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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

SP mountain data analysis

An analysis of synthetic aperture radar data of SP Mountain was undertaken to demonstrate the use of digital image processing techniques to aid in geologic interpretation of SAR data. These data were collected with the ERIM X- and L-band airborne SAR using like- and cross-polarizations. The resulting signal films were used to produce computer compatible tapes, from which four-channel imagery was generated. Slant range-to-ground range and range-azimuth-scale corrections were made in order to facilitate image registration; intensity corrections were also made. Manual interpretation of the imagery showed that L-band represented the geology of the area better than X-band. Several differences between the various images were also noted. Further digital analysis of the corrected data was done for enhancement purposes. This analysis included application of an MSS differencing routine and development of a routine for removal of relief displacement. It was found that accurate registration of the SAR channels is critical to the effectiveness of the differencing routine. Use of the relief displacement algorithm on the SP Mountain data demonstrated the feasibility of the technique.

Rawson, R. F.↗
NASA NTRS1978

Concentration-response data on toxicity of pyrolysis gases from six synthetic polymers

Concentration-response data are presented on the toxic effects of the pyrolysis gases from six synthetic polymers on Swiss Webster male mice, using a toxicity screening test method. The polymers consisted of one sample each of polycarbonate and polystyrene, and two samples each of polyethylene and acrylonitrile-butadiene-styrene (ABS). All six samples were evaluated using the rising temperature program, and three samples were evaluated using the fixed temperature program. The pyrolysis gases from polycarbonate, polyethylene, and ABS appeared to exhibit the concentration-response relationships commonly encountered in toxicology. The polystyrene sample gave variable results; probably due to poor reproducibility of the essentially anaerobic pyrolysis. Carbon monoxide seemed to be the principal toxicant in the pyrolysis gases from polycarbonate and polyethylene, but did not appear to be the principal toxicant in the pyrolysis gases from ABS.

Hilado, C. J.↗
NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗
NASA NTRS1991

Automatic analysis of stereoscopic satellite image pairs for determination of cloud-top height and structure

Results are presented on an automatic stereo analysis of cloud-top heights from nearly simultaneous satellite image pairs from the GOES and NOAA satellites, using a massively parallel processor computer. Comparisons of computer-derived height fields and manually analyzed fields show that the automatic analysis technique shows promise for performing routine stereo analysis in a real-time environment, providing a useful forecasting tool by augmenting observational data sets of severe thunderstorms and hurricanes. Simulations using synthetic stereo data show that it is possible to automatically resolve small-scale features such as 4000-m-diam clouds to about 1500 m in the vertical.

Hasler, A. F.↗
NASA NTRS1987

Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data

Space-borne synthetic aperture radar (SAR) data acquired by the Shuttle Imaging Radar-B (SIR-B) Program and Landsat Multispectral Scanner Subsystem (MSS) Data from Landsat 4 were used to map flood boundaries for the assessment of flood damage in the Peoples Republic of Bangladesh. The cloud penetrating capabilities of the L-band radar provided a clear picture of the hydrologic conditions of the surface during a period of inclement weather at the end of the wet phase of the 1984 monsoon. The radar image data were digitally processed to geometrically rectify the pixel geometry and were filtered to subdue radar image speckle effects. Contrast enhancement techniques and density slicing were used to create discrete land-cover categories corresponding to surface conditions present at the time of the Shuttle overflight. The radar image classification map was digitally registered to a spectral signature classification map of the area derived from Landsat MSS data collected two weeks prior to the SIR-B mission. Classification accuracy comparisons were made between the radar and MSS classification maps, and flood boundary and flood damage assessment measurements were made with the merged data by adding the classifications and inventorying the land-cover classes inundated at the time of flooding.

Imhoff, Marc L.↗
NASA NTRS1979

Improvement of selected satellite applications through the use of microwave data

This paper describes an analysis of a data set in which satellite acquired microwave data (Seasat Synthetic Aperture Radar) have been registered with Landsat Multispectral Scanner data and the combined data processed using conventional multichannel spectral pattern recognition programs. Results of this analysis indicate that the combined data set offers improvement in surface classification that is significant to certain applications. A brief description of the registration procedure is given. The improvement in results rendered to selected resource management applications is discussed. The presented results are preliminary due to the short time that the satellite microwave data have been available; however, a more comprehensive analysis is in progress and will be completed during the coming year.

Mooneyhan, D. W.↗
NASA NTRS1988

The Laboratory for Oceans Computing Facility

The first VAX computer in the Laboratory for Oceans Computing Facility (LOCF) was installed and the facility was largely expanded. The growth is not only in hardware and software, but also in the number of users and in supporting research and development projects. The LOCF serves as a general purpose computing facility for: ocean color research projects, sea ice research projects, processing of the Nimbus-7 Coastal Zone Color Scanner data set, real time ingest and analysis of TIROS-N satellite data, study of the Synthetic Aperture Radar data, study of LANDSAT data, and many others. The physical space and the electrical power layout of the computing room were modified to accommodate all the equipment. The LOCF has several image processing stations which include two International Imaging Systems (IIS) model 75 processors and one Adage processor. The facility has the capability of ingesting the TIROS-N HRPT satellite data on a real time basis. More than 30 software packages were installed on the systems. System software packages, network software, FORTRAN and C compilers, database management software, image processing software, graphics, mathematics and statistics packages, TAE, Catalog Manager, GEMPAK, LAS and many other software developed on the LOCF computers such as SEAPAK have greatly advanced the capability of the LOCF.

Kao, R.↗
NASA NTRS1989

Adaptation of the ISCCP cloud detection algorithm to combined AVHRR and SMMR arctic data

The International Satellite Cloud Climatology Project (ISCCP) cloud detection algorithm is applied to artic data, and modifications are suggested. Both Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data are examined. Synthetic AVHRR and SMMR data are also generated. Modifications suggested include the use of snow and ice data sets for the estimation of surface parameters, additional AVHRR channels, and surface class characteristic values when clear sky values cannot be obtained. Greatest improvement in computed cloud fraction is realized over snow and ice surfaces; over other surfaces all versions perform similarly. Since the use of SMMR for surface analysis increases the computational burden, its use may be justified only over snow and ice-covered regions.

Key, J.↗
NASA NTRS1990

Tropical forest tree stands characterization with L-band polarimetric radar

The effectiveness of using L-band polarimetric data to determine tropical tree-stand parameters is discussed with specific attention given to the correction of the radar data. Tree-parameter data from ground studies is compared to L-band polarimetric data (in both uncorrected and topographically corrected forms) for two test areas. The test sites are at two different elevations but both include 81 test plots with topographic data and tree-characteristic data given. Synthetic-aperture radar (SAR) data are found to be related to bole volume and tree volume, and the topographically corrected data show results similar to the uncorrected data. Similar r-values are given for both data sets because the data with incidence-angle values below 35 and above 55 are removed. Topographical correction is important when local incidence angles exceed the limits.

Wu, Shih-Tseng↗
NASA NTRS2007

The PICWidget

The Plug-in Image Component Widget (PICWidget) is a software component for building digital imaging applications. The component is part of a methodology described in GIS Methodology for Planning Planetary-Rover Operations (NPO-41812), which appears elsewhere in this issue of NASA Tech Briefs. Planetary rover missions return a large number and wide variety of image data products that vary in complexity in many ways. Supported by a powerful, flexible image-data-processing pipeline, the PICWidget can process and render many types of imagery, including (but not limited to) thumbnail, subframed, downsampled, stereoscopic, and mosaic images; images coregistred with orbital data; and synthetic red/green/blue images. The PICWidget is capable of efficiently rendering images from data representing many more pixels than are available at a computer workstation where the images are to be displayed. The PICWidget is implemented as an Eclipse plug-in using the Standard Widget Toolkit, which provides a straightforward interface for re-use of the PICWidget in any number of application programs built upon the Eclipse application framework. Because the PICWidget is tile-based and performs aggressive tile caching, it has flexibility to perform faster or slower, depending whether more or less memory is available.

Norris, Jeffrey↗
NASA NTRS2016

NASA Soil Moisture Active Passive Mission Status and Science Performance

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational since mid-April 2015. The SMAP radiometer has been operating flawlessly, but the radar transmitter ceased operation on July 7. This paper provides a status summary of the calibration and validation of the SMAP instruments and the quality assessment of its soil moisture and freeze/thaw products. Since the loss of the radar in July, the SMAP project has been conducting two parallel activities to enhance the resolution of soil moisture products. One of them explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The other investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic radar data to obtain soil moisture products at about 1 to 3 kilometers resolution. In addition, SMAP's L-band data have found many new applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.

hydroclimatology↗
NASA NTRS2023

Commercial Smallsat Data Acquisition Program: Airbus U.S. Synthetic Aperture Radar Quality Assessment Summary

Quality assessment of the Airbus X-band Synthetic Aperture Radar (SAR) satellite products was conducted by the Commercial Smallsat Data Acquisition (CSDA) program’s radar subject matter experts, following the Joint NASA/ESA (European Space Agency) assessment draft guidelines. All three Airbus SAR spacecraft (TerraSAR-X, TanDEM-X, and PAZ) are based on the TerraSAR-X platform, and each have an active phased array antenna that is 4.8 x 0.7 m in the along-track and cross-track dimensions, respectively. TerraSAR-X and TanDEM-X are in a helical orbit, creating a bistatic imaging geometry, in addition to being capable of independent monostatic observations. The PAZ mission follows TerraSAR-X and TanDEM-X in the same 11-day orbit with a 5.5-day lag. TerraSAR-X and TanDEM-X are designed, developed, and operated through a Public-Private Partnership, while PAZ is a dual-use mission (civil and defense agencies), funded and owned by the Spanish Ministry of Defense and managed by Hisdesat (Hisdesat Servicios Estratégicos, S.A.), a Spanish private communications company. The assessment presented in this document is divided into two main parts: documentation review and the assessment of test datasets. The documentation review in sections 2.1 through 2.4 includes the assessment of the Airbus documentation provided to the CSDA evaluation team. The grading of these documents is given in columns 1-4 of the maturity matrix shown in section 1.1. Section 2.5 summarizes the evaluation performed by NASA using the data purchased through the CSDA program. The grading for this is given in the last column of the maturity matrix. Section 3 provides more detailed explanations on the methods and the results of the data analysis performed by NASA. Only the documents provided by Airbus for the evaluation were considered for the review. Additional documentation with more detailed description of the calibration and validation procedures may be available online but were not considered for this evaluation. The product information provided in the available documentation (RD-1, RD-2) and the product metadata together provided adequate information to work with the data. The product details in the metadata included the required information to work with the data in the common XML file format. Metrological traceability documentation was not provided to CSDA. All relevant characterization of the SAR system and data were provided, and the metadata include all relevant ancillary information. Documentation provided to CSDA included limited pre-flight and post-launch calibration information.

Batuhan Osmanoglu↗
DOE OSTI2024

Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM

This archive is the data companion to the bonney_et-al_2026_erc metarepo which generates synthetic data, trains an LSTM model, and generates performance metrics on the trained model. While the generation of the synthetic data is fully reprodicible, it is a computationally expensive process. This data archive contains the synthetic datasets needed for training and testing an LSTM model and reproduction of figures and tables. In addition, supplemenatary data products generating and visualizing results is also included, such as geospatial data for the basin. Contents There are two high level directories: `WRAP_archive/` and `repo_data/`. The `WRAP_archive` directory contains compressed intermediate dataproducts from the dataset generation workflow (marked as "I_Dataset_Generation" in the metarepo). These data products are not required by any scripts in the metarepo, but they are archived as they are expensive to generate and may have useful information for other analyses. The `repo_data` directory contains the necessary data for reproducing the workflow in the metarepo and should be decompressed and moved into the top level of the metarepo. Additional details are provided in README.md.

drought↗
NASA NTRS2017

An Application Programming Interface for Synthetic Snowflake Particle Structure and Scattering Data

The work by Kuo and colleagues on growing synthetic snowflakes and calculating their single-scattering properties has demonstrated great potential to improve the retrievals of snowfall. To grant colleagues flexible and targeted access to their large collection of sizes and shapes at fifteen (15) microwave frequencies, we have developed a web-based Application Programming Interface (API) integrated with NASA Goddard's Precipitation Processing System (PPS) Group. It is our hope that the API will enable convenient programmatic utilization of the database. To help users better understand the API's capabilities, we have developed an interactive web interface called the OpenSSP API Query Builder, which implements an intuitive system of mechanisms for selecting shapes, sizes, and frequencies to generate queries, with which the API can then extract and return data from the database. The Query Builder also allows for the specification of normalized particle size distributions by setting pertinent parameters, with which the API can also return mean geometric and scattering properties for each size bin. Additionally, the Query Builder interface enables downloading of raw scattering and particle structure data packages. This presentation will describe some of the challenges and successes associated with developing such an API. Examples of its usage will be shown both through downloading output and pulling it into a spreadsheet, as well as querying the API programmatically and working with the output in code.

reflectivity properties of snowflakes↗
DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗