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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Scale covariance and G-varying cosmology. IV - The log N-log S relation

The traditional radio counts N(S) and radio source models are studied within the framework of the scale-covariant cosmology developed to investigate whether the relative strength of the gravitational and electromagnetic constants is a function of cosmological epoch. It is found that a gravitational constant G varying as the inverse of t, where t is the epoch in atomic units, is consistent with all the data analyzed. For a wide class of models the present cosmology allows a finer discrimination of the deceleration parameter than does standard theory. The results, when combined with those of previous papers, namely, those from radio and optical flux and angular-diameter data analysis, favor an open universe.

Canuto, V. M.↗
NASA NTRS1982

A complete X-ray sample of the high-latitude /absolute value of b greater than 20 deg/ sky from HEAO 1 A-2 - Log N-log S and luminosity functions

An all-sky survey of X-ray sources was performed, complete to a limiting sensitivity of 3.1 x 10 to the -11 ergs/sq cm/s in the 2-10 keV band. The complete sample has allowed construction of luminosity functions based on a flux-limited sample for clusters of galaxies and active galactic nuclei. Integration of the best-fit luminosity functions indicates that clusters of galaxies contribute about 4% of the 2-10 keV DXRB, and active galactic nuclei about 20%. It is predicted that many of the objects seen in the deep survey should be local, relatively low luminosity active galactic nuclei and clusters of galaxies.

Piccinotti, G.↗
NASA NTRS1982

A medium sensitivity X-ray survey using the Einstein Observatory - The log N-log S relation for extragalactic X-ray sources

The paper presents results of an X-ray survey of about 50 sq deg of the high galactic latitude sky at sensitivities in the range of 7 x 10 to the -14 to 5 x 10 to the -12 ergs/sq cm/s. The number-flux relation is derived for the extragalactic population to yield a best-fit power-law slope of 1.53 + or - 0.16, and the content of the sample is analyzed in terms of types of sources, appearing to be significantly different from the content of similar samples selected at higher fluxes. The medium sensitivity sample of extragalactic sources is dominated by active galactic nuclei, while samples selected at higher fluxes and higher energies are dominated by clusters of galaxies. Thus, the number-flux relation for extragalactic sources may be interpreted to a first approximation as the sum of the two different distributions with flatter and steeper slopes describing clusters and AGNs, respectively.

Maccacaro, T.↗
NASA NTRS1982

Comments on the gamma-ray burst log N /greater than S/ log S point of Beurle et al

Statistical improbability is demonstrated for the Beurle et al (1981) determination of an isotropic cumulative burst rate of 7030 + 10,000 or - 6000/year, at a fluence of 8.47 x to the -9th/erg per sq cm, from their observation of two gamma-ray bursts. The difficulty identified is Beurle's assumption that the power law cumulative distribution function index equals one. Reconsideration of the Beurle observations is undertaken, and an alternative upper limit of the annual number of bursts at the fluence cited of less than 5400/year is proposed.

Jennings, M. C.↗
NASA NTRS1992

A deep x ray survey in the Lockman hole and the soft x ray log N - log S

The longest pointed observation (152 ksec) with the Rosat position sensitive proportional counter in the direction of the absolutely lowest neutral hydrogen column density is discussed. 26 shallower fields from the Rosat medium sensivity survey are analyzed. 1176 X-ray sources were detected in at least one Rosat energy band in these fields covering a total solid angle of 9.3 deg; 661 of these sources constitute a statistically complete sample detected in the hard band with 0.5 to 2 keV fluxes greater than 2.5 times 10 to the power of minus 15 erg/sq cm s. The faintest limiting flux of the survey is analyzed. Detailed simulations show that confusion effects and other selection biases are relatively small and can be corrected for in the sample. From an analysis in the Lockman field, a best fit slope of approximately 1.8 is found for the extrapolation of the differential X-ray counts below 2.5 times 10 to the power of minus 15 erg/sq cm s. On the basis of this analysis an upper limit of approximately 25% can be found for a truly diffuse background component in the Rosat hard band.

Hasinger, G.↗
DOE OSTI2020

Hindsight logging for model training

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.

Computer Science↗
NASA NTRS2002

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗
DOE OSTI2016

Utah FORGE: Milford Digitized Geophysical Logs from Acord 1

This submission includes digitalized versions of the following: McCulloch Geothermal Corp Acord 1-26 Cover Letter McCulloch Geothermal Corp Acord 1-26 Drilling Plan McCulloch Geothermal Corp Acord 1-26 Bond Documents Division of Water Rights Permission to Drill Drillers Log Geothermal Data (Mud) Log Compensated Densilog - Neutron Log Dual Induction Focused Log BHC Acoustilog Differential Temperature Log Dual Induction Focused Log Gamma Ray Neutron Log Temperature Log Caliper Temperature Log (Run 3) Densilog Gamma Ray Neutron Log Temperature Log (Run 4) Compensated Densilog Sample Log (Page 1 of 2) Report of Well Driller Stratigraphic Report (J.E. Welsh) Photographs and Negatives of Acord 1-26 Well Site (7) Petrography Report (M.J. Sweeney) Cuttings Samples (21 Boxes at Utah Core Research Center)

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Generation of synthetic dielectric dispersion logs in organic-rich shale formations using neural-network models

Dielectric dispersion (DD) logs acquired in subsurface geologic formations generally are composed of conductivity ([Formula: see text]) and relative permittivity ([Formula: see text]) measurements at four discrete frequencies in the range of 10 MHz to 1 GHz. Acquisition of DD logs in subsurface formations is operationally challenging, and it requires a hard-to-deploy infrastructure. We developed three supervised neural-network-based predictive methods to process conventional, easy-to-acquire subsurface logs for generating the eight DD logs acquired at four frequencies. These predictive methods will improve reservoir characterization in the absence of a DD logging tool. The predictive methods are tested in three wells intersecting organic-rich shale formations of the Permian Basin and the Bakken Shale. The first method predicts the eight dispersion logs simultaneously using a single artificial neural network (ANN) model, whereas the second method simultaneously predicts the four conductivity dispersion logs using one ANN model, followed by simultaneous prediction of four permittivity dispersion logs using a second ANN model. The third method sequentially predicts the eight dispersion logs, one at a time using eight sequential ANN models, based on a predetermined ranking of the prediction accuracy for each of the eight DD logs when simultaneously generated. Considering that the conventional and DD logs are recorded more than 10,000 ft deep in the subsurface using logging tools that are run at different times in rugose boreholes for sensing the near-wellbore geologic formation, the data used in this predictive work is prone to noise and biases that tend to adversely affect the prediction performances of the proposed methods. In terms of normalized root-mean square error (Nrms error), the prediction performances of the second predictive method are 8.5% worse and 6.2% better for the conductivity and permittivity dispersion logs, respectively, as compared with those of the first predictive method. The third method has best prediction performance for permittivity dispersion logs, which is 0.089 in terms of the Nrms error.

Geochemistry & Geophysics↗