, while the croplands are on average four times that amount. Estimates of technical potential for soil sequestration within the region in cropland are in the range of 2.0 M mt C/yr over 20 year time horizon. This is equivalent to approximately 7.0 M mt CO{sub 2}e/yr. The forestry sinks are well documented, and the potential in the Big Sky region ranges from 9-15 M mt CO{sub 2} equivalent per year. Value-added benefits include enhanced yields, reduced erosion, and increased wildlife habitat. Thus the terrestrial sinks provide a viable, environmentally beneficial, and relatively low cost sink that is available to sequester C in the current time frame. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts in developing and implementing MMV technologies for geological and terrestrial sequestration re

Susan Capalbo

2005-12-31T23:59:59.000Z

73

What Do the Sun and the Sky Tell Us About the Camera? Jean-Francois Lalonde, Srinivasa G. Narasimhan, and Alexei A. Efros  

E-Print Network [OSTI]

What Do the Sun and the Sky Tell Us About the Camera? Jean-Fran¸cois Lalonde, Srinivasa G in computer vision because its appearance in an image depends on the sun position, weather conditions sources of information available within the visible portion of the sky region: the sun position

Treuille, Adrien

74

TOTAL Full-TOTAL Full-  

E-Print Network [OSTI]

Conducting - Orchestral 6 . . 6 5 1 . 6 5 . . 5 Conducting - Wind Ensemble 3 . . 3 2 . . 2 . 1 . 1 Early- X TOTAL Full- Part- X TOTAL Alternative Energy 6 . . 6 11 . . 11 13 2 . 15 Biomedical Engineering 52 English 71 . 4 75 70 . 4 74 72 . 3 75 Geosciences 9 . 1 10 15 . . 15 19 . . 19 History 37 1 2 40 28 3 3 34

Portman, Douglas

75

Cool White Dwarfs in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

A reduced proper motion diagram utilizing Sloan Digital Sky Survey (SDSS) photometry and astrometry and USNO-B plate astrometry is used to separate cool white dwarf candidates from metal-weak, high-velocity main sequence Population II stars (subdwarfs) in the SDSS Data Release 2 imaging area. Follow-up spectroscopy using the Hobby-Eberly Telescope, the MMT, and the McDonald 2.7m Telescope is used to demonstrate that the white dwarf and subdwarf loci separate cleanly in the reduced proper motion diagram, and that the contamination by subdwarfs is small near the cool white dwarf locus. This enables large statistically complete samples of white dwarfs, particularly the poorly understood cool white dwarfs, to be created from the SDSS imaging survey, with important implications for white dwarf luminosity function studies. SDSS photometry for our sample of cool white dwarfs is compared to current white dwarf models.

Mukremin Kilic; Jeffrey A. Munn; Hugh C. Harris; James Liebert; Ted von Hippel; Kurtis A. Williams; Travis S. Metcalfe; D. E. Winget; Stephen E. Levine

2005-03-28T23:59:59.000Z

76

Total Imports  

U.S. Energy Information Administration (EIA) Indexed Site

Data Series: Imports - Total Imports - Crude Oil Imports - Crude Oil, Commercial Imports - by SPR Imports - into SPR by Others Imports - Total Products Imports - Total Motor Gasoline Imports - Finished Motor Gasoline Imports - Reformulated Gasoline Imports - Reformulated Gasoline Blended w/ Fuel Ethanol Imports - Other Reformulated Gasoline Imports - Conventional Gasoline Imports - Conv. Gasoline Blended w/ Fuel Ethanol Imports - Conv. Gasoline Blended w/ Fuel Ethanol, Ed55 & Ed55 Imports - Other Conventional Gasoline Imports - Motor Gasoline Blend. Components Imports - Motor Gasoline Blend. Components, RBOB Imports - Motor Gasoline Blend. Components, RBOB w/ Ether Imports - Motor Gasoline Blend. Components, RBOB w/ Alcohol Imports - Motor Gasoline Blend. Components, CBOB Imports - Motor Gasoline Blend. Components, GTAB Imports - Motor Gasoline Blend. Components, Other Imports - Fuel Ethanol Imports - Kerosene-Type Jet Fuel Imports - Distillate Fuel Oil Imports - Distillate F.O., 15 ppm Sulfur and Under Imports - Distillate F.O., > 15 ppm to 500 ppm Sulfur Imports - Distillate F.O., > 500 ppm to 2000 ppm Sulfur Imports - Distillate F.O., > 2000 ppm Sulfur Imports - Residual Fuel Oil Imports - Propane/Propylene Imports - Other Other Oils Imports - Kerosene Imports - NGPLs/LRGs (Excluding Propane/Propylene) Exports - Total Crude Oil and Products Exports - Crude Oil Exports - Products Exports - Finished Motor Gasoline Exports - Kerosene-Type Jet Fuel Exports - Distillate Fuel Oil Exports - Residual Fuel Oil Exports - Propane/Propylene Exports - Other Oils Net Imports - Total Crude Oil and Products Net Imports - Crude Oil Net Imports - Petroleum Products Period: Weekly 4-Week Avg.

77

The Second Data Release of the Sloan Digital Sky Survey  

E-Print Network [OSTI]

The Sloan Digital Sky Survey has validated and made publicly available its Second Data Release. This data release consists of 3324 square degrees of five-band (u g r i z) imaging data with photometry for over 88 million unique objects, 367,360 spectra of galaxies, quasars, stars and calibrating blank sky patches selected over 2627 degrees of this area, and tables of measured parameters from these data. The imaging data reach a depth of r ~ 22.2 (95% completeness limit for point sources) and are photometrically and astrometrically calibrated to 2% rms and 100 milli-arcsec rms per coordinate, respectively. The imaging data have all been processed through a new version of the SDSS imaging pipeline, in which the most important improvement since the last data release is fixing an error in the model fits to each object. The result is that model magnitudes are now a good proxy for point spread function (PSF) magnitudes for point sources, and Petrosian magnitudes for extended sources. The spectroscopy extends from 3800 A to 9200 A at a resolution of 2000. The spectroscopic software now repairs a systematic error in the radial velocities of certain types of stars, and has substantially improved spectrophotometry. All data included in the SDSS Early Data Release and First Data Release are reprocessed with the improved pipelines, and included in the Second Data Release. The data are publically available as of 2004 March 15 via the web sites http://www.sdss.org/dr2 and http://skyserver.sdss.org .

K. Abazajian et al.

2004-03-13T23:59:59.000Z

78

Sloan Digital Sky Survey III (SDSS-III), Data Release 8  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Building on the legacy of the Sloan Digital Sky Survey (SDSS) and SDSS-II, the SDSS-III Collaboration is working to map the Milky Way, search for extrasolar planets, and solve the mystery of dark energy. SDSS-III's first release, Data Release 8 (DR8), became available in the first half of 2012. DR8 contains all the images ever taken by the SDSS telescope. Together, these images make up the largest color image of the sky ever made. A version of the DR8 image is shown to the right. DR8 also includes measurements for nearly 500 million stars, galaxies, and quasars, and spectra for nearly two million. All of DR8's images, spectra, and measurements are available to anyone online. You can browse through sky images, look up data for individual objects, or search for objects anywhere using any criteria. SDSS-III will collect data from 2008 to 2014, using the 2.5-meter telescope at Apache Point Observatory. SDSS-III consists of four surveys, each focused on a different scientific theme. These four surveys are: 1) Baryon Oscillation Spectroscopic Survey (BOSS); 2) SEGUE-2 (Sloan Extension for Galactic Understanding and Exploration); 3) The APO Galactic Evolution Experiment (APOGEE); and 4) The Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). [Copied with edits from http://www.sdss3.org/index.php

79

SolarSkies | Open Energy Information  

Open Energy Info (EERE)

SolarSkies SolarSkies Jump to: navigation, search Name SolarSkies Address 106 Donovan Drive Place Alexandria, Minnesota Zip 56308 Country United States Sector Solar Coordinates 45.88897°, -95.3536576° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.88897,"lon":-95.3536576,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

80

Remote sensing of total integrated water vapor, wind speed, and cloud liquid water over the ocean using the Special Sensor Microwave/Imager (SSM/I)  

E-Print Network [OSTI]

A modified D-matrix retrieval method is the basis of the refined total integrated water vapor (TIWV), total integrated cloud liquid water (CLW), and surface wind speed (WS) retrieval methods that are developed. The 85 GHZ polarization difference...

Manning, Norman Willis William

2012-06-07T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Exploring the Variable Sky with the Sloan Digital Sky Survey  

E-Print Network [OSTI]

We quantify the variability of faint unresolved optical sources using a catalog based on multiple SDSS imaging observations. The catalog covers SDSS Stripe 82, and contains 58 million photometric observations in the SDSS ugriz system for 1.4 million unresolved sources. In each photometric bandpass we compute various low-order lightcurve statistics and use them to select and study variable sources. We find that 2% of unresolved optical sources brighter than g=20.5 appear variable at the 0.05 mag level (rms) simultaneously in the g and r bands. The majority (2/3) of these variable sources are low-redshift (<2) quasars, although they represent only 2% of all sources in the adopted flux-limited sample. We find that at least 90% of quasars are variable at the 0.03 mag level (rms) and confirm that variability is as good a method for finding low-redshift quasars as is the UV excess color selection (at high Galactic latitudes). We analyze the distribution of lightcurve skewness for quasars and find that is centered on zero. We find that about 1/4 of the variable stars are RR Lyrae stars, and that only 0.5% of stars from the main stellar locus are variable at the 0.05 mag level. The distribution of lightcurve skewness in the g-r vs. u-g color-color diagram on the main stellar locus is found to be bimodal (with one mode consistent with Algol-like behavior). Using over six hundred RR Lyrae stars, we demonstrate rich halo substructure out to distances of 100 kpc. We extrapolate these results to expected performance by the Large Synoptic Survey Telescope and estimate that it will obtain well-sampled 2% accurate, multi-color lightcurves for ~2 million low-redshift quasars, and will discover at least 50 million variable stars.

Branimir Sesar; Zeljko Ivezic; Robert H. Lupton; Mario Juric; James E. Gunn; Gillian R. Knapp; Nathan De Lee; J. Allyn Smith; Gajus Miknaitis; Huan Lin; Douglas Tucker; Mamoru Doi; Masayuki Tanaka; Masataka Fukugita; Jon Holtzman; Steve Kent; Brian Yanny; David Schlegel; Douglas Finkbeiner; Nikhil Padmanabhan; Constance M. Rockosi; Nicholas Bond; Brian Lee; Chris Stoughton; Sebastian Jester; Hugh Harris; Paul Harding; Jon Brinkmann; Donald P. Schneider; Donald York; Michael W. Richmond; Daniel Vanden Berk

2007-04-04T23:59:59.000Z

82

Desert Sky Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Desert Sky Wind Farm Desert Sky Wind Farm Jump to: navigation, search Name Desert Sky Wind Farm Facility Desert Sky Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner American Electric Power Developer GE Energy Energy Purchaser City of San Antonio Texas (Utility Company) Location Pecos County TX Coordinates 30.926626°, -102.100067° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":30.926626,"lon":-102.100067,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

83

SkyFuel | Open Energy Information  

Open Energy Info (EERE)

SkyFuel SkyFuel Jump to: navigation, search Logo: SkyFuel Name SkyFuel Address 18300 West Highway 72 Place Arvada, Colorado Zip 80007 Sector Solar Product Parabolic Trough Solar Collector Year founded 2007 Number of employees 11-50 Phone number 303.330.0276 Website http://www.skyfuel.com Coordinates 39.8630176°, -105.2064482° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.8630176,"lon":-105.2064482,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

84

American Clean Skies Foundation | Open Energy Information  

Open Energy Info (EERE)

Skies Foundation Skies Foundation Jump to: navigation, search Logo: American Clean Skies Foundation Name American Clean Skies Foundation Address 750 1st Street NE, Suite 1100 Place Washington, DC Zip 20002 Region Northeast - NY NJ CT PA Area Year founded 2007 Phone number (202) 682-6294 Website http://www.cleanskies.org/ Coordinates 38.899704°, -77.007068° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.899704,"lon":-77.007068,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

85

Sublime endeavours : connecting earth to sky  

E-Print Network [OSTI]

We live in an era in which we come closer everyday to conquering the elements, to a degree that earlier humans could scarcely dream of. Nearly one hundred years ago, we took to the skies, and learned to fly. Today the act ...

Kain, Jacob E. (Jacob Evelyn), 1974-

2000-01-01T23:59:59.000Z

86

Cosmological Simulations for Large-Scale Sky Surveys | Argonne Leadership  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Instantaneous velocity magnitude in a flow through an open valve in a valve/piston assembly. Instantaneous velocity magnitude in a flow through an open valve in a valve/piston assembly. Instantaneous velocity magnitude in a flow through an open valve in a valve/piston assembly. Christos Altantzis, MIT, and Martin Schmitt, LAV. All the images were generated from their work at LAV. Cosmological Simulations for Large-Scale Sky Surveys PI Name: Christos Frouzakis PI Email: frouzakis@lav.mavt.ethz.ch Institution: Swiss Federal Institute of Technology Zurich Allocation Program: INCITE Allocation Hours at ALCF: 100 Million Year: 2014 Research Domain: Chemistry The combustion of coal and petroleum-based fuels supply most of the energy needed to meet the world's transportation and power generation demands. To address the anticipated petroleum shortage, along with increasing energy

87

NETL: News Release - Eyes in the Sky...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

August 28, 2003 August 28, 2003 Eyes in the Sky... Remote Sensing Technology Maps Flow of Groundwater from the Air Photo: Remote Sensor Suspended Beneath a Helicopter Detects Groundwater Beneath the Surface DOE is using remote sensors suspended from helicopters to map the flow of groundwater that may be affected by energy projects. In four states this past spring and summer, eyes have turned skyward as helicopters zig-zagged over hills and valleys, towing torpedo- or spiderweb-like contraptions that conjured up thoughts of Superman - "Look! Up in the sky!" But the "x-ray vision" in this case isn't comic-book fantasy. Instead, using aerial remote sensing techniques, researchers working with the U.S. Department of Energy are "seeing" through solid ground to create

88

New Sky Energy | Open Energy Information  

Open Energy Info (EERE)

New Sky Energy New Sky Energy Place Boulder, Colorado Sector Carbon Product Colorado-based startup that focuses on using chemical technology to convert carbon dioxide to usable outputs. Coordinates 42.74962°, -109.714163° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.74962,"lon":-109.714163,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

89

Einstein and the Daytime Sky - A  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

The distinction between a fluid's liquid and gaseous phases breaks down at a certain temperature and pressure; when illuminated under these conditions, the fluid looks milky white, like a common opal. Einstein found how this relates to the reason the sky is blue. A B C D A. A path with a detour If you look at many artists' renderings of Albert Einstein, you are likely to find some that depict Einstein with some representation of the universe as a whole, or black holes, or other objects in deep space. Because many such pictures exist, we may, somewhat unconsciously, associate Einstein with the dark nighttime sky. This is a quite reasonable association, since Einstein's theories of space and time deal with the universe as a whole and with certain astrophysical

90

Blue Sky Optimum Energy | Open Energy Information  

Open Energy Info (EERE)

Optimum Energy Optimum Energy Jump to: navigation, search Name Blue Sky Optimum Energy Place Buffalo, New York Product Blue Sky offers a processing system to produce biodiesel at a cheaper price. Coordinates 42.88544°, -78.878464° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.88544,"lon":-78.878464,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

91

Blue Sky Bio Fuels | Open Energy Information  

Open Energy Info (EERE)

Bio Fuels Bio Fuels Jump to: navigation, search Name Blue Sky Bio-Fuels Place Oakland, California Zip 94602 Product Blue Sky owns and operates a biodiesel plant in Idaho with a capacity of 37.9mLpa (10m gallons annually). Coordinates 37.805065°, -122.273024° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.805065,"lon":-122.273024,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

92

Cogenra Solar formerly SkyWatch Energy | Open Energy Information  

Open Energy Info (EERE)

Cogenra Solar formerly SkyWatch Energy Cogenra Solar formerly SkyWatch Energy Jump to: navigation, search Name Cogenra Solar (formerly SkyWatch Energy) Place Mountain View, California Zip 94043 Sector Solar Product California-based and founded by a former Applied Materials executive, Cogenra Solar is a stealth mode solar company. References Cogenra Solar (formerly SkyWatch Energy)[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Cogenra Solar (formerly SkyWatch Energy) is a company located in Mountain View, California . References ↑ "Cogenra Solar (formerly SkyWatch Energy)" Retrieved from "http://en.openei.org/w/index.php?title=Cogenra_Solar_formerly_SkyWatch_Energy&oldid=343766"

93

Conergy SkyPower JV | Open Energy Information  

Open Energy Info (EERE)

SkyPower JV SkyPower JV Jump to: navigation, search Name Conergy & SkyPower JV Place Canada Sector Solar Product Canada-based solar project developer. References Conergy & SkyPower JV[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Conergy & SkyPower JV is a company located in Canada . References ↑ "Conergy & SkyPower JV" Retrieved from "http://en.openei.org/w/index.php?title=Conergy_SkyPower_JV&oldid=343842" Categories: Clean Energy Organizations Companies Organizations Stubs What links here Related changes Special pages Printable version Permanent link Browse properties 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load)

94

Sloan Digital Sky Survey (SDSS and SDSS-II): Data from the SDSS Legacy Survey (Data Release 7)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is a series of three interlocking imaging and spectroscopic surveys, carried out over an eight-year period with a dedicated 2.5m telescope located at Apache Point Observatory in Southern New Mexico. The seventh data release (DR7) from the SDSS represents a completion of the overall, original project, though SDSS-III began in 2008 and will build upon the knowledge gained already. The SDSS Legacy Survey provided a uniform, well-calibrated map of more than 7,500 square degrees of the North Galactic Cap, and three stripes in the South Galactic Cap totaling 740 square degrees. The central stripe in the South Galactic Gap, Stripe 82, was scanned multiple times to enable a deep co-addition of the data and to enable discovery of variable objects. Legacy data supported studies ranging from asteroids and nearby stars to the large-scale structure of the universe. All of the imaging data have been processed to yield calibrated astrometric and photometric parameters and classifications. These parameters are available in one or more tables in a database accessible via the Catalog Archive Server (CAS) at http://cas.sdss.org/astro. [taken and edited from the Legacy page at http://www.sdss.org/legacy/index.html] All three surveys summarized are: 1) Legacy: an imaging survey in five bands over a contiguous 7646 deg2 high-latitude elliptical region in the Northern Galactic Cap, plus an additional 750 deg2 in the Southern Galactic Cap, together with spectroscopy of complete samples of galaxies and quasars covering about 8200 square degrees. The total imaging area in the Legacy survey is 8423 square degrees; 2) SEGUE: (Sloan Extension for Galactic Understanding and Exploration): additional imaging of 3240 deg2 of sky at lower Galactic latitudes, together with spectroscopy of 240,000 stars towards 200 sight lines covering 1400 square degrees (spread throughout the Legacy and SEGUE imaging footprints), to study the structure of the Milky Way; 3) Supernova: the equivalent of about 80 repeated imaging scans of the Southern Equatorial Stripe (ra > 310 or ra < 59; -1.25 > dec < 1.25) obtained in variable weather conditions (some clouds) to search for supernovae in the redshift range 0.1 < z < 0.4. The catalog derived from the images includes more than 350 million celestial objects, and spectra of 930,000 galaxies, 120,000 quasars, and 460,000 stars. The data are fully calibrated and reduced, carefully checked for quality, and publicly accessible through efficient databases. The data have been publicly released in a series of annual data releases, culminating in the final data release, DR7.[Copied from http://www.sdss.org/dr7/start/aboutdr7.html

95

The Millimeter Sky Transparency Imager (MiSTI)  

Science Journals Connector (OSTI)

......devices for full operation (i.e., power...used for receiver operation and data acquisition...devices for full operation (including motor...of a RAM, a flat heater (SAMICON 230...C. An optional cold temperature standard...b) under cloudy weather ( 0.5). Two......

Yoichi Tamura; Ryohei Kawabe; Kotaro Kohno; Masayuki Fukuhara; Munetake Momose; Hajime Ezawa; Akihito Kuboi; Tomohiko Sekiguchi; Takeshi Kamazaki; Baltasar Vila-VilarÓ; Yuki Nakagawa; Norio Okada

2011-04-25T23:59:59.000Z

96

The Animated Gamma-ray Sky Revealed by the Fermi Gamma-ray Space Telescope  

ScienceCinema (OSTI)

The Fermi Gamma-ray Space Telescope has been observing the sky in gamma-rays since August 2008.  In addition to breakthrough capabilities in energy coverage (20 MeV-300 GeV) and angular resolution, the wide field of view of the Large Area Telescope enables observations of 20% of the sky at any instant, and of the whole sky every three hours. It has revealed a very animated sky with bright gamma-ray bursts flashing and vanishing in minutes, powerful active galactic nuclei flaring over hours and days, many pulsars twinkling in the Milky Way, and X-ray binaries shimmering along their orbit. Most of these variable sources had not been seen by the Fermi predecessor, EGRET, and the wealth of new data already brings important clues to the origin of the high-energy emission and particles powered by the compact objects. The telescope also brings crisp images of the bright gamma-ray emission produced by cosmic-ray interactions in the interstellar medium, thus allowing to measure the cosmic nuclei and electron spectra across the Galaxy, to weigh interstellar clouds, in particular in the dark-gas phase. The telescope sensitivity at high energy will soon provide useful constraints on dark-matter annihilations in a variety of environments. I will review the current results and future prospects of the Fermi mission.

Isabelle Grenier

2010-01-08T23:59:59.000Z

97

Building Energy Software Tools Directory: SkyVision  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

SkyVision SkyVision SkyVision logo. Calculates the overall optical characteristics (transmittance, absorptance, reflectance and Solar Heat Gain Coefficient) of conventional and tubular skylights, performance indicators of skylight/room interfaces (well efficiency and coefficient of utilization), indoor daylight availability (daylight factor and illuminance) and daily/annual lighting energy savings. SkyVision accounts for the skylight shape and glazing, geometry of the indoor space (curb, well, room), skylight layouts, lighting and shading controls, site location and sky/ground conditions. SkyVision is unique--it uses the state-of-art glazing models and ray-tracing-based methods to compute the optical characteristics of skylights and indoor daylight availability. Screen Shots

98

ARM: Gridded (0.25 x 0.25 lat/lon) fractional cloud cover, clear-sky and all-sky shortwave flux over the SGP site.  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Gridded (0.25 x 0.25 lat/lon) fractional cloud cover, clear-sky and all-sky shortwave flux over the SGP site.

Gaustad, Krista; Gaustad, Krista; McFarlane, Sally; McFarlane, Sally

99

Imaging  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Imaging Print Imaging Print The wavelengths of soft x-ray photons (1-15 nm) are very well matched to the creation of "nanoscopes" capable of probing the interior structure of biological cells and inorganic mesoscopic systems.Topics addressed by soft x-ray imaging techniques include cell biology, nanomagnetism, environmental science, and polymers. The tunability of synchrotron radiation is absolutely essential for the creation of contrast mechanisms. Cell biology CAT scans are performed in the "water window" (300-500 eV). Nanomagnetism studies require the energy range characteristic of iron, cobalt, and nickel (600-900 eV). Mid- and far-infrared (energies below 1 eV) microprobes using synchrotron radiation are being used to address problems such as chemistry in biological tissues, chemical identification and molecular conformation, environmental biodegradation, mineral phases in geological and astronomical specimens, and electronic properties of novel materials. Infrared synchrotron radiation is focused through, or reflected from, a small spot on the specimen and then analyzed using a spectrometer. Tuning to characteristic vibrational frequencies serves as a sensitive fingerprint for molecular species. Images of the various species are built up by raster scanning the specimen through the small illuminated spot.

100

Image-Guided Total-Marrow Irradiation Using Helical Tomotherapy in Patients With Multiple Myeloma and Acute Leukemia Undergoing Hematopoietic Cell Transplantation  

SciTech Connect (OSTI)

Purpose: Total-body irradiation (TBI) has an important role in patients undergoing hematopoietic cell transplantation (HCT), but is associated with significant toxicities. Targeted TBI using helical tomotherapy results in reduced doses to normal organs, which predicts for reduced toxicities compared with standard TBI. Methods and Materials: Thirteen patients with multiple myeloma were treated in an autologous tandem transplantation Phase I trial with high-dose melphalan, followed 6 weeks later by total-marrow irradiation (TMI) to skeletal bone. Dose levels were 10, 12, 14, and 16 Gy at 2 Gy daily/twice daily. In a separate allogeneic HCT trial, 8 patients (5 with acute myelogenous leukemia, 1 with acute lymphoblastic leukemia, 1 with non-Hodgkin's lymphoma, and 1 with multiple myeloma) were treated with TMI plus total lymphoid irradiation plus splenic radiotherapy to 12 Gy (1.5 Gy twice daily) combined with fludarabine/melphalan. Results: For the 13 patients in the tandem autologous HCT trial, median age was 54 years (range, 42-66 years). Median organ doses were 15-65% that of the gross target volume dose. Primarily Grades 1-2 acute toxicities were observed. Six patients reported no vomiting; 9 patients, no mucositis; 6 patients, no fatigue; and 8 patients, no diarrhea. For the 8 patients in the allogeneic HCT trial, median age was 52 years (range, 24-61 years). Grades 2-3 nausea, vomiting, mucositis, and diarrhea were observed. In both trials, no Grade 4 nonhematologic toxicity was observed, and all patients underwent successful engraftment. Conclusions: This study shows that TMI using helical tomotherapy is clinically feasible. The reduced acute toxicities observed compare favorably with those seen with standard TBI. Initial results are encouraging and warrant further evaluation as a method to dose escalate with acceptable toxicity or to offer TBI-containing regimens to patients unable to tolerate standard approaches.

Wong, Jeffrey Y.C. [Division of Radiation Oncology and Radiation Research, City of Hope Cancer Center, Duarte, CA (United States)], E-mail: jwong@coh.org; Rosenthal, Joseph [Division of Hematology and Hematopoietic Cell Transplantation, City of Hope Cancer Center, Duarte, CA (United States); Liu An; Schultheiss, Timothy [Division of Radiation Oncology and Radiation Research, City of Hope Cancer Center, Duarte, CA (United States); Forman, Stephen; Somlo, George [Division of Hematology and Hematopoietic Cell Transplantation, City of Hope Cancer Center, Duarte, CA (United States)

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

NETL: Carbon Storage - Big Sky Carbon Sequestration Partnership  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

BSCSP BSCSP Carbon Storage Big Sky Carbon Sequestration Partnership MORE INFO Additional information related to ongoing BSCSP efforts can be found on their website. The Big Sky Carbon Sequestration Partnership (BSCSP) is led by Montana State University-Bozeman and represents a coalition of more than 60 organizations including universities, national laboratories, private companies, state agencies, Native American tribes, and international collaborators. The partners are engaged in several aspects of BSCSP projects and contribute to the efforts to deploy carbon storage projects in the BSCSP region. The BSCSP region encompasses Montana, Wyoming, Idaho, South Dakota, and eastern Washington and Oregon. BSCSP Big Sky Carbon Sequestration Partnership Region Big Sky Carbon Sequestration Partnership Region

102

SkyPower Pekon Electronics JV | Open Energy Information  

Open Energy Info (EERE)

Pekon Electronics JV Jump to: navigation, search Name: SkyPower-Pekon Electronics JV Place: India Sector: Wind energy Product: Joint venture for development of Indian wind farms....

103

Sky WindPower Corp | Open Energy Information  

Open Energy Info (EERE)

California Zip: 92065 Sector: Wind energy Product: Sky WindPower is working on turbines that would look like airborne balloons or kites, tethered to the ground. References:...

104

NREL Success Stories - SkyFuel Partnership Reflects Bright Future  

ScienceCinema (OSTI)

NREL Scientists and SkyFuel share a story about how their partnership has resulted in a revolutionary concentrating solar power technology ReflecTech Mirror Film.

Jorgensen, Gary; Gee, Randy

2013-05-29T23:59:59.000Z

105

Climatology of Mid-latitude Ionospheric Disturbances from the Very Large Array Low-frequency Sky Survey  

E-Print Network [OSTI]

The results of a climatological study of ionospheric disturbances derived from observations of cosmic sources from the Very Large Array (VLA) Low-frequency Sky Survey (VLSS) are presented. We have used the ionospheric corrections applied to the 74 MHz interferometric data within the VLSS imaging process to obtain fluctuation spectra for the total electron content (TEC) gradient on spatial scales from a few to hundreds of kilometers and temporal scales from less than one minute to nearly an hour. The observations sample nearly all times of day and all seasons. They also span latitudes and longitudes from 28 deg. N to 40 deg. N and 95 deg. W to 114 deg. W, respectively. We have binned and averaged the fluctuation spectra according to time of day, season, and geomagnetic (Kp index) and solar (F10.7) activity. These spectra provide a detailed, multi-scale account of seasonal and intraday variations in ionospheric activity with wavelike structures detected at wavelengths between about 35 and 250 km. In some cases,...

Helmboldt, J F; Cotton, W D

2012-01-01T23:59:59.000Z

106

Satellite measurements of the clear-sky greenhouse effect from  

E-Print Network [OSTI]

LETTERS Satellite measurements of the clear-sky greenhouse effect from tropospheric ozone HELEN M of 0.48±0.14 W m-2 between 45 S and 45 N. This estimate of the clear-sky greenhouse effect from

Waliser, Duane E.

107

ROSAT All-Sky Survey observations of IRAS galaxies; I. Soft X-ray and far-infrared properties  

E-Print Network [OSTI]

The 120,000 X-ray sources detected in the RASS II processing of the ROSAT All-Sky Survey are correlated with the 14,315 IRAS galaxies selected from the IRAS Point Source Catalogue: 372 IRAS galaxies show X-ray emission within a distance of 100 arcsec from the infrared position. By inspecting the structure of the X-ray emission in overlays on optical images we quantify the likelihood that the X-rays originate from the IRAS galaxy. For 197 objects the soft X-ray emission is very likely associated with the IRAS galaxy. Their soft X-ray properties are determined and compared with their far-infrared emission. X-ray contour plots overlaid on Palomar Digitized Sky Survey images are given for each of the 372 potential identifications. All images and tables displayed here are also available in electronic form.

Th. Boller; F. Bertoldi; M. Dennefeld; W. Voges

1997-10-16T23:59:59.000Z

108

The Sloan Digital Sky Survey Monitor Telescope Pipeline  

E-Print Network [OSTI]

The photometric calibration of the Sloan Digital Sky Survey (SDSS) is a multi-step process which involves data from three different telescopes: the 1.0-m telescope at the US Naval Observatory (USNO), Flagstaff Station, Arizona (which was used to establish the SDSS standard star network); the SDSS 0.5-m Photometric Telescope (PT) at the Apache Point Observatory (APO), New Mexico (which calculates nightly extinctions and calibrates secondary patch transfer fields); and the SDSS 2.5-m telescope at APO (which obtains the imaging data for the SDSS proper). In this paper, we describe the Monitor Telescope Pipeline, MTPIPE, the software pipeline used in processing the data from the single-CCD telescopes used in the photometric calibration of the SDSS (i.e., the USNO 1.0-m and the PT). We also describe transformation equations that convert photometry on the USNO-1.0m u'g'r'i'z' system to photometry the SDSS 2.5m ugriz system and the results of various validation tests of the MTPIPE software. Further, we discuss the semi-automated PT factory, which runs MTPIPE in the day-to-day standard SDSS operations at Fermilab. Finally, we discuss the use of MTPIPE in current SDSS-related projects, including the Southern u'g'r'i'z' Standard Star project, the u'g'r'i'z' Open Star Clusters project, and the SDSS extension (SDSS-II).

D. L. Tucker; S. Kent; M. W. Richmond; J. Annis; J. A. Smith; S. S. Allam; C. T. Rodgers; J. L. Stute; J. K. Adelman-McCarthy; J. Brinkmann; M. Doi; D. Finkbeiner; M. Fukugita; J. Goldston; B. Greenway; J. E. Gunn; J. S. Hendry; D. W. Hogg; S. -I. Ichikawa; Z. Ivezic; G. R. Knapp; H. Lampeitl; B. C. Lee; H. Lin; T. A. McKay; A. Merrelli; J. A. Munn; E. H. Neilsen, Jr.; H. J. Newberg; G. T. Richards; D. J. Schlegel; C. Stoughton; A. Uomoto; B. Yanny

2006-08-26T23:59:59.000Z

109

DOE Zero Energy Ready Home Case Study: One Sky Homes, San Jose...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

One Sky Homes, San Jose, CA DOE Zero Energy Ready Home Case Study: One Sky Homes, San Jose, CA DOE Zero Energy Ready Home Case Study: One Sky Homes, San Jose, CA Case study of a...

110

Barge Truck Total  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

Barge Truck Total delivered cost per short ton Shipments with transportation rates over total shipments Total delivered cost per short ton Shipments with transportation rates over...

111

EA-1886: Big Sky Regional Carbon Sequestration Partnership - Phase III:  

Broader source: Energy.gov (indexed) [DOE]

886: Big Sky Regional Carbon Sequestration Partnership - Phase 886: Big Sky Regional Carbon Sequestration Partnership - Phase III: Large Volume CO2 Injection-Site Characterization, Well Drilling, and Infrastructure Development, Injection, MVA, and Site Closure, Kevin Dome, Toole County, Montana EA-1886: Big Sky Regional Carbon Sequestration Partnership - Phase III: Large Volume CO2 Injection-Site Characterization, Well Drilling, and Infrastructure Development, Injection, MVA, and Site Closure, Kevin Dome, Toole County, Montana SUMMARY This EA will evaluate the environmental impacts of a proposal for the Big Sky Carbon Sequestration Regional Partnership to demonstrate the viability and safety of CO2 storage in a regionally significant subsurface formation in Toole County, Montana and to promote the commercialization of future

112

Mobile Climate Monitoring Facility to Sample Skies in Africa | Department  

Broader source: Energy.gov (indexed) [DOE]

Mobile Climate Monitoring Facility to Sample Skies in Africa Mobile Climate Monitoring Facility to Sample Skies in Africa Mobile Climate Monitoring Facility to Sample Skies in Africa January 18, 2006 - 10:47am Addthis WASHINGTON, D.C. -- The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program is placing a new, portable atmospheric laboratory with sophisticated instruments and data systems in Niger, Africa, to gain a better understanding of the potential impacts of Saharan dust on global climate. Dust from Africa's Sahara desert-the largest source of dust on the planet-reaches halfway around the globe. Carried by winds and clouds, the dust travels through West African, Mediterranean, and European skies, and across the Atlantic into North America. Unfortunately, Africa is one of the most under-sampled climate regimes in the world, leaving scientists to

113

EA-1886: Big Sky Regional Carbon Sequestration Partnership - Phase III:  

Broader source: Energy.gov (indexed) [DOE]

6: Big Sky Regional Carbon Sequestration Partnership - Phase 6: Big Sky Regional Carbon Sequestration Partnership - Phase III: Large Volume CO2 Injection-Site Characterization, Well Drilling, and Infrastructure Development, Injection, MVA, and Site Closure, Kevin Dome, Toole County, Montana EA-1886: Big Sky Regional Carbon Sequestration Partnership - Phase III: Large Volume CO2 Injection-Site Characterization, Well Drilling, and Infrastructure Development, Injection, MVA, and Site Closure, Kevin Dome, Toole County, Montana SUMMARY This EA will evaluate the environmental impacts of a proposal for the Big Sky Carbon Sequestration Regional Partnership to demonstrate the viability and safety of CO2 storage in a regionally significant subsurface formation in Toole County, Montana and to promote the commercialization of future

114

Big Sky Trust Fund (Montana) | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

Big Sky Trust Fund (Montana) Big Sky Trust Fund (Montana) Big Sky Trust Fund (Montana) < Back Eligibility Commercial Local Government Tribal Government Savings Category Alternative Fuel Vehicles Hydrogen & Fuel Cells Buying & Making Electricity Water Wind Home Weatherization Solar Program Info Start Date 2005 State Montana Program Type Grant Program Provider Montana Department of Commerce The Big Sky Trust Fund reimburses expenses incurred in the purchase, leasing, or relocation of real assets for direct use of the assisted business or employee training costs. A local or tribal government on behalf of any business may apply. The funding limit of the program is $5,000 per new qualifying job created or $7,500 per qualifying job created in a high poverty county. A dollar for dollar match (or 50% match in a high poverty

115

Big Sky Carbon Sequestration Partnership--Validation Phase  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Big Sky Carbon Sequestration Big Sky Carbon Sequestration Partnership-Validation Phase Background The U.S. Department of Energy (DOE) has selected seven partnerships, through its Regional Carbon Sequestration Partnership (RCSP) initiative, to determine the best approaches for capturing and permanently storing carbon dioxide (CO 2 ), a greenhouse gas (GHG) which can contribute to global climate change. The RCSPs are made up of state and local agencies, coal companies, oil and gas companies, electric utilities,

116

Slater-DW  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Total Sky ImagerWhole Sky Imager Cloud Fraction Comparison D. W. Slater and C. N. Long Pacific Northwest National Laboratory Richland, Washington T. P. Tooman Sandia National...

117

Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates  

SciTech Connect (OSTI)

This report describes an intercomparison of three popular broadband clear sky solar irradiance model results with measured data, as well as satellite-based model clear sky results compared to measured clear sky data. The authors conclude that one of the popular clear sky models (the Bird clear sky model developed by Richard Bird and Roland Hulstrom) could serve as a more accurate replacement for current satellite-model clear sky estimations. Additionally, the analysis of the model results with respect to model input parameters indicates that rather than climatological, annual, or monthly mean input data, higher-time-resolution input parameters improve the general clear sky model performance.

Sengupta, M.; Gotseff, P.

2013-12-01T23:59:59.000Z

118

LOW-FREQUENCY IMAGING OF FIELDS AT HIGH GALACTIC LATITUDE WITH THE MURCHISON WIDEFIELD ARRAY 32 ELEMENT PROTOTYPE  

SciTech Connect (OSTI)

The Murchison Widefield Array (MWA) is a new low-frequency, wide-field-of-view radio interferometer under development at the Murchison Radio-astronomy Observatory in Western Australia. We have used a 32 element MWA prototype interferometer (MWA-32T) to observe two 50 Degree-Sign diameter fields in the southern sky, covering a total of {approx}2700 deg{sup 2}, in order to evaluate the performance of the MWA-32T, to develop techniques for epoch of reionization experiments, and to make measurements of astronomical foregrounds. We developed a calibration and imaging pipeline for the MWA-32T, and used it to produce {approx}15' angular resolution maps of the two fields in the 110-200 MHz band. We perform a blind source extraction using these confusion-limited images, and detect 655 sources at high significance with an additional 871 lower significance source candidates. We compare these sources with existing low-frequency radio surveys in order to assess the MWA-32T system performance, wide-field analysis algorithms, and catalog quality. Our source catalog is found to agree well with existing low-frequency surveys in these regions of the sky and with statistical distributions of point sources derived from Northern Hemisphere surveys; it represents one of the deepest surveys to date of this sky field in the 110-200 MHz band.

Williams, Christopher L.; Hewitt, Jacqueline N.; Levine, Alan M. [MIT Kavli Institute for Astrophysics and Space Research, Cambridge, MA (United States); De Oliveira-Costa, Angelica; Hernquist, Lars L.; Bernardi, Gianni [Harvard-Smithsonian Center for Astrophysics, Cambridge, MA (United States); Bowman, Judd D. [School of Earth and Space Exploration, Arizona State University, Tempe, AZ (United States); Briggs, Frank H. [Research School of Astronomy and Astrophysics, The Australian National University, Canberra (Australia); Gaensler, B. M.; Mitchell, Daniel A.; Subrahmanyan, Ravi; Sadler, Elaine M. [ARC Centre of Excellence for All-sky Astrophysics (CAASTRO) (Australia); Morales, Miguel F. [Department of Physics, University of Washington, Seattle, WA (United States); Sethi, Shiv K. [Raman Research Institute, Bangalore (India); Arcus, Wayne; Crosse, Brian W. [International Centre for Radio Astronomy Research, Curtin University, Perth (Australia); Barnes, David G. [Center for Astrophysics and Supercomputing, Swinburne University of Technology, Melbourne (Australia); Bunton, John D. [CSIRO Astronomy and Space Science, Epping (Australia); Cappallo, Roger C.; Corey, Brian E., E-mail: clmw@mit.edu [MIT Haystack Observatory, Westford, MA (United States); and others

2012-08-10T23:59:59.000Z

119

Zhenjiang Sky Solar Co Ltd | Open Energy Information  

Open Energy Info (EERE)

Zhenjiang Sky Solar Co Ltd Zhenjiang Sky Solar Co Ltd Jump to: navigation, search Name Zhenjiang Sky-Solar Co Ltd Place Zhenjiang, Jiangsu Province, China Zip 212009 Sector Solar Product A high-tech enterprise specialized in developing and manufacturing solar series lights and solar panels. Coordinates 31.966261°, 119.472687° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":31.966261,"lon":119.472687,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

120

Development test results of the Portable, Reconfigurable Sky Sensor (PRSS)  

SciTech Connect (OSTI)

The protection of assets against surreptitious access from the sky is a continuing problem. The Portable, Reconfigurable Sky Sensor is designed to provide volumetric intruder detection against low-observable aircraft, helicopters, and parachutists in the sky. Multiple systems may be joined to form continuous detection volume for applications such as borders. The PRSS is resistant to nuisance alarms due to wind up to 70 mph, rain/snow up to 6 inches/hour or small targets such as birds. The PRSS has been successfully tested against multiple intrusions with altitude range from 50 to 3,000 feet and cross-range up to 3,000 feet. This paper summarizes some of these field tests and lists specifications and potential uses.

Blattman, D.A. [Racon, Inc., Seattle, WA (United States)

1993-12-31T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Microsoft Word - NEPA Big Sky Final EA .doc  

Broader source: Energy.gov (indexed) [DOE]

886 886 FINAL ENVIRONMENTAL ASSESSMENT For The Big Sky Regional Carbon Sequestration Partnership - Phase III: Kevin Dome Carbon Storage Project U.S. DEPARTMENT OF ENERGY NATIONAL ENERGY TECHNOLOGY LABORATORY April 2013 U.S. Department of Energy Kevin Dome Carbon Storage Project National Energy Technology Laboratory Final Environmental Assessment i April 2013 COVER SHEET Responsible Agency: The U.S. Department of Energy (DOE) Title: Environmental Assessment for the Big Sky Regional Carbon Sequestration Partnership - Phase III: Kevin Dome Carbon Storage Project (DOE/EA-1886) Contact: For additional copies or more information about this Environmental Assessment, please contact: Mr. Bill Gwilliam U.S. Department of Energy

122

Mapping the nano-Hertz gravitational wave sky  

E-Print Network [OSTI]

We describe a new method for extracting gravitational wave signals from pulsar timing data. We show that any gravitational wave signal can be decomposed into an orthogonal set of sky maps, with the number of maps equal to the number of pulsars in the timing array. These maps may be used as a basis to construct gravitational wave templates for any type of source, including collections of point sources. A variant of the standard Hellings-Downs correlation analysis is recovered for statistically isotropic signals. The template based approach allows us to probe potential anisotropies in the signal and produce maps of the gravitational wave sky.

Neil J. Cornish; Rutger van Haasteren

2014-06-19T23:59:59.000Z

123

Variations of Total Domination  

Science Journals Connector (OSTI)

The study of locating–dominating sets in graphs was pioneered by Slater [186, 187...], and this concept was later extended to total domination in graphs. A locating–total dominating set, abbreviated LTD-set, in G

Michael A. Henning; Anders Yeo

2013-01-01T23:59:59.000Z

124

Total Crude by Pipeline  

U.S. Energy Information Administration (EIA) Indexed Site

Product: Total Crude by All Transport Methods Domestic Crude by All Transport Methods Foreign Crude by All Transport Methods Total Crude by Pipeline Domestic Crude by Pipeline Foreign Crude by Pipeline Total Crude by Tanker Domestic Crude by Tanker Foreign Crude by Tanker Total Crude by Barge Domestic Crude by Barge Foreign Crude by Barge Total Crude by Tank Cars (Rail) Domestic Crude by Tank Cars (Rail) Foreign Crude by Tank Cars (Rail) Total Crude by Trucks Domestic Crude by Trucks Foreign Crude by Trucks Period: Product: Total Crude by All Transport Methods Domestic Crude by All Transport Methods Foreign Crude by All Transport Methods Total Crude by Pipeline Domestic Crude by Pipeline Foreign Crude by Pipeline Total Crude by Tanker Domestic Crude by Tanker Foreign Crude by Tanker Total Crude by Barge Domestic Crude by Barge Foreign Crude by Barge Total Crude by Tank Cars (Rail) Domestic Crude by Tank Cars (Rail) Foreign Crude by Tank Cars (Rail) Total Crude by Trucks Domestic Crude by Trucks Foreign Crude by Trucks Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Product Area 2007 2008 2009 2010 2011 2012 View

125

Big Sky Carbon Sequestration Partnership | Open Energy Information  

Open Energy Info (EERE)

Sky Carbon Sequestration Partnership Sky Carbon Sequestration Partnership Jump to: navigation, search Logo: Big Sky Carbon Sequestration Partnership Name Big Sky Carbon Sequestration Partnership Address 2327 University Way, 3rd Floor Place Bozeman, Montana Zip 59715 Region Pacific Northwest Area Phone number 406-994-3755 Notes One of the US DOE's seven regional carbon sequestration partnerships. Coordinates 45.6565752°, -111.041813° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.6565752,"lon":-111.041813,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

126

KPV: A clear-sky index for photovoltaics  

Science Journals Connector (OSTI)

Abstract The rapidly growing installed base of distributed solar photovoltaic (PV) systems is causing increased interest in forecasting their power output. A key step towards this is accurately estimating the output from a PV system based on the known output from a nearby PV system. However, each PV system is unique with its own hardware configuration, orientation, shading, etc. Thus, the process of using the power output from one system to estimate the power output of another nearby system is not necessarily straightforward. In order to address these challenges, a modified clear-sky index for photovoltaics is proposed. This index is the ratio of the instantaneous PV power output to the instantaneous theoretical clear-sky power output derived from a clear-sky radiation model and PV system simulation routine. This definition performs better than previous clear-sky indices when both PV systems’ characteristics are known and the two PV systems have similar orientations. Through this index, the performance of a nearby PV system can be predicted quite accurately. This is demonstrated through the analysis of power output data from five residential PV systems in Canberra, Australia.

N.A. Engerer; F.P. Mills

2014-01-01T23:59:59.000Z

127

Bluer Skies and Brighter Days: The U.S. and India Collaborate...  

Office of Environmental Management (EM)

Bluer Skies and Brighter Days: The U.S. and India Collaborate in First Long-Term Climate Experiment Bluer Skies and Brighter Days: The U.S. and India Collaborate in First Long-Term...

128

E-Print Network 3.0 - all-sky survey view Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

All-Sky Survey. Since the simulated light curve is quite stable... .1. All-Sky Survey FIS is primarily designed ... Source: Pak, Soojong - Department of Astronomy and Space...

129

E-Print Network 3.0 - all-sky survey mission Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

All-Sky Survey. Since the simulated light curve is quite stable... .1. All-Sky Survey FIS is primarily designed ... Source: Pak, Soojong - Department of Astronomy and Space...

130

E-Print Network 3.0 - all-sky survey bright Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

All-Sky Survey. Since the simulated light curve is quite stable... .1. All-Sky Survey FIS is primarily designed ... Source: Pak, Soojong - Department of Astronomy and Space...

131

E-Print Network 3.0 - artificial sky brightness Sample Search...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

even if the entire sky is assumed to have uniform brightness. 2 Theory Consider a patch of sky which... to be 1335.4 ohm. Using again the ... Source: Ellingson, Steven W. -...

132

Total Space Heat-  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

Buildings Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration...

133

White Dwarf Luminosity and Mass Functions from Sloan Digital Sky Survey Spectra  

E-Print Network [OSTI]

We present the first phase in our ongoing work to use Sloan Digital Sky Survey (SDSS) data to create separate white dwarf (WD) luminosity functions for two or more different mass ranges. In this paper, we determine the completeness of the SDSS spectroscopic white dwarf sample by comparing a proper-motion selected sample of WDs from SDSS imaging data with a large catalog of spectroscopically determined WDs. We derive a selection probability as a function of a single color (g-i) and apparent magnitude (g) that covers the range -1.0 white dwarfs with Teff white dwarfs with Teff white dwarf luminosity function with nearly an order of magnitude (3,358) more spectroscopically confirmed white dwarfs than any previous work.

Steven DeGennaro; Ted von Hippel; D. E. Winget; S. O. Kepler; Atsuko Nitta; Detlev Koester; Leandro Althaus

2007-09-14T23:59:59.000Z

134

Ground-based All-sky Mid-infrared and Visible Imagery for Purposes of Characterizing Cloud Properties  

SciTech Connect (OSTI)

This paper describes the All Sky Infrared Visible Analyzer (ASIVA), a multi-purpose visible and infrared sky imaging and analysis instrument whose primary functionality is to provide radiometrically calibrated imagery in the mid-infrared (mid-IR) atmospheric window. This functionality enables the determination of diurnal hemispherical cloud fraction (HCF) and estimates of sky/cloud temperature from which one can derive estimates of cloud emissivity and cloud height. This paper describes the calibration methods and performance of the ASIVA instrument with particular emphasis on data products being developed for the meteorological community. Data presented here were collected during a field campaign conducted at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Climate Research Facility from May 21 to July 27, 2009. The purpose of this campaign was to determine the efficacy of IR technology in providing reliable nighttime HCF data. Significant progress has been made in the analysis of the campaign data over the past several years and the ASIVA has proven to be an excellent instrument for determining HCF as well as several other important cloud properties.

Klebe, Dimitri; Blatherwick, R. D.; Morris, Victor R.

2014-02-24T23:59:59.000Z

135

THE 37 MONTH MAXI/GSC SOURCE CATALOG OF THE HIGH GALACTIC-LATITUDE SKY  

SciTech Connect (OSTI)

We present a catalog of high Galactic-latitude (|b| > 10 Degree-Sign ) X-ray sources detected in the first 37 months of data of the Monitor of All-sky X-ray Image/Gas Slit Camera (MAXI/GSC). To achieve the best sensitivity, we develop a background model of the GSC that well reproduces the data based on the detailed on-board calibration. Source detection is performed through image fits with a Poisson likelihood algorithm. The catalog contains 500 objects detected with significances of s{sub D,4-10keV} {>=} 7 in the 4-10 keV band. The limiting sensitivity is Almost-Equal-To 7.5 Multiplication-Sign 10{sup -12} erg cm{sup -2} s{sup -1} ( Almost-Equal-To 0.6 mCrab) in the 4-10 keV band for 50% of the survey area, which is the highest ever achieved in an all-sky survey mission covering this energy band. We summarize the statistical properties of the catalog and results from cross matching with the Swift/BAT 70 month catalog, the meta-catalog of X-ray detected clusters of galaxies, and the MAXI/GSC 7 month catalog. Our catalog lists the source name (2MAXI), position and its error, detection significances and fluxes in the 4-10 keV and 3-4 keV bands, the hardness ratio, and the basic information of the likely counterpart available for 296 sources.

Hiroi, Kazuo; Ueda, Yoshihiro; Hayashida, Masaaki; Shidatsu, Megumi; Sato, Ryosuke; Kawamuro, Taiki [Department of Astronomy, Kyoto University, Oiwake-cho, Sakyo-ku, Kyoto 606-8502 (Japan); Sugizaki, Mutsumi; Serino, Motoko; Matsuoka, Masaru; Mihara, Tatehiro [Institute of Physical and Chemical Research (RIKEN), 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Nakahira, Satoshi; Tomida, Hiroshi; Ueno, Shiro [ISS Science Project Office, Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA), 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505 (Japan); Kawai, Nobuyuki; Morii, Mikio [Department of Physics, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551 (Japan); Nakajima, Motoki [School of Dentistry at Matsudo, Nihon University, 2-870-1 Sakaecho-nishi, Matsudo, Chiba 101-8308 (Japan); Negoro, Hitoshi [Department of Physics, Nihon University, 1-8-14 Kanda-Surugadai, Chiyoda-ku, Tokyo 101-8308 (Japan); Sakamoto, Takanori [Department of Physics and Mathematics, Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252-5258 (Japan); Tsuboi, Yohko [Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551 (Japan); Tsunemi, Hiroshi, E-mail: hiroi@kusastro.kyoto-u.ac.jp [Department of Earth and Space Science, Osaka University, 1-1 Machikaneyama, Toyonaka, Osaka 560-0043 (Japan); and others

2013-08-15T23:59:59.000Z

136

Big Sky, Montana: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Sky, Montana: Energy Resources Sky, Montana: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 45.2846507°, -111.368292° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.2846507,"lon":-111.368292,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

137

Preliminary Analysis of the Nauru Island Effect Study Data  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Eppley pyranometer and pyrheliometer, total sky imager (TSI), and infrared sky thermometer (IRT) were installed near the Menen Hotel (see Figure 1) in early November, 2001....

138

Dreams of Earth and Sky: Interviews with Nine Kansas Poets  

E-Print Network [OSTI]

: The Landscape of Possibility………………………………………..81 Donald Levering: The Thread of the Past, the Life of the Present………………..103 Kathleen Johnson: Searching for a Spark…………………………………………126 Harley Elliott: Earth, Sky, and Stone……………………………………………..137 Kim..., attended the University of Kansas, and felt a longing for this place all of his adult life. It was reading interviews with him that inspired me to love the poetry interview as a window into personal life, ideas, and personality. Stafford’s discipline...

Bosnak, Kirsten Ann Meenen

2010-07-28T23:59:59.000Z

139

Cosmology using the Parkes Multibeam Southern-Sky HI Survey  

E-Print Network [OSTI]

I discuss the implications of the Parkes HI Multibeam Southern Sky Survey for cosmology. It will determine the local mass function of HI clouds, detecting several hundred per decade of mass. Each of these will come with a redshift and, for the more massive clouds, an estimate of the velocity width. This will provide an ideal database for peculiar motion studies and for measurements of biasing of galaxies relative to the underlying matter distribution.

P. A. Thomas

1996-07-02T23:59:59.000Z

140

OZZ Solar Inc Sky Ozz International | Open Energy Information  

Open Energy Info (EERE)

OZZ Solar Inc Sky Ozz International OZZ Solar Inc Sky Ozz International Jump to: navigation, search Name OZZ Solar Inc. (Sky Ozz International) Place Concord, Ontario, Canada Zip L4K 4R1 Sector Solar Product Ontario-based OZZ Solar was formed to build commercial and residential rooftop solar projects under the province's feed-in tariff programme. Coordinates 37.344704°, -78.975299° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.344704,"lon":-78.975299,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Bright Skies Ahead for Moapa | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

Bright Skies Ahead for Moapa Bright Skies Ahead for Moapa Bright Skies Ahead for Moapa March 1, 2013 - 7:19pm Addthis In addition to the planned 250-MW solar farm set to begin construction in June 2013, the Moapa Band of Paiutes is working on a second 150-MW project that would use both PV and concentrated solar technologies to generate power for the Tribe. Photo from Moapa Band of Paiutes. In addition to the planned 250-MW solar farm set to begin construction in June 2013, the Moapa Band of Paiutes is working on a second 150-MW project that would use both PV and concentrated solar technologies to generate power for the Tribe. Photo from Moapa Band of Paiutes. Photo from Moapa Band of Paiutes. Photo from Moapa Band of Paiutes. Moapa Band of Paiutes Chairman William Anderson. In addition to the planned 250-MW solar farm set to begin construction in June 2013, the Moapa Band of Paiutes is working on a second 150-MW project that would use both PV and concentrated solar technologies to generate power for the Tribe. Photo from Moapa Band of Paiutes.

142

Back to the future: The Open Skies talks  

SciTech Connect (OSTI)

Seventeen months after President Bush launched an expanded version of Eisenhower's 1955 Open Skies initiative, the proposal for reciprocal reconnaissance flights over the US, the Soviet Union, and their European allies remains mired in disputes over a wide range of issues. While US officials originally viewed Open Skies as a straightforward negotiation that might be concluded by mid-May of this year, two rounds of multilateral talks in Ottawa and Budapest have been unable to bridge the gap between NATO and Soviet proposals. A third round was postponed until after the treaty on Conventional Armed Forces in Europe (CFE) has been signed. Open Skies overflights would complement satellite reconnaissance, offering additional coverage of targets of particular interest. Aircraft would be able to fly below the persistent cloud cover that masks much of the Soviet Union from all but radar satellites for weeks at a time; they might also carry air-sampling devices that could help detect chemical weapons production or the telltale venting of radioactive material from nuclear tests. Operating over the entire Soviet Union, such flights could enhance verification of a variety of arms control agreements. 4 refs.

Tucker, J.B. (Dept. of State, Washington, DC (USA))

1990-10-01T23:59:59.000Z

143

Inconsistencies in air quality metrics: 'Blue Sky' days and  

Science Journals Connector (OSTI)

International attention is focused on Beijing's efforts to improve air quality. The number of days reported as attaining the daily Chinese National Ambient Air Quality Standard for cities, called 'Blue Sky' days, has increased yearly from 100 in 1998 to 246 in 2007. However, analysis of publicly reported daily air pollution index (API) values for fine particulate matter (diameter?10 µm, PM10), indicates a discrepancy between the reported 'Blue Sky' days (defined as API?100, PM10?150 µg m?3) and published monitoring station data. Here I show that reported improvements in air quality for 2006–2007 over 2002 levels can be attributed to (a) a shift in reported daily PM10 concentrations from just above to just below the national standard, and (b) a shift of monitoring stations in 2006 to less polluted areas. I found that calculating daily Beijing API for 2006 and 2007 using data from the original monitoring stations eliminates a bias in reported PM10 concentrations near the 'Blue Sky' boundary, and results in a number of 'Blue Sky' days and annual PM10 concentration near 2002 levels in 2006 and 2007 (203 days and ~167 µg m?3 calculated for 2006—38 days fewer and a PM10 concentration ~6 µg m?3 higher than reported; 191 'Blue Sky' days and ~161 µg m?3 calculated for 2007—55 days fewer and a PM10 concentration ~12 µg m?3 higher than reported; 203 days and 166 µg m?3 were reported in 2002). Furthermore, although different pollutants were monitored before daily reporting began and less stringent standards were implemented in June 2000, reported annual average concentrations of particulate (diameter?100 µm, TSP) and nitrogen dioxide (NO2) indicate no improvement between 1998 and 2002. This analysis highlights the sensitivity of monitoring data in the evaluation of air quality trends, and the potential for the misinterpretation or manipulation of these trends on the basis of inconsistent metrics.

Steven Q Andrews

2008-01-01T23:59:59.000Z

144

Jade Sky Technologies Partners with CLTC on LED Replacement Lamp Upgrade Project UC Davis' California Lighting Technology Center will utilize Jade Sky Technologies' driver ICs to help spur  

E-Print Network [OSTI]

Jade Sky Technologies Partners with CLTC on LED Replacement Lamp Upgrade Project UC Davis of cost-effective, easy-to-use LED lighting solutions Milpitas, Calif. ­ October 15, 2013 ­ Jade Sky Technologies (JST), a clean-tech start-up manufacturer of driver ICs for LED lighting applications, announces

California at Davis, University of

145

Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America  

Science Journals Connector (OSTI)

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada-wide and North America continental scale clear-sky composites at 250 m spatial resolution for all seven MODIS land spectral bands (B1–B7). The MODIS Level 1B (MOD02) swath level data are used as input to circumvent the problems with image distortion in the mid latitude and polar regions inherent to the global sinusoidal (SIN) projection utilized for the standard MODIS data products. The MODIS 500 m land bands B3 to B7 are first downscaled to 250 m resolution using an adaptive regression and normalization scheme for compatibility with the 250 m bands B1 and B2. A new method has been developed to produce the mask of clear-sky, cloud and cloud shadow at 250 m resolution. It shows substantial advantages in comparison with the MODIS 250 m standard cloud masks. The testing of new cloud mask showed that it is in reasonable agreement with the MODIS 1-km standard product once it is aggregated to 1-km scale, while the cloud shadow detection looks more reliable with the new methodology. Nevertheless, more quantitative analyses of the presented scene identification technique are required to understand its performance over the range of input scenes in various seasons. The new clear-sky compositing scheme employs a scene-dependent decision matrix. It is demonstrated that this new scheme provides better results than any others based on a single compositing criterion, such as maximum NDVI or minimum visible reflectance. To account for surface bi-directional properties, two clear-sky composites for the same time period are produced by separating backward scattering and forward scattering geometries, which separate pixels with the sun-satellite relative azimuth angles within 90°–270° and outside of this range. Comparison with Landsat imagery and with MODIS standard composite products demonstrated the advantage of the new technique for screening cloud and cloud shadow, and generating high spatial resolution MODIS clear-sky composites. The new data products are mapped in the Lambert Conformal Conic (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. Presently this activity is limited to MODIS/TERRA due to known problems with band-to-band registration and noisy SWIR channels on MODIS/AQUA.

Yi Luo; Alexander P. Trishchenko; Konstantin V. Khlopenkov

2008-01-01T23:59:59.000Z

146

21 briefing pages total  

Broader source: Energy.gov (indexed) [DOE]

briefing pages total p. 1 briefing pages total p. 1 Reservist Differential Briefing U.S. Office of Personnel Management December 11, 2009 p. 2 Agenda - Introduction of Speakers - Background - References/Tools - Overview of Reservist Differential Authority - Qualifying Active Duty Service and Military Orders - Understanding Military Leave and Earnings Statements p. 3 Background 5 U.S.C. 5538 (Section 751 of the Omnibus Appropriations Act, 2009, March 11, 2009) (Public Law 111-8) Law requires OPM to consult with DOD Law effective first day of first pay period on or after March 11, 2009 (March 15 for most executive branch employees) Number of affected employees unclear p. 4 Next Steps

147

Barge Truck Total  

U.S. Energy Information Administration (EIA) Indexed Site

Barge Barge Truck Total delivered cost per short ton Shipments with transportation rates over total shipments Total delivered cost per short ton Shipments with transportation rates over total shipments Year (nominal) (real) (real) (percent) (nominal) (real) (real) (percent) 2008 $6.26 $5.77 $36.50 15.8% 42.3% $6.12 $5.64 $36.36 15.5% 22.2% 2009 $6.23 $5.67 $52.71 10.8% 94.8% $4.90 $4.46 $33.18 13.5% 25.1% 2010 $6.41 $5.77 $50.83 11.4% 96.8% $6.20 $5.59 $36.26 15.4% 38.9% Annual Percent Change First to Last Year 1.2% 0.0% 18.0% - - 0.7% -0.4% -0.1% - - Latest 2 Years 2.9% 1.7% -3.6% - - 26.6% 25.2% 9.3% - - - = No data reported or value not applicable STB Data Source: The Surface Transportation Board's 900-Byte Carload Waybill Sample EIA Data Source: Form EIA-923 Power Plant Operations Report

148

Summary Max Total Units  

Broader source: Energy.gov (indexed) [DOE]

Max Total Units Max Total Units *If All Splits, No Rack Units **If Only FW, AC Splits 1000 52 28 28 2000 87 59 35 3000 61 33 15 4000 61 33 15 Totals 261 153 93 ***Costs $1,957,500.00 $1,147,500.00 $697,500.00 Notes: added several refrigerants removed bins from analysis removed R-22 from list 1000lb, no Glycol, CO2 or ammonia Seawater R-404A only * includes seawater units ** no seawater units included *** Costs = (total units) X (estimate of $7500 per unit) 1000lb, air cooled split systems, fresh water Refrig Voltage Cond Unit IF-CU Combos 2 4 5 28 References Refrig Voltage C-U type Compressor HP R-404A 208/1/60 Hermetic SA 2.5 R-507 230/1/60 Hermetic MA 2.5 208/3/60 SemiHerm SA 1.5 230/3/60 SemiHerm MA 1.5 SemiHerm HA 1.5 1000lb, remote rack systems, fresh water Refrig/system Voltage Combos 12 2 24 References Refrig/system Voltage IF only

149

Total Precipitable Water  

SciTech Connect (OSTI)

The simulation was performed on 64K cores of Intrepid, running at 0.25 simulated-years-per-day and taking 25 million core-hours. This is the first simulation using both the CAM5 physics and the highly scalable spectral element dynamical core. The animation of Total Precipitable Water clearly shows hurricanes developing in the Atlantic and Pacific.

None

2012-01-01T23:59:59.000Z

150

Total Sustainability Humber College  

E-Print Network [OSTI]

1 Total Sustainability Management Humber College November, 2012 SUSTAINABILITY SYMPOSIUM Green An Impending Global Disaster #12;3 Sustainability is NOT Climate Remediation #12;Our Premises "We cannot, you cannot improve it" (Lord Kelvin) "First rule of sustainability is to align with natural forces

Thompson, Michael

151

Cool White Dwarfs Identified in the Second Data Release of the UKIRT Infrared Deep Sky Survey  

E-Print Network [OSTI]

We have paired the Second Data Release of the Large Area Survey of the UKIRT Infrared Deep Sky Survey with the Fifth Data Release of the Sloan Digital Sky Survey to identify ten cool white dwarf candidates, from their photometry and astrometry. Of these ten, one was previously known to be a very cool white dwarf. We have obtained optical spectroscopy for seven of the candidates using the GMOS-N spectrograph on Gemini North, and have confirmed all seven as white dwarfs. Our photometry and astrometry indicates that the remaining two objects are also white dwarfs. Model analysis of the photometry and available spectroscopy shows that the seven confirmed new white dwarfs, and the two new likely white dwarfs, have effective temperatures in the range Teff = 5400-6600 K. Our analysis of the previously known white dwarf confirms that it is cool, with Teff = 3800 K. The cooling age for this dwarf is 8.7 Gyr, while that of the nine ~6000 K white dwarfs is 1.8-3.6 Gyr. We are unable to determine the masses of the white dwarfs from the existing data, and therefore we cannot constrain the total ages of the white dwarfs. The large cooling age for the coolest white dwarf in the sample, combined with its low estimated tangential velocity, suggests that it is an old member of the thin disk, or a member of the thick disk of the Galaxy, with an age 10-11 Gyr. The warmer white dwarfs appear to have velocities typical of the thick disk or even halo; these may be very old remnants of low-mass stars, or they may be relatively young thin disk objects with unusually high space motion.

N. Lodieu; S. K. Leggett; P. Bergeron; A. Nitta

2008-10-22T23:59:59.000Z

152

Confusion of Diffuse Objects in the X-ray Sky  

E-Print Network [OSTI]

Most of the baryons in the present-day universe are thought to reside in intergalactic space at temperatures of 10^5-10^7 K. X-ray emission from these baryons contributes a modest (~10%) fraction of the ~ 1 keV background whose prominence within the large-scale cosmic web depends on the amount of non-gravitational energy injected into intergalactic space by supernovae and AGNs. Here we show that the virialized regions of groups and clusters cover over a third of the sky, creating a source-confusion problem that may hinder X-ray searches for individual intercluster filaments and contaminate observations of distant groups.

G. Mark Voit; August E. Evrard; Greg L. Bryan

2000-12-08T23:59:59.000Z

153

Nuclear Dynamics with the Sky3D code  

E-Print Network [OSTI]

A description is presented of how to use the Sky3D time-dependent Hartree-Fock code to calculate giant monopole resonances. This requires modification to the code, and a step-by-step guide of how to make the necessary modification is given. An example of how to analyse the output of the code to obtain quantities of physics interest is included. Together, the modifications and the post-processing are intended to serve as a typical example of how the code, which was designed to be extendable to particular users' needs, can be extended.

Stevenson, P D

2014-01-01T23:59:59.000Z

154

Milagro all-sky TeV gamma ray observatory.  

SciTech Connect (OSTI)

Milagro is a water Cherenkov telescope sensitive to gamma rays with energies above 100 GeV. Unlike air-Cherenkov telescopes, Milagro continuously views the entire overhead sky. This capability makes it well suited to search for transient phenomena such as gamma-ray bursts and to discover new phenomena. I will review the design and construction of Milagro, detail the sensitivity of the instrument, including a discussion of background rejection with Milagro. Recent and ongoing upgrades to the instrument are discussed. The paper concludes with a summary of some recent physics results with Milagro.

Sinnis, C. (Constantine)

2002-01-01T23:59:59.000Z

155

Clean Cities: Land of Sky Clean Vehicles coalition (Western North Carolina)  

Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (EERE)]

Land of Sky Clean Vehicles Coalition (Western North Carolina) Land of Sky Clean Vehicles Coalition (Western North Carolina) The Land of Sky Clean Vehicles coalition (Western North Carolina) works with vehicle fleets, fuel providers, community leaders, and other stakeholders to reduce petroleum use in transportation. Land of Sky Clean Vehicles coalition (Western North Carolina) Contact Information Bill Eaker 828-251-6622 x142 bill@landofsky.org Coalition Website Clean Cities Coordinator Bill Eaker Photo of Bill Eaker Bill Eaker established the Land of Sky Clean Vehicles Coalition, serving the Western North Carolina region, in 2004 and has served as the coalition's coordinator since then. Eaker has over 31 years of experience in environmental, land use, and growth management planning at the local, regional, and state scales. He has worked at Land of Sky Regional Council

156

Total isomerization gains flexibility  

SciTech Connect (OSTI)

Isomerization extends refinery flexibility to meet changing markets. TIP (Total Isomerization Process) allows conversion of paraffin fractions in the gasoline boiling region including straight run naptha, light reformate, aromatic unit raffinate, and hydrocrackate. The hysomer isomerization is compared to catalytic reforming. Isomerization routes are graphed. Cost estimates and suggestions on the use of other feedstocks are given. TIP can maximize gas production, reduce crude runs, and complement cat reforming. In four examples, TIP reduces reformer severity and increases reformer yield.

Symoniak, M.F.; Holcombe, T.C.

1983-05-01T23:59:59.000Z

157

Blue Sky Green Field Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Green Field Wind Farm Green Field Wind Farm Jump to: navigation, search Name Blue Sky Green Field Wind Farm Facility Blue Sky Green Field Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner We Energies Developer We Energies Energy Purchaser We Energies Location Fond du Lac County WI Coordinates 43.908549°, -88.305384° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.908549,"lon":-88.305384,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

158

E-Print Network 3.0 - akari-galex all-sky surveys Sample Search...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Science: pulsars, transients Continuous all-sky monitor with 1... min buffer Gamma-ray bursts Exoplanets Pulsar survey of complete local population Lofar detection Source:...

159

Autonomous global sky monitoring with real-time robotic follow-up  

SciTech Connect (OSTI)

We discuss the development of prototypes for a global grid of advanced 'thinking' sky sentinels and robotic follow-up telescopes that observe the full night sky to provide real-time monitoring of the night sky by autonomously recognizing anomalous behavior, selecting targets for detailed investigation, and making real-time anomaly detection to enable rapid recognition and a swift response to transients as they emerge. This T3 global EO grid avoids the limitations imposed by geography and weather to provide persistent monitoring of the night sky.

Vestrand, W Thomas [Los Alamos National Laboratory; Davis, H [Los Alamos National Laboratory; Wren, J [Los Alamos National Laboratory; Wozniak, P [Los Alamos National Laboratory; Norman, B [Los Alamos National Laboratory; White, R [Los Alamos National Laboratory; Bloch, J [Los Alamos National Laboratory; Fenimore, E [Los Alamos National Laboratory; Hodge, Barry [AFRL; Jah, Moriba [AFRL; Rast, Richard [AFRL

2008-01-01T23:59:59.000Z

160

Total Sales of Kerosene  

U.S. Energy Information Administration (EIA) Indexed Site

End Use: Total Residential Commercial Industrial Farm All Other Period: End Use: Total Residential Commercial Industrial Farm All Other Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: End Use Area 2007 2008 2009 2010 2011 2012 View History U.S. 492,702 218,736 269,010 305,508 187,656 81,102 1984-2012 East Coast (PADD 1) 353,765 159,323 198,762 237,397 142,189 63,075 1984-2012 New England (PADD 1A) 94,635 42,570 56,661 53,363 38,448 15,983 1984-2012 Connecticut 13,006 6,710 8,800 7,437 7,087 2,143 1984-2012 Maine 46,431 19,923 25,158 24,281 17,396 7,394 1984-2012 Massachusetts 7,913 3,510 5,332 6,300 2,866 1,291 1984-2012 New Hampshire 14,454 6,675 8,353 7,435 5,472 1,977 1984-2012

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Determination of Total Solids in Biomass and Total Dissolved...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Total Solids in Biomass and Total Dissolved Solids in Liquid Process Samples Laboratory Analytical Procedure (LAP) Issue Date: 3312008 A. Sluiter, B. Hames, D. Hyman, C. Payne,...

162

Total Marketed Production ..............  

Gasoline and Diesel Fuel Update (EIA)

billion cubic feet per day) billion cubic feet per day) Total Marketed Production .............. 68.95 69.77 70.45 71.64 71.91 71.70 71.46 71.57 72.61 72.68 72.41 72.62 70.21 71.66 72.58 Alaska ......................................... 1.04 0.91 0.79 0.96 1.00 0.85 0.77 0.93 0.97 0.83 0.75 0.91 0.93 0.88 0.87 Federal GOM (a) ......................... 3.93 3.64 3.44 3.82 3.83 3.77 3.73 3.50 3.71 3.67 3.63 3.46 3.71 3.70 3.62 Lower 48 States (excl GOM) ...... 63.97 65.21 66.21 66.86 67.08 67.08 66.96 67.14 67.92 68.18 68.02 68.24 65.58 67.07 68.09 Total Dry Gas Production .............. 65.46 66.21 66.69 67.79 68.03 67.83 67.61 67.71 68.69 68.76 68.50 68.70 66.55 67.79 68.66 Gross Imports ................................ 8.48 7.60 7.80 7.95 8.27 7.59 7.96 7.91 7.89 7.17 7.61 7.73 7.96 7.93 7.60 Pipeline ........................................

163

Blue Sky Energy Inc BSE | Open Energy Information  

Open Energy Info (EERE)

Energy Inc BSE Energy Inc BSE Jump to: navigation, search Name Blue Sky Energy Inc (BSE) Place Vista, California Zip 92081 Product MPPT (Maximum Power Point Tracking) technology. Own a patented technology allowing an increase of power from a PV array of up to 30% more than conventional controllers. Coordinates 37.989712°, -93.665689° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.989712,"lon":-93.665689,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

164

Big Sky Regional Carbon Sequestration Partnership--Validation Phase  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Program Technology Program Technology Manager National Energy Technology Laboratory 3610 Collins Ferry Road P.O. Box 880 Morgantown, WV 26507 304-285-1345 traci.rodosta@netl.doe.gov William Aljoe Project Manager National Energy Technology Laboratory 626 Cochrans Mill Road P.O. Box 10940 Pittsburgh, PA 15236-0940 412-386-6569 william.aljoe@netl.doe.gov Leslie L. Schmidt Business Contact Montana State University-Bozeman 309 Montana Hall Bozeman, MT 59717-2470 406-994-2381 lschmidt@montana.edu Lee Spangler Technical Contact Montana State University-Bozeman P.O. Box 172460 Bozeman, MT 59717-2470 406-994-4399 spangler@montana.edu PARTNERS Battelle Pacific Northwest Division Center for Advanced Energy Studies Cimarex Energy Columbia University, Lamont-Doherty Earth Observatory Crow Tribe Big Sky Regional Carbon Sequestration

165

Clusters and Superclusters in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

Two-dimensional high-resolution density field of galaxies of the Early Data Release of the Sloan Digital Sky Survey with a smoothing lengths 0.8 h^{-1} Mpc is applied to extract clusters of galaxies, and a low-resolution field with smoothing lengths 10^{-1} Mpc to extract superclusters of galaxies. We compare properties of density field clusters and superclusters with Abell clusters, and superclusters found on the basis of Abell clusters. We found that clusters in high-density environment have a luminosity a factor of about 5 higher than in low-density environment. There exists a large anisotropy between the SDSS Northern and Southern sample in the properties of clusters and superclusters: most luminous clusters and superclusters in the Northern sample are a factor of 2 more luminous than the respective systems in the Southern sample.

J. Einasto; G. H"utsi; M. Einasto; E. Saar; D. L. Tucker; V. M"uller; P. Hein"am"aki; S. S. Allam

2002-12-13T23:59:59.000Z

166

OurStory: Exploring the Sky From the Internet to Outer Space  

E-Print Network [OSTI]

OurStory: Exploring the Sky From the Internet to Outer Space Read the "Directions" sheets for step Guide, page 1 of 2 #12;OurStory: Exploring the Sky From the Internet to Outer Space Parent Guide, page 2 (attached) Computer with Internet access Pen or pencil More information at http

Mathis, Wayne N.

167

202 Western Birds 41:202230, 2010 SHORT-TAILED HAWKS NESTING IN THE SKY  

E-Print Network [OSTI]

, and southwestern Chihuahua commenced in the 1980s and since then have become increasingly numerous throughout the sky islands of Arizona, New Mexico, Sonora, and Chihuahua. In this report we summarize previously the exception of one bird in northwestern Chihuahua), have been of the light morph. The sky islands

Montana, University of

168

HOT WHITE DWARFS IN DETACHED BINARIES FROM THE ROSAT WFC ALL SKY SURVEY  

E-Print Network [OSTI]

HOT WHITE DWARFS IN DETACHED BINARIES FROM THE ROSAT WFC ALL SKY SURVEY Thesis submitted 1997 #12; HOT WHITE DWARFS IN DETACHED BINARIES FROM THE ROSAT WFC ALL SKY SURVEY Matthew R. Burleigh ABSTRACT White dwarfs in unresolved pairs with normal stars (spectral type K or earlier) are invisible

Burleigh, Matt

169

E-Print Network 3.0 - all-sky survey 2mass Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

12;18 PSCzPSCz dipoledipole 12;19 2MASS survey 2MASS all-sky... survey: ground-based near-infrared survey whole sky, J(1.2 mm), H(1.6 mm), K(2.2 mm) ... Source: Weijgaert,...

170

The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III  

E-Print Network [OSTI]

The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 sq. deg of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 s...

Alam, Shadab; Prieto, Carlos Allende; Anders, F; Anderson, Scott F; Andrews, Brett H; Armengaud, Eric; Aubourg, Éric; Bailey, Stephen; Bautista, Julian E; Beaton, Rachael L; Bender, Chad F; Berlind, Andreas A; Beutler, Florian; Bhardwaj, Vaishali; Bird, Jonathan C; Bizyaev, Dmitry; Blanton, Michael R; Blomqvist, Michael; Bochanski, John J; Bolton, Adam S; Bovy, Jo; Bradley, A Shelden; Brandt, W N; Brauer, D E; Brinkmann, J; Brown, Peter J; Brownstein, Joel R; Burden, Angela; Burtin, Etienne; Busca, Nicolás G; Cai, Zheng; Capozzi, Diego; Rosell, Aurelio Carnero; Carrera, Ricardo; Chen, Yen-Chi; Chiappini, Cristina; Chojnowski, S Drew; Chuang, Chia-Hsun; Clerc, Nicolas; Comparat, Johan; Covey, Kevin; Croft, Rupert A C; Cuesta, Antonio J; Cunha, Katia; da Costa, Luiz N; Da Rio, Nicola; Davenport, James R A; Dawson, Kyle S; De Lee, Nathan; Delubac, Timothée; Deshpande, Rohit; Dutra-Ferreira, Letícia; Dwelly, Tom; Ealet, Anne; Ebelke, Garrett L; Edmondson, Edward M; Eisenstein, Daniel J; Escoffier, Stephanie; Esposito, Massimiliano; Fan, Xiaohui; Fernández-Alvar, Emma; Feuillet, Diane; Ak, Nurten Filiz; Finley, Hayley; Flaherty, Kevin; Fleming, Scott W; Font-Ribera, Andreu; Foster, Jonathan; Frinchaboy, Peter M; Galbraith-Frew, J G; García-Hernández, D A; Pérez, Ana E García; Gaulme, Patrick; Ge, Jian; Génova-Santos, R; Ghezzi, Luan; Gillespie, Bruce A; Girardi, Léo; Goddard, Daniel; Gontcho, Satya Gontcho A; Hernández, Jonay I González; Grebel, Eva K; Grieb, Jan Niklas; Grieves, Nolan; Gunn, James E; Guo, Hong; Harding, Paul; Hasselquist, Sten; Hawley, Suzanne L; Hayden, Michael; Hearty, Fred R; Ho, Shirley; Hogg, David W; Holley-Bockelmann, Kelly; Holtzman, Jon A; Honscheid, Klaus; Huehnerhoff, Joseph; Jiang, Linhua; Johnson, Jennifer A; Kinemuchi, Karen; Kirkby, David; Kitaura, Francisco; Klaene, Mark A; Kneib, Jean-Paul; Koenig, Xavier P; Lam, Charles R; Lan, Ting-Wen; Lang, Dustin; Laurent, Pierre; Goff, Jean-Marc Le; Lee, Khee-Gan; Lee, Young Sun; Licquia, Timothy C; Liu, Jian; Long, Daniel C; López-Corredoira, Martín; Lorenzo-Oliveira, Diego; Lucatello, Sara; Lundgren, Britt; Lupton, Robert H; Mack, Claude E; Mahadevan, Suvrath; Maia, Marcio A G; Majewski, Steven R; Malanushenko, Elena; Malanushenko, Viktor; Manchado, A; Manera, Marc; Mao, Qingqing; Maraston, Claudia; Marchwinski, Robert C; Margala, Daniel; Martell, Sarah L; Martig, Marie; Masters, Karen L; McBride, Cameron K; McGehee, Peregrine M; McGreer, Ian D; McMahon, Richard G; Ménard, Brice; Menzel, Marie-Luise; Merloni, Andrea; Mészáros, Szabolcs; Miralda-Escudé, Jordi; Miyatake, Hironao; Montero-Dorta, Antonio D; Morice-Atkinson, Xan; Morrison, Heather L; Muna, Demitri; Myers, Adam D; Newman, Jeffrey A; Neyrinck, Mark; Nguyen, Duy Cuong; Nichol, Robert C; Nidever, David L; Noterdaeme, Pasquier; Nuza, Sebastián E; O'Connell, Julia E; O'Connell, Robert W; O'Connell, Ross; Ogando, Ricardo L C; Olmstead, Matthew D; Oravetz, Audrey E; Oravetz, Daniel J; Osumi, Keisuke; Owen, Russell; Padgett, Deborah L; Padmanabhan, Nikhil; Paegert, Martin; Palanque-Delabrouille, Nathalie; Pan, Kaike; Parejko, John K; Park, Changbom; Pâris, Isabelle; Pattarakijwanich, Petchara; Pellejero-Ibanez, M; Pepper, Joshua; Percival, Will J; Pérez-Fournon, Ismael; Pérez-Ràfols, Ignasi; Petitjean, Patrick; Pieri, Matthew M; Pinsonneault, Marc H; de Mello, Gustavo F Porto; Prada, Francisco; Prakash, Abhishek; Price-Whelan, Adrian M; Raddick, M Jordan; Rahman, Mubdi; Reid, Beth A; Rich, James; Rix, Hans-Walter; Robin, Annie C; Rockosi, Constance M; Rodrigues, Thaíse S; Rodríguez-Rottes, Sergio; Roe, Natalie A; Ross, Ashley J; Ross, Nicholas P; Rossi, Graziano; Ruan, John J; Rubiño-Martín, J A; Salazar-Albornoz, Salvador; Salvato, Mara; Samushia, Lado; Sánchez, Ariel G; Santiago, Basílio; Sayres, Conor; Schiavon, Ricardo P; Schlegel, David J; Schmidt, Sarah J; Schneider, Donald P; Schultheis, Mathias; Schwope, Axel D; Scóccola, C G; Sellgren, Kris; Seo, Hee-Jong; Shane, Neville; Shen, Yue; Shetrone, Matthew; Shu, Yiping; Skrutskie, M F; Slosar, Anže; Smith, Verne V; Sobreira, Flávia; Stassun, Keivan G; Steinmetz, Matthias; Strauss, Michael A; Streblyanska, Alina; Swanson, Molly E C; Tan, Jonathan C; Tayar, Jamie; Terrien, Ryan C; Thakar, Aniruddha R; Thomas, Daniel; Thompson, Benjamin A; Tinker, Jeremy L; Tojeiro, Rita; Troup, Nicholas W; Vargas-Magaña, Mariana; Verde, Licia; Viel, Matteo; Vogt, Nicole P; Wake, David A; Wang, Ji; Weaver, Benjamin A; Weinberg, David H; Weiner, Benjamin J; White, Martin; Wilson, John C; Wisniewski, John P; Wood-Vasey, W M

2015-01-01T23:59:59.000Z

171

Sensitivity of Clear-Sky Diffuse Radiation to In Situ Aerosol Scattering Parameters  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Sensitivity of Clear-Sky Diffuse Radiation to In Situ Sensitivity of Clear-Sky Diffuse Radiation to In Situ Aerosol Scattering Parameters P. J. Ricchiazzi and C. Gautier University of California Santa Barbara, California Introduction Recent studies of clear-sky radiation indicate that current radiative transfer (RT) models underestimate atmospheric absorption when standard aerosol properties are used. This so-called clear-sky anomaly is manifested in predicted levels of diffuse radiation significantly below those observed at Southern Great Plains (SGP) and other sites in the continental United States (e.g., Halthore et al. 1998 GRL). Other observations at pristine sites do not show a discrepancy (Barnard and Powell 2001, 2001; Kato et al. 1997; Halthore 1998). These results may indicate that the clear-sky anomaly is only observed at sites

172

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Released: September, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings* ........................... 3,037 115 397 384 52 1,143 22 354 64 148 357 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 386 19 43 18 11 93 7 137 8 12 38 5,001 to 10,000 .......................... 262 12 35 17 5 83 4 56 6 9 35 10,001 to 25,000 ........................ 407 20 46 44 8 151 3 53 9 19 54 25,001 to 50,000 ........................ 350 15 55 50 9 121 2 34 7 16 42 50,001 to 100,000 ...................... 405 16 57 65 7 158 2 29 6 18 45 100,001 to 200,000 .................... 483 16 62 80 5 195 1 24 Q 31 56 200,001 to 500,000 .................... 361 8 51 54 5 162 1 9 8 19 43 Over 500,000 ............................. 383 8 47 56 3 181 2 12 8 23 43 Principal Building Activity

173

A serendipitous all sky survey for bright objects in the outer solar system  

E-Print Network [OSTI]

We use seven year's worth of observations from the Catalina Sky Survey and the Siding Spring Survey covering most of the northern and southern hemisphere at galactic latitudes higher than 20 degrees to search for serendipitously imaged moving objects in the outer solar system. These slowly moving objects would appear as stationary transients in these fast cadence asteroids surveys, so we develop methods to discover objects in the outer solar system using individual observations spaced by months, rather than spaced by hours, as is typically done. While we independently discover 8 known bright objects in the outer solar system, the faintest having $V=19.8\\pm0.1$, no new objects are discovered. We find that the survey is nearly 100% efficient at detecting objects beyond 25 AU for $V\\lesssim 19.1$ ($V\\lesssim18.6$ in the southern hemisphere) and that the probability that there is one or more remaining outer solar system object of this brightness left to be discovered in the unsurveyed regions of the galactic plan...

Brown, M E; Schmidt, B P; Drake, A J; Djorgovski, S G; Graham, M J; Mahabal, A; Donalek, C; Larson, S; Christensen, E; Beshore, E; McNaught, R

2015-01-01T23:59:59.000Z

174

THE SLOAN DIGITAL SKY SURVEY CO-ADD: A GALAXY PHOTOMETRIC REDSHIFT CATALOG  

SciTech Connect (OSTI)

We present and describe a catalog of galaxy photometric redshifts (photo-z) for the Sloan Digital Sky Survey (SDSS) Co-add Data. We use the artificial neural network (ANN) technique to calculate the photo-z and the nearest neighbor error method to estimate photo-z errors for {approx}13 million objects classified as galaxies in the co-add with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of {approx}83,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey, the Deep Extragalactic Evolutionary Probe Data Release 3, the VIsible imaging Multi-Object Spectrograph-Very Large Telescope Deep Survey, and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than {sigma}{sub 68} = 0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data.

Reis, Ribamar R. R.; Soares-Santos, Marcelle; Annis, James; Dodelson, Scott; Hao Jiangang; Johnston, David; Kubo, Jeffrey; Lin Huan [Center for Particle Astrophysics, Fermi National Accelerator Laboratory, Batavia, IL 60510 (United States); Seo, Hee-Jong [Berkeley Center for Cosmological Physics, LBL and Department of Physics, University of California, Berkeley, CA 94720 (United States); Simet, Melanie [Department of Astronomy and Astrophysics, The University of Chicago, Chicago, IL 60637 (United States)

2012-03-01T23:59:59.000Z

175

Determination of Total Petroleum Hydrocarbons (TPH) Using Total Carbon Analysis  

SciTech Connect (OSTI)

Several methods have been proposed to replace the Freon(TM)-extraction method to determine total petroleum hydrocarbon (TPH) content. For reasons of cost, sensitivity, precision, or simplicity, none of the replacement methods are feasible for analysis of radioactive samples at our facility. We have developed a method to measure total petroleum hydrocarbon content in aqueous sample matrixes using total organic carbon (total carbon) determination. The total carbon content (TC1) of the sample is measured using a total organic carbon analyzer. The sample is then contacted with a small volume of non-pokar solvent to extract the total petroleum hydrocarbons. The total carbon content of the resultant aqueous phase of the extracted sample (TC2) is measured. Total petroleum hydrocarbon content is calculated (TPH = TC1-TC2). The resultant data are consistent with results obtained using Freon(TM) extraction followed by infrared absorbance.

Ekechukwu, A.A.

2002-05-10T23:59:59.000Z

176

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings ............................. 91.0 33.0 7.2 6.1 7.0 18.7 2.7 5.3 1.0 2.2 7.9 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 99.0 30.7 6.7 2.7 7.1 13.9 7.1 19.9 1.1 1.7 8.2 5,001 to 10,000 .......................... 80.0 30.1 5.5 2.6 6.1 13.6 5.2 8.2 0.8 1.4 6.6 10,001 to 25,000 ........................ 71.0 28.2 4.5 4.1 4.1 14.5 2.3 4.5 0.8 1.6 6.5 25,001 to 50,000 ........................ 79.0 29.9 6.8 5.9 6.3 14.9 1.7 3.9 0.8 1.8 7.1 50,001 to 100,000 ...................... 88.7 31.6 7.6 7.6 6.5 19.6 1.7 3.4 0.7 2.0 8.1 100,001 to 200,000 .................... 104.2 39.1 8.2 8.9 7.9 22.9 1.1 2.9 Q 3.2 8.7 200,001 to 500,000 ....................

177

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings ............................. 91.0 33.0 7.2 6.1 7.0 18.7 2.7 5.3 1.0 2.2 7.9 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 99.0 30.7 6.7 2.7 7.1 13.9 7.1 19.9 1.1 1.7 8.2 5,001 to 10,000 .......................... 80.0 30.1 5.5 2.6 6.1 13.6 5.2 8.2 0.8 1.4 6.6 10,001 to 25,000 ........................ 71.0 28.2 4.5 4.1 4.1 14.5 2.3 4.5 0.8 1.6 6.5 25,001 to 50,000 ........................ 79.0 29.9 6.8 5.9 6.3 14.9 1.7 3.9 0.8 1.8 7.1 50,001 to 100,000 ...................... 88.7 31.6 7.6 7.6 6.5 19.6 1.7 3.4 0.7 2.0 8.1 100,001 to 200,000 .................... 104.2 39.1 8.2 8.9 7.9 22.9 1.1 2.9 Q 3.2 8.7 200,001 to 500,000 ....................

178

U.S. Total Exports  

Gasoline and Diesel Fuel Update (EIA)

Babb, MT Havre, MT Port of Morgan, MT Pittsburg, NH Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Sweetgrass, MT Total to Chile Sabine Pass, LA Total to China Kenai, AK Sabine Pass, LA Total to India Freeport, TX Sabine Pass, LA Total to Japan Cameron, LA Kenai, AK Sabine Pass, LA Total to Mexico Douglas, AZ Nogales, AZ Calexico, CA Ogilby Mesa, CA Otay Mesa, CA Alamo, TX Clint, TX Del Rio, TX Eagle Pass, TX El Paso, TX Hidalgo, TX McAllen, TX Penitas, TX Rio Bravo, TX Roma, TX Total to Portugal Sabine Pass, LA Total to Russia Total to South Korea Freeport, TX Sabine Pass, LA Total to Spain Cameron, LA Sabine Pass, LA Total to United Kingdom Sabine Pass, LA Period: Monthly Annual

179

The Sloan Digital Sky Survey Quasar Lens Search. IV. Statistical Lens Sample from the Fifth Data Release  

SciTech Connect (OSTI)

We present the second report of our systematic search for strongly lensed quasars from the data of the Sloan Digital Sky Survey (SDSS). From extensive follow-up observations of 136 candidate objects, we find 36 lenses in the full sample of 77,429 spectroscopically confirmed quasars in the SDSS Data Release 5. We then define a complete sample of 19 lenses, including 11 from our previous search in the SDSS Data Release 3, from the sample of 36,287 quasars with i < 19.1 in the redshift range 0.6 < z < 2.2, where we require the lenses to have image separations of 1 < {theta} < 20 and i-band magnitude differences between the two images smaller than 1.25 mag. Among the 19 lensed quasars, 3 have quadruple-image configurations, while the remaining 16 show double images. This lens sample constrains the cosmological constant to be {Omega}{sub {Lambda}} = 0.84{sub -0.08}{sup +0.06}(stat.){sub -0.07}{sup + 0.09}(syst.) assuming a flat universe, which is in good agreement with other cosmological observations. We also report the discoveries of 7 binary quasars with separations ranging from 1.1 to 16.6, which are identified in the course of our lens survey. This study concludes the construction of our statistical lens sample in the full SDSS-I data set.

Inada, Naohisa; /Wako, RIKEN /Tokyo U., ICEPP; Oguri, Masamune; /Natl. Astron. Observ. of Japan /Stanford U., Phys. Dept.; Shin, Min-Su; /Michigan U. /Princeton U. Observ.; Kayo, Issha; /Tokyo U., ICRR; Strauss, Michael A.; /Princeton U. Observ.; Hennawi, Joseph F.; /UC, Berkeley /Heidelberg, Max Planck Inst. Astron.; Morokuma, Tomoki; /Natl. Astron. Observ. of Japan; Becker, Robert H.; /LLNL, Livermore /UC, Davis; White, Richard L.; /Baltimore, Space Telescope Sci.; Kochanek, Christopher S.; /Ohio State U.; Gregg, Michael D.; /LLNL, Livermore /UC, Davis /Exeter U.

2010-05-01T23:59:59.000Z

180

Energy Perspectives, Total Energy - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Total Energy Total Energy Glossary › FAQS › Overview Data Monthly Annual Analysis & Projections this will be filled with a highchart PREVIOUSNEXT Energy Perspectives 1949-2011 September 2012 PDF | previous editions Release Date: September 27, 2012 Introduction Energy Perspectives is a graphical overview of energy history in the United States. The 42 graphs shown here reveal sweeping trends related to the Nation's production, consumption, and trade of energy from 1949 through 2011. Energy Flow, 2011 (Quadrillion Btu) Total Energy Flow diagram image For footnotes see here. Energy can be grouped into three broad categories. First, and by far the largest, is the fossil fuels-coal, petroleum, and natural gas. Fossil fuels have stored the sun's energy over millennia past, and it is primarily

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Neutrino tomography: Tevatron mapping versus the neutrino sky  

Science Journals Connector (OSTI)

... adequate reconstruction for noninvasive imaging in medicine. Geophysical densities follow from the mapping of the Radon or Fourier transform of certain neutrino projections, and not from the Volkova-Zatsepin scheme, ... problem was first addressed by Radon24 who derived what has now become known as the Radon transform. Tomography10'19'20'25 can be defined as reconstructive imaging by means of ...

Thomas L. Wilson

1984-05-03T23:59:59.000Z

182

Non-smooth optimization in the 1D-Var data assimilation of all-sky infrared satellite observations  

E-Print Network [OSTI]

Non-smooth optimization in the 1D-Var data assimilation of all-sky infrared satellite observations of clear-sky data assimilation using infrared satellites is well understood (e.g. [8], [5]), and while of non-smooth optimization algorithms to improve the variational data assimilation of all-sky infrared

Navon, Michael

183

Relation between total quanta and total energy for aquatic ...  

Science Journals Connector (OSTI)

Jan 22, 1974 ... havior of the ratio of total quanta to total energy (Q : W) within the spectral region of photosynthetic ..... For blue-green waters, where hRmax lies.

2000-01-02T23:59:59.000Z

184

Oxygen abundance in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

We present two samples of $\\hii$ galaxies from the Sloan Digital Sky Survey (SDSS) spectroscopic observations data release 3. The electron temperatures($T_e$) of 225 galaxies are calculated with the photoionized $\\hii$ model and $T_e$ of 3997 galaxies are calculated with an empirical method. The oxygen abundances from the $T_e$ methods of the two samples are determined reliably. The oxygen abundances from a strong line metallicity indicator, such as $R_{23}$, $P$, $N2$, and $O3N2$, are also calculated. We compared oxygen abundances of $\\hii$ galaxies obtained with the $T_e$ method, $R_{23}$ method, $P$ method, $N2$ method, and $O3N2$method. The oxygen abundances derived with the $T_e$ method are systematically lower by $\\sim$0.2 dex than those derived with the $R_{23}$ method, consistent with previous studies based on $\\hii$ region samples. No clear offset for oxygen abundance was found between $T_e$ metallicity and $P$, $N2$ and $O3N2$ metallicity. When we studied the relation between N/O and O/H, we found that in the metallicity regime of $\\zoh > 7.95$, the large scatter of the relation can be explained by the contribution of small mass stars to the production of nitrogen. In the high metallicity regime, $\\zoh > 8.2$, nitrogen is primarily a secondary element produced by stars of all masses.

F. Shi; X. Kong; F. Z. Cheng

2006-03-10T23:59:59.000Z

185

Measurement and Evaluation of Cloud free line of sight with Digital Whole Sky Imagers  

E-Print Network [OSTI]

by Lund and Shanklin are showing quite different behavior near the horizon, particularly in the presence

Buckingham, Michael

186

NREL: Technology Transfer - NREL and SkyFuel Partnership Reflects Bright  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

NREL and SkyFuel Partnership Reflects Bright Future for Solar Energy NREL and SkyFuel Partnership Reflects Bright Future for Solar Energy In this video, NREL Principal Scientist Gary Jorgensen and SkyFuel Chief Technology Officer Randy Gee talk about their partnership to develop a thin film to substitute for bulkier glass mirrors on solar-collecting parabolic troughs. Get the Adobe Flash Player to see this video. Credit: Fireside Production More Information For more information about NREL's partnership with SkyFuel, read Award-Winning Reflector to Cut Solar Cost and New Solar Technology Concentrates on Cost, Efficiency. Learn more about NREL's Concentrating Solar Power Research. Printable Version Technology Transfer Home About Technology Transfer Technology Partnership Agreements Licensing Agreements Nondisclosure Agreements Research Facilities

187

Cloudy Sky RRTM Shortwave Radiative Transfer and Comparison to the Revised ECMWF Shortwave Model  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Cloudy Sky RRTM Shortwave Radiative Transfer and Cloudy Sky RRTM Shortwave Radiative Transfer and Comparison to the Revised ECMWF Shortwave Model M. J. Iacono, J. S. Delamere, E. J. Mlawer, and S. A. Clough Atmospheric and Environmental Research, Inc. Lexington, Massachusetts J.-J. Morcrette European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Introduction An important step toward improving radiative transfer codes in general circulation models (GCMs) is their thorough evaluation by comparison to measurements directly, or to other data-validated radiation models. This work extends the clear-sky shortwave (SW) GCM evaluation presented by Iacono et al. (2001) to computations including clouds. The rapid radiative transfer model (RRTM) SW radiation model accurately reproduces clear-sky direct beam fluxes from the Line-By-Line Radiative Transfer

188

First all-sky search for continuous gravitational waves from unknown sources in binary systems  

E-Print Network [OSTI]

We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect ...

Aggarwal, Nancy

189

E-Print Network 3.0 - aura big sky Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

bomb, a- way: 15 to Ching Ter- Health Why 12 the dus- for- ka ching. skies Source: Nightingale, Peter - Department of Physics, University of Rhode Island Collection: Physics 38...

190

Physical parameters of 62 eclipsing binary stars using the All Sky Automated Survey-3 data – I  

Science Journals Connector (OSTI)

......Sky Catalog of point sources. NASA/IPAC Infrared Science Archive, http://irsa.ipac.caltech.edu/applications/Gator/ . Deb S. , Tiwari S. K., Singh H. P., Seshadri T. R., Chaubey U. S., 2009, Bull. Astron. Soc. India, 37......

Sukanta Deb; Harinder P. Singh

2011-04-11T23:59:59.000Z

191

E-Print Network 3.0 - average clear-sky broadband Sample Search...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

which used data from the 15-yr... the oceans and showed that the clear-sky greenhouse effect was an ... Source: Allan, Richard P. - Department of Meteorology, University of...

192

Surveying The TeV Sky With Milagro G. P. Walker for the Milagro Collaboration  

E-Print Network [OSTI]

Surveying The TeV Sky With Milagro G. P. Walker for the Milagro Collaboration Los Alamos National been reported by the Milagro collaboration [5]. In this analysis, the emission is resolved into regions

California at Santa Cruz, University of

193

Spectrometer for Sky-Scanning Sun-Tracking Atmospheric Research (4STAR): Instrument Technology  

SciTech Connect (OSTI)

The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) combines airborne sun tracking and sky scanning with diffraction spectroscopy, to improve knowledge of atmospheric constituents and their links to air-pollution/climate. Direct beam hyper-spectral measurement of optical depth improves retrievals of gas constituents and determination of aerosol properties. Sky scanning enhances retrievals of aerosol type and size distribution. 4STAR measurements will tighten the closure between satellite and ground-based measurements. 4STAR incorporates a modular sun-tracking/ sky-scanning optical head with fiber optic signal transmission to rack mounted spectrometers, permitting miniaturization of the external optical head, and future detector evolution. Technical challenges include compact optical collector design, radiometric dynamic range and stability, and broad spectral coverage. Test results establishing the performance of the instrument against the full range of operational requirements are presented, along with calibration, engineering flight test, and scientific field campaign data and results.

Dunagan, Stephen; Johnson, Roy; Zavaleta, Jhony; Russell, P. B.; Schmid, Beat; Flynn, Connor J.; Redemann, Jens; Shinozuka, Yohei; Livingston, J.; Segal Rozenhaimer, Michal

2013-08-06T23:59:59.000Z

194

OT 060420: A Seemingly Optical Transient Recorded by All-Sky Cameras  

E-Print Network [OSTI]

We report on a ~5th magnitude flash detected for approximately 10 minutes by two CONCAM all-sky cameras located in Cerro Pachon - Chile and La Palma - Spain. A third all-sky camera, located in Cerro Paranal - Chile did not detect the flash, and therefore the authors of this paper suggest that the flash was a series of cosmic-ray hits, meteors, or satellite glints. Another proposed hypothesis is that the flash was an astronomical transient with variable luminosity. In this paper we discuss bright optical transient detection using fish-eye all-sky monitors, analyze the apparently false-positive optical transient, and propose possible causes to false optical transient detection in all-sky cameras.

Lior Shamir; Robert J. Nemiroff

2006-07-03T23:59:59.000Z

195

Mujeres Hombres Total Hombres Total 16 5 21 0 10  

E-Print Network [OSTI]

Julio de 2011 Tipo de Discapacidad Sexo CENTRO 5-Distribución del estudiantado con discapacidad por centro, tipo de discapacidad, sexo y totales. #12;

Autonoma de Madrid, Universidad

196

Relation between total quanta and total energy for aquatic ...  

Science Journals Connector (OSTI)

Jan 22, 1974 ... ment of the total energy and vice versa. From a measurement of spectral irradi- ance ... unit energy (for the wavelength region specified).

2000-01-02T23:59:59.000Z

197

Tropical Africa: Total Forest Biomass (By Country)  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Tropical Africa: Total Forest Biomass (By Country) Tropical Africa: Total Forest Biomass (By Country) image Brown, S., and G. Gaston. 1996. Tropical Africa: Land Use, Biomass, and Carbon Estimates For 1980. ORNL/CDIAC-92, NDP-055. Carbon Dioxide Information Analysis Center, U.S. Department of Energy, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A. More Maps Calculated Actual Aboveground Live Biomass in Forests (1980) Maximum Potential Biomass Density Land Use (1980) Area of Closed Forests (By Country) Mean Biomass of Closed Forests (By County) Area of Open Forests (By Country) Mean Biomass of Open Forests (By County) Percent Forest Cover (By Country) Population Density - 1990 (By Administrative Unit) Population Density - 1980 (By Administrative Unit) Population Density - 1970 (By Administrative Unit)

198

Big Sky Carbon Sequestration Partnership--Phase I  

SciTech Connect (OSTI)

The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I are organized into four areas: (1) Evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; (2) Development of GIS-based reporting framework that links with national networks; (3) Design of an integrated suite of monitoring, measuring, and verification technologies, market-based opportunities for carbon management, and an economic/risk assessment framework (referred to below as the Advanced Concepts component of the Phase I efforts); and (4) Initiation of a comprehensive education and outreach program. As a result of the Phase I activities, the groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that complements the ongoing DOE research agenda in Carbon Sequestration. The geology of the Big Sky Carbon Sequestration Partnership Region is favorable for the potential sequestration of enormous volume of CO{sub 2}. The United States Geological Survey (USGS 1995) identified 10 geologic provinces and 111 plays in the region. These provinces and plays include both sedimentary rock types characteristic of oil, gas, and coal productions as well as large areas of mafic volcanic rocks. Of the 10 provinces and 111 plays, 1 province and 4 plays are located within Idaho. The remaining 9 provinces and 107 plays are dominated by sedimentary rocks and located in the states of Montana and Wyoming. The potential sequestration capacity of the 9 sedimentary provinces within the region ranges from 25,000 to almost 900,000 million metric tons of CO{sub 2}. Overall every sedimentary formation investigated has significant potential to sequester large amounts of CO{sub 2}. Simulations conducted to evaluate mineral trapping potential of mafic volcanic rock formations located in the Idaho province suggest that supercritical CO{sub 2} is converted to solid carbonate mineral within a few hundred years and permanently entombs the carbon. Although MMV for this rock type may be challenging, a carefully chosen combination of geophysical and geochemical techniques should allow assessment of the fate of CO{sub 2} in deep basalt hosted aquifers. Terrestrial carbon sequestration relies on land management practices and technologies to remove atmospheric CO{sub 2} where it is stored in trees, plants, and soil. This indirect sequestration can be implemented today and is on the front line of voluntary, market-based approaches to reduce CO{sub 2} emissions. Initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil Carbon (C) on rangelands, and forested, agricultural, and reclaimed lands. Rangelands can store up to an additional 0.05 mt C/ha/yr, while the croplands are on average four times that amount. Estimates of technical potential for soil sequestration within the region in cropland are in the range of 2.0 M mt C/yr over 20 year time horizon. This is equivalent to approximately 7.0 M mt CO{sub 2}e/yr. The forestry sinks are well documented, and the potential in the Big Sky region ranges from 9-15 M mt CO{sub 2} equivalent per year. Value-added benefits include enhanced yields, reduced erosion, and increased wildlife habitat. Thus the terrestrial sinks provide a viable, environmentally beneficial, and relatively low cost sink that is available to sequester C in the current time frame. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts in developing and implementing MMV technologies for geological and terrestrial sequestration re

Susan M. Capalbo

2005-10-01T23:59:59.000Z

199

Big Sky Carbon Sequestration Partnership--Phase I  

SciTech Connect (OSTI)

The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I are organized into four areas: (1) Evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; (2) Development of GIS-based reporting framework that links with national networks; (3) Design of an integrated suite of monitoring, measuring, and verification technologies, market-based opportunities for carbon management, and an economic/risk assessment framework (referred to below as the Advanced Concepts component of the Phase I efforts); and (4) Initiation of a comprehensive education and outreach program. As a result of the Phase I activities, the groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that complements the ongoing DOE research agenda in Carbon Sequestration. The geology of the Big Sky Carbon Sequestration Partnership Region is favorable for the potential sequestration of enormous volume of CO{sub 2}. The United States Geological Survey (USGS 1995) identified 10 geologic provinces and 111 plays in the region. These provinces and plays include both sedimentary rock types characteristic of oil, gas, and coal productions as well as large areas of mafic volcanic rocks. Of the 10 provinces and 111 plays, 1 province and 4 plays are located within Idaho. The remaining 9 provinces and 107 plays are dominated by sedimentary rocks and located in the states of Montana and Wyoming. The potential sequestration capacity of the 9 sedimentary provinces within the region ranges from 25,000 to almost 900,000 million metric tons of CO{sub 2}. Overall every sedimentary formation investigated has significant potential to sequester large amounts of CO{sub 2}. Simulations conducted to evaluate mineral trapping potential of mafic volcanic rock formations located in the Idaho province suggest that supercritical CO{sub 2} is converted to solid carbonate mineral within a few hundred years and permanently entombs the carbon. Although MMV for this rock type may be challenging, a carefully chosen combination of geophysical and geochemical techniques should allow assessment of the fate of CO{sub 2} in deep basalt hosted aquifers. Terrestrial carbon sequestration relies on land management practices and technologies to remove atmospheric CO{sub 2} where it is stored in trees, plants, and soil. This indirect sequestration can be implemented today and is on the front line of voluntary, market-based approaches to reduce CO{sub 2} emissions. Initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil Carbon (C) on rangelands, and forested, agricultural, and reclaimed lands. Rangelands can store up to an additional 0.05 mt C/ha/yr, while the croplands are on average four times that amount. Estimates of technical potential for soil sequestration within the region in cropland are in the range of 2.0 M mt C/yr over 20 year time horizon. This is equivalent to approximately 7.0 M mt CO{sub 2}e/yr. The forestry sinks are well documented, and the potential in the Big Sky region ranges from 9-15 M mt CO{sub 2} equivalent per year. Value-added benefits include enhanced yields, reduced erosion, and increased wildlife habitat. Thus the terrestrial sinks provide a viable, environmentally beneficial, and relatively low cost sink that is available to sequester C in the current time frame. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts in developing and implementing MMV technologies for geological and terrestrial sequestration re

Susan M. Capalbo

2006-01-01T23:59:59.000Z

200

Total.................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

49.2 49.2 15.1 15.6 11.1 7.0 5.2 8.0 Have Cooling Equipment............................... 93.3 31.3 15.1 15.6 11.1 7.0 5.2 8.0 Use Cooling Equipment................................ 91.4 30.4 14.6 15.4 11.1 6.9 5.2 7.9 Have Equipment But Do Not Use it............... 1.9 1.0 0.5 Q Q Q Q Q Do Not Have Cooling Equipment................... 17.8 17.8 N N N N N N Air-Conditioning Equipment 1, 2 Central System............................................. 65.9 3.9 15.1 15.6 11.1 7.0 5.2 8.0 Without a Heat Pump................................ 53.5 3.5 12.9 12.7 8.6 5.5 4.2 6.2 With a Heat Pump..................................... 12.3 0.4 2.2 2.9 2.5 1.5 1.0 1.8 Window/Wall Units........................................ 28.9 27.5 0.5 Q 0.3 Q Q Q 1 Unit......................................................... 14.5 13.5 0.3 Q Q Q N Q 2 Units.......................................................

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

7.1 7.1 7.0 8.0 12.1 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.2 Have Main Space Heating Equipment.................. 109.8 7.1 6.8 7.9 11.9 Use Main Space Heating Equipment.................... 109.1 7.1 6.6 7.9 11.4 Have Equipment But Do Not Use It...................... 0.8 N Q N 0.5 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 3.8 0.4 3.8 8.4 Central Warm-Air Furnace................................ 44.7 1.8 Q 3.1 6.0 For One Housing Unit................................... 42.9 1.5 Q 3.1 6.0 For Two Housing Units................................. 1.8 Q N Q Q Steam or Hot Water System............................. 8.2 1.9 Q Q 0.2 For One Housing Unit................................... 5.1 0.8 Q N Q For Two Housing Units.................................

202

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

5.6 5.6 17.7 7.9 Do Not Have Space Heating Equipment............... 1.2 Q Q N Have Main Space Heating Equipment.................. 109.8 25.6 17.7 7.9 Use Main Space Heating Equipment.................... 109.1 25.6 17.7 7.9 Have Equipment But Do Not Use It...................... 0.8 N N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 18.4 13.1 5.3 Central Warm-Air Furnace................................ 44.7 16.2 11.6 4.7 For One Housing Unit................................... 42.9 15.5 11.0 4.5 For Two Housing Units................................. 1.8 0.7 0.6 Q Steam or Hot Water System............................. 8.2 1.6 1.2 0.4 For One Housing Unit................................... 5.1 1.1 0.9 Q For Two Housing Units.................................

203

Total...........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

4.2 4.2 7.6 16.6 Do Not Have Cooling Equipment............................. 17.8 10.3 3.1 7.3 Have Cooling Equipment.......................................... 93.3 13.9 4.5 9.4 Use Cooling Equipment........................................... 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it.......................... 1.9 1.0 Q 0.8 Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat Pump........................................... 53.5 8.7 3.2 5.5 With a Heat Pump............................................... 12.3 1.7 0.7 1.0 Window/Wall Units.................................................. 28.9 3.6 0.6 3.0 1 Unit................................................................... 14.5 2.9 0.5 2.4 2 Units.................................................................

204

Total...........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Q Q Million U.S. Housing Units Renter- Occupied Housing Units (millions) Type of Renter-Occupied Housing Unit U.S. Housing Units (millions Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Table HC4.2 Living Space Characteristics by Renter-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing Units Renter- Occupied Housing Units (millions) Type of Renter-Occupied Housing Unit U.S. Housing Units (millions Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Table HC4.2 Living Space Characteristics by Renter-Occupied Housing Units, 2005

205

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Personal Computers Personal Computers Do Not Use a Personal Computer.................................. 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer.............................................. 75.6 26.6 14.5 4.1 7.9 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 20.5 11.0 3.4 6.1 Laptop Model............................................................. 16.9 6.1 3.5 0.7 1.9 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 5.0 2.6 1.0 1.3 2 to 15 Hours............................................................. 29.1 10.3 5.9 1.6 2.9 16 to 40 Hours........................................................... 13.5 4.1 2.3 0.6 1.2 41 to 167 Hours.........................................................

206

Total..............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

,171 ,171 1,618 1,031 845 630 401 Census Region and Division Northeast................................................... 20.6 2,334 1,664 562 911 649 220 New England.......................................... 5.5 2,472 1,680 265 1,057 719 113 Middle Atlantic........................................ 15.1 2,284 1,658 670 864 627 254 Midwest...................................................... 25.6 2,421 1,927 1,360 981 781 551 East North Central.................................. 17.7 2,483 1,926 1,269 999 775 510 West North Central................................. 7.9 2,281 1,930 1,566 940 796 646 South.......................................................... 40.7 2,161 1,551 1,295 856 615 513 South Atlantic......................................... 21.7 2,243 1,607 1,359 896 642 543 East South Central.................................

207

Total.........................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

..... ..... 111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer...................................... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer.................................................. 75.6 4.2 5.0 5.3 9.0 Most-Used Personal Computer Type of PC Desk-top Model............................................................. 58.6 3.2 3.9 4.0 6.7 Laptop Model................................................................. 16.9 1.0 1.1 1.3 2.4 Hours Turned on Per Week Less than 2 Hours......................................................... 13.6 0.7 0.9 0.9 1.4 2 to 15 Hours................................................................. 29.1 1.7 2.1 1.9 3.4 16 to 40 Hours............................................................... 13.5 0.9 0.9 0.9 1.8 41 to 167 Hours.............................................................

208

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 2.6 0.7 1.9 2 Times A Day...................................................... 24.6 6.6 2.0 4.6 Once a Day........................................................... 42.3 8.8 2.9 5.8 A Few Times Each Week...................................... 27.2 4.7 1.5 3.1 About Once a Week.............................................. 3.9 0.7 Q 0.6 Less Than Once a Week....................................... 4.1 0.7 0.3 0.4 No Hot Meals Cooked........................................... 0.9 0.2 Q Q Conventional Oven Use an Oven......................................................... 109.6 23.7 7.5 16.2 More Than Once a Day..................................... 8.9 1.7 0.4 1.3 Once a Day.......................................................

209

Total..............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

0.7 0.7 21.7 6.9 12.1 Do Not Have Cooling Equipment................................ 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................. 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment.............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................. 1.9 0.5 Q Q Q Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 32.1 17.6 5.2 9.3 Without a Heat Pump.............................................. 53.5 23.2 10.9 3.8 8.4 With a Heat Pump................................................... 12.3 9.0 6.7 1.4 0.9 Window/Wall Units..................................................... 28.9 8.0 3.4 1.7 2.9 1 Unit......................................................................

210

Total....................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

14.7 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Household Size 1 Person.......................................................... 30.0 4.6 2.5 3.7 3.2 5.4 5.5 3.7 1.6 2 Persons......................................................... 34.8 4.3 1.9 4.4 4.1 5.9 5.3 5.5 3.4 3 Persons......................................................... 18.4 2.5 1.3 1.7 1.9 2.9 3.5 2.8 1.6 4 Persons......................................................... 15.9 1.9 0.8 1.5 1.6 3.0 2.5 3.1 1.4 5 Persons......................................................... 7.9 0.8 0.4 1.0 1.1 1.2 1.1 1.5 0.9 6 or More Persons........................................... 4.1 0.5 0.3 0.3 0.6 0.5 0.7 0.8 0.4 2005 Annual Household Income Category Less than $9,999............................................. 9.9 1.9 1.1 1.3 0.9 1.7 1.3 1.1 0.5 $10,000 to $14,999..........................................

211

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

25.6 25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer.............................................. 75.6 13.7 17.5 26.6 17.8 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 10.4 14.1 20.5 13.7 Laptop Model............................................................. 16.9 3.3 3.4 6.1 4.1 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 2.4 3.4 5.0 2.9 2 to 15 Hours............................................................. 29.1 5.2 7.0 10.3 6.6 16 to 40 Hours........................................................... 13.5 3.1 2.8 4.1 3.4 41 to 167 Hours.........................................................

212

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

4.2 4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.4 2.2 4.2 Use a Personal Computer.............................................. 75.6 17.8 5.3 12.5 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 13.7 4.2 9.5 Laptop Model............................................................. 16.9 4.1 1.1 3.0 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 2.9 0.9 2.0 2 to 15 Hours............................................................. 29.1 6.6 2.0 4.6 16 to 40 Hours........................................................... 13.5 3.4 0.9 2.5 41 to 167 Hours......................................................... 6.3

213

Total..................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

33.0 33.0 8.0 3.4 5.9 14.4 1.2 Do Not Have Cooling Equipment..................... 17.8 6.5 1.6 0.9 1.3 2.4 0.2 Have Cooling Equipment................................. 93.3 26.5 6.5 2.5 4.6 12.0 1.0 Use Cooling Equipment.................................. 91.4 25.7 6.3 2.5 4.4 11.7 0.8 Have Equipment But Do Not Use it................. 1.9 0.8 Q Q 0.2 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 14.1 3.6 1.5 2.1 6.4 0.6 Without a Heat Pump.................................. 53.5 12.4 3.1 1.3 1.8 5.7 0.6 With a Heat Pump....................................... 12.3 1.7 0.6 Q 0.3 0.6 Q Window/Wall Units....................................... 28.9 12.4 2.9 1.0 2.5 5.6 0.4 1 Unit.......................................................... 14.5 7.3 1.2 0.5 1.4 3.9 0.2 2 Units.........................................................

214

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day................................................. 8.2 3.7 1.6 1.4 1.5 2 Times A Day.............................................................. 24.6 10.8 4.1 4.3 5.5 Once a Day................................................................... 42.3 17.0 7.2 8.7 9.3 A Few Times Each Week............................................. 27.2 11.4 4.7 6.4 4.8 About Once a Week..................................................... 3.9 1.7 0.6 0.9 0.8 Less Than Once a Week.............................................. 4.1 2.2 0.6 0.8 0.5 No Hot Meals Cooked................................................... 0.9 0.4 Q Q Q Conventional Oven Use an Oven................................................................. 109.6 46.2 18.8

215

Total...................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Single-Family Units Single-Family Units Detached Type of Housing Unit Table HC2.7 Air Conditioning Usage Indicators by Type of Housing Unit, 2005 Million U.S. Housing Units Air Conditioning Usage Indicators Attached 2 to 4 Units 5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Single-Family Units Detached Type of Housing Unit Table HC2.7 Air Conditioning Usage Indicators by Type of Housing Unit, 2005 Million U.S. Housing Units Air Conditioning Usage Indicators Attached 2 to 4 Units 5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) At Home Behavior Home Used for Business

216

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 2.1 1.8 0.3 Have Cooling Equipment............................................ 93.3 23.5 16.0 7.5 Use Cooling Equipment............................................. 91.4 23.4 15.9 7.5 Have Equipment But Do Not Use it............................ 1.9 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 17.3 11.3 6.0 Without a Heat Pump............................................. 53.5 16.2 10.6 5.6 With a Heat Pump................................................. 12.3 1.1 0.8 0.4 Window/Wall Units.................................................. 28.9 6.6 4.9 1.7 1 Unit..................................................................... 14.5 4.1 2.9 1.2 2 Units...................................................................

217

Total..............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

20.6 20.6 25.6 40.7 24.2 Do Not Have Cooling Equipment................................ 17.8 4.0 2.1 1.4 10.3 Have Cooling Equipment............................................. 93.3 16.5 23.5 39.3 13.9 Use Cooling Equipment.............................................. 91.4 16.3 23.4 38.9 12.9 Have Equipment But Do Not Use it............................. 1.9 0.3 Q 0.5 1.0 Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 6.0 17.3 32.1 10.5 Without a Heat Pump.............................................. 53.5 5.5 16.2 23.2 8.7 With a Heat Pump................................................... 12.3 0.5 1.1 9.0 1.7 Window/Wall Units..................................................... 28.9 10.7 6.6 8.0 3.6 1 Unit......................................................................

218

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

5.6 5.6 17.7 7.9 Personal Computers Do Not Use a Personal Computer.................................. 35.5 8.1 5.6 2.5 Use a Personal Computer.............................................. 75.6 17.5 12.1 5.4 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 14.1 10.0 4.0 Laptop Model............................................................. 16.9 3.4 2.1 1.3 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 3.4 2.5 0.9 2 to 15 Hours............................................................. 29.1 7.0 4.8 2.3 16 to 40 Hours........................................................... 13.5 2.8 2.1 0.7 41 to 167 Hours......................................................... 6.3

219

Total...................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

15.2 15.2 7.8 1.0 1.2 3.3 1.9 For Two Housing Units............................. 0.9 Q N Q 0.6 N Heat Pump.................................................. 9.2 7.4 0.3 Q 0.7 0.5 Portable Electric Heater............................... 1.6 0.8 Q Q Q 0.3 Other Equipment......................................... 1.9 0.7 Q Q 0.7 Q Fuel Oil........................................................... 7.7 5.5 0.4 0.8 0.9 0.2 Steam or Hot Water System........................ 4.7 2.9 Q 0.7 0.8 N For One Housing Unit.............................. 3.3 2.9 Q Q Q N For Two Housing Units............................. 1.4 Q Q 0.5 0.8 N Central Warm-Air Furnace........................... 2.8 2.4 Q Q Q 0.2 Other Equipment......................................... 0.3 0.2 Q N Q N Wood..............................................................

220

Total...............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment................. Do Not Have Cooling Equipment................. 17.8 5.3 4.7 2.8 1.9 3.1 3.6 7.5 Have Cooling Equipment.............................. 93.3 21.5 24.1 17.8 11.2 18.8 13.0 31.1 Use Cooling Equipment............................... 91.4 21.0 23.5 17.4 11.0 18.6 12.6 30.3 Have Equipment But Do Not Use it............. 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Air-Conditioning Equipment 1, 2 Central System............................................ 65.9 11.0 16.5 13.5 8.7 16.1 6.4 17.2 Without a Heat Pump.............................. 53.5 9.4 13.6 10.7 7.1 12.7 5.4 14.5 With a Heat Pump................................... 12.3 1.7 2.8 2.8 1.6 3.4 1.0 2.7 Window/Wall Units...................................... 28.9 10.5 8.1 4.5 2.7 3.1 6.7 14.1 1 Unit....................................................... 14.5 5.8 4.3 2.0 1.1 1.3 3.4 7.4 2 Units.....................................................

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.4 1.0 0.4 2 Times A Day...................................................... 24.6 5.8 3.5 2.3 Once a Day........................................................... 42.3 10.7 7.8 2.9 A Few Times Each Week...................................... 27.2 5.6 4.0 1.6 About Once a Week.............................................. 3.9 0.9 0.6 0.3 Less Than Once a Week....................................... 4.1 1.1 0.7 0.4 No Hot Meals Cooked........................................... 0.9 Q Q N Conventional Oven Use an Oven......................................................... 109.6 25.3 17.6 7.7 More Than Once a Day..................................... 8.9 1.3 0.8 0.5 Once a Day.......................................................

222

Total...............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Personal Computers Do Not Use a Personal Computer ........... 35.5 17.1 10.8 4.2 1.8 1.6 10.3 20.6 Use a Personal Computer......................... 75.6 9.6 18.0 16.4 11.3 20.3 6.4 17.9 Number of Desktop PCs 1.......................................................... 50.3 8.3 14.2 11.4 7.2 9.2 5.3 14.2 2.......................................................... 16.2 0.9 2.6 3.7 2.9 6.2 0.8 2.6 3 or More............................................. 9.0 0.4 1.2 1.3 1.2 5.0 0.3 1.1 Number of Laptop PCs 1.......................................................... 22.5 2.2 4.6 4.5 2.9 8.3 1.4 4.0 2.......................................................... 4.0 Q 0.4 0.6 0.4 2.4 Q 0.5 3 or More............................................. 0.7 Q Q Q Q 0.4 Q Q Type of Monitor Used on Most-Used PC Desk-top

223

Total...............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

20.6 20.6 25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer ........... 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer......................... 75.6 13.7 17.5 26.6 17.8 Number of Desktop PCs 1.......................................................... 50.3 9.3 11.9 18.2 11.0 2.......................................................... 16.2 2.9 3.5 5.5 4.4 3 or More............................................. 9.0 1.5 2.1 2.9 2.5 Number of Laptop PCs 1.......................................................... 22.5 4.7 4.6 7.7 5.4 2.......................................................... 4.0 0.6 0.9 1.5 1.1 3 or More............................................. 0.7 Q Q Q 0.3 Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 7.9 11.4 15.4 10.2 Flat-panel LCD.................................

224

Total................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

111.1 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Do Not Have Space Heating Equipment....... 1.2 0.5 0.3 0.2 Q 0.2 0.3 0.6 Have Main Space Heating Equipment.......... 109.8 26.2 28.5 20.4 13.0 21.8 16.3 37.9 Use Main Space Heating Equipment............ 109.1 25.9 28.1 20.3 12.9 21.8 16.0 37.3 Have Equipment But Do Not Use It.............. 0.8 0.3 0.3 Q Q N 0.4 0.6 Main Heating Fuel and Equipment Natural Gas.................................................. 58.2 12.2 14.4 11.3 7.1 13.2 7.6 18.3 Central Warm-Air Furnace........................ 44.7 7.5 10.8 9.3 5.6 11.4 4.6 12.0 For One Housing Unit........................... 42.9 6.9 10.3 9.1 5.4 11.3 4.1 11.0 For Two Housing Units......................... 1.8 0.6 0.6 Q Q Q 0.4 0.9 Steam or Hot Water System..................... 8.2 2.4 2.5 1.0 1.0 1.3 1.5 3.6 For One Housing Unit...........................

225

Total...........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Q Q Table HC3.2 Living Space Characteristics by Owner-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Million U.S. Housing Units Owner- Occupied Housing Units (millions) Type of Owner-Occupied Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC3.2 Living Space Characteristics by Owner-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Million U.S. Housing Units Owner- Occupied Housing Units (millions) Type of Owner-Occupied Housing Unit Housing Units (millions)

226

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

25.6 25.6 40.7 24.2 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.7 Have Main Space Heating Equipment.................. 109.8 20.5 25.6 40.3 23.4 Use Main Space Heating Equipment.................... 109.1 20.5 25.6 40.1 22.9 Have Equipment But Do Not Use It...................... 0.8 N N Q 0.6 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 11.4 18.4 13.6 14.7 Central Warm-Air Furnace................................ 44.7 6.1 16.2 11.0 11.4 For One Housing Unit................................... 42.9 5.6 15.5 10.7 11.1 For Two Housing Units................................. 1.8 0.5 0.7 Q 0.3 Steam or Hot Water System............................. 8.2 4.9 1.6 1.0 0.6 For One Housing Unit................................... 5.1 3.2 1.1 0.4

227

Total...........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

0.6 0.6 15.1 5.5 Do Not Have Cooling Equipment............................. 17.8 4.0 2.4 1.7 Have Cooling Equipment.......................................... 93.3 16.5 12.8 3.8 Use Cooling Equipment........................................... 91.4 16.3 12.6 3.7 Have Equipment But Do Not Use it.......................... 1.9 0.3 Q Q Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 6.0 5.2 0.8 Without a Heat Pump........................................... 53.5 5.5 4.8 0.7 With a Heat Pump............................................... 12.3 0.5 0.4 Q Window/Wall Units.................................................. 28.9 10.7 7.6 3.1 1 Unit................................................................... 14.5 4.3 2.9 1.4 2 Units.................................................................

228

Total.......................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

4.2 4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer ................... 35.5 6.4 2.2 4.2 Use a Personal Computer................................ 75.6 17.8 5.3 12.5 Number of Desktop PCs 1.................................................................. 50.3 11.0 3.4 7.6 2.................................................................. 16.2 4.4 1.3 3.1 3 or More..................................................... 9.0 2.5 0.7 1.8 Number of Laptop PCs 1.................................................................. 22.5 5.4 1.5 3.9 2.................................................................. 4.0 1.1 0.3 0.8 3 or More..................................................... 0.7 0.3 Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)...........................

229

Total....................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

111.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer.................................. 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer.............................................. 75.6 30.3 12.5 18.1 14.7 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 22.9 9.8 14.1 11.9 Laptop Model............................................................. 16.9 7.4 2.7 4.0 2.9 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 5.7 1.8 2.9 3.2 2 to 15 Hours............................................................. 29.1 11.9 5.1 6.5 5.7 16 to 40 Hours........................................................... 13.5 5.5 2.5 3.3 2.2 41 to 167 Hours.........................................................

230

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

7.1 7.1 19.0 22.7 22.3 Do Not Have Space Heating Equipment............... 1.2 0.7 Q 0.2 Q Have Main Space Heating Equipment.................. 109.8 46.3 18.9 22.5 22.1 Use Main Space Heating Equipment.................... 109.1 45.6 18.8 22.5 22.1 Have Equipment But Do Not Use It...................... 0.8 0.7 Q N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 27.0 11.9 14.9 4.3 Central Warm-Air Furnace................................ 44.7 19.8 8.6 12.8 3.6 For One Housing Unit................................... 42.9 18.8 8.3 12.3 3.5 For Two Housing Units................................. 1.8 1.0 0.3 0.4 Q Steam or Hot Water System............................. 8.2 4.4 2.1 1.4 0.3 For One Housing Unit................................... 5.1 2.1 1.6 1.0

231

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

15.1 15.1 5.5 Do Not Have Space Heating Equipment............... 1.2 Q Q Q Have Main Space Heating Equipment.................. 109.8 20.5 15.1 5.4 Use Main Space Heating Equipment.................... 109.1 20.5 15.1 5.4 Have Equipment But Do Not Use It...................... 0.8 N N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 11.4 9.1 2.3 Central Warm-Air Furnace................................ 44.7 6.1 5.3 0.8 For One Housing Unit................................... 42.9 5.6 4.9 0.7 For Two Housing Units................................. 1.8 0.5 0.4 Q Steam or Hot Water System............................. 8.2 4.9 3.6 1.3 For One Housing Unit................................... 5.1 3.2 2.2 1.0 For Two Housing Units.................................

232

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 2.8 0.7 0.5 0.2 Million U.S. Housing Units Home Electronics Usage Indicators Table HC12.12 Home Electronics Usage Indicators by Midwest Census Region,...

233

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 13.2 1.8 1.2 0.5 Table HC11.10 Home Appliances Usage Indicators by Northeast Census Region, 2005 Million U.S. Housing Units Home Appliances...

234

Total..........................................................  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

... 2.8 1.1 0.7 Q 0.4 Million U.S. Housing Units Home Electronics Usage Indicators Table HC13.12 Home Electronics Usage Indicators by South Census Region,...

235

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 13.2 3.1 1.0 2.2 Table HC14.10 Home Appliances Usage Indicators by West Census Region, 2005 Million U.S. Housing Units Home Appliances...

236

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

States New York Florida Texas California Million U.S. Housing Units Home Electronics Usage Indicators Table HC15.12 Home Electronics Usage Indicators by Four Most Populated...

237

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 13.2 2.7 3.5 2.2 1.3 3.5 1.3 3.8 Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line Eligible for Federal...

238

Total..........................................................  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

... 13.2 3.4 2.0 1.4 Table HC12.10 Home Appliances Usage Indicators by Midwest Census Region, 2005 Million U.S. Housing Units Home Appliances...

239

Total..........................................................  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

Census Region Northeast Midwest South West Million U.S. Housing Units Home Electronics Usage Indicators Table HC10.12 Home Electronics Usage Indicators by U.S. Census Region, 2005...

240

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

(as Self-Reported) City Town Suburbs Rural Million U.S. Housing Units Home Electronics Usage Indicators Table HC8.12 Home Electronics Usage Indicators by UrbanRural Location,...

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 13.2 4.4 2.5 3.0 3.4 Table HC8.10 Home Appliances Usage Indicators by UrbanRural Location, 2005 Million U.S. Housing Units UrbanRural...

242

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 2.8 0.6 Q 0.5 Million U.S. Housing Units Home Electronics Usage Indicators Table HC14.12 Home Electronics Usage Indicators by West Census Region, 2005...

243

Total..........................................................  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

... 13.2 4.9 2.3 1.1 1.5 Table HC13.10 Home Appliances Usage Indicators by South Census Region, 2005 Million U.S. Housing Units South Census Region...

244

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

... 51.9 7.0 4.8 2.2 Not Asked (Mobile Homes or Apartment in Buildings with 5 or More Units)... 23.7...

245

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

Housing Units Living Space Characteristics Attached 2 to 4 Units 5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) Single-Family Units Detached...

246

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

0.7 21.7 6.9 12.1 Do Not Have Space Heating Equipment... 1.2 Q Q N Q Have Main Space Heating Equipment... 109.8 40.3 21.4 6.9 12.0 Use Main Space Heating...

247

Total  

U.S. Energy Information Administration (EIA) Indexed Site

Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Other Renewable Diesel Fuel Other Renewable Fuels Gasoline Blending...

248

Total  

U.S. Energy Information Administration (EIA) Indexed Site

Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Fuel Other Renewable Diesel Fuel Other Renewable Fuels Gasoline Blending...

249

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.2 1.0 0.2 2 Times A Day...................................................... 24.6 4.0 2.7 1.2 Once a Day........................................................... 42.3 7.9 5.4 2.5 A Few Times Each Week...................................... 27.2 6.0 4.8 1.2 About Once a Week.............................................. 3.9 0.6 0.5 Q Less Than Once a Week....................................... 4.1 0.6 0.4 Q No Hot Meals Cooked........................................... 0.9 0.3 Q Q Conventional Oven Use an Oven......................................................... 109.6 20.3 14.9 5.4 More Than Once a Day..................................... 8.9 1.4 1.2 0.3 Once a Day.......................................................

250

Total...............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

47.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer ........... 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer......................... 75.6 30.3 12.5 18.1 14.7 Number of Desktop PCs 1.......................................................... 50.3 21.1 8.3 10.7 10.1 2.......................................................... 16.2 6.2 2.8 4.1 3.0 3 or More............................................. 9.0 2.9 1.4 3.2 1.6 Number of Laptop PCs 1.......................................................... 22.5 9.1 3.6 6.0 3.8 2.......................................................... 4.0 1.5 0.6 1.3 0.7 3 or More............................................. 0.7 0.3 Q Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 17.7 7.5 10.2 9.6 Flat-panel LCD.................................

251

Total........................................................  

U.S. Energy Information Administration (EIA) Indexed Site

111.1 24.5 1,090 902 341 872 780 441 Census Region and Division Northeast............................................. 20.6 6.7 1,247 1,032 Q 811 788 147 New England.................................... 5.5 1.9 1,365 1,127 Q 814 748 107 Middle Atlantic.................................. 15.1 4.8 1,182 978 Q 810 800 159 Midwest................................................ 25.6 4.6 1,349 1,133 506 895 810 346 East North Central............................ 17.7 3.2 1,483 1,239 560 968 842 351 West North Central........................... 7.9 1.4 913 789 329 751 745 337 South................................................... 40.7 7.8 881 752 572 942 873 797 South Atlantic................................... 21.7 4.9 875 707 522 1,035 934 926 East South Central........................... 6.9 0.7 Q Q Q 852 826 432 West South Central..........................

252

Total...............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

0.7 0.7 21.7 6.9 12.1 Personal Computers Do Not Use a Personal Computer ........... 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer......................... 75.6 26.6 14.5 4.1 7.9 Number of Desktop PCs 1.......................................................... 50.3 18.2 10.0 2.9 5.3 2.......................................................... 16.2 5.5 3.0 0.7 1.8 3 or More............................................. 9.0 2.9 1.5 0.5 0.8 Number of Laptop PCs 1.......................................................... 22.5 7.7 4.3 1.1 2.4 2.......................................................... 4.0 1.5 0.9 Q 0.4 3 or More............................................. 0.7 Q Q Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 15.4 7.9 2.8 4.8 Flat-panel LCD.................................

253

Total.................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day.............................. 8.2 2.9 2.5 1.3 0.5 1.0 2.4 4.6 2 Times A Day........................................... 24.6 6.5 7.0 4.3 3.2 3.6 4.8 10.3 Once a Day................................................ 42.3 8.8 9.8 8.7 5.1 10.0 5.0 12.9 A Few Times Each Week........................... 27.2 5.6 7.2 4.7 3.3 6.3 3.2 7.5 About Once a Week................................... 3.9 1.1 1.1 0.6 0.5 0.6 0.4 1.4 Less Than Once a Week............................ 4.1 1.3 1.0 0.9 0.5 0.4 0.7 1.4 No Hot Meals Cooked................................ 0.9 0.5 Q Q Q Q 0.2 0.5 Conventional Oven Use an Oven.............................................. 109.6 26.1 28.5 20.2 12.9 21.8 16.3 37.8 More Than Once a Day..........................

254

Total..................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

. . 111.1 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Do Not Have Cooling Equipment..................... 17.8 3.9 1.8 2.2 2.1 3.1 2.6 1.7 0.4 Have Cooling Equipment................................. 93.3 10.8 5.6 10.3 10.4 15.8 16.0 15.6 8.8 Use Cooling Equipment.................................. 91.4 10.6 5.5 10.3 10.3 15.3 15.7 15.3 8.6 Have Equipment But Do Not Use it................. 1.9 Q Q Q Q 0.6 0.4 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 3.7 2.6 6.1 6.8 11.2 13.2 13.9 8.2 Without a Heat Pump.................................. 53.5 3.6 2.3 5.5 5.8 9.5 10.1 10.3 6.4 With a Heat Pump....................................... 12.3 Q 0.3 0.6 1.0 1.7 3.1 3.6 1.7 Window/Wall Units....................................... 28.9 7.3 3.2 4.5 3.7 4.8 3.0 1.9 0.7 1 Unit..........................................................

255

Total..............................................  

U.S. Energy Information Administration (EIA) Indexed Site

111.1 86.6 2,720 1,970 1,310 1,941 1,475 821 1,059 944 554 Census Region and Division Northeast.................................... 20.6 13.9 3,224 2,173 836 2,219 1,619 583 903 830 Q New England.......................... 5.5 3.6 3,365 2,154 313 2,634 1,826 Q 951 940 Q Middle Atlantic........................ 15.1 10.3 3,167 2,181 1,049 2,188 1,603 582 Q Q Q Midwest...................................... 25.6 21.0 2,823 2,239 1,624 2,356 1,669 1,336 1,081 961 778 East North Central.................. 17.7 14.5 2,864 2,217 1,490 2,514 1,715 1,408 907 839 553 West North Central................. 7.9 6.4 2,729 2,289 1,924 1,806 1,510 1,085 1,299 1,113 1,059 South.......................................... 40.7 33.0 2,707 1,849 1,563 1,605 1,350 954 1,064 970 685 South Atlantic......................... 21.7 16.8 2,945 1,996 1,695 1,573 1,359 909 1,044 955

256

Total.................................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

... ... 111.1 20.6 15.1 5.5 Do Not Have Cooling Equipment................................. 17.8 4.0 2.4 1.7 Have Cooling Equipment............................................. 93.3 16.5 12.8 3.8 Use Cooling Equipment............................................... 91.4 16.3 12.6 3.7 Have Equipment But Do Not Use it............................. 1.9 0.3 Q Q Type of Air-Conditioning Equipment 1, 2 Central System.......................................................... 65.9 6.0 5.2 0.8 Without a Heat Pump.............................................. 53.5 5.5 4.8 0.7 With a Heat Pump................................................... 12.3 0.5 0.4 Q Window/Wall Units.................................................... 28.9 10.7 7.6 3.1 1 Unit.......................................................................

257

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 8.5 2.7 2.6 4.0 Have Cooling Equipment............................................ 93.3 38.6 16.2 20.1 18.4 Use Cooling Equipment............................................. 91.4 37.8 15.9 19.8 18.0 Have Equipment But Do Not Use it............................ 1.9 0.9 0.3 0.3 0.4 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 25.8 10.9 16.6 12.5 Without a Heat Pump............................................. 53.5 21.2 9.7 13.7 8.9 With a Heat Pump................................................. 12.3 4.6 1.2 2.8 3.6 Window/Wall Units.................................................. 28.9 13.4 5.6 3.9 6.1 1 Unit.....................................................................

258

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 10.3 3.1 7.3 Have Cooling Equipment............................................ 93.3 13.9 4.5 9.4 Use Cooling Equipment............................................. 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it............................ 1.9 1.0 Q 0.8 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat Pump............................................. 53.5 8.7 3.2 5.5 With a Heat Pump................................................. 12.3 1.7 0.7 1.0 Window/Wall Units.................................................. 28.9 3.6 0.6 3.0 1 Unit..................................................................... 14.5 2.9 0.5 2.4 2 Units...................................................................

259

Total..................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

78.1 78.1 64.1 4.2 1.8 2.3 5.7 Do Not Have Cooling Equipment..................... 17.8 11.3 9.3 0.6 Q 0.4 0.9 Have Cooling Equipment................................. 93.3 66.8 54.7 3.6 1.7 1.9 4.8 Use Cooling Equipment.................................. 91.4 65.8 54.0 3.6 1.7 1.9 4.7 Have Equipment But Do Not Use it................. 1.9 1.1 0.8 Q N Q Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 51.7 43.9 2.5 0.7 1.6 3.1 Without a Heat Pump.................................. 53.5 41.1 34.8 2.1 0.5 1.2 2.6 With a Heat Pump....................................... 12.3 10.6 9.1 0.4 Q 0.3 0.6 Window/Wall Units....................................... 28.9 16.5 12.0 1.3 1.0 0.4 1.7 1 Unit.......................................................... 14.5 7.2 5.4 0.5 0.2 Q 0.9 2 Units.........................................................

260

Total.............................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................ 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................ 1.9 0.5 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 32.1 17.6 5.2 9.3 Without a Heat Pump............................................. 53.5 23.2 10.9 3.8 8.4 With a Heat Pump................................................. 12.3 9.0 6.7 1.4 0.9 Window/Wall Units.................................................. 28.9 8.0 3.4 1.7 2.9 1 Unit.....................................................................

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Total........................................................................  

U.S. Energy Information Administration (EIA) Indexed Site

4.2 4.2 7.6 16.6 Do Not Have Space Heating Equipment............... 1.2 0.7 Q 0.7 Have Main Space Heating Equipment.................. 109.8 23.4 7.5 16.0 Use Main Space Heating Equipment.................... 109.1 22.9 7.4 15.4 Have Equipment But Do Not Use It...................... 0.8 0.6 Q 0.5 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 14.7 4.6 10.1 Central Warm-Air Furnace................................ 44.7 11.4 4.0 7.4 For One Housing Unit................................... 42.9 11.1 3.8 7.3 For Two Housing Units................................. 1.8 0.3 Q Q Steam or Hot Water System............................. 8.2 0.6 0.3 0.3 For One Housing Unit................................... 5.1 0.4 0.2 0.1 For Two Housing Units.................................

262

Total..............................................................  

U.S. Energy Information Administration (EIA) Indexed Site

Do Not Have Cooling Equipment................ Do Not Have Cooling Equipment................ 17.8 5.3 4.7 2.8 1.9 3.1 3.6 7.5 Have Cooling Equipment............................. 93.3 21.5 24.1 17.8 11.2 18.8 13.0 31.1 Use Cooling Equipment.............................. 91.4 21.0 23.5 17.4 11.0 18.6 12.6 30.3 Have Equipment But Do Not Use it............. 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Type of Air-Conditioning Equipment 1, 2 Central System.......................................... 65.9 11.0 16.5 13.5 8.7 16.1 6.4 17.2 Without a Heat Pump.............................. 53.5 9.4 13.6 10.7 7.1 12.7 5.4 14.5 With a Heat Pump................................... 12.3 1.7 2.8 2.8 1.6 3.4 1.0 2.7 Window/Wall Units................................... 28.9 10.5 8.1 4.5 2.7 3.1 6.7 14.1 1 Unit...................................................... 14.5 5.8 4.3 2.0 1.1 1.3 3.4 7.4 2 Units....................................................

263

Idle Operating Total Stream Day  

U.S. Energy Information Administration (EIA) Indexed Site

3 3 Idle Operating Total Stream Day Barrels per Idle Operating Total Calendar Day Barrels per Atmospheric Crude Oil Distillation Capacity Idle Operating Total Operable Refineries Number of State and PAD District a b b 11 10 1 1,293,200 1,265,200 28,000 1,361,700 1,329,700 32,000 ............................................................................................................................................... PAD District I 1 1 0 182,200 182,200 0 190,200 190,200 0 ................................................................................................................................................................................................................................................................................................ Delaware......................................

264

Properties of Luminous Red Galaxies in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

We perform population synthesis modelling of a magnitude-limited sample of 4391 Luminous Red Galaxies selected from the Sloan Digital Sky Survey Data Release 4 (SDSS DR4). We fit measured spectral indices using a large library of high resolution spectra, covering a wide range of metallicities and assuming an exponentially decaying star-formation rate punctuated by bursts, to obtain median-likelihood estimates for the light-weighted age, metallicity, stellar mass and extinction for the galaxies. The ages lie in the range 4-10 Gyr, peaking near 6 Gyr, with metallicities in the range -0.4<[Z/H]<0.4, peaking at [Z/H] ~ 0.2. Only a few per cent of the spectra are better fit allowing for a burst in addition to continuous star-formation. The total stellar masses of all the galaxies are confined to a very narrow range. Our results broadly agree with those of previous groups using an independent population synthesis code. We find, however, that our choice in priors results in ages 1-2 Gyr smaller, decreasing the peak star formation epoch from z=2.3 to z=1.3. We develop a metal evolution model incorporating stochastic star-formation quenching motivated by recent attempts to solve the `anti-hierarchical' formation problem of ellipticals. Two scenarios emerge, a closed box with an effective stellar yield of 0.26, and an accreting box with an effective stellar yield of 0.10. Both scenarios require an IMF weighted towards massive stars and characteristic star-formation quenching times of about 100 Myr, the expected lifetime of luminous QSOs. The models predict an anti-correlation between the age and mean metallicity similar to that observed.

T. Barber; A. Meiksin; T. Murphy

2006-11-02T23:59:59.000Z

265

total energy | OpenEI  

Open Energy Info (EERE)

total energy total energy Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion BTUs, and quantifies the energy prices using U.S. dollars. The data is broken down into total production, imports, exports, consumption, and prices for energy types. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO consumption EIA export import production reference case total energy Data application/vnd.ms-excel icon AEO2011: Total Energy Supply, Disposition, and Price Summary - Reference Case (xls, 112.8 KiB) Quality Metrics Level of Review Peer Reviewed

266

Sloan Digital Sky Survey Extension for Galactic Understanding and Exploration (SEGUE): Data from a Spectroscopic Survey of 240,000 Stars with g=14-20  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

The Sloan Digital Sky Survey (SDSS) is a series of three interlocking imaging and spectroscopic surveys, carried out over an eight-year period with a dedicated 2.5m telescope located at Apache Point Observatory in Southern New Mexico. The seventh data release (DR7) from the SDSS represented a completion of the overall, original project, though SDSS-III began in 2008. SEGUE, which stands for the Sloan Digital Sky Survey Extension for Galactic Understanding and Exploration, was one of those three SDSS surveys. The images and spectra obtained by SEGUE allowed astronomers to map the positions and velocities of hundreds of thousands of stars, from faint, relatively near-by (within about 100 pc or roughly 300 light-years) ancient stellar embers known as white dwarfs to bright stellar giants located in the outer reaches of the stellar halo, more than 100,000 light-years away. Encoded within the spectral data are the composition and temperature of these stars, vital clues for determining the age and origin of different populations of stars within the Galaxy. [from the SEGUE page at http://www.sdss.org/segue/

Yanny, Brian; Rockosi, Constance; Newberg, Heidi Jo; Knapp, Gillian R.

267

High resolution image reconstruction with constrained, total-variation minimization  

E-Print Network [OSTI]

in computed tomography (CT), see for example [1], because it is possible to account for noise in the data the resolution is arbitrarily high, because the system resolution is still limited by the discrete data sampling of the volume and ideal conditions of perfect data consistency: g = Xf, (1) where g represents the projection

Kurien, Susan

268

Gamma-Ray Imaging with the Coded Mask IBIS Telescope  

E-Print Network [OSTI]

The IBIS telescope onboard INTEGRAL, the ESA gamma-ray space mission to be launched in 2002, is a soft gamma-ray (20 keV - 10 MeV) device based on a coded aperture imaging system. We describe here basic concepts of coded masks, the imaging system of the IBIS telescope, and the standard data analysis procedures to reconstruct sky images. This analysis includes, for both the low-energy detector layer (ISGRI) and the high energy layer (PICSIT), iterative procedures which decode recorded shadowgrams, search for and locate sources, clean for secondary lobes, and then rotate and compose sky images. These procedures will be implemented in the Quick Look and Standard Analysis of the INTEGRAL Science Data Center (ISDC) as IBIS Instrument Specific Software.

Goldwurm, A; Gros, A; Stephen, J; Foschini, L; Gianotti, F; Natalucci, L; De Cesare, G; Santo, M D

2000-01-01T23:59:59.000Z

269

Effects of Extreme Drought and Megafires on Sky Island Conifer Forests of the Peninsular Ranges, Southern California.  

E-Print Network [OSTI]

??Conifer populations in the Peninsular Range of southern California and Baja California form isolated biogeographic "sky-islands" on mountains with orographic enhanced precipitation. Fire suppression management… (more)

Goforth, Brett Russell

2009-01-01T23:59:59.000Z

270

Breast ultrasound tomography with total-variation regularization  

SciTech Connect (OSTI)

Breast ultrasound tomography is a rapidly developing imaging modality that has the potential to impact breast cancer screening and diagnosis. A new ultrasound breast imaging device (CURE) with a ring array of transducers has been designed and built at Karmanos Cancer Institute, which acquires both reflection and transmission ultrasound signals. To extract the sound-speed information from the breast data acquired by CURE, we have developed an iterative sound-speed image reconstruction algorithm for breast ultrasound transmission tomography based on total-variation (TV) minimization. We investigate applicability of the TV tomography algorithm using in vivo ultrasound breast data from 61 patients, and compare the results with those obtained using the Tikhonov regularization method. We demonstrate that, compared to the Tikhonov regularization scheme, the TV regularization method significantly improves image quality, resulting in sound-speed tomography images with sharp (preserved) edges of abnormalities and few artifacts.

Huang, Lianjie [Los Alamos National Laboratory; Li, Cuiping [KARMANOS CANCER INSTIT.; Duric, Neb [KARMANOS CANCER INSTIT

2009-01-01T23:59:59.000Z

271

Tracking Santa With Our Eyes in the Sky | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

Tracking Santa With Our Eyes in the Sky Tracking Santa With Our Eyes in the Sky Tracking Santa With Our Eyes in the Sky December 24, 2013 - 10:00am Addthis The Energy Department's Los Alamos National Lab is tracking Santa Claus as he circles the globe the night before Christmas. The Energy Department's Los Alamos National Lab is tracking Santa Claus as he circles the globe the night before Christmas. Michael Hess Michael Hess Former Digital Communications Specialist, Office of Public Affairs Every year since 1998, the Energy Department's Los Alamos National Lab has been using state-of-the-art technology to track Santa Claus as he circles the globe the night before Christmas. You'll be able to monitor St. Nick's journey here starting at 6 a.m. ET on Christmas Eve. Since Santa doesn't file his flight path with the Federal Aviation

272

Bluer Skies and Brighter Days: The U.S. and India Collaborate in First  

Broader source: Energy.gov (indexed) [DOE]

Bluer Skies and Brighter Days: The U.S. and India Collaborate in Bluer Skies and Brighter Days: The U.S. and India Collaborate in First Long-Term Climate Experiment Bluer Skies and Brighter Days: The U.S. and India Collaborate in First Long-Term Climate Experiment June 27, 2011 - 12:42pm Addthis ARM Mobile Facility instrumentation is installed in June 2011 at the ARIES Observatory in Nainital, India, for the Ganges Valley Aerosol Experiment (GVAX). | Courtesy of ARM.gov ARM Mobile Facility instrumentation is installed in June 2011 at the ARIES Observatory in Nainital, India, for the Ganges Valley Aerosol Experiment (GVAX). | Courtesy of ARM.gov Charles Rousseaux Charles Rousseaux Senior Writer, Office of Science What are the key facts? Energy Department's Atmospheric Radiation Measurement (ARM) Climate Research Facility recently deployed its mobile facility to

273

Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection |  

Broader source: Energy.gov (indexed) [DOE]

Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection August 13, 2013 - 1:59pm Addthis Photo by J.D. Griggs, courtesy of U.S.Geological Survey Photo by J.D. Griggs, courtesy of U.S.Geological Survey For Additional Information To learn more about the carbon storage projects in which NETL is involved, please visit the NETL Carbon Storage website How can a prehistoric volcanic eruption help us reduce the amount of CO2 released into the atmosphere today? The answer is found in the basalt formations created by the lava - formations that can be used as sites for injecting carbon dioxide (CO2) captured from industrial sources in a process called carbon capture and storage (CCS).

274

3.2B Pixel Camera to Shed Light on Southern Sky | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

3.2B Pixel Camera to Shed Light on Southern Sky 3.2B Pixel Camera to Shed Light on Southern Sky 3.2B Pixel Camera to Shed Light on Southern Sky July 24, 2012 - 10:56am Addthis This is an artist's rendering of the Large Synoptic Survey Telescope (LSST), the 8.4 meter wide-field telescope that the National Science Board recently approved to advance to its final design stage. Construction is expected to begin in 2014 and take about five years. | Photo courtesy of LSST Corporation. This is an artist's rendering of the Large Synoptic Survey Telescope (LSST), the 8.4 meter wide-field telescope that the National Science Board recently approved to advance to its final design stage. Construction is expected to begin in 2014 and take about five years. | Photo courtesy of LSST Corporation. Charles Rousseaux Charles Rousseaux

275

Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection |  

Broader source: Energy.gov (indexed) [DOE]

Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection Ancient Lava Flows Trap CO2 for Long-Term Storage in Big Sky Injection August 13, 2013 - 1:59pm Addthis Photo by J.D. Griggs, courtesy of U.S.Geological Survey Photo by J.D. Griggs, courtesy of U.S.Geological Survey For Additional Information To learn more about the carbon storage projects in which NETL is involved, please visit the NETL Carbon Storage website How can a prehistoric volcanic eruption help us reduce the amount of CO2 released into the atmosphere today? The answer is found in the basalt formations created by the lava - formations that can be used as sites for injecting carbon dioxide (CO2) captured from industrial sources in a process called carbon capture and storage (CCS).

276

Use of Aeronet Aerosol Retrievals to Calculate Clear-Sky Irradiance at the Surface  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

AERONET Aerosol Retrievals to AERONET Aerosol Retrievals to Calculate Clear-Sky Irradiance at the Surface G. L. Schuster National Aeronautics and Space Administration Langley Research Center Hampton, Virginia O. Dubovik National Aeronautics and Space Administration Goddard Space Flight Center Laboratory for Terrestrial Physics Greenbelt, Maryland Motivation The worldwide aerosol robotic network (AERONET) of ground-based radiometers was developed (in part) as a satellite validation tool (Holben et al. 1998). These sites utilize spectral sky-scanning radiometers, providing more information for aerosol retrievals than conventional sunphotometer measurements. The use of the almucantar sky radiance scans in conjunction with the aerosol optical thicknesses are the basis of the AERONET Dubovik retrievals, which provide the aerosol size

277

A SEARCH FOR CONCENTRIC CIRCLES IN THE 7 YEAR WILKINSON MICROWAVE ANISOTROPY PROBE TEMPERATURE SKY MAPS  

SciTech Connect (OSTI)

In this Letter, we search for concentric circles with low variance in cosmic microwave background sky maps. The detection of such circles would hint at new physics beyond the current cosmological concordance model, which states that the universe is isotropic and homogeneous, and filled with Gaussian fluctuations. We first describe a set of methods designed to detect such circles, based on matched filters and {chi}{sup 2} statistics, and then apply these methods to the best current publicly available data, the 7 year Wilkinson Microwave Anisotropy Probe (WMAP) temperature sky maps. We compare the observations with an ensemble of 1000 Gaussian {Lambda}CDM simulations. Based on these tests, we conclude that the WMAP sky maps are fully compatible with the Gaussian and isotropic hypothesis as measured by low-variance ring statistics.

Wehus, I. K. [Department of Physics, University of Oslo, P.O. Box 1048 Blindern, N-0316 Oslo (Norway); Eriksen, H. K., E-mail: i.k.wehus@fys.uio.no [Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, N-0315 Oslo (Norway)

2011-06-01T23:59:59.000Z

278

Sky maps without anisotropies in the cosmic microwave background are a better fit to WMAP's uncalibrated time ordered data than the official sky maps  

E-Print Network [OSTI]

The purpose of this reanalysis of the WMAP uncalibrated time ordered data (TOD) was two fold. The first was to reassess the reliability of the detection of the anisotropies in the official WMAP sky maps of the cosmic microwave background (CMB). The second was to assess the performance of a proposed criterion in avoiding systematic error in detecting a signal of interest. The criterion was implemented by testing the null hypothesis that the uncalibrated TOD was consistent with no anisotropies when WMAP's hourly calibration parameters were allowed to vary. It was shown independently for all 20 WMAP channels that sky maps with no anisotropies were a better fit to the TOD than those from the official analysis. The recently launched Planck satellite should help sort out this perplexing result.

Keith S. Cover

2009-09-02T23:59:59.000Z

279

ADONIS high contrast infrared imaging of Sirius-B  

E-Print Network [OSTI]

Sirius is the brightest star in the sky and a strong source of diffuse light for modern telescopes so that the immediate surroundings of the star are still poorly known. We study the close surroundings of the star (2 to 25 arcsec) by means of adaptive optics and coronographic device in the near-infrared, using the ESO/ADONIS system. The resulting high contrast images in the JHKs bands have a resolution of ~ 0.2 arcsec and limiting apparent magnitude ranging from mK = 9.5 at 3 arcsec, from Sirius-A to mK = 13.1 at 10 arcsec. These are the first and deepest images of the Sirius system in this infrared range. From these observations, accurate infrared photometry of the Sirius-B white dwarf companion is obtained. The JH magnitudes of Sirius-B are found to agree with expectations for a DA white dwarf of temperature (T=25000K) and gravity (log(g) = 8.5), consistent with the characteristics determined from optical observations. However, a small, significant excess is measurable for the K band, similar to that detected for "dusty" isolated white dwarfs harbouring suspected planetary debris. The possible existence of such circumstellar material around Sirius-B has still to be confirmed by further observations. These deep images allow us to search for small but yet undetected companions to Sirius. Apart from Sirius-B, no other source is detected within the total 25 arcsec field. The minimum detectable mass is around 10 MJup inside the planetary limit, indicating that an extrasolar planet at a projected distance of ~ 25 AU from Sirius would have been detected (abridged abstract).

Jean-Marc Bonnet-Bidaud; Eric Pantin

2008-09-28T23:59:59.000Z

280

Testing foundations of modern cosmology with SKA all-sky surveys  

E-Print Network [OSTI]

Continuum and HI surveys with the Square Kilometre Array (SKA) will allow us to probe some of the most fundamental assumptions of modern cosmology, including the Cosmological Principle. SKA all-sky surveys will map an enormous slice of space-time and reveal cosmology at superhorizon scales and redshifts of order unity. We illustrate the potential of these surveys and discuss the prospects to measure the cosmic radio dipole at high fidelity. We outline several potentially transformational tests of cosmology to be carried out by means of SKA all-sky surveys.

Schwarz, Dominik J; Chen, Song; Clarkson, Chris; Huterer, Dragan; Kunz, Martin; Maartens, Roy; Raccanelli, Alvise; Rubart, Matthias; Starck, Jean-Luc

2015-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

The Cosmic Lens All-Sky Survey:II. Gravitational lens candidate selection and follow-up  

E-Print Network [OSTI]

We report the final results of the search for gravitationally lensed flat-spectrum radio sources found in the combination of CLASS (Cosmic Lens All Sky Survey) and JVAS (Jodrell-Bank VLA Astrometric Survey). VLA observations of 16,503 sources have been made, resulting in the largest sample of arcsec-scale lens systems available. Contained within the 16,503 sources is a complete sample of 11,685 sources having two-point spectral indices between 1.4 and 5 GHz flatter than -0.5 and 5 GHz flux densities $\\geq$30 mJy. A subset of 8,958 sources form a well-defined statistical sample suitable for analysis of the lens statistics. We describe the systematic process by which 149 candidate lensed sources were picked from the statistical sample on the basis of possessing multiple compact components in the 0.2 arcsec-resolution VLA maps. Candidates were followed up with 0.05 arcsec resolution MERLIN and 0.003 arcsec VLBA observations at 5 GHz and rejected as lens systems if they failed well-defined surface brightness and/or morphological tests. Maps for all the candidates can be found on the World Wide Web at http://www.jb.man.ac.uk/research/gravlens/index.html We summarize the properties of each of the 22 gravitational lens systems in JVAS/CLASS. Twelve are double-image systems, nine are four-image systems and one is a six-image system. Thirteen constitute a statistically well-defined sample giving a point-source lensing rate of 1:690$\\pm$190. The interpretation of the results in terms of the properties of the lensing galaxy population and cosmological parameters will be published elsewhere. (Abridged)

I. W. A. Browne; P. N. Wilkinson; N. J. F. Jackson; S. T. Myers; C. D. Fassnacht; L. V. E. Koopmans; D. R. Marlow; M. Norbury; D. Rusin; C. M. Sykes; A. D. Biggs; R. D. Blandford; A. G. de Bruyn; K-H. Chae; P. Helbig; L. J. King; J. P. McKean; T. J. Pearson; P. M. Phillips; A. C. S. Readhead; E. Xanthopoulos; T. York

2002-11-04T23:59:59.000Z

282

Bistatic SAR: Signal Processing and Image Formation.  

SciTech Connect (OSTI)

This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013 on Kirtland Air Force Base, New Mexico.

Wahl, Daniel E.; Yocky, David A.

2014-10-01T23:59:59.000Z

283

Total variation based Fourier reconstruction and regularization for computer tomography  

E-Print Network [OSTI]

of the reconstruted image. Insufficiency of data may be caused by the undersampling of projections, by the limitedTotal variation based Fourier reconstruction and regularization for computer tomography Xiao. Index Terms-- Computer tomography, reconstruction, regular- ization, iterative method, Fourier method

Zhang, Xiaoqun

284

Rapid mapping of the sky at 240 MHz using the antennas of the Giant Metrewave Radio Telescope  

Science Journals Connector (OSTI)

......map of the sky at 240 MHz using the Giant Metrewave...frequency range of 30-1000 MHz are necessary to obtain...distribution of thermal plasma in the interstellar medium...surveys made below 500 MHz are the all-sky map...and ground radiation and atmospheric emission. The effects......

S. K. Sirothia

2009-09-11T23:59:59.000Z

285

Ground-based zenith sky abundances and in situ gas cross sections for ozone and nitrogen dioxide  

E-Print Network [OSTI]

Ground-based zenith sky abundances and in situ gas cross sections for ozone and nitrogen dioxide, in situ ambient absorption gas cell mea- surements for ozone and nitrogen dioxide, and ground-based zenith for ozone and nitrogen dioxide that are retrieved from measured spectra of the zenith sky

Dirksen, Ruud

286

PROJECTED CENTRAL DARK MATTER FRACTIONS AND DENSITIES IN MASSIVE EARLY-TYPE GALAXIES FROM THE SLOAN DIGITAL SKY SURVEY  

SciTech Connect (OSTI)

We investigate in massive early-type galaxies the variation of their two-dimensional central fraction of dark over total mass and dark matter density as a function of stellar mass, central stellar velocity dispersion, effective radius, and central surface stellar mass density. We use a sample of approximately 1.7 x 10{sup 5} galaxies from the Sloan Digital Sky Survey Data Release Seven (SDSS DR7) at redshift smaller than 0.33. We apply conservative photometric and spectroscopic cuts on the SDSS DR7 and the MPA/JHU value-added galaxy catalogs, to select galaxies with physical properties similar to those of the lenses studied in the Sloan Lens ACS Survey. The values of the galaxy stellar and total mass projected inside a cylinder of radius equal to the effective radius are obtained, respectively, by fitting the SDSS multicolor photometry with stellar population synthesis models, under the assumption of a Chabrier stellar initial mass function (IMF), and adopting a one-component isothermal total mass model with effective velocity dispersion approximated by the central stellar velocity dispersion. The plausibility of an isothermal model to represent the galaxy total mass distribution is supported by independent gravitational lensing and stellar-dynamical analyses performed in the lens subsample, which is found here to represent nicely the entire galaxy sample. We find that within the effective radius the stellar mass estimates differ from the total ones by only a relatively constant proportionality factor. In detail, we observe that the values of the projected fraction of dark over total mass and the logarithmic values of the central surface dark matter density (measured in M{sub sun} kpc{sup -2}) have almost Gaussian probability distribution functions, with median values of 0.64{sup +0.08}{sub -0.11} and 9.1{sup +0.2}{sub -0.2}, respectively. We discuss the observed correlations between these quantities and other galaxy global parameters and show that our results disfavor an interpretation of the tilt of the fundamental plane in terms of differences in the galaxy dark matter content and give useful information on the possible variations of the galaxy stellar IMF and dark matter density profile. Finally, we provide some observational evidence on the likely significant contribution of dry minor mergers, feedback from active galactic nuclei, and/or coalescence of binary black holes on the formation and evolution of massive early-type galaxies.

Grillo, C., E-mail: cgrillo@eso.or [Excellence Cluster Universe, Technische Universitaet Muenchen, Boltzmannstr. 2, D-85748, Garching (Germany); Max-Planck-Institut fuer extraterrestrische Physik, Giessenbachstr., D-85748, Garching (Germany); Universitaets-Sternwarte Muenchen, Scheinerstr. 1, D-81679 Muenchen (Germany)

2010-10-10T23:59:59.000Z

287

Contributions of artificial lighting sources on light pollution in Hong Kong measured through a night sky brightness monitoring network  

E-Print Network [OSTI]

Light pollution is a form of environmental degradation in which excessive artificial outdoor lighting, such as street lamps, neon signs, and illuminated signboards, affects the natural environment and the ecosystem. Poorly designed outdoor lighting not only wastes energy, money, and valuable Earth resources, but also robs us of our beautiful night sky. Effects of light pollution on the night sky can be evaluated by the skyglow caused by these artificial lighting sources, through measurements of the night sky brightness (NSB). The Hong Kong Night Sky Brightness Monitoring Network (NSN) was established to monitor in detail the conditions of light pollution in Hong Kong. Monitoring stations were set up throughout the city covering a wide range of urban and rural settings to continuously measure the variations of the NSB. Over 4.6 million night sky measurements were collected from 18 distinct locations between May 2010 and March 2013. This huge dataset, over two thousand times larger than our previous survey, for...

Pun, Chun Shing Jason; Leung, Wai Yan; Wong, Chung Fai

2014-01-01T23:59:59.000Z

288

A generalized algorithm for retrieving cloudy sky skin temperature from satellite thermal infrared radiances  

E-Print Network [OSTI]

A generalized algorithm for retrieving cloudy sky skin temperature from satellite thermal infrared Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta Abstract. A physical algorithm that of Jin [2000]. Two neighboring pixels over the same land cover have a difference in temperature largely

Jin, Menglin

289

Einstein@Home all-sky search for periodic gravitational waves in LIGO S5 data  

E-Print Network [OSTI]

This paper presents results of an all-sky search for periodic gravitational waves in the frequency range [50,1?190]??Hz and with frequency derivative range of ?[-20,1.1]×10[superscript -10]??Hz?s[superscript -1] for the ...

Barsotti, Lisa

290

All-sky search for periodic gravitational waves in the full S5 LIGO data  

E-Print Network [OSTI]

We report on an all-sky search for periodic gravitational waves in the frequency band 50–800 Hz and with the frequency time derivative in the range of 0 through -6×10[superscript -9]??Hz/s. Such a signal could be produced ...

Barsotti, Lisa

291

The size distribution of galaxies in the Sloan Digital Sky Survey  

Science Journals Connector (OSTI)

......redshifts. The SDSS spectroscopic pipelines have an overall performance...targeted only when the local and global sky values are within 0.05...assumed to form a bar due to a global instability; the bar is then...transformed into a bulge through a buckling instability (e.g. Kormendy......

Shiyin Shen; H. J. Mo; Simon D. M. White; Michael R. Blanton; Guinevere Kauffmann; Wolfgang Voges; J. Brinkmann; Istvan Csabai

2003-08-11T23:59:59.000Z

292

Blue not brown: UKIRT Infrared Deep Sky Survey T dwarfs with suppressed K-band flux  

Science Journals Connector (OSTI)

......Cutri R. M. et al., 2003, 2MASS All Sky Catalog of point sources. http://irsa.ipac.caltech.edu/applications/Gator/ . Day-Jones A. C. et al., 2008, MNRAS, 388, 838. Dekker H. , Delabre B., Dodorico S., 1986, inCrawford D......

D. N. Murray; B. Burningham; H. R. A. Jones; D. J. Pinfield; P. W. Lucas; S. K. Leggett; C. G. Tinney; A. C. Day-Jones; D. J. Weights; N. Lodieu; J. A. Pérez Prieto; E. Nickson; Z. H. Zhang; J. R. A. Clarke; J. S. Jenkins; M. Tamura

2011-06-11T23:59:59.000Z

293

Diagnostic analysis of atmospheric moisture and clear-sky radiative feedback in the Hadley  

E-Print Network [OSTI]

Diagnostic analysis of atmospheric moisture and clear-sky radiative feedback in the Hadley Centre and Geophysical Fluid Dynamics Laboratory (GFDL) climate models Richard P. Allan Hadley Centre, Met Office Jersey, USA A. Slingo1 Hadley Centre, Met Office, Bracknell, UK Received 23 July 2001; revised 20

Allan, Richard P.

294

Performance Period Total Fee Paid  

Broader source: Energy.gov (indexed) [DOE]

Period Period Total Fee Paid 4/29/2012 - 9/30/2012 $418,348 10/1/2012 - 9/30/2013 $0 10/1/2013 - 9/30/2014 $0 10/1/2014 - 9/30/2015 $0 10/1/2015 - 9/30/2016 $0 Cumulative Fee Paid $418,348 Contract Type: Cost Plus Award Fee Contract Period: $116,769,139 November 2011 - September 2016 $475,395 $0 Fee Information Total Estimated Contract Cost $1,141,623 $1,140,948 $1,140,948 $5,039,862 $1,140,948 Maximum Fee $5,039,862 Minimum Fee Fee Available Portage, Inc. DE-DT0002936 EM Contractor Fee Site: MOAB Uranium Mill Tailings - MOAB, UT Contract Name: MOAB Uranium Mill Tailings Remedial Action Contract September 2013 Contractor: Contract Number:

295

Buildings","Total  

U.S. Energy Information Administration (EIA) Indexed Site

L1. Floorspace Lit by Lighting Type for Non-Mall Buildings, 1995" L1. Floorspace Lit by Lighting Type for Non-Mall Buildings, 1995" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings*",54068,51570,45773,6746,34910,1161,3725,779 "Building Floorspace" "(Square Feet)" "1,001 to 5,000",6272,5718,4824,986,3767,50,22,54 "5,001 to 10,000",7299,6667,5728,1240,4341,61,169,45 "10,001 to 25,000",10829,10350,8544,1495,6442,154,553,"Q"

296

ARM - Measurement - Total cloud water  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

cloud water cloud water ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Total cloud water The total concentration (mass/vol) of ice and liquid water particles in a cloud; this includes condensed water content (CWC). Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available measurements, including those recorded for diagnostic or quality assurance purposes. External Instruments NCEPGFS : National Centers for Environment Prediction Global Forecast System Field Campaign Instruments CSI : Cloud Spectrometer and Impactor PDI : Phase Doppler Interferometer

297

Buildings","Total  

U.S. Energy Information Administration (EIA) Indexed Site

L2. Floorspace Lit by Lighting Types (Non-Mall Buildings), 1999" L2. Floorspace Lit by Lighting Types (Non-Mall Buildings), 1999" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings* ...............",61707,58693,49779,6496,37150,3058,5343,1913 "Building Floorspace" "(Square Feet)" "1,001 to 5,000 ...............",6750,5836,4878,757,3838,231,109,162 "5,001 to 10,000 ..............",7940,7166,5369,1044,4073,288,160,109 "10,001 to 25,000 .............",10534,9773,7783,1312,5712,358,633,232

298

Buildings","Total  

U.S. Energy Information Administration (EIA) Indexed Site

L3. Floorspace Lit by Lighting Type (Non-Mall Buildings), 2003" L3. Floorspace Lit by Lighting Type (Non-Mall Buildings), 2003" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings* ...............",64783,62060,51342,5556,37918,4004,4950,2403 "Building Floorspace" "(Square Feet)" "1,001 to 5,000 ...............",6789,6038,4826,678,3932,206,76,124 "5,001 to 10,000 ..............",6585,6090,4974,739,3829,192,238,248 "10,001 to 25,000 .............",11535,11229,8618,1197,6525,454,506,289

299

A robust distance measurement and dark energy constraints from the spherically averaged correlation function of Sloan Digital Sky Survey luminous red Galaxies  

Science Journals Connector (OSTI)

......measurement and dark energy constraints...Digital Sky Survey luminous red...Digital Sky Survey (SDSS) data...assuming a dark energy model or a...constraints on the dark energy and cosmological...largest effective survey volume to date......

Chia-Hsun Chuang; Yun Wang; Maddumage Don P. Hemantha

2012-06-21T23:59:59.000Z

300

TThhee EEsssseennttiiaall JJoouurrnnaall ffoorr AAmmaatteeuurr AAssttrroonnoommeerrss AArroouunndd tthhee WWoorrlldd!! Summer 2011 Star Party Calendar * Shorts From Down Under * Astro Trivia * Deep Sky Treasures * Golden  

E-Print Network [OSTI]

tthhee WWoorrlldd!! Summer 2011 Star Party Calendar * Shorts From Down Under * Astro Trivia * Deep Sky Project * Building A Home Observatory * Sky Sketching * Cleaning Optics *Short Subjects *Star People Treasures * Golden State Star Party * Periodic Error * Laser Collimation * MSRAL * Stationary Eyepiece

Blaber, Michael

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

GALACTIC ALL-SKY SURVEY HIGH-VELOCITY CLOUDS IN THE REGION OF THE MAGELLANIC LEADING ARM  

SciTech Connect (OSTI)

We present a catalog of high-velocity clouds in the region of the Magellanic Leading Arm. The catalog is based on neutral hydrogen (H I) observations from the Parkes Galactic All-Sky Survey. Excellent spectral resolution allows clouds with narrow-line components to be resolved. The total number of detected clouds is 419. We describe the method of cataloging and present the basic parameters of the clouds. We discuss the general distribution of the high-velocity clouds and classify the clouds based on their morphological type. The presence of a significant number of head-tail clouds and their distribution in the region is discussed in the context of Magellanic System simulations. We suggest that ram-pressure stripping is a more important factor than tidal forces for the morphology and formation of the Magellanic Leading Arm and that different environmental conditions might explain the morphological difference between the Magellanic Leading Arm and Magellanic Stream. We also discuss a newly identified population of clouds that forms the LA IV and a new diffuse bridge-like feature connecting the LA II and III complexes.

For, Bi-Qing; Staveley-Smith, Lister [International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia)] [International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia); McClure-Griffiths, N. M., E-mail: biqing.for@uwa.edu.au [Australia Telescope National Facility, CSIRO Astronomy and Space Science, PO Box 76, Epping, NSW 1710 (Australia)

2013-02-10T23:59:59.000Z

302

Co-evolution of Extreme Star Formation and Quasar: hints from {\\it Herschel} and the Sloan Digital Sky Survey  

E-Print Network [OSTI]

Using the public data from the Herschel very wide field surveys, we study the far-infrared properties of optical-selected quasars from the Sloan Digital Sky Survey. Within the common area of $\\sim 172~deg^2$, we have identified the far-infrared counterparts for 372 quasars, among which 134 are highly secure detections in the Herschel 250$\\mu$m band (signal-to-noise ratios $\\geq 5$). This sample is the largest far-infrared quasar sample of its kind, and spans a wide redshift range of $0.14\\leq z \\leq 4.7$. Their far-infrared spectral energy distributions are consistent with heated dust emission due to active star formation, and the vast majority of them ($\\gtrsim 80$\\%) have total infrared luminosities $L_{IR}>10^{12}L_{\\odot}$ and thus qualify as ultra-luminous infrared galaxies. Their infrared luminosities are not correlated with the absolute magnitudes or the black hole masses of the quasars, which further support the interpretation that their far-infrared emissions are not due to their active galactic nucl...

Ma, Zhiyuan

2015-01-01T23:59:59.000Z

303

What causes the excessive response of clear-sky greenhouse effect to El Nin~o warming in Community Atmosphere Models?  

E-Print Network [OSTI]

What causes the excessive response of clear-sky greenhouse effect to El Nin~o warming in Community-sky greenhouse effect to El Nin~o warming in the Community Atmosphere Models (CAMs), the response of both water in the lapse rate response to the discrepancies seen in the clear-sky greenhouse effect. The results confirm

Sun, Dezheng

304

Total Energy - Data - U.S. Energy Information Administration (EIA)  

Gasoline and Diesel Fuel Update (EIA)

Total Energy Flow, (Quadrillion Btu) Total Energy Flow, (Quadrillion Btu) Total Energy Flow diagram image Footnotes: 1 Includes lease condensate. 2 Natural gas plant liquids. 3 Conventional hydroelectric power, biomass, geothermal, solar/photovoltaic, and wind. 4 Crude oil and petroleum products. Includes imports into the Strategic Petroleum Reserve. 5 Natural gas, coal, coal coke, biofuels, and electricity. 6 Adjustments, losses, and unaccounted for. 7 Natural gas only; excludes supplemental gaseous fuels. 8 Petroleum products, including natural gas plant liquids, and crude oil burned as fuel. 9 Includes 0.01 quadrillion Btu of coal coke net exports. 10 Includes 0.13 quadrillion Btu of electricity net imports. 11 Total energy consumption, which is the sum of primary energy consumption, electricity retail sales, and electrical system energy losses.

305

Total Adjusted Sales of Kerosene  

U.S. Energy Information Administration (EIA) Indexed Site

End Use: Total Residential Commercial Industrial Farm All Other Period: End Use: Total Residential Commercial Industrial Farm All Other Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: End Use Area 2007 2008 2009 2010 2011 2012 View History U.S. 492,702 218,736 269,010 305,508 187,656 81,102 1984-2012 East Coast (PADD 1) 353,765 159,323 198,762 237,397 142,189 63,075 1984-2012 New England (PADD 1A) 94,635 42,570 56,661 53,363 38,448 15,983 1984-2012 Connecticut 13,006 6,710 8,800 7,437 7,087 2,143 1984-2012 Maine 46,431 19,923 25,158 24,281 17,396 7,394 1984-2012 Massachusetts 7,913 3,510 5,332 6,300 2,866 1,291 1984-2012 New Hampshire 14,454 6,675 8,353 7,435 5,472 1,977 1984-2012

306

Solar total energy project Shenandoah  

SciTech Connect (OSTI)

This document presents the description of the final design for the Solar Total Energy System (STES) to be installed at the Shenandoah, Georgia, site for utilization by the Bleyle knitwear plant. The system is a fully cascaded total energy system design featuring high temperature paraboloidal dish solar collectors with a 235 concentration ratio, a steam Rankine cycle power conversion system capable of supplying 100 to 400 kW(e) output with an intermediate process steam take-off point, and a back pressure condenser for heating and cooling. The design also includes an integrated control system employing the supervisory control concept to allow maximum experimental flexibility. The system design criteria and requirements are presented including the performance criteria and operating requirements, environmental conditions of operation; interface requirements with the Bleyle plant and the Georgia Power Company lines; maintenance, reliability, and testing requirements; health and safety requirements; and other applicable ordinances and codes. The major subsystems of the STES are described including the Solar Collection Subysystem (SCS), the Power Conversion Subsystem (PCS), the Thermal Utilization Subsystem (TUS), the Control and Instrumentation Subsystem (CAIS), and the Electrical Subsystem (ES). Each of these sections include design criteria and operational requirements specific to the subsystem, including interface requirements with the other subsystems, maintenance and reliability requirements, and testing and acceptance criteria. (WHK)

None

1980-01-10T23:59:59.000Z

307

Grantee Total Number of Homes  

Broader source: Energy.gov (indexed) [DOE]

Grantee Grantee Total Number of Homes Weatherized through November 2011 [Recovery Act] Total Number of Homes Weatherized through November 2011 (Calendar Year 2009 - November 2011) [Recovery Act + Annual Program Funding] Alabama 6,704 7,867 1 Alaska 443 2,363 American Samoa 304 410 Arizona 6,354 7,518 Arkansas 5,231 6,949 California 41,649 50,002 Colorado 12,782 19,210 Connecticut 8,940 10,009 2 Delaware** 54 54 District of Columbia 962 1,399 Florida 18,953 20,075 Georgia 13,449 14,739 Guam 574 589 Hawaii 604 1,083 Idaho** 4,470 6,614 Illinois 35,530 44,493 Indiana** 18,768 21,689 Iowa 8,794 10,202 Kansas 6,339 7,638 Kentucky 7,639 10,902 Louisiana 4,698 6,946 Maine 5,130 6,664 Maryland 8,108 9,015 Massachusetts 17,687 21,645 Michigan 29,293 37,137 Minnesota 18,224 22,711 Mississippi 5,937 6,888 Missouri 17,334 20,319 Montana 3,310 6,860 Navajo Nation

308

Total Number of Operable Refineries  

U.S. Energy Information Administration (EIA) Indexed Site

Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge Capacity (B/SD) Thermal Cracking Downstream Charge Capacity (B/SD) Thermal Cracking Total Coking Downstream Charge Capacity (B/SD) Thermal Cracking Delayed Coking Downstream Charge Capacity (B/SD Thermal Cracking Fluid Coking Downstream Charge Capacity (B/SD) Thermal Cracking Visbreaking Downstream Charge Capacity (B/SD) Thermal Cracking Other/Gas Oil Charge Capacity (B/SD) Catalytic Cracking Fresh Feed Charge Capacity (B/SD) Catalytic Cracking Recycle Charge Capacity (B/SD) Catalytic Hydro-Cracking Charge Capacity (B/SD) Catalytic Hydro-Cracking Distillate Charge Capacity (B/SD) Catalytic Hydro-Cracking Gas Oil Charge Capacity (B/SD) Catalytic Hydro-Cracking Residual Charge Capacity (B/SD) Catalytic Reforming Charge Capacity (B/SD) Catalytic Reforming Low Pressure Charge Capacity (B/SD) Catalytic Reforming High Pressure Charge Capacity (B/SD) Catalytic Hydrotreating/Desulfurization Charge Capacity (B/SD) Catalytic Hydrotreating Naphtha/Reformer Feed Charge Cap (B/SD) Catalytic Hydrotreating Gasoline Charge Capacity (B/SD) Catalytic Hydrotreating Heavy Gas Oil Charge Capacity (B/SD) Catalytic Hydrotreating Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Kerosene/Jet Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Diesel Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Other Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Residual/Other Charge Capacity (B/SD) Catalytic Hydrotreating Residual Charge Capacity (B/SD) Catalytic Hydrotreating Other Oils Charge Capacity (B/SD) Fuels Solvent Deasphalting Charge Capacity (B/SD) Catalytic Reforming Downstream Charge Capacity (B/CD) Total Coking Downstream Charge Capacity (B/CD) Catalytic Cracking Fresh Feed Downstream Charge Capacity (B/CD) Catalytic Hydro-Cracking Downstream Charge Capacity (B/CD) Period:

309

Total quality management implementation guidelines  

SciTech Connect (OSTI)

These Guidelines were designed by the Energy Quality Council to help managers and supervisors in the Department of Energy Complex bring Total Quality Management to their organizations. Because the Department is composed of a rich mixture of diverse organizations, each with its own distinctive culture and quality history, these Guidelines are intended to be adapted by users to meet the particular needs of their organizations. For example, for organizations that are well along on their quality journeys and may already have achieved quality results, these Guidelines will provide a consistent methodology and terminology reference to foster their alignment with the overall Energy quality initiative. For organizations that are just beginning their quality journeys, these Guidelines will serve as a startup manual on quality principles applied in the Energy context.

Not Available

1993-12-01T23:59:59.000Z

310

Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR)  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Ames: Phil Russell, Jens Redemann, NASA Ames: Phil Russell, Jens Redemann, Ames: Phil Russell, Jens Redemann, NASA Ames: Phil Russell, Jens Redemann, Steve Dunagan, Roy Johnson: Steve Dunagan, Roy Johnson: Battelle PND: Connor Flynn, Beat Schmid, Battelle PND: Connor Flynn, Beat Schmid, Evgueni Kassianov Evgueni Kassianov NASA GSFC: Alexander Sinyuk, Brent NASA GSFC: Alexander Sinyuk, Brent Holben Holben , , & AERONET Team & AERONET Team Collaboration involving: Collaboration involving: NASA Ames, Battelle PND, NASA GSFC NASA Ames, Battelle PND, NASA GSFC 4S 4S TAR TAR : : S S pectrometer for pectrometer for S S ky ky - - S S canning, canning, S S un un - - T T racking racking A A tmospheric tmospheric R R esearch esearch 4STAR: 4STAR: Spectrometer Spectrometer for for Sky Sky - - Scanning Scanning , , Sun Sun - - Tracking Tracking Atmospheric Research Atmospheric Research

311

Total Heart Transplant: A Modern Overview  

E-Print Network [OSTI]

use of the total artificial heart. New England Journal ofJ. (1997). Artificial heart transplants. British medicala total artificial heart as a bridge to transplantation. New

Lingampalli, Nithya

2014-01-01T23:59:59.000Z

312

The Clustering Dipole of the Local Universe from the Two Micron All Sky Survey  

E-Print Network [OSTI]

The unprecedented sky coverage and photometric uniformity of the Two Micron All Sky Survey (2MASS) provides a rich resource for investigating the galaxies populating the local Universe. A full characterization of the large-scale clustering distribution is important for theoretical studies of structure formation. 2MASS offers an all-sky view of the local galaxy population at 2.15 micron, unbiased by young stellar light and minimally affected by dust. We use 2MASS to map the local distribution of galaxies, identifying the largest structures in the nearby universe. The inhomogeneity of these structures causes an acceleration on the Local Group of galaxies, which can be seen in the dipole of the Cosmic Microwave Background (CMB). We find that the direction of the 2MASS clustering dipole is 11 degrees from the CMB dipole, confirming that the local galaxy distribution accelerates the Local Group. From the magnitude of the dipole we find a value of the linear bias parameter b=1.37 +/- 0.3 in the K_s-band. The 2MASS clustering dipole is 19 degrees from the latest measurement of the dipole using galaxies detected by the Infrared Astronomical Satellite (IRAS) suggesting that bias may be non-linear in some wavebands.

Ariyeh H. Maller; Daniel H. McIntosh; Neal Katz; Martin D. Weinberg

2003-03-26T23:59:59.000Z

313

THE 70 MONTH SWIFT-BAT ALL-SKY HARD X-RAY SURVEY  

SciTech Connect (OSTI)

We present the catalog of sources detected in 70 months of observations with the Burst Alert Telescope (BAT) hard X-ray detector on the Swift gamma-ray burst observatory. The Swift-BAT 70 month survey has detected 1171 hard X-ray sources (more than twice as many sources as the previous 22 month survey) in the 14-195 keV band down to a significance level of 4.8{sigma}, associated with 1210 counterparts. The 70 month Swift-BAT survey is the most sensitive and uniform hard X-ray all-sky survey and reaches a flux level of 1.03 Multiplication-Sign 10{sup -11} erg s{sup -1} cm{sup -2} over 50% of the sky and 1.34 Multiplication-Sign 10{sup -11} erg s{sup -1} cm{sup -2} over 90% of the sky. The majority of new sources in the 70 month survey continue to be active galactic nuclei, with over 700 in the catalog. As part of this new edition of the Swift-BAT catalog, we also make available eight-channel spectra and monthly sampled light curves for each object detected in the survey in the online journal and at the Swift-BAT 70 month Web site.

Baumgartner, W. H.; Tueller, J.; Markwardt, C. B.; Skinner, G. K.; Barthelmy, S.; Gehrels, N. [NASA/Goddard Space Flight Center, Astrophysics Science Division, Greenbelt, MD 20771 (United States); Mushotzky, R. F. [Department of Astronomy, University of Maryland, College Park, MD 20742 (United States); Evans, P. A., E-mail: whbaumga@alum.mit.edu [X-Ray and Observational Astronomy Group/Department of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH (United Kingdom)

2013-08-15T23:59:59.000Z

314

SkyMouse: A smart interface for astronomical on-line resources and services  

E-Print Network [OSTI]

With the development of network and the World Wide Web (WWW), the Internet has been growing and changing dramatically. More and more on-line database systems and different kinds of services are available for astronomy research. How to help users find their way through the jungle of information services becomes an important challenge. Although astronomers have been aware of the importance of interoperability and introduced the concept of Virtual Observatory as a uniform environment for future astronomical on-line resources and services, transparent access to heterogeneous on-line information is still difficult. SkyMouse is a lightweight interface for distributed astronomical on-line resources and services, which is designed and developed by us, i.e., Chinese Virtual Observatory Project. Taking advantage of screen word-capturing technology, different kinds of information systems can be queried through simple mouse actions, and results are returned in a uniform web page. SkyMouse is an easy to use application, aiming to show basic information or to create a comprehensive overview of a specific astronomical object. In this paper current status of on-line resources and services access is reviewed; system architecture, features and functions of SkyMouse are described; challenges for intelligent interface for on-line astronomical resources and services are discussed.

Chen-Zhou CUI; Hua-Ping SUN; Yong-Heng ZHAO; Yu LUO; Da-Zhi QI

2007-11-27T23:59:59.000Z

315

Synoptic Sky Surveys and the Diffuse Supernova Neutrino Background: Removing Astrophysical Uncertainties and Revealing Invisible Supernovae  

E-Print Network [OSTI]

The cumulative (anti)neutrino production from all core-collapse supernovae within our cosmic horizon gives rise to the diffuse supernova neutrino background (DSNB), which is on the verge of detectability. The observed flux depends on supernova physics, but also on the cosmic history of supernova explosions; currently, the cosmic supernova rate introduces a substantial (+/-40%) uncertainty, largely through its absolute normalization. However, a new class of wide-field, repeated-scan (synoptic) optical sky surveys is coming online, and will map the sky in the time domain with unprecedented depth, completeness, and dynamic range. We show that these surveys will obtain the cosmic supernova rate by direct counting, in an unbiased way and with high statistics, and thus will allow for precise predictions of the DSNB. Upcoming sky surveys will substantially reduce the uncertainties in the DSNB source history to an anticipated +/-5% that is dominated by systematics, so that the observed high-energy flux thus will test supernova neutrino physics. The portion of the universe (z invisible supernovae, which may be unseen either due to unexpected large dust obscuration in host galaxies, or because some core-collapse events proceed directly to black hole formation and fail to give an optical outburst.

Amy Lien; Brian D. Fields; John F. Beacom

2010-01-20T23:59:59.000Z

316

All-sky search for gravitational-wave bursts in the first joint LIGO-GEO-Virgo run  

E-Print Network [OSTI]

We present results from an all-sky search for unmodeled gravitational-wave bursts in the data collected by the LIGO, GEO 600 and Virgo detectors between November 2006 and October 2007. The search is performed by three ...

Weiss, Rainer

317

Computations of the tropospheric radiation budget in the Caribbean and Gulf of Mexico area for clear sky conditions  

Science Journals Connector (OSTI)

Computations of solar heating and infrared cooling for clear sky conditions in the area of the Central American Seas are presented, as based on conventional radiosondes in 1960. Results are discussed with rega...

Stefan L. Hastenrath

1967-01-01T23:59:59.000Z

318

Total Imports of Residual Fuel  

Gasoline and Diesel Fuel Update (EIA)

May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 View May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 View History U.S. Total 5,752 5,180 7,707 9,056 6,880 6,008 1936-2013 PAD District 1 1,677 1,689 2,008 3,074 2,135 2,814 1981-2013 Connecticut 1995-2009 Delaware 1995-2012 Florida 359 410 439 392 704 824 1995-2013 Georgia 324 354 434 364 298 391 1995-2013 Maine 65 1995-2013 Maryland 1995-2013 Massachusetts 1995-2012 New Hampshire 1995-2010 New Jersey 903 756 948 1,148 1,008 1,206 1995-2013 New York 21 15 14 771 8 180 1995-2013 North Carolina 1995-2011 Pennsylvania 1995-2013 Rhode Island 1995-2013 South Carolina 150 137 194 209 1995-2013 Vermont 5 4 4 5 4 4 1995-2013 Virginia 32 200 113 1995-2013 PAD District 2 217 183 235 207 247 179 1981-2013 Illinois 1995-2013

319

U.S. Total Exports  

Gasoline and Diesel Fuel Update (EIA)

Noyes, MN Warroad, MN Babb, MT Port of Del Bonita, MT Port of Morgan, MT Sweetgrass, MT Whitlash, MT Portal, ND Sherwood, ND Pittsburg, NH Champlain, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Highgate Springs, VT U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Galvan Ranch, TX LNG Imports from Algeria LNG Imports from Australia LNG Imports from Brunei LNG Imports from Canada Highgate Springs, VT LNG Imports from Egypt Cameron, LA Elba Island, GA Freeport, TX Gulf LNG, MS LNG Imports from Equatorial Guinea LNG Imports from Indonesia LNG Imports from Malaysia LNG Imports from Nigeria Cove Point, MD LNG Imports from Norway Cove Point, MD Freeport, TX Sabine Pass, LA LNG Imports from Oman LNG Imports from Peru Cameron, LA Freeport, TX LNG Imports from Qatar Elba Island, GA Golden Pass, TX Sabine Pass, LA LNG Imports from Trinidad/Tobago Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf LNG, MS Lake Charles, LA Sabine Pass, LA LNG Imports from United Arab Emirates LNG Imports from Yemen Everett, MA Freeport, TX Sabine Pass, LA LNG Imports from Other Countries Period: Monthly Annual

320

Natural Gas Total Liquids Extracted  

U.S. Energy Information Administration (EIA) Indexed Site

Thousand Barrels) Thousand Barrels) Data Series: Natural Gas Processed Total Liquids Extracted NGPL Production, Gaseous Equivalent Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History U.S. 658,291 673,677 720,612 749,095 792,481 873,563 1983-2012 Alabama 13,381 11,753 11,667 13,065 1983-2010 Alaska 22,419 20,779 19,542 17,798 18,314 18,339 1983-2012 Arkansas 126 103 125 160 212 336 1983-2012 California 11,388 11,179 11,042 10,400 9,831 9,923 1983-2012 Colorado 27,447 37,804 47,705 57,924 1983-2010 Florida 103 16 1983-2008 Illinois 38 33 24 231 705 0 1983-2012

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Tiny images  

E-Print Network [OSTI]

The human visual system is remarkably tolerant to degradations in image resolution: in a scene recognition task, human performance is similar whether $32 \\times 32$ color images or multi-mega pixel images are used. With ...

Torralba, Antonio

2007-04-23T23:59:59.000Z

322

Total Petroleum Systems and Assessment Units (AU)  

E-Print Network [OSTI]

Total Petroleum Systems (TPS) and Assessment Units (AU) Field type Surface water Groundwater X X X X X X X X AU 00000003 Oil/ Gas X X X X X X X X Total X X X X X X X Total Petroleum Systems (TPS) and Assessment Units (AU) Field type Total undiscovered petroleum (MMBO or BCFG) Water per oil

Torgersen, Christian

323

Locating and total dominating sets in trees  

Science Journals Connector (OSTI)

A set S of vertices in a graph G = ( V , E ) is a total dominating set of G if every vertex of V is adjacent to a vertex in S. We consider total dominating sets of minimum cardinality which have the additional property that distinct vertices of V are totally dominated by distinct subsets of the total dominating set.

Teresa W. Haynes; Michael A. Henning; Jamie Howard

2006-01-01T23:59:59.000Z

324

Locating-total domination in graphs  

Science Journals Connector (OSTI)

In this paper, we continue the study of locating-total domination in graphs. A set S of vertices in a graph G is a total dominating set in G if every vertex of G is adjacent to a vertex in S . We consider total dominating sets S which have the additional property that distinct vertices in V ( G ) ? S are totally dominated by distinct subsets of the total dominating set. Such a set S is called a locating-total dominating set in G , and the locating-total domination number of G is the minimum cardinality of a locating-total dominating set in G . We obtain new lower and upper bounds on the locating-total domination number of a graph. Interpolation results are established, and the locating-total domination number in special families of graphs, including cubic graphs and grid graphs, is investigated.

Michael A. Henning; Nader Jafari Rad

2012-01-01T23:59:59.000Z

325

BRIGHT 22 ?m EXCESS CANDIDATES FROM THE WISE ALL-SKY CATALOG AND THE HIPPARCOS MAIN CATALOG  

SciTech Connect (OSTI)

In this paper, we present a catalog that includes 141 bright candidates (?10.27 mag, V band) showing an excess of infrared (IR) at 22 ?m. Of these 141 candidates, 38 stars are known IR-excess stars or disks, 23 stars are double or multiple stars, and 4 are Be stars while the remaining more than 70 stars are identified as 22 ?m excess candidates in our work. The criterion for selecting candidates is K{sub s} – [22]{sub ?m}. All these candidates are selected from the Wide-field Infrared Survey Explorer all-sky data cross-correlated with the Hipparcos main catalog and the likelihood-ratio technique is employed. Considering the effect of background, we introduce the IRAS 100 ?m level to exclude the high background. We also estimate the coincidence probability of these sources. In addition, we present the optical to mid-IR spectral energy distributions and optical images for all the candidates, and give the observed optical spectra of six stars with the National Astronomical Observatories, Chinese Academy of Sciences' 2.16 m telescope. To measure for the amount of dust around each star, the fractional luminosity is also provided. We also test whether our method of selecting IR-excess stars can be used to search for extra-solar planets; we cross-match our catalog with known IR-excess stars with planets but found no matches. Finally, we give the fraction of stars showing excess IR for different spectral types of main-sequence stars.

Wu, Chao-Jian; Wu, Hong; Lam, Man-I; Yang, Ming; Gao, Liang [National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Wen, Xiao-Qing [Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Li, Shuo [Department of Astronomy, Peking University, Beijing 100871 (China); Zhang, Tong-Jie [Department of Astronomy, Beijing Normal University, Beijing 100875 (China)

2013-10-01T23:59:59.000Z

326

SDSS J141624.08+134826.7: A NEARBY BLUE L DWARF FROM THE SLOAN DIGITAL SKY SURVEY  

SciTech Connect (OSTI)

We present the discovery of a bright (J = 13.1 mag) nearby L6 dwarf found in a search for L-type ultracool subdwarfs in the Sloan Digital Sky Survey (SDSS) Data Release 7. SDSS J141624.08+134826.7 exhibits blue near-infrared colors compared to other optically typed L6 objects, but its optical and near-infrared spectra do not show metal-poor features characteristic of known L-type ultracool subdwarfs. Instead, SDSS J141624.08+134826.7 is probably a nearby example of the class of L dwarfs with low condensate opacities that exhibit unusually blue near-infrared colors for a given spectral type. Its deep 1.4 and 1.9 {mu}m H{sub 2}O absorption bands, weak 2.3 {mu}m CO feature, strong 0.99 {mu}m FeH band, and shallow optical TiO and CaH bands resemble the spectra of other blue L dwarfs which are believed to have unusually thin or large-grained cloud structure. The luminosity of SDSS J141624.08+134826.7 implies that it is either a high-mass brown dwarf or a low-mass star, depending on its age, and its UVW space motion suggests a thin-disk membership. With a spectrophotometric distance of 8.4 +- 1.9 pc, SDSS J141624.08+134826.7 is one of the nearest L dwarfs to the Sun and is therefore an excellent target for high resolution imaging, spectroscopic, and astrometric follow-up observations.

Bowler, Brendan P. [Infrared Telescope Facility, National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program (United States); Liu, Michael C.; Dupuy, Trent J., E-mail: bpbowler@ifa.hawaii.ed [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States)

2010-02-10T23:59:59.000Z

327

U.S. Total Exports  

U.S. Energy Information Administration (EIA) Indexed Site

International Falls, MN Noyes, MN Warroad, MN Babb, MT Havre, MT Port of Del Bonita, MT Port of Morgan, MT Sweetgrass, MT Whitlash, MT Portal, ND Sherwood, ND Pittsburg, NH Champlain, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Highgate Springs, VT North Troy, VT LNG Imports into Cameron, LA LNG Imports into Cove Point, MD LNG Imports into Elba Island, GA LNG Imports into Everett, MA LNG Imports into Freeport, TX LNG Imports into Golden Pass, TX LNG Imports into Gulf Gateway, LA LNG Imports into Gulf LNG, MS LNG Imports into Lake Charles, LA LNG Imports into Neptune Deepwater Port LNG Imports into Northeast Gateway LNG Imports into Sabine Pass, LA U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Alamo, TX El Paso, TX Galvan Ranch, TX Hidalgo, TX McAllen, TX Penitas, TX LNG Imports from Algeria Cove Point, MD Everett, MA Lake Charles, LA LNG Imports from Australia Everett, MA Lake Charles, LA LNG Imports from Brunei Lake Charles, LA LNG Imports from Canada Highgate Springs, VT LNG Imports from Egypt Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf LNG, MS Lake Charles, LA Northeast Gateway Sabine Pass, LA LNG Imports from Equatorial Guinea Elba Island, GA Lake Charles, LA LNG Imports from Indonesia Lake Charles, LA LNG Imports from Malaysia Gulf Gateway, LA Lake Charles, LA LNG Imports from Nigeria Cove Point, MD Elba Island, GA Freeport, TX Gulf Gateway, LA Lake Charles, LA Sabine Pass, LA LNG Imports from Norway Cove Point, MD Sabine Pass, LA LNG Imports from Oman Lake Charles, LA LNG Imports from Peru Cameron, LA Freeport, TX Sabine Pass, LA LNG Imports from Qatar Cameron, LA Elba Island, GA Golden Pass, TX Gulf Gateway, LA Lake Charles, LA Northeast Gateway Sabine Pass, LA LNG Imports from Trinidad/Tobago Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf Gateway, LA Gulf LNG, MS Lake Charles, LA Neptune Deepwater Port Northeast Gateway Sabine Pass, LA LNG Imports from United Arab Emirates Lake Charles, LA LNG Imports from Yemen Everett, MA Freeport, TX Neptune Deepwater Port Sabine Pass, LA LNG Imports from Other Countries Lake Charles, LA Period: Monthly Annual

328

Cloudy Skies  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Analysis of Cloud Radiative Forcing andFeedback Analysis of Cloud Radiative Forcing andFeedback in a Climate General Circulation Model A. A. lacis NASA Goddard Space Flight Center Institute for Space Studies New York, NY 10225 The principal objectives of the Atmospheric Radiation Measurement (ARM) Program research at the Goddard Institute for Space Studies (GISS) are 1) to improve and validate the radiation parameterizations in the GISS general circulation model (GCM) through model intercomparisons with line-by-line calculations and through comparisons with ARM observations, 2) to improve the GCM diagnostic output to enable more effective comparisons to global cloud/radiation data sets, and 3) to use ARM Cloud and Radiation Testbed (CART) data to develop improved parameterization of clouds in the GCM and to study the

329

Clear Skies  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

A. A. lacis NASA Goddard Space Flight Center Institute for Space Studies New York, NY 10025 "convective adjustment"takes place. Energy is transported upward within the troposphere...

330

Electric skies?  

Science Journals Connector (OSTI)

... conscious that this concern could put a damper on the growth of their businesses. The Boeing 787, the first exemplar of which is expected to be rolled out of the ... construction — is incorporated, for the first time in civil aviation history, in the Boeing 787. Further improvements in the strength-to-weight ratio of aircraft structures will come ...

2007-06-27T23:59:59.000Z

331

HAWC: A Next Generation All-Sky VHE Gamma-Ray Telescope  

E-Print Network [OSTI]

The study of the universe at energies above 100 GeV is a relatively new and exciting field. The current generation of pointed instruments have detected TeV gamma rays from at least 10 sources and the next generation of detectors promises a large increase in sensitivity. We have also seen the development of a new type of all-sky monitor in this energy regime based on water Cherenkov technology (Milagro). To fully understand the universe at these extreme energies requires a highly sensitive detector capable of continuously monitoring the entire overhead sky. Such an instrument could observe prompt emission from gamma-ray bursts and probe the limits of Lorentz invariance at high energies. With sufficient sensitivity it could detect short transients ($\\sim$15 minutes) from active galaxies and study the time structure of flares at energies unattainable to space-based instruments. Unlike pointed instruments a wide-field instrument can make an unbiased study of all active galaxies and enable many multi-wavelength campaigns to study these objects. This paper describes the design and performance of a next generation water Cherenkov detector. To attain a low energy threshold and have high sensitivity the detector should be located at high altitude ($>$ 4km) and have a large area ($\\sim$40,000 m$^2$). Such an instrument could detect gamma ray bursts out to a redshift of 1, observe flares from active galaxies as short as 15 minutes in duration, and survey the overhead sky at a level of 50 mCrab in one year.

G. Sinnis; A. Smith; J. E. McEnery

2004-03-03T23:59:59.000Z

332

State Residential Commercial Industrial Transportation Total  

Gasoline and Diesel Fuel Update (EIA)

schedules 4A-D, EIA-861S and EIA-861U) State Residential Commercial Industrial Transportation Total 2012 Total Electric Industry- Average Retail Price (centskWh) (Data from...

333

Total cost model for making sourcing decisions  

E-Print Network [OSTI]

This thesis develops a total cost model based on the work done during a six month internship with ABB. In order to help ABB better focus on low cost country sourcing, a total cost model was developed for sourcing decisions. ...

Morita, Mark, M.B.A. Massachusetts Institute of Technology

2007-01-01T23:59:59.000Z

334

Team Total Points Beta Theta Pi 2271  

E-Print Network [OSTI]

Bubbles 40 Upset City 30 Team Success 30 #12;Team Total Points Sly Tye 16 Barringer 15 Fire Stinespring 15

Buehrer, R. Michael

335

Robotic observations of the most eccentric spectroscopic binary in the sky  

E-Print Network [OSTI]

The visual A component of the Gliese 586AB system is a double-lined spectroscopic binary consisting of two cool stars with the exceptional orbital eccentricity of 0.976. Such an extremely eccentric system may be important for our understanding of low-mass binary formation. We present a total of 598 high-resolution echelle spectra from our robotic facility STELLA from 2006-2012 which we used to compute orbital elements of unprecedented accuracy. The orbit constrains the eccentricity to 0.97608+/-0.00004 and the orbital period to 889.8195+/-0.0003d. The masses of the two components are 0.87+/-0.05 Msun and 0.58+/-0.03 Msun if the inclination is 5+/-1.5degr as determined from adaptive-optics images, that is good to only 6% due to the error of the inclination although the minimum masses reached a precision of 0.3%. The flux ratio Aa:Ab in the optical is betwee n 30:1 in Johnson-B and 11:1 in I. Radial velocities of the visual B-component (K0-1V) appear constant to within 130 m/s over six years. Sinusoidal modulat...

Strassmeier, K G; Granzer, T

2013-01-01T23:59:59.000Z

336

DOE/SC-ARM-14-024 ARM Climate Research Facility Data Management...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

(ORNL) (Giri Palanisamy and Raymond McCord) * Provided collection support: Infrared Thermometer (IRT), total precipitation sensor (TPS), Infrared Sky Imager (IRSI), and C-Band ARM...

337

Spectroscopic needs for imaging dark energy experiments  

Science Journals Connector (OSTI)

Abstract Ongoing and near-future imaging-based dark energy experiments are critically dependent upon photometric redshifts (a.k.a. photo-z’s): i.e., estimates of the redshifts of objects based only on flux information obtained through broad filters. Higher-quality, lower-scatter photo-z’s will result in smaller random errors on cosmological parameters; while systematic errors in photometric redshift estimates, if not constrained, may dominate all other uncertainties from these experiments. The desired optimization and calibration is dependent upon spectroscopic measurements for secure redshift information; this is the key application of galaxy spectroscopy for imaging-based dark energy experiments. Hence, to achieve their full potential, imaging-based experiments will require large sets of objects with spectroscopically-determined redshifts, for two purposes:• Training: Objects with known redshift are needed to map out the relationship between object color and z (or, equivalently, to determine empirically-calibrated templates describing the rest-frame spectra of the full range of galaxies, which may be used to predict the color-z relation). The ultimate goal of training is to minimize each moment of the distribution of differences between photometric redshift estimates and the true redshifts of objects, making the relationship between them as tight as possible. The larger and more complete our “training set” of spectroscopic redshifts is, the smaller the RMS photo-z errors should be, increasing the constraining power of imaging experiments. Requirements: Spectroscopic redshift measurements for ?30,000 objects over >?15 widely-separated regions, each at least ?20 arcmin in diameter, and reaching the faintest objects used in a given experiment, will likely be necessary if photometric redshifts are to be trained and calibrated with conventional techniques. Larger, more complete samples (i.e., with longer exposure times) can improve photo-z algorithms and reduce scatter further, enhancing the science return from planned experiments greatly (increasing the Dark Energy Task Force figure of merit by up to ?50%). Options: This spectroscopy will most efficiently be done by covering as much of the optical and near-infrared spectrum as possible at modestly high spectral resolution (?/?? > ?3000), while maximizing the telescope collecting area, field of view on the sky, and multiplexing of simultaneous spectra. The most efficient instrument for this would likely be either the proposed GMACS/MANIFEST spectrograph for the Giant Magellan Telescope or the OPTIMOS spectrograph for the European Extremely Large Telescope, depending on actual properties when built. The PFS spectrograph at Subaru would be next best and available considerably earlier, c. 2018; the proposed ngCFHT and SSST telescopes would have similar capabilities but start later. Other key options, in order of increasing total time required, are the WFOS spectrograph at TMT, MOONS at the VLT, and DESI at the Mayall 4 m telescope (or the similar 4MOST and WEAVE projects); of these, only DESI, MOONS, and PFS are expected to be available before 2020. Table 2-3 of this white paper summarizes the observation time required at each facility for strawman training samples. To attain secure redshift measurements for a high fraction of targeted objects and cover the full redshift span of future experiments, additional near-infrared spectroscopy will also be required; this is best done from space, particularly with WFIRST-2.4 and JWST. Calibration: The first several moments of redshift distributions (the mean, RMS redshift dispersion, etc.), must be known to high accuracy for cosmological constraints not to be systematics-dominated (equivalently, the moments of the distribution of differences between photometric and true redshifts could be determined instead). The ultimate goal of calibration is to characterize these moments for every subsample used in analyses - i.e., to minimize the uncertainty in their mean redshift, RMS dispersion, et

Jeffrey A. Newman; Alexandra Abate; Filipe B. Abdalla; Sahar Allam; Steven W. Allen; Réza Ansari; Stephen Bailey; Wayne A. Barkhouse; Timothy C. Beers; Michael R. Blanton; Mark Brodwin; Joel R. Brownstein; Robert J. Brunner; Matias Carrasco Kind; Jorge L. Cervantes-Cota; Elliott Cheu; Nora Elisa Chisari; Matthew Colless; Johan Comparat; Jean Coupon; Carlos E. Cunha; Axel de la Macorra; Ian P. Dell’Antonio; Brenda L. Frye; Eric J. Gawiser; Neil Gehrels; Kevin Grady; Alex Hagen; Patrick B. Hall; Andew P. Hearin; Hendrik Hildebrandt; Christopher M. Hirata; Shirley Ho; Klaus Honscheid; Dragan Huterer; Željko Ivezi?; Jean-Paul Kneib; Jeffrey W. Kruk; Ofer Lahav; Rachel Mandelbaum; Jennifer L. Marshall; Daniel J. Matthews; Brice Ménard; Ramon Miquel; Marc Moniez; H.W. Moos; John Moustakas; Adam D. Myers; Casey Papovich; John A. Peacock; Changbom Park; Mubdi Rahman; Jason Rhodes; Jean-Stephane Ricol; Iftach Sadeh; Anže Slozar; Samuel J. Schmidt; Daniel K. Stern; J. Anthony Tyson; Anja von der Linden; Risa H. Wechsler; W.M. Wood-Vasey; Andrew R. Zentner

2015-01-01T23:59:59.000Z

338

The MAXI Mission on the ISS: Science and Instruments for Monitoring All-Sky X-Ray Images  

Science Journals Connector (OSTI)

......collimators without a mirror system, the angular resolution is not good (i.e., the FWHM of slat collimators is 1 5), but...event to astronomers and dedicated users worldwide via the Internet from TKSC. This nova alert system has been developed......

Masaru Matsuoka; Kazuyoshi Kawasaki; Shiro Ueno; Hiroshi Tomida; Mitsuhiro Kohama; Motoko Suzuki; Yasuki Adachi; Masaki Ishikawa; Tatehiro Mihara; Mutsumi Sugizaki; Naoki Isobe; Yujin Nakagawa; Hiroshi Tsunemi; Emi Miyata; Nobuyuki Kawai; Jun Kataoka; Mikio Morii; Atsumasa Yoshida; Hitoshi Negoro; Motoki Nakajima; Yoshihiro Ueda; Hirotaka Chujo; Kazutaka Yamaoka; Osamu Yamazaki; Satoshi Nakahira; Tetsuya You; Ryoji Ishiwata; Sho Miyoshi; Satoshi Eguchi; Kazuo Hiroi; Haruyoshi Katayama; Ken Ebisawa

2009-10-25T23:59:59.000Z

339

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

38 38 Nevada - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S30. Summary statistics for natural gas - Nevada, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 4 4 4 3 4 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 4 4 4 3 4

340

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Idaho - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S14. Summary statistics for natural gas - Idaho, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Washington - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S49. Summary statistics for natural gas - Washington, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

342

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Maine - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S21. Summary statistics for natural gas - Maine, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

343

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 Minnesota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

344

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 South Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

345

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 North Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

346

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Iowa - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S17. Summary statistics for natural gas - Iowa, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

347

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 Massachusetts - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

348

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Minnesota - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

349

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 New Jersey - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

350

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Vermont - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S47. Summary statistics for natural gas - Vermont, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

351

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Wisconsin - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S51. Summary statistics for natural gas - Wisconsin, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

352

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 North Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

353

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 New Jersey - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

354

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Maryland - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 7 7 7 7 8 Production (million cubic feet) Gross Withdrawals From Gas Wells 35 28 43 43 34 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 35

355

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 New Hampshire - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S31. Summary statistics for natural gas - New Hampshire, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

356

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Maryland - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 7 7 7 8 9 Production (million cubic feet) Gross Withdrawals From Gas Wells 28 43 43 34 44 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 28

357

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Missouri - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S27. Summary statistics for natural gas - Missouri, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 53 100 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

358

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Massachusetts - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

359

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 South Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

360

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Rhode Island - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S41. Summary statistics for natural gas - Rhode Island, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

An Efficient Algorithm for Positioning Tiles in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

The Sloan Digital Sky Survey (SDSS) will observe around 10^6 spectra from targets distributed over an area of about 10,000 square degrees, using a multi-object fiber spectrograph which can simultaneously observe 640 objects in a circular field-of-view (referred to as a ``tile'') 1.49 degrees in radius. No two fibers can be placed closer than 55'' during the same observation; multiple targets closer than this distance are said to ``collide.'' We present here a method of allocating fibers to desired targets given a set of tile centers which includes the effects of collisions and which is nearly optimally efficient and uniform. Because of large-scale structure in the galaxy distribution (which form the bulk of the SDSS targets), a naive covering the sky with equally-spaced tiles does not yield uniform sampling. Thus, we present a heuristic for perturbing the centers of the tiles from the equally-spaced distribution which provides more uniform completeness. For the SDSS sample, we can attain a sampling rate greater than 92% for all targets, and greater than 99% for the set of targets which do not collide with each other, with an efficiency greater than 90% (defined as the fraction of available fibers assigned to targets).

M. R. Blanton; R. H. Lupton; F. Miller Maley; N. Young; I. Zehavi; J. Loveday

2001-05-30T23:59:59.000Z

362

First ground-based 200-um observing with THUMPER on JCMT - sky characterisation and planet maps  

E-Print Network [OSTI]

We present observations that were carried out with the Two HUndred Micron PhotometER (THUMPER) mounted on the James Clerk Maxwell Telescope (JCMT) in Hawaii, at a wavelength of 200 um (frequency 1.5 THz). The observations utilise a small atmospheric window that opens up at this wavelength under very dry conditions at high-altitude observing sites. The atmosphere was calibrated using the sky-dipping method and a relation was established between the optical depth, tau, at 1.5 THz and that at 225 GHz: tau_1.5THz = (95 +/- 10)*tau_225GHz. Mars and Jupiter were mapped from the ground at this wavelength for the first time, and the system characteristics measured. A noise equivalent flux density (NEFD) of ~65 +/- 10 Jy (1 sigma 1 second) was measured for the THUMPER-JCMT combination, consistent with predictions based upon our laboratory measurements. The main-beam resolution of 14 arcsec was confirmed and an extended error-beam detected at roughly two-thirds of the magnitude of the main beam. Measurements of the Sun allow us to estimate that the fraction of the power in the main beam is ~15%, consistent with predictions based on modelling the dish surface accuracy. It is therefore shown that the sky over Mauna Kea is suitable for astronomy at this wavelength under the best conditions. However, higher or drier sites should have a larger number of useable nights per year.

D. Ward-Thompson; P. A. R. Ade; H. Araujo; I. Coulson; J. Cox; G. R. Davis; Rh. Evans; M. J. Griffin; W. K. Gear; P. Hargrave; P. Hargreaves; D. Hayton; B. J. Kiernan; S. J. Leeks; P. Mauskopf; D. Naylor; N. Potter; S. A. Rinehart; R. Sudiwala; C. R. Tucker; R. J. Walker; S. L. Watkin

2005-09-22T23:59:59.000Z

363

Machine learning techniques for astrophysical modelling and photometric redshift estimation of quasars in optical sky surveys  

E-Print Network [OSTI]

Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual astronomical phenomena over time and the automated, simultaneous analysis of thousands of objects in large optical sky surveys. Specifically investigated are (1) techniques to approximate the precise orbits of the satellites of Jupiter and Saturn given Earth-based observations as well as (2) techniques to quickly estimate the distances of quasars observed in the Sloan Digital Sky Survey. Learning methods considered include genetic algorithms, particle swarm optimisation, artificial neural networks, and radial basis function networks. The first part of this dissertation demonstrates that GAs and PSO can both be efficiently used to model functions that are highly non-linear in several dimensions. It is subsequently demonstrated in the second part that ANNs and RBFNs can be used as effective predictors of spectroscopic redshift given accurate photometry, especially in combination with other learning-based approaches described in the literature. Careful application of these and other ML techniques to problems in astronomy and astrophysics will contribute to a better understanding of stellar evolution, binary star systems, cosmology, and the large-scale structure of the universe.

N. Daniel Kumar

2008-11-04T23:59:59.000Z

364

A DETAILED MODEL ATMOSPHERE ANALYSIS OF COOL WHITE DWARFS IN THE SLOAN DIGITAL SKY SURVEY  

SciTech Connect (OSTI)

We present optical spectroscopy and near-infrared photometry of 126 cool white dwarfs (WDs) in the Sloan Digital Sky Survey (SDSS). Our sample includes high proper motion targets selected using the SDSS and USNO-B astrometry and a dozen previously known ultracool WD candidates. Our optical spectroscopic observations demonstrate that a clean selection of large samples of cool WDs in the SDSS (and the SkyMapper, Pan-STARRS, and the Large Synoptic Survey Telescope data sets) is possible using a reduced proper motion diagram and a tangential velocity cut-off (depending on the proper motion accuracy) of 30 km s{sup -1}. Our near-infrared observations reveal eight new stars with significant absorption. We use the optical and near-infrared photometry to perform a detailed model atmosphere analysis. More than 80% of the stars in our sample are consistent with either pure hydrogen or pure helium atmospheres. However, the eight stars with significant infrared absorption and the majority of the previously known ultracool WD candidates are best explained with mixed hydrogen and helium atmosphere models. The age distribution of our sample is consistent with a Galactic disk age of 8 Gyr. A few ultracool WDs may be as old as 12-13 Gyr, but our models have problems matching the spectral energy distributions of these objects. There are only two halo WD candidates in our sample. However, trigonometric parallax observations are required for accurate mass and age determinations and to confirm their membership in the halo.

Kilic, Mukremin [Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138 (United States); Leggett, S. K. [Gemini Observatory, 670 N. A'ohoku Place, Hilo, HI 96720 (United States); Tremblay, P.-E.; Bergeron, P. [Departement de Physique, Universite de Montreal, C.P. 6128, Succursale Centre-Ville, Montreal, Quebec H3C 3J7 (Canada); Von Hippel, Ted [Physics Department, Siena College, 515 Loudon Road, Loudonville, NY 12211 (United States); Harris, Hugh C.; Munn, Jeffrey A. [U.S. Naval Observatory, Flagstaff Station, 10391 W. Naval Observatory Road, Flagstaff, AZ 86001 (United States); Williams, Kurtis A. [Department of Astronomy, 1 University Station C1400, Austin, TX 78712 (United States); Gates, Evalyn [Kavli Institute for Cosmological Physics and Department of Astronomy and Astrophysics, The University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 (United States); Farihi, J., E-mail: mkilic@cfa.harvard.ed [Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH (United Kingdom)

2010-09-15T23:59:59.000Z

365

Anisotropy in the Microwave Sky: Results from the First Flight of BAM  

E-Print Network [OSTI]

Results are reported from the first flight of a new balloon-borne instrument, BAM (Balloon-borne Anisotropy Measurement), designed to search for cosmic microwave background (CMB) anisotropy. The instrument uses a cryogenic differential Fourier transform spectrometer to obtain data in five spectral channels whose central frequencies lie in the range 3.7 cm^{-1} to 8.5 cm^{-1}. The spectrometer is coupled to an off-axis prime focus telescope; the combination yields difference spectra of two regions on the sky defined by 0\\fdg 7 FWHM beams separated by 3\\fdg 6. Single differences obtained at ten sky positions show statistically significant fluctuations. Assuming Gaussian correlated anisotropy, for the band average 3.1 cm^{-1} to 9.2 cm^{-1}, one finds $\\Delta T/T = 3.1^{+3.1}_{1.1}\\times 10^{-5}$ (90% confidence interval) for a correlation angle of 1\\fdg 2. This corresponds to $Q_{flat} = 35.9^{17.7}_{6.3} \\mu K$ (1\\sigma).

G. S. Tucker; H. P. Gush; M. Halpern; I. Shinkoda; W. Towlson

1996-11-27T23:59:59.000Z

366

Evryscope science: exploring the potential of all-sky gigapixel-scale telescopes  

E-Print Network [OSTI]

Low-cost mass-produced sensors and optics have recently made it feasible to build telescope arrays which observe the entire accessible sky simultaneously. In this article we discuss the scientific motivation for these telescopes, including exoplanets, stellar variability and extragalactic transients. To provide a concrete example we detail the goals and expectations for the Evryscope, an under-construction 780 MPix telescope which covers 8,660 square degrees in each two-minute exposure; each night, 18,400 square degrees will be continuously observed for an average of approximately 6 hours. Despite its small 61mm aperture, the system's large field of view provides an etendue which is ~10% of LSST. The Evryscope, which places 27 separate individual telescopes into a common mount which tracks the entire accessible sky with only one moving part, will return 1%-precision, many-year-length, high-cadence light curves for every accessible star brighter than mV=16.5, with brighter stars having few-millimagnitude photo...

Law, Nicholas M; Ratzloff, Jeffrey; Wulfken, Philip; Kavanaugh, Dustin; Sitar, David J; Pruett, Zachary; Birchart, Mariah; Barlow, Brad; Cannon, Kipp; Cenko, S Bradley; Dunlap, Bart; Kraus, Adam; Maccarone, Thomas J

2015-01-01T23:59:59.000Z

367

SPATIAL ANISOTROPY OF GALAXY KINEMATICS IN SLOAN DIGITAL SKY SURVEY GALAXY CLUSTERS  

SciTech Connect (OSTI)

Measurements of galaxy cluster kinematics are important in understanding the dynamical state and evolution of clusters of galaxies, as well as constraining cosmological models. While it is well established that clusters exhibit non-spherical geometries, evident in the distribution of galaxies on the sky, azimuthal variations of galaxy kinematics within clusters have yet to be observed. Here we measure the azimuthal dependence of the line-of-sight velocity dispersion profile in a stacked sample of 1743 galaxy clusters from the Sloan Digital Sky Survey (SDSS). The clusters are drawn from the SDSS DR8 redMaPPer catalog. We find that the line-of-sight velocity dispersion of galaxies lying along the major axis of the central galaxy is larger than those that lie along the minor axis. This is the first observational detection of anisotropic kinematics of galaxies in clusters. We show that the result is consistent with predictions from numerical simulations. Furthermore, we find that the degree of projected anisotropy is strongly dependent on the line-of-sight orientation of the galaxy cluster, opening new possibilities for assessing systematics in optical cluster finding.

Skielboe, Andreas; Wojtak, Radoslaw; Pedersen, Kristian [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, DK-2100 Copenhagen (Denmark); Rozo, Eduardo [Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 (United States); Rykoff, Eli S. [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States)

2012-10-10T23:59:59.000Z

368

Compare All CBECS Activities: Total Energy Use  

U.S. Energy Information Administration (EIA) Indexed Site

Total Energy Use Total Energy Use Compare Activities by ... Total Energy Use Total Major Fuel Consumption by Building Type Commercial buildings in the U.S. used a total of approximately 5.7 quadrillion Btu of all major fuels (electricity, natural gas, fuel oil, and district steam or hot water) in 1999. Office buildings used the most total energy of all the building types, which was not a surprise since they were the most common commercial building type and had an above average energy intensity. Figure showing total major fuel consumption by building type. If you need assistance viewing this page, please call 202-586-8800. Major Fuel Consumption per Building by Building Type Because there were relatively few inpatient health care buildings and they tend to be large, energy intensive buildings, their energy consumption per building was far above that of any other building type.

369

TotalView Parallel Debugger at NERSC  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Totalview Totalview Totalview Description TotalView from Rogue Wave Software is a parallel debugging tool that can be run with up to 512 processors. It provides both X Windows-based Graphical User Interface (GUI) and command line interface (CLI) environments for debugging. The performance of the GUI can be greatly improved if used in conjunction with free NX software. The TotalView documentation web page is a good resource for learning more about some of the advanced TotalView features. Accessing Totalview at NERSC To use TotalView at NERSC, first load the TotalView modulefile to set the correct environment settings with the following command: % module load totalview Compiling Code to Run with TotalView In order to use TotalView, code must be compiled with the -g option. We

370

ARM: W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)

Widener, Kevin; Nelson, Dan; Bharadwaj, Nitin; Lindenmaier, Iosif [Andrei; Johnson, Karen

371

ARM: Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan (6 horizon-to-horizon scans at 30-degree azimuth intervals)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan (6 horizon-to-horizon scans at 30-degree azimuth intervals)

Bharadwaj, Nitin; Widener, Kevin

372

ARM: X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)

Widener, Kevin; Nelson, Dan; Bharadwaj, Nitin; Lindenmaier, Iosif [Andrei; Johnson, Karen

373

People Images  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Images People Images Several hundred of the 1700 U.S. scientists contributing to the LHC accelerator and experiments gathered in June 2008 in CERN's building 40 CE0252 Joel...

374

VIMOS total transmission profiles for broad-band filters  

E-Print Network [OSTI]

VIMOS is a wide-field imager and spectrograph mounted on UT3 at the VLT, whose FOV consists of four 7'x8' quadrants. Here we present the measurements of total transmission profiles -- i.e. the throughput of telescope + instrument -- for the broad band filters U, B, V, R, I, and z for each of its four quadrants. Those measurements can also be downloaded from the public VIMOS web-page. The transmission profiles are compared with previous estimates from the VIMOS consortium.

S. Mieske; M. Rejkuba; S. Bagnulo; C. Izzo; G. Marconi

2007-04-13T23:59:59.000Z

375

Total Carbon Dioxide, Hydrographic, and Nitrate Measurements in the  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Total Carbon Dioxide, Hydrographic, and Nitrate Measurements in the Southwest Pacific during Austral Autumn, 1990: Results from NOAA/PMEL CGC-90 Cruise. Total Carbon Dioxide, Hydrographic, and Nitrate Measurements in the Southwest Pacific during Austral Autumn, 1990: Results from NOAA/PMEL CGC-90 Cruise. NDP-052 (1995) data Download the Data and ASCII Documentation files of NDP-052 PDF Download a PDF of NDP-052 image Contributed by Marilyn F. Lamb and Richard A. Feely Pacific Marine Environmental Laboratory Seattle, Washington and Lloyd Moore and Donald K. Atwood Atlantic Oceanographic and Meteorological Laboratory Miami, Florida Prepared by Alexander Kozyr* Carbon Dioxide Information Analysis Center Oak Ridge National Laboratory Oak Ridge, Tennessee, U.S.A. *Energy, Environment, and Resources Center The University of Tennessee Knoxville, Tennessee Environmental Sciences Division Publication No. 4420 Date Published: September 1995

376

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 Tennessee - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S44. Summary statistics for natural gas - Tennessee, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 285 310 230 210 212 Production (million cubic feet) Gross Withdrawals From Gas Wells 4,700 5,478 5,144 4,851 5,825 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

377

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Connecticut - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S7. Summary statistics for natural gas - Connecticut, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

378

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Oregon - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S39. Summary statistics for natural gas - Oregon, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 18 21 24 26 24 Production (million cubic feet) Gross Withdrawals From Gas Wells 409 778 821 1,407 1,344 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

379

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 District of Columbia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S9. Summary statistics for natural gas - District of Columbia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

380

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 Oregon - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S39. Summary statistics for natural gas - Oregon, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 21 24 26 24 27 Production (million cubic feet) Gross Withdrawals From Gas Wells 778 821 1,407 1,344 770 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Georgia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S11. Summary statistics for natural gas - Georgia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

382

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Delaware - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S8. Summary statistics for natural gas - Delaware, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

383

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 District of Columbia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S9. Summary statistics for natural gas - District of Columbia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

384

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Tennessee - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S44. Summary statistics for natural gas - Tennessee, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 305 285 310 230 210 Production (million cubic feet) Gross Withdrawals From Gas Wells NA 4,700 5,478 5,144 4,851 From Oil Wells 3,942 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

385

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Nebraska - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S29. Summary statistics for natural gas - Nebraska, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 186 322 285 276 322 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,331 2,862 2,734 2,092 1,854 From Oil Wells 228 221 182 163 126 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

386

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Georgia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S11. Summary statistics for natural gas - Georgia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

387

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Connecticut - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S7. Summary statistics for natural gas - Connecticut, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

388

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Florida - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S10. Summary statistics for natural gas - Florida, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 2,000 2,742 290 13,938 17,129 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

389

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 Delaware - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S8. Summary statistics for natural gas - Delaware, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

390

ARM - Measurement - Shortwave spectral total downwelling irradiance  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Shadowband Spectroradiometer SPEC-TOTDN : Shortwave Total Downwelling Spectrometer UAV-EGRETT : UAV-Egrett Value-Added Products VISST : Minnis Cloud Products Using Visst...

391

,"New York Natural Gas Total Consumption (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New York Natural Gas Total Consumption (MMcf)",1,"Annual",2013 ,"Release Date:","12312014"...

392

Total Supplemental Supply of Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Product: Total Supplemental Supply Synthetic Propane-Air Refinery Gas Biomass Other Period: Monthly Annual Download Series History Download Series History Definitions, Sources &...

393

Total Natural Gas Gross Withdrawals (Summary)  

Gasoline and Diesel Fuel Update (EIA)

Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to...

394

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Indiana - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S16. Summary statistics for natural gas - Indiana, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 525 563 620 914 819 Production (million cubic feet) Gross Withdrawals From Gas Wells 4,701 4,927 6,802 9,075 8,814 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

395

EXTENDED HOT HALOS AROUND ISOLATED GALAXIES OBSERVED IN THE ROSAT ALL-SKY SURVEY  

SciTech Connect (OSTI)

We place general constraints on the luminosity and mass of hot X-ray-emitting gas residing in extended 'hot halos' around nearby massive galaxies. We examine stacked images of 2165 galaxies from the 2MASS Isolated Galaxy Catalog as well as subsets of this sample based on galaxy morphology and K-band luminosity. We detect X-ray emission at high confidence (ranging up to nearly 10{sigma}) for each subsample of galaxies. The average L{sub X} within 50 kpc is 1.0 {+-} 0.1 (statistical) {+-}0.2 (systematic) Multiplication-Sign 10{sup 40} erg s{sup -1}, although the early-type galaxies are more than twice as luminous as the late-type galaxies. Using a spatial analysis, we also find evidence for extended emission around five out of seven subsamples (the full sample, the luminous galaxies, early-type galaxies, luminous late-type galaxies, and luminous early-type galaxies) at 92.7%, 99.3%, 89.3%, 98.7%, and 92.1% confidence, respectively. Several additional lines of evidence also support this conclusion and suggest that about 1/2 of the total emission is extended, and about 1/3 of the extended emission comes from hot gas. For the sample of luminous galaxies, which has the strongest evidence for extended emission, the average hot gas mass is 4 Multiplication-Sign 10{sup 9} M {sub Sun} within 50 kpc and the implied accretion rate is 0.4 M {sub Sun} yr{sup -1}.

Anderson, Michael E.; Bregman, Joel N. [Department of Astronomy, University of Michigan, Ann Arbor, MI 48109 (United States)] [Department of Astronomy, University of Michigan, Ann Arbor, MI 48109 (United States); Dai, Xinyu, E-mail: michevan@umich.edu, E-mail: jbregman@umich.edu, E-mail: xdai@ou.edu [Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK 73019 (United States)] [Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK 73019 (United States)

2013-01-10T23:59:59.000Z

396

Three Spectacular H II-Buried Active Galactic Nucleus Galaxies from the Sloan Digital Sky Survey  

Science Journals Connector (OSTI)

We present our analysis of the three H II-buried active galactic nucleus: SDSS J091053+333008, SDSS J121837+091324, and SDSS J153002-020415, by studying their optical spectra extracted from Sloan Digital Sky Survey. The location in the BPT diagnostic diagrams of the three galaxies indicates that the narrow emission lines are mainly exited from H II regions. However, after the removal of the host galaxy's stellar emission, the emission lines display the typical feature of Narrow-Line Seyfert 1-like. All the three objects have large Eddington ratio, small black hole mass, and low star formation rate. We propose that the three galaxies are at the transit stage from the starburst-dominated phase to AGN-dominated phase.

Yufeng Mao; Jing Wang; Jianyan Wei

2009-01-01T23:59:59.000Z

397

Two Rare Magnetic Cataclysmic Variables with Extreme Cyclotron Features Identified in the Sloan Digital Sky Survey  

E-Print Network [OSTI]

Two newly identified magnetic cataclysmic variables discovered in the Sloan Digital Sky Survey (SDSS), SDSSJ155331.12+551614.5 and SDSSJ132411.57+032050.5, have spectra showing highly prominent, narrow, strongly polarized cyclotron humps with amplitudes that vary on orbital periods of 4.39 and 2.6 hrs, respectively. In the former, the spacing of the humps indicates the 3rd and 4th harmonics in a magnetic field of ~60 MG. The narrowness of the cyclotron features and the lack of strong emission lines imply very low temperature plasmas and very low accretion rates, so that the accreting area is heated by particle collisions rather than accretion shocks. The detection of rare systems like these exemplifies the ability of the SDSS to find the lowest accretion rate close binaries.

Paula Szkody; Scott F. Anderson; Gary Schmidt; Patrick B. Hall; Bruce Margon; Antonino Miceli; Mark SubbaRao; James Frith; Hugh Harris

2002-08-12T23:59:59.000Z

398

Probing non-Gaussianities in the cosmic microwave background on an incomplete sky using surrogates  

Science Journals Connector (OSTI)

We demonstrate the feasibility to generate surrogates by Fourier-based methods for an incomplete data set. This is performed for the case of a cosmic microwave background analysis, where astrophysical foreground emission, mainly present in the Galactic plane, is a major challenge. The shuffling of the Fourier phases for generating surrogates is now enabled by transforming the spherical harmonics into a new set of basis functions that are orthonormal on the cut sky. The results show that non-Gaussianities and hemispherical asymmetries in the cosmic microwave background as identified in several former investigations, can still be detected even when the complete Galactic plane (|b|<30°) is removed. We conclude that the Galactic plane cannot be the dominant source for these anomalies. The results point towards a violation of statistical isotropy.

G. Rossmanith, H. Modest, C. Räth, A. J. Banday, K. M. Górski, and G. Morfill

2012-10-15T23:59:59.000Z

399

Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys  

E-Print Network [OSTI]

The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a k...

Djorgovski, S G; Donalek, C; Graham, M J; Drake, A J; Turmon, M; Fuchs, T

2014-01-01T23:59:59.000Z

400

Mapping the Heavens: Probing Cosmology with the Sloan Digital Sky Survey  

SciTech Connect (OSTI)

This talk will provide an overview of results from the on-going Sloan Digital Sky Survey (SDSS), the most ambitious mapping of the Universe yet undertaken, focusing on those with implications for cosmology. It will include a virtual fly-through of the survey that reveals the 3-dimensional large-scale structure of the galaxy distribution. Recent measurements of this large-scale structure, in combination with observations of the cosmic microwave background, have provided independent evidence for a Universe dominated by dark matter and dark energy as well as insights into how galaxies and larger-scale structures formed. I will also describe early results from the SDSS Supernova Survey, which aims to provide more precise constraints on the nature of dark energy. Future planned surveys from the ground and from space will build on these foundations to probe the history of the cosmic expansion--and thereby the dark energy--with even greater precision.

Professor Josh Frieman

2006-12-04T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Total Synthesis of Irciniastatin A (Psymberin)  

E-Print Network [OSTI]

Total Synthesis of Irciniastatin A (Psymberin) Michael T. Crimmins,* Jason M. Stevens, and Gregory, North Carolina 27599 crimmins@email.unc.edu Received July 21, 2009 ABSTRACT The total synthesis of a hemiaminal and acid chloride to complete the synthesis. In 2004, Pettit and Crews independently reported

402

TOTAL REFLUX OPERATION OF MULTIVESSEL BATCH DISTILLATION  

E-Print Network [OSTI]

TOTAL REFLUX OPERATION OF MULTIVESSEL BATCH DISTILLATION BERND WITTGENS, RAJAB LITTO, EVA S RENSEN a generalization of previously proposed batch distillation schemes. A simple feedback control strategy for total re verify the simulations. INTRODUCTION Although batch distillation generally is less energy e cient than

Skogestad, Sigurd

403

Our Star, The Sun Looking up at the sky with the naked eye, the Sun seems static, constant. It provides the warmth and light that  

E-Print Network [OSTI]

#12;Our Star, The Sun Looking up at the sky with the naked eye, the Sun seems static, constant in the Sun are its location (as it ravels across the sky) and its color (will the atmosphere make it turn red or orange?) Scientists have learned a lot about the Sun in the past 400 years. We know that the Sun

Christian, Eric

404

A SEARCH FOR OXYGEN IN THE LOW-DENSITY Ly{alpha} FOREST USING THE SLOAN DIGITAL SKY SURVEY  

SciTech Connect (OSTI)

We use 2167 Sloan Digital Sky Survey quasar spectra to search for low-density oxygen in the intergalactic medium (IGM). Oxygen absorption is detected on a pixel-by-pixel basis by its correlation with Ly{alpha} forest absorption. We have developed a novel locally calibrated pixel (LCP) search method that uses adjacent regions of the spectrum to calibrate interlopers and spectral artifacts, which would otherwise limit the measurement of O VI absorption. Despite the challenges presented by searching for weak O VI within the Ly{alpha} forest in spectra of moderate resolution and signal-to-noise, we find a highly significant detection of absorption by oxygen at 2.7 < z < 3.2 (the null hypothesis has a {chi}{sup 2} = 80 for nine data points). We interpret our results using synthetic spectra generated from a log-normal density field assuming a mixed quasar-galaxy photoionizing background and that it dominates the ionization fraction of detected O VI. The LCP search data can be fit by a constant metallicity model with [O/H] = -2.15{sup +0.07}{sub -0.09} but also by models in which low-density regions are unenriched and higher density regions have a higher metallicity. The density-dependent enrichment model by Aguirre et al. is also an acceptable fit. All our successful models have similar mass-weighted oxygen abundance, corresponding to [(O/H){sub MW}] = -2.45 {+-} 0.06. This result can be used to find the cosmic oxygen density in the Ly{alpha} forest, {Omega}{sub Oxy,IGM} = 1.4({+-}0.2) x 10{sup -6} {approx} 3 x 10{sup -4{Omega}}{sub b}. This is the tightest constraint on the mass-weighted mean oxygen abundance and the cosmic oxygen density in the Ly{alpha} forest to date and indicates that it contains {approx}16% of the total expected metal production by star formation up to z = 3.

Pieri, Matthew M.; Frank, Stephan; Mathur, Smita; Weinberg, David H. [Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States); York, Donald G. [Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 (United States); Oppenheimer, Benjamin D., E-mail: mpieri@astronomy.ohio-state.ed [Astronomy Department, University of Arizona, Tucson, AZ 85721 (United States)

2010-06-20T23:59:59.000Z

405

The Galaxy Angular Correlation Functions and Power Spectrum from the Two Micron All Sky Survey  

E-Print Network [OSTI]

We calculate the angular correlation function of galaxies in the Two Micron All Sky Survey. We minimize the possible contamination by stars, dust, seeing and sky brightness by studying their cross correlation with galaxy density, and limiting the galaxy sample accordingly. We measure the correlation function at scales between 1-18 arcdegs using a half million galaxies. We find a best fit power law to the correlation function has a slope of 0.76 and an amplitude of 0.11. However, there are statistically significant oscillations around this power law. The largest oscillation occurs at about 0.8 degrees, corresponding to 2.8 h^{-1} Mpc at the median redshift of our survey, as expected in halo occupation distribution descriptions of galaxy clustering. We invert the angular correlation function using Singular Value Decomposition to measure the three-dimensional power spectrum and find that it too is in good agreement with previous measurements. A dip seen in the power spectrum at small wavenumber k is statistically consistent with CDM-type power spectra. A fit of CDM-type power spectra to k < 0.2 h Mpc^{-1} give constraints of \\Gamma_{eff}=0.116 and \\sigma_8=0.96. This suggest a K_s-band linear bias of 1.1+/-0.2. This \\Gamma_{eff} is different from the WMAP CMB derived value. On small scales the power-law shape of our power spectrum is shallower than that derived for the SDSS. These facts together imply a biasing of these different galaxies that might be nonlinear, that might be either waveband or luminosity dependent, and that might have a nonlocal origin.

Ariyeh H. Maller; Daniel H. McIntosh; Neal Katz; Martin D. Weinberg

2003-04-01T23:59:59.000Z

406

The 60-month all-sky BAT Survey of AGN and the Anisotropy of Nearby AGN  

SciTech Connect (OSTI)

Surveys above 10 keV represent one of the the best resources to provide an unbiased census of the population of Active Galactic Nuclei (AGN). We present the results of 60 months of observation of the hard X-ray sky with Swift/BAT. In this timeframe, BAT detected (in the 15-55 keV band) 720 sources in an all-sky survey of which 428 are associated with AGN, most of which are nearby. Our sample has negligible incompleteness and statistics a factor of {approx}2 larger over similarly complete sets of AGN. Our sample contains (at least) 15 bona-fide Compton-thick AGN and 3 likely candidates. Compton-thick AGN represent a {approx}5% of AGN samples detected above 15 keV. We use the BAT dataset to refine the determination of the LogN-LogS of AGN which is extremely important, now that NuSTAR prepares for launch, towards assessing the AGN contribution to the cosmic X-ray background. We show that the LogN-LogS of AGN selected above 10 keV is now established to a {approx}10% precision. We derive the luminosity function of Compton-thick AGN and measure a space density of 7.9{sub -2.9}{sup +4.1} x 10{sup -5} Mpc{sup -3} for objects with a de-absorbed luminosity larger than 2 x 10{sup 42} erg s{sup -1}. As the BAT AGN are all mostly local, they allow us to investigate the spatial distribution of AGN in the nearby Universe regardless of absorption. We find concentrations of AGN that coincide spatially with the largest congregations of matter in the local ({le} 85 Mpc) Universe. There is some evidence that the fraction of Seyfert 2 objects is larger than average in the direction of these dense regions.

Ajello, M.; /KIPAC, Menlo Park; Alexander, D.M.; /Durham U.; Greiner, J.; /Garching, Max Planck Inst., MPE; Madejski, G.M.; /KIPAC, Menlo Park; Gehrels, N.; /NASA, Goddard; Burlon, D.; /Garching, Max Planck Inst., MPE

2012-04-02T23:59:59.000Z

407

CBER-DETR Nevada Coincident and Leading Employment Cloudy Skies Continue to Hang over the Nevada Employment Sector  

E-Print Network [OSTI]

the seasonally adjusted data reported by the Bureau of Labor Statistics. The Nevada Coincident Employment IndexCBER-DETR Nevada Coincident and Leading Employment Indexes1 Cloudy Skies Continue to Hang over the Nevada Employment Sector The Nevada Coincident Employment Index measures the ups and downs of the Nevada

Ahmad, Sajjad

408

All-Sky LIGO Search for Periodic Gravitational Waves in the Early Fifth-Science-Run Data  

E-Print Network [OSTI]

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50–1100 Hz and with the frequency’s time derivative in the range -5×10[superscript -9]–0??Hz?s[superscript -1]. ...

Zucker, Michael E.

409

DISCOVERIES FROM A NEAR-INFRARED PROPER MOTION SURVEY USING MULTI-EPOCH TWO MICRON ALL-SKY SURVEY DATA  

E-Print Network [OSTI]

We have conducted a 4030 deg[superscript 2] near-infrared proper motion survey using multi-epoch data from the Two Micron All-Sky Survey (2MASS). We find 2778 proper motion candidates, 647 of which are not listed in SIMBAD. ...

Kirkpatrick, J. Davy

410

The Sloan Digital Sky Survey Quasar Lens Search. III Constraints on Dark Energy From The Third Data Release Quasar Lens Catalog  

SciTech Connect (OSTI)

We present cosmological results from the statistics of lensed quasars in the Sloan Digital Sky Survey (SDSS) Quasar Lens Search. By taking proper account of the selection function, we compute the expected number of quasars lensed by early-type galaxies and their image separation distribution assuming a flat universe, which is then compared with 7 lenses found in the SDSS Data Release 3 to derive constraints on dark energy under strictly controlled criteria. For a cosmological constant model (w = -1) we obtain {Omega}{sub {Lambda}} = 0.74{sub -0.15}{sup +0.11}(stat.){sub -0.06}{sup +0.13}(syst.). Allowing w to be a free parameter we find {Omega}{sub M} = 0.26{sub -0.06}{sup +0.07}(stat.){sub -0.05}{sup +0.03}(syst.) and w = -1.1 {+-} 0.6(stat.){sub -0.5}{sup +0.3}(syst.) when combined with the constraint from the measurement of baryon acoustic oscillations in the SDSS luminous red galaxy sample. Our results are in good agreement with earlier lensing constraints obtained using radio lenses, and provide additional confirmation of the presence of dark energy consistent with a cosmological constant, derived independently of type Ia supernovae.

Oguri, M; Inada, N; Strauss, M A; Kochanek, C S; Richards, G T; Schneider, D P; Becker, R H; Fukugita, M; Gregg, M D; Hall, P B; Hennawi, J F; Johnston, D E; Kayo, I; Keeton, C R; Pindor, B; Shin, M; Turner, E; White, R L; York, D G; Anderson, S F; Bahcall, N A; Brunner, R J; Burles, S; Castander, F J; Chiu, K; Clocchiatti, A; Einsenstein, D; Frieman, J; Kawano, Y; Lupton, R; Morokuma, T; Rix, H; Scranton, R; Sheldon, E S

2007-09-12T23:59:59.000Z

411

The Sloan Digital Sky Survey Quasar Lens Search. III. Constraints on Dark Energy from the Third Data Release Quasar Lens Catalog  

Science Journals Connector (OSTI)

We present cosmological results from the statistics of lensed quasars in the Sloan Digital Sky Survey (SDSS) Quasar Lens Search. By taking proper account of the selection function, we compute the expected number of quasars lensed by early-type galaxies and their image separation distribution assuming a flat universe, which is then compared with seven lenses found in the SDSS Data Release 3 to derive constraints on dark energy under strictly controlled criteria. For a cosmological constant model (w = ?1) we obtain ?? = 0.74+0.11 ?0.15(stat.)+0.13 ?0.06(syst.). Allowing w to be a free parameter we find ?M = 0.26+0.07 ?0.06(stat.)+0.03 ?0.05(syst.) and w = ?1.1 ± 0.6(stat.)+0.3 ?0.5(syst.) when combined with the constraint from the measurement of baryon acoustic oscillations in the SDSS luminous red galaxy sample. Our results are in good agreement with earlier lensing constraints obtained using radio lenses, and provide additional confirmation of the presence of dark energy consistent with a cosmological constant, derived independently of type Ia supernovae.

Masamune Oguri; Naohisa Inada; Michael A. Strauss; Christopher S. Kochanek; Gordon T. Richards; Donald P. Schneider; Robert H. Becker; Masataka Fukugita; Michael D. Gregg; Patrick B. Hall; Joseph F. Hennawi; David E. Johnston; Issha Kayo; Charles R. Keeton; Bartosz Pindor; Min-Su Shin; Edwin L. Turner; Richard L. White; Donald G. York; Scott F. Anderson; Neta A. Bahcall; Robert J. Brunner; Scott Burles; Francisco J. Castander; Kuenley Chiu; Alejandro Clocchiatti; Daniel Eisenstein; Joshua A. Frieman; Yozo Kawano; Robert Lupton; Tomoki Morokuma; Hans-Walter Rix; Ryan Scranton; Erin Scott Sheldon

2008-01-01T23:59:59.000Z

412

EMSL - Imaging  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

imaging en Diffusional Motion of Redox Centers in Carbonate Electrolytes . http:www.emsl.pnl.govemslwebpublicationsdiffusional-motion-redox-centers-carbonate-electrolytes

413

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 Illinois - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S15. Summary statistics for natural gas - Illinois, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 45 51 50 40 40 Production (million cubic feet) Gross Withdrawals From Gas Wells E 1,188 E 1,438 E 1,697 2,114 2,125 From Oil Wells E 5 E 5 E 5 7 0 From Coalbed Wells E 0 E 0 0 0 0 From Shale Gas Wells 0

414

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

50 50 North Dakota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S36. Summary statistics for natural gas - North Dakota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 194 196 188 239 211 Production (million cubic feet) Gross Withdrawals From Gas Wells 13,738 11,263 10,501 14,287 22,261 From Oil Wells 54,896 45,776 38,306 27,739 17,434 From Coalbed Wells 0

415

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Mississippi - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S26. Summary statistics for natural gas - Mississippi, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 2,343 2,320 1,979 5,732 1,669 Production (million cubic feet) Gross Withdrawals From Gas Wells 331,673 337,168 387,026 429,829 404,457 From Oil Wells 7,542 8,934 8,714 8,159 43,421 From Coalbed Wells 7,250

416

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Virginia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S48. Summary statistics for natural gas - Virginia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 5,735 6,426 7,303 7,470 7,903 Production (million cubic feet) Gross Withdrawals From Gas Wells R 6,681 R 7,419 R 16,046 R 23,086 20,375 From Oil Wells 0 0 0 0 0 From Coalbed Wells R 86,275 R 101,567

417

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Michigan - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S24. Summary statistics for natural gas - Michigan, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 9,712 9,995 10,600 10,100 11,100 Production (million cubic feet) Gross Withdrawals From Gas Wells R 80,090 R 16,959 R 20,867 R 7,345 18,470 From Oil Wells 54,114 10,716 12,919 9,453 11,620 From Coalbed Wells 0

418

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Montana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S28. Summary statistics for natural gas - Montana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 6,925 7,095 7,031 6,059 6,477 Production (million cubic feet) Gross Withdrawals From Gas Wells R 69,741 R 67,399 R 57,396 R 51,117 37,937 From Oil Wells 23,092 22,995 21,522 19,292 21,777 From Coalbed Wells

419

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Mississippi - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S26. Summary statistics for natural gas - Mississippi, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 2,315 2,343 2,320 1,979 5,732 Production (million cubic feet) Gross Withdrawals From Gas Wells R 259,001 R 331,673 R 337,168 R 387,026 429,829 From Oil Wells 6,203 7,542 8,934 8,714 8,159 From Coalbed Wells

420

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Indiana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S16. Summary statistics for natural gas - Indiana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 2,350 525 563 620 914 Production (million cubic feet) Gross Withdrawals From Gas Wells 3,606 4,701 4,927 6,802 9,075 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 New York - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S34. Summary statistics for natural gas - New York, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 6,680 6,675 6,628 6,736 6,157 Production (million cubic feet) Gross Withdrawals From Gas Wells 54,232 49,607 44,273 35,163 30,495 From Oil Wells 710 714 576 650 629 From Coalbed Wells 0

422

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Texas - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S45. Summary statistics for natural gas - Texas, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 76,436 87,556 93,507 95,014 100,966 Production (million cubic feet) Gross Withdrawals From Gas Wells R 4,992,042 R 5,285,458 R 4,860,377 R 4,441,188 3,794,952 From Oil Wells 704,092 745,587 774,821 849,560 1,073,301

423

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Ohio - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S37. Summary statistics for natural gas - Ohio, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 34,416 34,963 34,931 46,717 35,104 Production (million cubic feet) Gross Withdrawals From Gas Wells 79,769 83,511 73,459 30,655 65,025 From Oil Wells 5,072 5,301 4,651 45,663 6,684 From Coalbed Wells 0

424

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Colorado - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S6. Summary statistics for natural gas - Colorado, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 25,716 27,021 28,813 30,101 32,000 Production (million cubic feet) Gross Withdrawals From Gas Wells 496,374 459,509 526,077 563,750 1,036,572 From Oil Wells 199,725 327,619 338,565

425

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 South Dakota - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S43. Summary statistics for natural gas - South Dakota, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 71 71 89 102 100 Production (million cubic feet) Gross Withdrawals From Gas Wells 422 R 1,098 R 1,561 1,300 933 From Oil Wells 11,458 10,909 11,366 11,240 11,516 From Coalbed Wells 0 0

426

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Illinois - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S15. Summary statistics for natural gas - Illinois, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 43 45 51 50 40 Production (million cubic feet) Gross Withdrawals From Gas Wells RE 1,389 RE 1,188 RE 1,438 RE 1,697 2,114 From Oil Wells E 5 E 5 E 5 E 5 7 From Coalbed Wells RE 0 RE

427

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Colorado - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S6. Summary statistics for natural gas - Colorado, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 22,949 25,716 27,021 28,813 30,101 Production (million cubic feet) Gross Withdrawals From Gas Wells R 436,330 R 496,374 R 459,509 R 526,077 563,750 From Oil Wells 160,833 199,725 327,619

428

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Alaska - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S2. Summary statistics for natural gas - Alaska, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 239 261 261 269 277 Production (million cubic feet) Gross Withdrawals From Gas Wells 165,624 150,483 137,639 127,417 112,268 From Oil Wells 3,313,666 3,265,401 3,174,747 3,069,683 3,050,654

429

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Ohio - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S37. Summary statistics for natural gas - Ohio, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 34,416 34,416 34,963 34,931 46,717 Production (million cubic feet) Gross Withdrawals From Gas Wells R 82,812 R 79,769 R 83,511 R 73,459 30,655 From Oil Wells 5,268 5,072 5,301 4,651 45,663 From Coalbed Wells

430

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Kentucky - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S19. Summary statistics for natural gas - Kentucky, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 16,563 16,290 17,152 17,670 14,632 Production (million cubic feet) Gross Withdrawals From Gas Wells 95,437 R 112,587 R 111,782 133,521 122,578 From Oil Wells 0 1,529 1,518 1,809 1,665 From Coalbed Wells 0

431

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Utah - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S46. Summary statistics for natural gas - Utah, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 5,197 5,578 5,774 6,075 6,469 Production (million cubic feet) Gross Withdrawals From Gas Wells R 271,890 R 331,143 R 340,224 R 328,135 351,168 From Oil Wells 35,104 36,056 36,795 42,526 49,947 From Coalbed Wells

432

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 California - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S5. Summary statistics for natural gas - California, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 1,540 1,645 1,643 1,580 1,308 Production (million cubic feet) Gross Withdrawals From Gas Wells 93,249 91,460 82,288 73,017 63,902 From Oil Wells R 116,652 R 122,345 R 121,949 R 151,369 120,880

433

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Utah - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S46. Summary statistics for natural gas - Utah, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 5,578 5,774 6,075 6,469 6,900 Production (million cubic feet) Gross Withdrawals From Gas Wells 331,143 340,224 328,135 351,168 402,899 From Oil Wells 36,056 36,795 42,526 49,947 31,440 From Coalbed Wells 74,399

434

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Louisiana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S20. Summary statistics for natural gas - Louisiana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 18,145 19,213 18,860 19,137 21,235 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,261,539 R 1,288,559 R 1,100,007 R 911,967 883,712 From Oil Wells 106,303 61,663 58,037 63,638 68,505

435

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Oklahoma - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S38. Summary statistics for natural gas - Oklahoma, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 38,364 41,921 43,600 44,000 41,238 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,583,356 R 1,452,148 R 1,413,759 R 1,140,111 1,281,794 From Oil Wells 35,186 153,227 92,467 210,492 104,703

436

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 New Mexico - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S33. Summary statistics for natural gas - New Mexico, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 42,644 44,241 44,784 44,748 32,302 Production (million cubic feet) Gross Withdrawals From Gas Wells R 657,593 R 732,483 R 682,334 R 616,134 556,024 From Oil Wells 227,352 211,496 223,493 238,580 252,326

437

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 West Virginia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S50. Summary statistics for natural gas - West Virginia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 48,215 49,364 50,602 52,498 56,813 Production (million cubic feet) Gross Withdrawals From Gas Wells R 189,968 R 191,444 R 192,896 R 151,401 167,113 From Oil Wells 701 0 0 0 0 From Coalbed Wells

438

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 Michigan - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S24. Summary statistics for natural gas - Michigan, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 9,995 10,600 10,100 11,100 10,900 Production (million cubic feet) Gross Withdrawals From Gas Wells 16,959 20,867 7,345 18,470 17,041 From Oil Wells 10,716 12,919 9,453 11,620 4,470 From Coalbed Wells 0

439

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 West Virginia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S50. Summary statistics for natural gas - West Virginia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 49,364 50,602 52,498 56,813 50,700 Production (million cubic feet) Gross Withdrawals From Gas Wells 191,444 192,896 151,401 167,113 397,313 From Oil Wells 0 0 0 0 1,477 From Coalbed Wells 0

440

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

80 80 Wyoming - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S52. Summary statistics for natural gas - Wyoming, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 27,350 28,969 25,710 26,124 26,180 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,649,284 R 1,764,084 R 1,806,807 R 1,787,599 1,709,218 From Oil Wells 159,039 156,133 135,269 151,871 152,589

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 New York - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S34. Summary statistics for natural gas - New York, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,675 6,628 6,736 6,157 7,176 Production (million cubic feet) Gross Withdrawals From Gas Wells 49,607 44,273 35,163 30,495 25,985 From Oil Wells 714 576 650 629 439 From Coalbed Wells 0

442

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Wyoming - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S52. Summary statistics for natural gas - Wyoming, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 28,969 25,710 26,124 26,180 22,171 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,764,084 1,806,807 1,787,599 1,709,218 1,762,095 From Oil Wells 156,133 135,269 151,871 152,589 24,544

443

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 Virginia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S48. Summary statistics for natural gas - Virginia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,426 7,303 7,470 7,903 7,843 Production (million cubic feet) Gross Withdrawals From Gas Wells 7,419 16,046 23,086 20,375 21,802 From Oil Wells 0 0 0 0 9 From Coalbed Wells 101,567 106,408

444

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 Kentucky - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S19. Summary statistics for natural gas - Kentucky, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 16,290 17,152 17,670 14,632 17,936 Production (million cubic feet) Gross Withdrawals From Gas Wells 112,587 111,782 133,521 122,578 106,122 From Oil Wells 1,529 1,518 1,809 1,665 0 From Coalbed Wells 0

445

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Pennsylvania - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S40. Summary statistics for natural gas - Pennsylvania, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 52,700 55,631 57,356 44,500 54,347 Production (million cubic feet) Gross Withdrawals From Gas Wells 182,277 R 188,538 R 184,795 R 173,450 242,305 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0

446

Total synthesis and study of myrmicarin alkaloids  

E-Print Network [OSTI]

I. Enantioselective Total Synthesis of Tricyclic Myrmicarin Alkaloids An enantioselective gram-scale synthesis of a key dihydroindolizine intermediate for the preparation of myrmicarin alkaloids is described. Key transformations ...

Ondrus, Alison Evelynn, 1981-

2009-01-01T23:59:59.000Z

447

Total synthesis of cyclotryptamine and diketopiperazine alkaloids  

E-Print Network [OSTI]

I. Total Synthesis of the (+)-12,12'-Dideoxyverticillin A The fungal metabolite (+)-12,12'-dideoxyverticillin A, a cytotoxic alkaloid isolated from a marine Penicillium sp., belongs to a fascinating family of densely ...

Kim, Justin, Ph. D. Massachusetts Institute of Technology

2013-01-01T23:59:59.000Z

448

Provides Total Tuition Charge to Source Contribution  

E-Print Network [OSTI]

,262 1,938 TGR 4-20 0-3 2,871 2,871 - % of time appointed Hours of Work/Week Units TAL Provides Total

Kay, Mark A.

449

Enantioselective Total Synthesis of (?)-Acylfulvene and (?)- Irofulven  

E-Print Network [OSTI]

We report our full account of the enantioselective total synthesis of (?)-acylfulvene (1) and (?)-irofulven (2), which features metathesis reactions for the rapid assembly of the molecular framework of these antitumor ...

Movassaghi, Mohammad

450

A GENUINELY HIGH ORDER TOTAL VARIATION DIMINISHING ...  

E-Print Network [OSTI]

(TVD) schemes solving one-dimensional scalar conservation laws degenerate to first order .... where the total variation is measured by the standard bounded variation ..... interval Ij and into the jump discontinuities at cell interfaces, see [12].

451

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 Texas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S45. Summary statistics for natural gas - Texas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 87,556 93,507 95,014 100,966 96,617 Production (million cubic feet) Gross Withdrawals From Gas Wells 5,285,458 4,860,377 4,441,188 3,794,952 3,619,901 From Oil Wells 745,587 774,821 849,560 1,073,301 860,675

452

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

0 0 Alabama - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S1. Summary statistics for natural gas - Alabama, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,860 6,913 7,026 7,063 6,327 Production (million cubic feet) Gross Withdrawals From Gas Wells 158,964 142,509 131,448 116,872 114,407 From Oil Wells 6,368 5,758 6,195 5,975 10,978

453

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 Louisiana - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S20. Summary statistics for natural gas - Louisiana, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 19,213 18,860 19,137 21,235 19,792 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,288,559 1,100,007 911,967 883,712 775,506 From Oil Wells 61,663 58,037 63,638 68,505 49,380

454

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 South Dakota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S43. Summary statistics for natural gas - South Dakota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 71 89 102 100 95 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,098 1,561 1,300 933 14,396 From Oil Wells 10,909 11,366 11,240 11,516 689 From Coalbed Wells 0 0 0 0 0

455

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 Kansas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S18. Summary statistics for natural gas - Kansas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 17,862 21,243 22,145 25,758 24,697 Production (million cubic feet) Gross Withdrawals From Gas Wells 286,210 269,086 247,651 236,834 264,610 From Oil Wells 45,038 42,647 39,071 37,194 0 From Coalbed Wells 44,066

456

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

6 6 Arkansas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S4. Summary statistics for natural gas - Arkansas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 5,592 6,314 7,397 8,388 8,538 Production (million cubic feet) Gross Withdrawals From Gas Wells 173,975 164,316 152,108 132,230 121,684 From Oil Wells 7,378 5,743 5,691 9,291 3,000

457

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

8 8 California - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S5. Summary statistics for natural gas - California, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 1,645 1,643 1,580 1,308 1,423 Production (million cubic feet) Gross Withdrawals From Gas Wells 91,460 82,288 73,017 63,902 120,579 From Oil Wells 122,345 121,949 151,369 120,880 70,900

458

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

4 4 Oklahoma - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S38. Summary statistics for natural gas - Oklahoma, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 41,921 43,600 44,000 41,238 40,000 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,452,148 1,413,759 1,140,111 1,281,794 1,394,859 From Oil Wells 153,227 92,467 210,492 104,703 53,720

459

Million Cu. Feet Percent of National Total  

U.S. Energy Information Administration (EIA) Indexed Site

2 2 Alaska - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S2. Summary statistics for natural gas - Alaska, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 261 261 269 277 185 Production (million cubic feet) Gross Withdrawals From Gas Wells 150,483 137,639 127,417 112,268 107,873 From Oil Wells 3,265,401 3,174,747 3,069,683 3,050,654 3,056,918

460

Data Image  

Science Journals Connector (OSTI)

Data image refers to the sum of all information 74/100,000 available in all datasets linked to a specific name; to all those who have access to databases that name is actually the data image of the real person...

2008-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "total sky imager" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

| Los Alamos National Laboratory | Total Scattering Developments forTotal Scattering Developments for  

E-Print Network [OSTI]

Laboratory | Total Scattering at the Lujan Center Neutron Powder Diffractometer (NPDF) High-Intensity Powder. Shoemaker, et al., Reverse Monte Carlo neutron scattering study of disordered crystalline materials neutron| Los Alamos National Laboratory | Total Scattering Developments forTotal Scattering Developments

Magee, Joseph W.

462

CONSTRAINTS ON THE SHAPE OF THE MILKY WAY DARK MATTER HALO FROM JEANS EQUATIONS APPLIED TO SLOAN DIGITAL SKY SURVEY DATA  

SciTech Connect (OSTI)

We search for evidence of dark matter in the Milky Way by utilizing the stellar number density distribution and kinematics measured by the Sloan Digital Sky Survey (SDSS) to heliocentric distances exceeding {approx}10 kpc. We employ the cylindrically symmetric form of Jeans equations and focus on the morphology of the resulting acceleration maps, rather than the normalization of the total mass as done in previous, mostly local, studies. Jeans equations are first applied to a mock catalog based on a cosmologically derived N-body+SPH simulation, and the known acceleration (gradient of gravitational potential) is successfully recovered. The same simulation is also used to quantify the impact of dark matter on the total acceleration. We use Galfast, a code designed to quantitatively reproduce SDSS measurements and selection effects, to generate a synthetic stellar catalog. We apply Jeans equations to this catalog and produce two-dimensional maps of stellar acceleration. These maps reveal that in a Newtonian framework, the implied gravitational potential cannot be explained by visible matter alone. The acceleration experienced by stars at galactocentric distances of {approx}20 kpc is three times larger than what can be explained by purely visible matter. The application of an analytic method for estimating the dark matter halo axis ratio to SDSS data implies an oblate halo with q{sub DM} = 0.47 {+-} 0.14 within the same distance range. These techniques can be used to map the dark matter halo to much larger distances from the Galactic center using upcoming deep optical surveys, such as LSST.

Loebman, Sarah R.; Ivezic, Zeljko; Quinn, Thomas R.; Governato, Fabio [Astronomy Department, University of Washington, Box 351580, Seattle, WA 98195-1580 (United States); Brooks, Alyson M. [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Christensen, Charlotte R. [Astronomy Department, University of Arizona, Tucson, AZ (United States); Juric, Mario, E-mail: sloebman@astro.washington.edu [LSST Corporation, 933 North Cherry Avenue, Tucson, AZ 85721 (United States)

2012-10-10T23:59:59.000Z

463

Property:TotalValue | Open Energy Information  

Open Energy Info (EERE)

TotalValue TotalValue Jump to: navigation, search This is a property of type Number. Pages using the property "TotalValue" Showing 25 pages using this property. (previous 25) (next 25) 4 44 Tech Inc. Smart Grid Demonstration Project + 10,000,000 + A ALLETE Inc., d/b/a Minnesota Power Smart Grid Project + 3,088,007 + Amber Kinetics, Inc. Smart Grid Demonstration Project + 10,000,000 + American Transmission Company LLC II Smart Grid Project + 22,888,360 + American Transmission Company LLC Smart Grid Project + 2,661,650 + Atlantic City Electric Company Smart Grid Project + 37,400,000 + Avista Utilities Smart Grid Project + 40,000,000 + B Baltimore Gas and Electric Company Smart Grid Project + 451,814,234 + Battelle Memorial Institute, Pacific Northwest Division Smart Grid Demonstration Project + 177,642,503 +

464

ARM - Measurement - Net broadband total irradiance  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

govMeasurementsNet broadband total irradiance govMeasurementsNet broadband total irradiance ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Net broadband total irradiance The difference between upwelling and downwelling, covering longwave and shortwave radiation. Categories Radiometric Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available measurements, including those recorded for diagnostic or quality assurance purposes. ARM Instruments EBBR : Energy Balance Bowen Ratio Station SEBS : Surface Energy Balance System External Instruments ECMWF : European Centre for Medium Range Weather Forecasts Model

465

SolarTotal | Open Energy Information  

Open Energy Info (EERE)

SolarTotal SolarTotal Jump to: navigation, search Name SolarTotal Place Bemmel, Netherlands Zip 6681 LN Sector Solar Product The company sells and installs PV solar instalations Coordinates 51.894112°, 5.89881° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":51.894112,"lon":5.89881,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

466

First all-sky search for continuous gravitational waves from unknown sources in binary systems  

E-Print Network [OSTI]

We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO Science Run and the second and third Virgo Science Runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ~2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semi-major axes of the orbit from ~0.6e-3 ls to ~6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3e-24 at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.

The LIGO Scientific Collaboration; the Virgo Collaboration; J. Aasi; B. P. Abbott; R. Abbott; T. Abbott; M. R. Abernathy; T. Accadia; F. Acernese; K. Ackley; C. Adams; T. Adams; P. Addesso; R. X. Adhikari; C. Affeldt; M. Agathos; N. Aggarwal; O. D. Aguiar; A. Ain; P. Ajith; A. Alemic; B. Allen; A. Allocca; D. Amariutei; M. Andersen; R. Anderson; S. B. Anderson; W. G. Anderson; K. Arai; M. C. Araya; C. Arceneaux; J. Areeda; S. M. Aston; P. Astone; P. Aufmuth; C. Aulbert; L. Austin; B. E. Aylott; S. Babak; P. T. Baker; G. Ballardin; S. W. Ballmer; J. C. Barayoga; M. Barbet; B. C. Barish; D. Barker; F. Barone; B. Barr; L. Barsotti; M. Barsuglia; M. A. Barton; I. Bartos; R. Bassiri; A. Basti; J. C. Batch; J. Bauchrowitz; Th. S. Bauer; B. Behnke; M. Bejger; M. G. Beker; C. Belczynski; A. S. Bell; C. Bell; G. Bergmann; D. Bersanetti; A. Bertolini; J. Betzwieser; P. T. Beyersdorf; I. A. Bilenko; G. Billingsley; J. Birch; S. Biscans; M. Bitossi; M. A. Bizouard; E. Black; J. K. Blackburn; L. Blackburn; D. Blair; S. Bloemen; M. Blom; O. Bock; T. P. Bodiya; M. Boer; G. Bogaert; C. Bogan; C. Bond; F. Bondu; L. Bonelli; R. Bonnand; R. Bork; M. Born; V. Boschi; Sukanta Bose; L. Bosi; C. Bradaschia; P. R. Brady; V. B. Braginsky; M. Branchesi; J. E. Brau; T. Briant; D. O. Bridges; A. Brillet; M. Brinkmann; V. Brisson; A. F. Brooks; D. A. Brown; D. D. Brown; F. Brückner; S. Buchman; T. Bulik; H. J. Bulten; A. Buonanno; R. Burman; D. Buskulic; C. Buy; L. Cadonati; G. Cagnoli; J. Calderón Bustillo; E. Calloni; J. B. Camp; P. Campsie; K. C. Cannon; B. Canuel; J. Cao; C. D. Capano; F. Carbognani; L. Carbone; S. Caride; A. Castiglia; S. Caudill; M. Cavaglià; F. Cavalier; R. Cavalieri; C. Celerier; G. Cella; C. Cepeda; E. Cesarini; R. Chakraborty; T. Chalermsongsak; S. J. Chamberlin; S. Chao; P. Charlton; E. Chassande-Mottin; X. Chen; Y. Chen; A. Chincarini; A. Chiummo; H. S. Cho; J. Chow; N. Christensen; Q. Chu; S. S. Y. Chua; S. Chung; G. Ciani; F. Clara; J. A. Clark; F. Cleva; E. Coccia; P. -F. Cohadon; A. Colla; C. Collette; M. Colombini; L. Cominsky; M. Constancio Jr.; A. Conte; D. Cook; T. R. Corbitt; M. Cordier; N. Cornish; A. Corpuz; A. Corsi; C. A. Costa; M. W. Coughlin; S. Coughlin; J. -P. Coulon; S. Countryman; P. Couvares; D. M. Coward; M. Cowart; D. C. Coyne; R. Coyne; K. Craig; J. D. E. Creighton; T. D. Creighton; S. G. Crowder; A. Cumming; L. Cunningham; E. Cuoco; K. Dahl; T. Dal Canton; M. Damjanic; S. L. Danilishin; S. D'Antonio; K. Danzmann; V. Dattilo; H. Daveloza; M. Davier; G. S. Davies; E. J. Daw; R. Day; T. Dayanga; G. Debreczeni; J. Degallaix; S. Deléglise; W. Del Pozzo; T. Denker; T. Dent; H. Dereli; V. Dergachev; R. De Rosa; R. T. DeRosa; R. DeSalvo; S. Dhurandhar; M. Díaz; L. Di Fiore; A. Di Lieto; I. Di Palma; A. Di Virgilio; A. Donath; F. Donovan; K. L. Dooley; S. Doravari; S. Dossa; R. Douglas; T. P. Downes; M. Drago; R. W. P. Drever; J. C. Driggers; Z. Du; S. Dwyer; T. Eberle; T. Edo; M. Edwards; A. Effler; H. Eggenstein; P. Ehrens; J. Eichholz; S. S. Eikenberry; G. Endr?czi; R. Essick; T. Etzel; M. Evans; T. Evans; M. Factourovich; V. Fafone; S. Fairhurst; Q. Fang; S. Farinon; B. Farr; W. M. Farr; M. Favata; H. Fehrmann; M. M. Fejer; D. Feldbaum; F. Feroz; I. Ferrante; F. Ferrini; F. Fidecaro; L. S. Finn; I. Fiori; R. P. Fisher; R. Flaminio; J. -D. Fournier; S. Franco; S. Frasca; F. Frasconi; M. Frede; Z. Frei; A. Freise; R. Frey; T. T. Fricke; P. Fritschel; V. V. Frolov; P. Fulda; M. Fyffe; J. Gair; L. Gammaitoni; S. Gaonkar; F. Garufi; N. Gehrels; G. Gemme; E. Genin; A. Gennai; S. Ghosh; J. A. Giaime; K. D. Giardina; A. Giazotto; C. Gill; J. Gleason; E. Goetz; R. Goetz; L. Gondan; G. González; N. Gordon; M. L. Gorodetsky; S. Gossan; S. Goßler; R. Gouaty; C. Gräf; P. B. Graff; M. Granata; A. Grant; S. Gras; C. Gray; R. J. S. Greenhalgh; A. M. Gretarsson; P. Groot; H. Grote; K. Grover; S. Grunewald; G. M. Guidi; C. Guido; K. Gushwa; E. K. Gustafson; R. Gustafson; D. Hammer; G. Hammond; M. Hanke; J. Hanks; C. Hanna; J. Hanson; J. Harms; G. M. Harry; I. W. Harry; E. D. Harstad; M. Hart; M. T. Hartman; C. -J. Haster; K. Haughian; A. Heidmann; M. Heintze; H. Heitmann; P. Hello; G. Hemming; M. Hendry; I. S. Heng; A. W. Heptonstall; M. Heurs; M. Hewitson; S. Hild; D. Hoak; K. A. Hodge; K. Holt; S. Hooper; P. Hopkins; D. J. Hosken; J. Hough; E. J. Howell; Y. Hu; E. Huerta; B. Hughey; S. Husa; S. H. Huttner; M. Huynh; T. Huynh-Dinh; D. R. Ingram; R. Inta; T. Isogai; A. Ivanov; B. R. Iyer; K. Izumi; M. Jacobson; E. James; H. Jang; P. Jaranowski; Y. Ji; F. Jiménez-Forteza; W. W. Johnson; D. I. Jones; R. Jones; R. J. G. Jonker; L. Ju; Haris K; P. Kalmus; V. Kalogera; S. Kandhasamy; G. Kang; J. B. Kanner; J. Karlen; M. Kasprzack; E. Katsavounidis; W. Katzman; H. Kaufer; K. Kawabe; F. Kawazoe; F. Kéfélian; G. M. Keiser; D. Keitel; D. B. Kelley; W. Kells; A. Khalaidovski

2014-05-30T23:59:59.000Z

467

First all-sky search for continuous gravitational waves from unknown sources in binary systems  

E-Print Network [OSTI]

We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO Science Run and the second and third Virgo Science Runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ~2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semi-major axes of the orbit from ~0.6e-3 ls to ~6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3e-24 at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for ci...

Aasi, J; Abbott, R; Abbott, T; Abernathy, M R; Accadia, T; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Affeldt, C; Agathos, M; Aggarwal, N; Aguiar, O D; Ain, A; Ajith, P; Alemic, A; Allen, B; Allocca, A; Amariutei, D; Andersen, M; Anderson, R; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Arceneaux, C; Areeda, J; Aston, S M; Astone, P; Aufmuth, P; Aulbert, C; Austin, L; Aylott, B E; Babak, S; Baker, P T; Ballardin, G; Ballmer, S W; Barayoga, J C; Barbet, M; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bauchrowitz, J; Bauer, Th S; Behnke, B; Bejger, M; Beker, M G; Belczynski, C; Bell, A S; Bell, C; Bergmann, G; Bersanetti, D; Bertolini, A; Betzwieser, J; Beyersdorf, P T; Bilenko, I A; Billingsley, G; Birch, J; Biscans, S; Bitossi, M; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bloemen, S; Blom, M; Bock, O; Bodiya, T P; Boer, M; Bogaert, G; Bogan, C; Bond, C; Bondu, F; Bonelli, L; Bonnand, R; Bork, R; Born, M; Boschi, V; Bose, Sukanta; Bosi, L; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Bridges, D O; Brillet, A; Brinkmann, M; Brisson, V; Brooks, A F; Brown, D A; Brown, D D; Brückner, F; Buchman, S; Bulik, T; Bulten, H J; Buonanno, A; Burman, R; Buskulic, D; Buy, C; Cadonati, L; Cagnoli, G; Bustillo, J Calderón; Calloni, E; Camp, J B; Campsie, P; Cannon, K C; Canuel, B; Cao, J; Capano, C D; Carbognani, F; Carbone, L; Caride, S; Castiglia, A; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Celerier, C; Cella, G; Cepeda, C; Cesarini, E; Chakraborty, R; Chalermsongsak, T; Chamberlin, S J; Chao, S; Charlton, P; Chassande-Mottin, E; Chen, X; Chen, Y; Chincarini, A; Chiummo, A; Cho, H S; Chow, J; Christensen, N; Chu, Q; Chua, S S Y; Chung, S; Ciani, G; Clara, F; Clark, J A; Cleva, F; Coccia, E; Cohadon, P -F; Colla, A; Collette, C; Colombini, M; Cominsky, L; Constancio, M; Conte, A; Cook, D; Corbitt, T R; Cordier, M; Cornish, N; Corpuz, A; Corsi, A; Costa, C A; Coughlin, M W; Coughlin, S; Coulon, J -P; Countryman, S; Couvares, P; Coward, D M; Cowart, M; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Creighton, T D; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Dahl, K; Canton, T Dal; Damjanic, M; Danilishin, S L; D'Antonio, S; Danzmann, K; Dattilo, V; Daveloza, H; Davier, M; Davies, G S; Daw, E J; Day, R; Dayanga, T; Debreczeni, G; Degallaix, J; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dereli, H; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Dhurandhar, S; Díaz, M; Di Fiore, L; Di Lieto, A; Di Palma, I; Di Virgilio, A; Donath, A; Donovan, F; Dooley, K L; Doravari, S; Dossa, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Dwyer, S; Eberle, T; Edo, T; Edwards, M; Effler, A; Eggenstein, H; Ehrens, P; Eichholz, J; Eikenberry, S S; Endr\\Hoczi, G; Essick, R; Etzel, T; Evans, M; Evans, T; Factourovich, M; Fafone, V; Fairhurst, S; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fehrmann, H; Fejer, M M; Feldbaum, D; Feroz, F; Ferrante, I; Ferrini, F; Fidecaro, F; Finn, L S; Fiori, I; Fisher, R P; Flaminio, R; Fournier, J -D; Franco, S; Frasca, S; Frasconi, F; Frede, M; Frei, Z; Freise, A; Frey, R; Fricke, T T; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gair, J; Gammaitoni, L; Gaonkar, S; Garufi, F; Gehrels, N; Gemme, G; Genin, E; Gennai, A; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, C; Gleason, J; Goetz, E; Goetz, R; Gondan, L; González, G; Gordon, N; Gorodetsky, M L; Gossan, S; Goßler, S; Gouaty, R; Gräf, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greenhalgh, R J S; Gretarsson, A M; Groot, P; Grote, H; Grover, K; Grunewald, S; Guidi, G M; Guido, C; Gushwa, K; Gustafson, E K; Gustafson, R; Hammer, D; Hammond, G; Hanke, M; Hanks, J; Hanna, C; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Hart, M; Hartman, M T; Haster, C -J; Haughian, K; Heidmann, A; Heintze, M; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Heptonstall, A W; Heurs, M; Hewitson, M; Hild, S; Hoak, D; Hodge, K A; Holt, K; Hooper, S; Hopkins, P; Hosken, D J; Hough, J; Howell, E J; Hu, Y; Huerta, E; Hughey, B; Husa, S; Huttner, S H; Huynh, M; Huynh-Dinh, T; Ingram, D R; Inta, R; Isogai, T; Ivanov, A; Iyer, B R; Izumi, K; Jacobson, M; James, E; Jang, H; Jaranowski, P; Ji, Y; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; K, Haris; Kalmus, P; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karlen, J; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, H; Kawabe, K; Kawazoe, F; Kéfélian, F; Keiser, G M; Keitel, D; Kelley, D B; Kells, W; Khalaidovski, A; Khalili, F Y; Khazanov, E A; Kim, C; Kim, K; Kim, N; Kim, N G; Kim, Y -M; King, E J; King, P J; Kinzel, D L; Kissel, J S; Klimenko, S; Kline, J; Koehlenbeck, S; Kokeyama, K; Kondrashov, V; Koranda, S

2014-01-01T23:59:59.000Z

468

All-sky search for periodic gravitational waves in LIGO S4 data  

E-Print Network [OSTI]

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50-1000 Hz and with the frequency's time derivative in the range -1.0E-8 Hz/s to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semi-coherent methods of transforming and summing strain power from Short Fourier Transforms (SFTs) of the calibrated data have been used. The first, known as "StackSlide", averages normalized power from each SFT. A "weighted Hough" scheme is also developed and used, and which also allows for a multi-interferometer search. The third method, known as "PowerFlux", is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitationa...

Abbott, B; Adhikari, R; Agresti, J; Ajith, P; Allen, B; Amin, R; Anderson, S B; Anderson, W G; Arain, M; Araya, M; Armandula, H; Ashley, M; Aston, S; Aufmuth, P; Aulbert, C; Babak, S; Ballmer, S; Bantilan, H; Barish, B C; Barker, C; Barker, D; Barr, B; Barriga, P; Barton, M A; Bayer, K; Belczynski, K; Betzwieser, J; Beyersdorf, P T; Bhawal, B; Bilenko, I A; Billingsley, G; Biswas, R; Black, E; Blackburn, K; Blackburn, L; Blair, D; Bland, B; Bogenstahl, J; Bogue, L; Bork, R; Boschi, V; Bose, S; Brady, P R; Braginsky, V B; Brau, J E; Brinkmann, M; Brooks, A; Brown, D A; Bullington, A; Bunkowski, A; Buonanno, A; Burmeister, O; Busby, D; Byer, R L; Cadonati, L; Cagnoli, G; Camp, J B; Cannizzo, J; Cannon, K; Cantley, C A; Cao, J; Cardenas, L; Casey, M M; Castaldi, G; Cepeda, C; Chalkey, E; Charlton, P; Chatterji, S; Chelkowski, S; Chen, Y; Chiadini, F; Chin, D; Chin, E; Chow, J; Christensen, N; Clark, J; Cochrane, P; Cokelaer, T; Colacino, C N; Coldwell, R; Conte, R; Cook, D; Corbitt, T; Coward, D; Coyne, D; Creighton, J D E; Creighton, T D; Croce, R P; Crooks, D R M; Cruise, A M; Cumming, A; Dalrymple, J; D'Ambrosio, E; Danzmann, K; Davies, G; De Bra, D; Degallaix, J; Degree, M; Demma, T; Dergachev, V; Desai, S; DeSalvo, R; Dhurandhar, S; Daz, M; Dickson, J; Di Credico, A; Diederichs, G; Dietz, A; Doomes, E E; Drever, R W P; Dumas, J C; Dupuis, R J; Dwyer, J G; Ehrens, P; Espinoza, E; Etzel, T; Evans, M; Evans, T; Fairhurst, S; Fan, Y; Fazi, D; Fejer, M M; Finn, L S; Fiumara, V; Fotopoulos, N; Franzen, A; Franzen, K Y; Freise, A; Frey, R; Fricke, T; Fritschel, P; Frolov, V V; Fyffe, M; Galdi, V; Garofoli, J; Gholami, I; Giaime, J A; Giampanis, S; Giardina, K D; Goda, K; Goetz, E; Goggin, L M; González, G; Gossler, S; Grant, A; Gras, S; Gray, a C; Gray, M; Greenhalgh, J; Gretarsson, A M; Grosso, R; Grote, H; Grünewald, S; Günther, M; Gustafson, R; Hage, B; Hammer, D; Hanna, C; Hanson, J; Harms, J; Harry, G; Harstad, E; Hayler, T; Heefner, J; Heng, I S; Heptonstall, A; Heurs, M; Hewitson, M; Hild, S; Hirose, E; Hoak, D; Hosken, D; Hough, J; Howell, E; Hoyland, D; Huttner, S H; Ingram, D; Innerhofer, E; Ito, M; Itoh, Y; Ivanov, A; Jackrel, D; Johnson, B; Johnson, W W; Jones, D I; Jones, G; Jones, R; Ju, L; Kalmus, Peter Ignaz Paul; Kalogera, V; Kasprzyk, D; Katsavounidis, E; Kawabe, K; Kawamura, S; Kawazoe, F; Kells, W; Keppel, D G; Khalili, F Ya; Kim, C; King, P; Kissel, J S; Klimenko, S; Kokeyama, K; Kondrashov, V; Kopparapu, R K; Kozak, D; Krishnan, B; Kwee, P; Lam, P K; Landry, M; Lantz, B; Lazzarini, A; Lee, B; Lei, M; Leiner, J; Leonhardt, V; Leonor, I; Libbrecht, K; Lindquist, P; Lockerbie, N A; Longo, M; Lormand, M; Lubinski, M; Luck, H; Machenschalk, B; MacInnis, M; Mageswaran, M; Mailand, K; Malec, M; Mandic, V; Marano, S; Marka, S; Markowitz, J; Maros, E; Martin, I; Marx, J N; Mason, K; Matone, L; Matta, V; Mavalvala, a N; McCarthy, R; McClelland, D E; McGuire, S C; McHugh, M; McKenzie, K; McNabb, J W C; McWilliams, S; Meier, T; Melissinos, A; Mendell, G; Mercer, R A; Meshkov, S; Messaritaki, E; Messenger, C J; Meyers, D; Mikhailov, E; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Miyakawa, O; Mohanty, S; Moreno, G; Mossavi, K; Mow Lowry, C; Moylan, A; Mudge, D; Müller, G; Mukherjee, S; Muller-Ebhardt, H; Munch, J; Murray, P; Myers, E; Myers, J; Nash, T; Newton, G; Nishizawa, A; Numata, K; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Overmier, H; Owen, B J; Pan, Y; Papa, M A; Parameshwaraiah, V; Patel, P; Pedraza, M; Penn, S; Pierro, V; Pinto, I M; Pitkin, M; Pletsch, H; Plissi, M V; Postiglione, F; Prix, R; Quetschke, V; Raab, F; Rabeling, D; Radkins, H; Rahkola, R; Rainer, N; Rakhmanov, M; Ramsunder, M; Rawlins, K; Ray-Majumder, S; Re, V; Rehbein, H; Reid, S; Reitze, D H; Ribichini, L; Riesen, R; Riles, K; Rivera, B; Robertson, N A; Robinson, C; Robinson, E L; Roddy, S; Rodríguez, A; Rogan, A M; Rollins, J; Romano, J D; Romie, J; Route, R; Rowan, S; Rüdiger, A; Ruet, L; Russell, P; Ryan, K; Sakata, S; Samidi, M; Sancho de la Jordana, L; Sandberg, V; Sannibale, V; Saraf, S; Sarin, P; Sathyaprakash, B S; Sato, S; Saulson, P R; Savage, R; Savov, P; Schediwy, S; Schilling, R; Schnabel, R; Schofield, R; Schutz, B F; Schwinberg, P; Scott, S M; Searle, A C; Sears, B; Seifert, F; Sellers, D; Sengupta, A S; Shawhan, P; Shoemaker, D H; Sibley, A; Sidles, J A; Siemens, X; Sigg, D; Sinha, S; Sintes, A M; Slagmolen, B J J; Slutsky, J; Smith, J R; Smith, M R; Somiya, K; Strain, K A; Strom, D M; Stuver, A; Summerscales, T Z; Sun, K X; Sung, M; Sutton, P J; Takahashi, H; Tanner, D B; Tarallo, M; Taylor, R; Taylor, R; Thacker, J; Thorne, K A; Thorne, K S; Thüring, A; Tokmakov, K V; Torres, C; Torrie, C; Traylor, G; Trias, M; Tyler, W; Ugolini, D; Ungarelli, C; Urbanek, K; Vahlbruch, H; Vallisneri, M; Van Den Broeck, C; Varvella, M; Vass, S; Vecchio, A; Veitch, J; Veitch, P; Villar, A; Vorvick, C

2007-01-01T23:59:59.000Z

469

Impact of Kuwait`s oil-fire smoke cloud on the sky of Bahrain  

SciTech Connect (OSTI)

The effects of the Kuwaiti oil well fires of 1991 on the atmospheric parameters of Bahrain (approximately 600 km southeast of Kuwait) were observed. Solar radiation, optical thickness, ultraviolet radiation, horizontal visibility, temperature, and solar spectral distribution were measured for 1991 and compared to the long-term values of 1985-1990. The relative monthly solar radiation in Bahrain was reduced by 8% (February) when 50 oil wells were burning and reduced further to 20% when 470 oil wells were on fire (April-July). In November 1991, when there were 12 oil wells burning, the recorded solar radiation became nearly equal to the long-term average. The monthly average daily optical thickness, {tau}, for the direct or beam solar radiation was calculated. The values of {tau} were found to be larger in 1991 than the average for the years 1985-1990 by nearly 58% during June and returned to normal in October (after nearly all the oil well fires were extinguished). The clear and smoked sky solar spectra distribution were detected before and during the burning of the Kuwait oil wells. Large absorption of the solar radiation was noticed on the 2nd and 3rd of March, 1991. The daily average infrared radiation during 1990 was found to be 6700.4 Whm{sup -2} and shifted to 9182.1 Whm{sup -2} in 1991. Comparison was also made between 1990 and 1991 data of the global solar radiation and the temperature. 13 refs., 12 figs., 1 tab.

Alnaser, W.E. [Univ. of Bahrain (Bahrain)] [Univ. of Bahrain (Bahrain)

1995-06-01T23:59:59.000Z

470

Testing the Randomness in the Sky-Distribution of Gamma-Ray Bursts  

E-Print Network [OSTI]

We studied the complete randomness of the angular distribution of gamma-ray bursts (GRBs) detected by BATSE. Since GRBs seem to be a mixture of objects of different physical nature we divided the BATSE sample into 5 subsamples (short1, short2, intermediate, long1, long2) based on their durations and peak fluxes and studied the angular distributions separately. We used three methods, Voronoi tesselation, minimal spanning tree and multifractal spectra to search for non-randomness in the subsamples. To investigate the eventual non-randomness in the subsamples we defined 13 test-variables (9 from the Voronoi tesselation, 3 from the minimal spanning tree and one from the multifractal spectrum). Assuming that the point patterns obtained from the BATSE subsamples are fully random we made Monte Carlo simulations taking into account the BATSE's sky-exposure function. The MC simulations enabled us to test the null hypothesis i.e. that the angular distributions are fully random. We tested the randomness by binomial test and introducing squared Euclidean distances in the parameter space of the test-variables. We concluded that the short1, short2 groups deviate significantly (99.90%, 99.98%) from the fully randomness in the distribution of the squared Euclidean distances but it is not the case at the long samples. At the intermediate group the squared Euclidean distances also give significant deviation (98.51%).

R. Vavrek; L. G. Balázs; A. Mészáros; I. Horváth; Z. Bagoly

2008-06-24T23:59:59.000Z

471

A NEAR-INFRARED SPECTROSCOPIC SURVEY OF COOL WHITE DWARFS IN THE SLOAN DIGITAL SKY SURVEY  

SciTech Connect (OSTI)

We present near-infrared photometric observations of 15 and spectroscopic observations of 38 cool white dwarfs (WDs). This is the largest near-infrared spectroscopic survey of cool WDs to date. Combining the Sloan Digital Sky Survey photometry and our near-infrared data, we perform a detailed model atmosphere analysis. The spectral energy distributions of our objects are explained fairly well by model atmospheres with temperatures ranging from 6300 K down to 4200 K. Two WDs show significant absorption in the infrared, and are best explained with mixed H/He atmosphere models. Based on the up-to-date model atmosphere calculations by Kowalski and Saumon, we find that the majority of the stars in our sample have hydrogen-rich atmospheres. We do not find any pure helium atmosphere WDs below 5000 K, and we find a trend of increasing hydrogen to helium ratio with decreasing temperature. These findings present an important challenge to understanding the spectral evolution of WDs.

Kilic, Mukremin [Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138 (United States); Kowalski, Piotr M. [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, 44780 Bochum (Germany); Von Hippel, Ted [Physics Department, Siena College, 515 Loudon Road, Loudonville, NY 12211 (United States)], E-mail: mkilic@cfa.harvard.edu

2009-07-15T23:59:59.000Z

472

A 250 GHz Survey of High Redshift QSOs from the Sloan Digital Sky Survey  

E-Print Network [OSTI]

We present observations at 250 GHz (1.2 mm), 43 GHz, and 1.4 GHz of a sample of 41 QSOs at z > 3.7 found in the Sloan Digital Sky Survey. We detect 16 sources with a 250 GHz flux density greater than 1.4 mJy. The combination of centimeter and millimeter wavelength observations indicates that the 250 GHz emission is most likely thermal dust emission. Assuming a dust temperature of 50 K, the implied dust masses for the 16 detected sources are in the range 1.5e8 to 5.9e8 Msun, and the dust emitting regions are likely to be larger than 1 kpc in extent. The radio-through-optical spectral energy distributions for these sources are within the broad range defined by lower redshift, lower optical luminosity QSOs. We consider possible dust heating mechanisms, including UV emission from the active nucleus (AGN) and a starburst concurrent with the AGN, with implied star formation rates between 500 and 2000 Msun/year.

Carilli, C L; Rupen, M P; Fan, X; Strauss, M A; Menten, K M; Kreysa, E; Schneider, D P; Bertarini, A; Yun, M S; Zylka, R; Fan, Xiaohui; Strauss, Michael A.; Schneider, Donald P.

2001-01-01T23:59:59.000Z

473

Monte Carlo simulations of alternative sky observation modes with the Cherenkov Telescope Array  

E-Print Network [OSTI]

We investigate possible sky survey modes with the Middle Sized Telescopes (MST, aimed at covering the energy range from $\\sim$100 GeV to 10 TeV) subsystem of the Cherenkov Telescope Array (CTA). We use the standard CTA tools, CORSIKA and sim_telarray, to simulate the development of gamma-ray showers, proton background and the telescope response. We perform simulations for the H.E.S.S.-site in Namibia, which is one of the candidate sites for the CTA experiment. We study two previously considered modes, parallel and divergent, and we propose a new, convergent mode with telescopes tilted toward the array center. For each mode we provide performance parameters crucial for choosing the most efficient survey strategy. For the non-parallel modes we study the dependence on the telescope offset angle. We show that use of both the divergent and convergent modes results in potential advantages in comparison with use of the parallel mode. The fastest source detection can be achieved in the divergent mode with larger offs...

Szanecki, M; Nied?wiecki, A; Sitarek, J; Bednarek, W

2015-01-01T23:59:59.000Z

474

The Oxygen Abundance of Nearby Galaxies from Sloan Digital Sky Survey Spectra  

E-Print Network [OSTI]

We have derived the oxygen abundance for a sample of nearby galaxies in the Data Release 5 of the Sloan Digital Sky Survey (SDSS) which possess at least two independent spectra of one or several HII reg