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All sources NASA NTRS DOE OSTI

DOE OSTI2018

Marine Corps Base Camp Lejeune New River Estuary (NRE) Regions

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.The New River Estuary regions were digitized using desktop GIS and elevation data to determine the watershed boundaries and bathymetry data is determined by the water depth. Geographic representation of box and flow model produced by DCERP aquatic & esturaine module team, includes micro-watershed data. A spatial approximation of the areas defined in the Estuarine Simulation Model developed by Mark Brush.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

HAPI: An API Standard for Accessing Heliophysics Time Series Data

Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires intercomparison of data from multiple data providers difficult. The Heliophysics Application Programmer's Interface (HAPI) is a recently developed standard for accessing distributed time series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI-related open-source projects offer server-side implementation tools and client-side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

Robert S. Weigel↗

DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

17 WIND ENERGY↗

DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Cambio

Cambio opens data files from common gamma radiation detectors, displays a visual representation of it, and allows the user to edit the meta-data, as well as convert the data to a different file format.

Johnson, William↗

NASA NTRS1978

Image registration using binary boundary maps

Registration technique that matches binary boundary maps extracted from raw data, rather than matching actual data, is considerably faster than other techniques. Boundary maps, which are digital representations of regions where image amplitudes change significantly, typically represent data compression of 60 to 70 percent. Maps allow average products to be computed with addition rather than multiplication, further reducing computation time.

Andrus, J. F.↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6022; 80 deg S to 90 deg S Latitude, South Periapsis - Volume 22

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U.S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6020; 80 deg N to 90 deg N Latitude, South Periapsis - Volume 20

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6021; 80 deg S to 90 deg S Latitude, North Periapsis - Volume 21

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U. S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6019; 80 deg N to 90 deg N Latitude, North Periapsis - Volume 19

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U. S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

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↗

NASA NTRS1984

Utilization of Historic Information in an Optimisation Task

One of the basic components of a discrete model of motor behavior and decision making, which describes tracking and supervisory control in unitary terms, is assumed to be a filtering mechanism which is tied to the representational principles of human memory for time-series information. In a series of experiments subjects used the time-series information with certain significant limitations: there is a range-effect; asymmetric distributions seem to be recognized, but it does not seem to be possible to optimize performance based on skewed distributions. Thus there is a transformation of the displayed data between the perceptual system and representation in memory involving a loss of information. This rules out a number of representational principles for time-series information in memory and fits very well into the framework of a comprehensive discrete model for control of complex systems, modelling continuous control (tracking), discrete responses, supervisory behavior and learning.

Boesser, T.↗

DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗

DOE OSTI2020

NCBI’s Virus Discovery Codeathon: Building “FIVE” —The Federated Index of Viral Experiments API Index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Evaluation of the GaAs displacement damage metric using updated nuclear data

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 {sup 75}As and TENDL-2019 {sup 71}Ga/{sup 69}Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97 ± 0.05 and the average updated double ratio was found to be 0.94 ± 0.05. (authors)

36 MATERIALS SCIENCE↗

DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗

DOE OSTI2024

Event-to-Video Conversion for Overhead Object Detection

Collecting overhead imagery using an event camera is desirable due to the energy efficiency of the image sensor compared to standard cameras. However, event cameras complicate downstream image processing, especially for complex tasks such as object detection. In this paper, we investigate the viability of event streams for overhead object detection. We demonstrate that across a number of standard modeling approaches, there is a significant gap in performance between dense event representations and corresponding RGB frames. We establish that this gap is, in part, due to a lack of overlap between the event representations and the pre-training data that the object detectors were initially trained on through a number of experiments. Then, apply an off-the-shelf event-to-video conversion tool that converts event streams into gray-scale video to close this gap. We demonstrate that this approach results in a large performance increase, outperforming even event-specific object detection techniques on our overhead target task. These results suggest that better aligning event representations with existing large pre-trained models may result in greater short-term performance gains compared to end-to-end event-specific architectural improvements.

machine learning (ML), computer vision, Neuromorph↗

NASA NTRS1991

Theoretical foundations for information representation and constraint specification

Research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University is described. Outlined here are the theoretical foundations necessary to construct a Neutral Information Representation Scheme (NIRS), which will allow for automated data transfer and translation between model languages, procedural programming languages, database languages, transaction and process languages, and knowledge representation and reasoning control languages for information system specification.

Menzel, Christopher P.↗