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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

The Burst and Transient Source Experiment (BATSE) Earth Occultation Catalog of Low-Energy Gamma-Ray Sources

The Burst and Transient Source Experiment (BATSE), aboard the Compton Gamma Ray Observatory (CGRO), provided a record of the low-energy gamma-ray sky (approx. 20-1000 keV) between 1991 April and 2000 May (9.1 yr). BATSE monitored the high-energy sky using the Earth occultation technique (EOT) for point sources whose emission extended for times on the order of the CGRO orbital period (approx. 92 min) or greater. Using the EOT to extract flux information, a catalog of sources using data from the BATSE Large Area Detectors has been prepared. The first part of the catalog consists of results from the all-sky monitoring of 58 sources, mostly Galactic, with intrinsic variability on timescales of hours to years. For these sources, we have included tables of flux and spectral data, and outburst times for transients. Light curves (or flux histories) have been placed on the World Wide Web. We then performed a deep sampling of these 58 objects, plus a selection of 121 more objects, combining data from the entire 9.1 yr BATSE data set. Source types considered were primarily accreting binaries, but a small number of representative active galaxies, X-ray-emitting stars, and supernova remnants were also included. The sample represents a compilation of sources monitored and/or discovered with BATSE and other high-energy instruments between 1991 and 2000, known sources taken from the HEAO 1 A-4 and Macomb & Gehrels catalogs. The deep sample results include definite detections of 83 objects and possible detections of 36 additional objects. The definite detections spanned three classes of sources: accreting black hole and neutron star binaries, active galaxies, and Supernova remnants. The average fluxes measured for the fourth class, the X-ray emitting stars, were below the confidence limit for definite detection.

Harmon, B. A.↗
DOE OSTI2023

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

25 ENERGY STORAGE↗
DOE OSTI2021

Nuclear Data Sheets for A=194

Experimental nuclear structure and decay data are evaluated for all of 14 known nuclides of mass 194 (Ta, W, Re, Os, Ir, Pt, Au, Hg, Tl, Pb, Bi, Po, At, Rn). Detailed evaluated spectroscopic information is presented for each reaction and decay experiment, and by combining all the available data, recommended values are provided for energies, spins and parities and half-lives of levels, together with energies, branching ratios and multipolarities of gamma radiations from the levels, and characteristics of beta and alpha radiations in radioactive decays. No excited states have yet been identified in 194 Ta, 194 W, and 194 Rn. Only limited spectroscopic data are available for 194 Re, with a 45–μs isomer identified, but its absolute energy yet to be determined. Only two levels at unknown absolute energies are reported for 194 At, both of which are isomeric but neither can be unambiguously identified as the ground state. Data are scarce for 194 Os, 194 Bi and 194 Po, while 194 Pt, 194 Au, 194 Hg, 194 Tl and 194 Pb are well studied through decays and reactions. For 194 Ir, although a major (n, γ), E=thermal study and two particle-transfer studies provide large amounts of spectral data, still several problematic issues are found in the assignment of multipolarities for the secondary gamma transitions in the (n, γ) study by 2008Ba25, 1998Ba85 and 1998Ba42, all from the same group. Some issues have been resolved by the evaluators, but we suggest that further experimental work is needed on 194 Ir structure to resolve standing issues, and verify the conclusions made by 2008Ba25. Furthermore, our attempt to contact the prime authors of the 2008Ba25 paper was not successful. The present work supersedes all the earlier evaluations of A=194 nuclides by 2006Si17, 1996Br26, 1989Si01 and 1972Au11.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1987

AIS-2 spectra of California wetland vegetation

Spectral data gathered by Airborne Imaging Spectrometers-2 from wetlands were analyzed. Spectra representing stands of green Salicornia virginica, green Sesuvium verrucosum, senescing Distichlis spicata, a mixture of senescing Scirpus acutus and Scirpus californicus, senescing Scirpus paludosus, senescent S. paludosus, mowed senescent S. paludosus, and soil were isolated. No difference among narrowband spectral reflectance of the cover types was apparent between 0.8 to 1.6 micron. There were, however, broadband differences in brightness. These differences were sufficient to permit a fairly accurate decomposition of the image into its major cover type components using a procedure that assumes an additive linear mixture of surface spectra.

Gross, Michael F.↗
NASA NTRS2010

Processing AIRS Scientific Data Through Level 3

The Atmospheric Infra-Red Sounder (AIRS) Science Processing System (SPS) is a collection of computer programs, known as product generation executives (PGEs). The AIRS SPS PGEs are used for processing measurements received from the AIRS suite of infrared and microwave instruments orbiting the Earth onboard NASA's Aqua spacecraft. Early stages of the AIRS SPS development were described in a prior NASA Tech Briefs article: Initial Processing of Infrared Spectral Data (NPO-35243), Vol. 28, No. 11 (November 2004), page 39. In summary: Starting from Level 0 (representing raw AIRS data), the AIRS SPS PGEs and the data products they produce are identified by alphanumeric labels (1A, 1B, 2, and 3) representing successive stages or levels of processing. The previous NASA Tech Briefs article described processing through Level 2, the output of which comprises geo-located atmospheric data products such as temperature and humidity profiles among others. The AIRS Level 3 PGE samples selected information from the Level 2 standard products to produce a single global gridded product. One Level 3 product is generated for each day s collection of Level 2 data. In addition, daily Level 3 products are aggregated into two multiday products: an eight-day (half the orbital repeat cycle) product and monthly (calendar month) product.

Granger, Stephanie↗
NASA NTRS1972

Noise generated by impingement of a jet upon a large flat board

Data were obtained on the noise generated by an air jet impinging on a large flat board. The board was large enough so that the flow leaving the edges of the board generated no significant noise. The impingement angle, nozzle shape and size, jet velocity, and the distance from the nozzle to the board were varied in the experiment. Far-field noise data are presented. The nozzle-alone noise contribution to the total noise was generally small and was subtracted from the total, leaving the impingement-only noise. The impingement-only noise was adequately correlated by eighth power of the peak impingement velocity and first power of the impingement area. The spectral data were correlated by a Strouhal number based on the peak impingement velocity and a characteristic impingement diameter.

Olsen, W. A.↗
NASA NTRS1982

A climate index derived from satellite measured spectral infrared radiation

The vertical infrared radiative emitting structure (VIRES) climate index, based on radiative transfer theory and derived from the spectral radiances typically used to retrieve temperature profiles, is introduced. It is assumed that clouds and climate are closely related and a change in one will result in a change in the other. The index is a function of the cloud, temperature, and moisture distributions. It is more accurately retrieved from satellite data than is cloudiness per se. The VIRES index is based upon the shape and relative magnitude of the broadband weighting function of the infrared radiative transfer equation. The broadband weighting curves are retrieved from simulated satellite infrared sounder data (spectral radiances). The retrieval procedure is described and the error error sensitivities of the method investigated. Index measuring options and possible applications of the VIRES index are proposed.

Abel, M. D.↗
NASA NTRS1984

An integrated LANDSAT/ancillary data classification of desert rangeland

Range inventorying methods using LANDSAT MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, Utah. The vegetation is predominately desert shrub and annual grasses, with some annual forbs. Three LANDSAT scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Price, K. P.↗
NASA NTRS1985

An integrated Landsat/ancillary data classification of desert rangeland

Range inventorying methods using Landsat MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, UT. The vegetation is predominately desert shrub and annual grasses, with same annual forbs. Three Landsat scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Price, K. P.↗
DOE OSTI2024

Carbon fiber classification using raman spectroscopy

Carbon fiber characterization processes are described that include multi-condition Raman spectroscopy-based examination combined with multivariate data analyses. Methods are a nondestructive material characterization approach that can provide predictions as to carbon fiber bulk physical properties, as well as identification of unknown carbon fiber materials for quality control purposes. The framework of the multivariate analysis methods includes a principal component-based identification protocol including comparison of Raman spectral data from an unknown carbon fiber with a data library of multiple principal component spaces.

Houk, Amanda L.↗
NASA NTRS2002

A Search for Evidence of Non-Thermal Emission from the Supernova Remnants 37A/B

The ADP grant NAG5-9211 entitled 'A Search for Evidence of Non-Thermal Emission from the Supernova Remnants 37 A/B' was not used to support an analysis of the ASCA data for these two remnants because the ASCA mission ended before the remnants were observed. The grant was used to support similar research on two remnants in the Large Magellanic Cloud, N132D and N 103B. An analysis of the Chandra data for these two remnants exhibits some evidence of non-thermal emission from small regions in the remnants. The X-ray spectra for these regions can not be adequately described by a single thermal X-ray emission model. However, if an X-ray synchrotron component is also included, the spectral data can be well described by the model and the values of the fit parameters are consistent with the values expected. These results were presented at the 199th Meeting of the American Astronomical Society. In summary, the grant has enabled us to strengthen the evidence that supernova remnants outside our Galaxy can also accelerate electrons to very-high energies. The results of this analysis will be published soon in the Astrophysical Journal,

Oliversen, R.↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗
DOE OSTI2022

Enhanced analysis of experimental x-ray spectra through deep learning

X-ray spectroscopic data from high-energy-density laser-produced plasmas has long required thorough, time-consuming analysis to extract meaningful source conditions. There are often confounding factors due to rapidly evolving states and finite spatial gradients (e.g., the existence of multi-temperature, multi-density, multi-ionization states, etc.) that make spectral measurements and analysis difficult. Here, in this paper, we demonstrate how deep learning can be applied to enhance x-ray spectral data analysis in both speed and intricacy. Neural networks (NNs) are trained on ensemble atomic physics simulations so that they can subsequently construct a model capable of extracting plasma parameters directly from experimental spectra. Through deep learning, the models can extract temperature distributions as opposed to single or dual temperature/density fits from standard trial-and-error atomic modeling at a significantly reduced computational cost compared to traditional trial-and-error methods. These NNs are envisioned to be deployed with high repetition rate x-ray spectrometers in order to provide detailed real-time analysis of experimental spectra.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

MIRACAL: A mission radiation calculation program for analysis of lunar and interplanetary missions

A computational procedure and data base are developed for manned space exploration missions for which estimates are made for the energetic particle fluences encountered and the resulting dose equivalent incurred. The data base includes the following options: statistical or continuum model for ordinary solar proton events, selection of up to six large proton flare spectra, and galactic cosmic ray fluxes for elemental nuclei of charge numbers 1 through 92. The program requires an input trajectory definition information and specifications of optional parameters, which include desired spectral data and nominal shield thickness. The procedure may be implemented as an independent program or as a subroutine in trajectory codes. This code should be most useful in mission optimization and selection studies for which radiation exposure is of special importance.

Nealy, John E.↗
NASA NTRS2017

Vegetation Classification of Coffea on Hawaii Island using Worldview-2 Satellite Imagery

Coffee is an important crop in tropical regions of the world; about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012.

Gaertner, Julie↗
NASA NTRS1985

Tentative Identification of the 780/cm nu(sub 4) Band Q Branch of Chlorine Nitrate in High-Resolution Solar Absorption Spectra of the Stratosphere

Absorption by the Q branch of the nu(sub 4), band of ClONO2 at 780.2/cm has been tentatively identified in a series of 0.02/cm resolution balloon-borne solar absorption spectra of the stratosphere. The spectral data were recorded at sunset from a flot altitude of 33.5 km during a balloon flight from Holloman Air Force Base (32.8deg N, 106.0 deg W) near Alamogordo, New Mexico, on March 23 1998. A preliminary ClONO2 vertical profile has been determined from the stratospheric spectra by using the technique of nonlinear least squares spectral curve fitting and new spectroscopic parameters deduced from high-resolution laboratory spectra of ClONO2 and O3.

Rinsland, C. P.↗
NASA NTRS1982

A comparative study of the thematic mapper and Landsat spectral bands from field measurement data

Principal component and factor analysis techniques were applied to the spectral data collected over 27 field plots of various crops under varying agronomic conditions. The spectral data was integrated over the proposed thematic mapper bands and Landsat MSS spectral bands. The results were examined to compare the discrimination power of the thematic mapper. Previously announced in STAR as N81-33549

Badhwar, G. D.↗
DOE OSTI2013

Adapting Raman Spectra from Laboratory Spectrometers to Portable Detection Libraries

Raman spectral data collected with high-resolution laboratory spectrometers are processed into a for- mat suitable for importing as a user library on a 1064nm DeltaNu rst generation, eld-deployable spectrometer prototype. The two laboratory systems used are a 1064nm Bruker spectrometer and a 785nm Kaiser spectrometer. The steps taken to compensate for device-dependent spectral resolution, wavenumber shifts between instruments, and wavenumber sensitivity variation are described.

Weatherall, James↗