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All sources NASA NTRS DOE OSTI

DOE OSTI2022

A method and tool to determine the colorimetric and photobiological properties of light transmitted through glass and other optical materials

Here we describe a framework and provides a computational tool to characterize the color quality and biological potential of light that is transmitted through glass and other optical materials. The IES TM-30 framework and Excel computation tool were adapted to evaluate color quality, with measures from CIE S 026, UL 24480, and the WELL Building Standard v2 added to evaluate biological potential. The user selects a pre-transmittance spectral power distribution (SPD), such as for a CIE D-Series Illuminant, Planckian radiation, electric lamp, or measurement of daylight at a building site. The tool allows a user to populate a database with spectral data for glazing or other optical materials, comprising spectral transmission and the spectral reflectance of both sides. The user creates a unit with one, two, or three panes of glazing or other optical materials, and the tool calculates the composite spectral transmittance accounting for reflections between materials. The tool computes the transmitted SPD, then determines colorimetric and photobiological outputs using the transmitted SPD as the test source. Glass, window, and skylight manufacturers can employ the tool to optimize glazing spectral transmission to achieve intentional colorimetric and photobiological performance with transmitted light. Electric lighting manufacturers, designers, and researchers can use the tool to evaluate the impact of glazing units and other optical materials on the color quality and biological potential of transmitted light.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Supplemental Data for the Manuscript: Quantification of Manganese for ChemCam Mars and Laboratory Spectra Using a Multivariate Model

This dataset includes all of the data needed to validate and/or reproduce the manganese calibration model described in the manuscript. The reference database contains metadata for the new Mn-bearing standards, minerals, and mixtures that are > 2.9 wt.% MnO. In addition, the files include the MnO composition data for all standards used, non-normalized spectral data, mean peak area spectrum, results of outlier determination, RMSECV data, regression vectors, and Test Set predictions.

58 GEOSCIENCES↗

NASA NTRS1990

Digital filtering of plume emission spectra

Fourier transformation and digital filtering techniques were used to separate the superpositioned spectral phenomena observed in the exhaust plumes of liquid propellant rocket engines. Space shuttle main engine (SSME) spectral data were used to show that extraction of spectral lines in the spatial frequency domain does not introduce error, and extraction of the background continuum introduces only minimal error. Error introduced during band extraction could not be quantified due to poor spectrometer resolution. Based on the atomic and molecular species found in the SSME plume, it was determined that spectrometer resolution must be 0.03 nm for SSME plume spectral monitoring.

Madzsar, George C.↗

NASA NTRS1990

Digital filtering of plume emission spectra

Madzsar, George C.↗

NASA NTRS2004

NASA Tech Briefs, August 2004

Topics covered include: Data Relay Board with Protocol for High-Speed, Free-Space Optical Communications; Software and Algorithms for Biomedical Image Data Processing and Visualization; Rapid Chemometric Filtering of Spectral Data; Prioritizing Scientific Data for Transmission; Determining Sizes of Particles in a Flow from DPIV Data; Faster Processing for Inverting GPS Occultation Data; FPGA-Based, Self-Checking, Fault-Tolerant Computers; Ultralow-Power Digital Correlator for Microwave Polarimetry; Grounding Headphones for Protection Against ESD; Lightweight Stacks of Direct Methanol Fuel Cells; Highly Efficient Vector-Inversion Pulse Generators; Estimating Basic Preliminary Design Performances of Aerospace Vehicles; Framework for Development of Object-Oriented Software; Analyzing Spacecraft Telecommunication Systems; Collaborative Planning of Robotic Exploration; Tools for Administration of a UNIX-Based Network; Preparing and Analyzing Iced Airfoils; Evaluating Performance of Components; Fuels Containing Methane of Natural Gas in Solution; Direct Electrolytic Deposition of Mats of MnxOy Nanowires; Bubble Eliminator Based on Centrifugal Flow; Inflatable Emergency Atmospheric-Entry Vehicles; Lightweight Deployable Mirrors with Tensegrity Supports; Centrifugal Adsorption Cartridge System; Ultrasonic Apparatus for Pulverizing Brittle Material; Transplanting Retinal Cells using Bucky Paper for Support; Using an Ultrasonic Instrument to Size Extravascular Bubbles; Coronagraphic Notch Filter for Raman Spectroscopy; On-the-Fly Mapping for Calibrating Directional Antennas; Working Fluids for Increasing Capacities of Heat Pipes; Computationally-Efficient Minimum-Time Aircraft Routes in the Presence of Winds; Liquid-Metal-Fed Pulsed Plasma Thrusters; Personal Radiation Protection System; and Attitude Control for a Solar-Sail Spacecraft.

Source record↗

DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Independent Gamma Spectroscopy Analysis of a Plutonium Oxide Sample

Independent gamma spectroscopy data analysis of a plutonium oxide sample was requested on July 23, 2021. The primary request was to assess the Pu-239 activity/mass of the sample using previously collected gamma spectral data. Using the provided gamma spectral analysis report and spectral files, an independent evaluation of the data was conducted without any prior knowledge of the isotopic activity/mass of the sample.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1989

Mapping surface energy balance components by combining Landsat Thematic Mapper and ground-based meteorological data

Surface energy balance components were evaluated by combining satellite-based spectral data with on-site measurements of solar irradiance, air temperature, wind speed, and vapor pressure. Maps of latent heat flux density and net radiant flux density were produced using Landsat TM data for three dates. The TM-based estimates differed from Bowen-ratio and aircraft-based estimates by less than 12 percent over mature fields of cotton, wheat, and alfalfa.

Moran, M. Susan↗

NASA NTRS1983

Observations of optical emissions on STS-4

Following the discovery of intense vehicle glow of the Space Shuttle on the STS-3 mission, a new experiment to probe this phenomenon was developed and flown on the Shuttle STS-4 mission. A conventional camera was equipped with an objective transmission grating and several photographic exposures were taken of the Shuttle tail from the aft flight deck window. Vehicle glow surrounding Shuttle surfaces exposed to the ram direction was observed on a long (400 second) exposure. From analysis of the resulting spectral data it appears that the Shuttle glow has a diffuse spectral component in the region 6300 to 8000A. In addition, it has been possible to assess the spectral character of light emitted by the firing of a reaction control system thruster. Our data show that the light emitted from the thruster gases is confined to wavelengths shorter than 7200A.

Mende, S. B.↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

Preliminary measurements of plasma fluctuations in an 8-cm mercury ion thruster

The investigation considered included an observation of the effects on the plasma fluctuations of a variation of several neutralizer parameters. The apparatus used in the investigation and the experimental techniques are discussed, taking into account the thruster, the thruster power supply, questions of thruster operation, transducers, and an analysis of time-varying data. The results presented are related to rms magnitudes, spectral data, and fluctuation correlations.

Serafini, J. S.↗

NASA NTRS1977

Effect of vegetation on rock and soil type discrimination

The effect of naturally occurring vegetation on the spectral reflectance of earth materials in the wavelength region of 0.45 to 2.4 microns is determined by computer averaging of in situ acquired spectral data. The amount and type of vegetation and the spectral reflectance of the ground are considered. Low albedo materials may be altered beyond recognition with only ten per cent green vegetation cover. Dead or dry vegetation does not greatly alter the shape of the spectral reflectance curve and only changes the albedo with minimum wavelength dependency. With increasing amounts of vegetation the Landsat MSS band ratios 4/6, 4/7, 5/6, and 5/7 are significantly decreased whereas MSS ratios 4/5 and 6/7 remain entirely constant.

Siegal, B. S.↗

NASA NTRS1978

Are two photographic infrared sensors required

The possible redundancy of photographic infrared (0.70-1.00 micron) sensors for monitoring vegetation and plant stresses was investigated using previously published data and the analysis of in situ narrow bandpass spectral data from experimental grass plots. Results of the analysis showed that a 0.76-0.90 micron photographic infrared sensor would combine excellent general vegetational utility with the ability to discriminate photographic infrared plateau-rounding stress conditions. A 0.76-0.90 micron sensor is therefore suggested for Landsat-D.

Tucker, C. J.↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Lunar Remote Sensing: Fire, Ice, and Regolith

The session "Lunar Remote Sensing: Fire, Ice, and Regolith" included the following:Compositional and Structural Study of the Aristarchus Plateau from Integrated UV-VIS-NIR Spectral Data; Clementine 2.7-?m Data: Mapping the Mare and Searching for Water; On the Search for Water at the Lunar Poles: Results of Forward Modeling of Permanently Shaded Areas and Lunar Prospector Measurements; Searching the Moon for Aluminous Mare Basalts Using Compositional Remote-Sensing Constraints I: Finding the Regions of Interest; Semi-automated Extraction of Contours from Lunar Topographic Maps; Basalts in Mare Humorum and S.E. Procellarum; The Hansteen and Helmet Volcanic Dome Regions on the Moon: Stratigraphy and Ages; Derivation of Elemental Abundance Maps at 15-km Spatial Resolution from the Merging of Clementine Optical and Lunar Prospector Geochemical Data; Remote Sensing and Geologic Studies of the Balmer Region of the Moon; Lava Flows in Mare Nubium and Mare Cognitum: A Geological History Based on Analysis of Multispectral Data; Development of Ground-based Lunar VIS/NEAR IR Spectral Imager; A BRDF Measurement Apparatus for Lab-based Samples; A New Source of High Resolution Lunar Images: Amateur Astronomers! ; Leakage of Gamma Rays and Neutrons from Thick Targets Bombarded by Energetic Protons; Progress on Reviving Lunar Orbiter: Scanning, Archiving, and Cartographic Processing at USGS; Modeling Lateral and Vertical Mixing by Impact Cratering with Applications for the Moon; Optical Maturity Study of Stuart#s Crater Candidate Impact; Evidence for Three Basins Beneath Oceanus Procellarum; and Ellipses of the South Pole-Aitken Basin: Implications for Basin Formation.

Source record↗

DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗

NASA NTRS1989

Column atmospheric water vapor retrievals from airborne imaging spectrometer data

High-spatial-resolution column atmospheric water vapor amounts were derived from spectral data collected by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). The quantitative derivation is made by curve fitting observed spectra with calculated spectra in the 1.14- and 0.94-micron water-vapor band absorption regions with a nonlinear least-squares technique. The precision of the retrieved column water vapor is approximately 5 percent. The derived column water vapor amounts are independent of the absolute surface reflectance. Curve fitting of spectra near 1 micron from areas covered with vegetation indicates that both the amount of atmospheric water vapor and the moisture content of vegetation can be retrieved simultaneously. It should be possible to measure column water vapor over land areas from satellite altitude with the proposed high-resolution imaging spectrometer or even the moderate-resolution imaging spectrometer.

Gao, Bo-Cai↗

NASA NTRSDate not supplied

Updates on the DebriSat Hypervelocity Experiment and Characterization of Fragments in Support of Environmental Models

To develop, maintain, and update orbital debris environmental and break-up models, such as the NASA Orbital Debris Engineering Model (ORDEM) and the NASA Standard Satellite Breakup Model (SSBM), the NASA Orbital Debris Program Office (ODPO) relies on the most recent validated datasets from various measurement sources, both laboratory and environmental. One key project that will provide insight for break-up events using modern-day spacecraft materials and construction techniques is the DebriSat laboratory hypervelocity-impact test. Based on the mass of the target, the projectile, and the impact velocity, the expected number of fragments greater than 2 mm, generated using the NASA SSBM, was estimated to be close to 85,000. To date, the DebriSat fragment database continues to grow, with over 200,000 fragments collected that will help inform updates to the SSBM. Additionally, the growing fragment ensemble will support key parameters for the next release of NASA’s environmental models, employing fragment shapes, densities, and size distributions. To further support these environmental models, specifically the size parameter used in ground-based optical measurements, optical characterization on a subset of DebriSat fragments is being conducted in ODPO’s Optical Measurement Center (OMC). Broadband bidirectional reflectance distribution (BRDF) measurements will provide insight into the optical-based NASA Size Estimation Model (OSEM). The OSEM equates an object’s brightness to size (e.g., diameter of a disk or sphere) given several assumed parameters, including a defined phase function, albedo, and range. To address the first defined parameter, the ODPO has been using ray-tracing software to simulate light conditions in the OMC and to generate phase functions (i.e., specular, Lambertian, and experimentally-derived) of known shapes, materials, and sizes. This simulated data, in addition to the experimentally derived measurements collected in the OMC, will aid in determining whether a new phase function would be suitable for an updated OSEM. The OSEM also assumes a single-value albedo, thus preimpact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization and to provide insight into spacecraft material taxonomies. This DebriSat spectral data, along with spectral measurements from other known spacecraft material samples, will allow for better analyses of albedo variations and the effect on size calculations of defined laboratory targets, thus further supporting OSEM updates. This paper provides an overview of DebriSat, the status of the project, updates on the parameter distributions, an overview of the NASA SSBM, and ongoing fragment characterization efforts within the OMC.

Heather Cowardin↗

DOE OSTI2014

1 H NMR metabolomics study of metastatic melanoma in C57BL/6J mouse spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. As such, in this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e., spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide ,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. Furthermore, these significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

1H NMR metabolomics↗