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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Midcourse Space Experiment Data Certification and Technology Transfer

The Midcourse Space Experiment spacecraft, launched April 24, 1996, is expected to have a 5 year useful lifetime with a 12 month lifetime for the cryogenically cooled IR sensor. A pre-launch, ground based calibration of the instruments provided a basis for the pre-launch certification of the Level 2 data base these instruments produce. With the spacecraft in-orbit the certification of the instrument's Level 2 data base is being extended to the in-orbit environment.

Pollock, David B.↗
NASA NTRS1996

An optimal user-interface for EPIMS database conversions and SSQ 25002 EEE parts screening

The Electrical, Electronic, and Electromechanical (EEE) Parts Information Management System (EPIMS) database was selected by the International Space Station Parts Control Board for providing parts information to NASA managers and contractors. Parts data is transferred to the EPIMS database by converting parts list data to the EP1MS Data Exchange File Format. In general, parts list information received from contractors and suppliers does not convert directly into the EPIMS Data Exchange File Format. Often parts lists use different variable and record field assignments. Many of the EPES variables are not defined in the parts lists received. The objective of this work was to develop an automated system for translating parts lists into the EPIMS Data Exchange File Format for upload into the EPIMS database. Once EEE parts information has been transferred to the EPIMS database it is necessary to screen parts data in accordance with the provisions of the SSQ 25002 Supplemental List of Qualified Electrical, Electronic, and Electromechanical Parts, Manufacturers, and Laboratories (QEPM&L). The SSQ 2S002 standards are used to identify parts which satisfy the requirements for spacecraft applications. An additional objective for this work was to develop an automated system which would screen EEE parts information against the SSQ 2S002 to inform managers of the qualification status of parts used in spacecraft applications. The EPIMS Database Conversion and SSQ 25002 User Interfaces are designed to interface through the World-Wide-Web(WWW)/Internet to provide accessibility by NASA managers and contractors.

Watson, John C.↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Classification of animal sounds in a hyperdiverse rainforest using convolutional neural networks with data augmentation

To protect tropical forest biodiversity, we need to be able to detect it reliably, cheaply, and at scale. Automated detection of sound producing animals from passively recorded soundscapes via machine-learning approaches is a promising technique towards this goal, but it is constrained by the necessity of large training data sets. Using soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN), we investigate i) the minimum viable training data set size for accurate prediction of call types (‘sonotypes’), and ii) the extent to which data augmentation and transfer learning can overcome the issue of small and imbalanced training data sets. We found that even relatively high sample sizes (>80 per sonotype) lead to mediocre accuracy, which however improved significantly with data augmentation and transfer learning, including at extremely small sample sizes (3 per sonotype), regardless of taxonomic group or call characteristics. Neither transfer learning nor data augmentation alone achieved high accuracy. Our results suggest that transfer learning and data augmentation could make the use of CNNs to classify species’ vocalizations feasible even for small soundscape-based projects with many rare species. Retraining our open-source model requires only basic programming skills which makes it possible for individual conservation initiatives to match their local context, in order to enable more evidence-informed management of biodiversity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

The Seasat surface truth experiments

A surface truth program for Seasat A is formulated in two phases: pre- and post-launch. The pre-launch phase (which includes the Marineland experiments, the JONSWAP-75 experiment, the West Coast experiment, and the altimeter experiment) is designed to provide data from aircraft over instrumented ocean sites during desirable geophysical events. The objective is to gather sufficient data for the development of algorithms which transfer space data into geophysical variables useful for applications. In the post-launch phase, the surface truth program is designed to verify and improve the algorithms developed in the pre-launch phase and also to evaluate the performance of spaceborne sensors.

Shemdin, O. H.↗
NASA NTRS1979

Satellite time transfer via Tracking and Data Relay Satellite System (TDRSS) and applications

With two geosynchronous relay satellites the tracking and data relay satellite system (TDRSS) can provide nearly worldwide coverage for communication between all near orbiting satellites and the satellite control center at Goddard Space Flight Center. Each future NASA satellite will carry a TDRSS transponder with which the satellite can communicate through a TDRSS to the ground station at White Sands, New Mexico. It is using this system that the ground station master clock time signal can be transmitted to the near Earth orbiting satellite in which a clock may be maintained independently to the accuracy required by the experimenters. The satellite time transfer terminal design concept and the application of the time signal in autonomously operated spacecraft clock are discussed. Some pertinent TDRSS parameters and corrections for the propagation delay measurement as well as the time code used to transfer the time signal are given.

Chi, A. R.↗
NASA NTRS1988

Vehicle for Space Transfer and Recovery (VSTAR), volume 2: Substantiating analyses and data

The Vehicle Space Transfer and Recovery (VSTAR) system is designed as a manned orbital transfer vehicle (MOTV) with the primary mission of Satellite Launch and Repair (SLR). Reference materials, calculations and trade studies used in the analysis and selection of VSTAR components. Each major VSTAR system is examined separately. Simple graphs and tables are used to make qualitative comparisons of various VSTAR component candidates. Equations and/or calculations used for a particular analysis are also included where applicable.

Source record↗
DOE OSTI2024

Verification of RESRAD-OFFSITE Code (V.4)

This report documents the verification of RESRAD-OFFSITE Version 4.0 and describes, where necessary, the verification of the following: • The data comprising the standard dose and risk coefficient libraries in the RESRAD database files Master_dcf_ICRP07.mdb and Master_dcf_2k.mdb. • The extraction and transfer of the data from the selected database file to the computational code by the RESRAD-OFFSITE 4.0 interface, ResOWin.exe. • The different processes that are modeled by the main computational code in RESRAD OFFSITE 4.0, ResOMain.exe. • The data displayed in the graphical and text reports. Many verifications were performed as part of the quality assurance quality control program associated with the development and release of RESRAD-OFFSITE 4.0, namely: • developer testing, • internal independent testing, and • release testing. Some were also performed in response to questions from users regarding the performance of the code. The main text of the report focuses on summarizing a subset of those tests, both independent and developer tests that verified the computations performed by the code. The verifications included in this report served as the basis for the development of the release tests of the computational executables and provided the quantitative results to be compared with the code output. The input and output interfaces and the data transfers between the various executables of the code were tested while performing the verification testing. They were tested intentionally during release testing. This report also provides some basic information to help in understanding the activities that were verified. The report: • outlines the components of RESRAD-OFFSITE 4.0 and the interconnections between these components, • outlines the processes modeled by the computational code, • provides summary figures and tables to offer confirmation of the verification of the computational components of the code, • reproduces the verifiers’ reports, if available, in individual appendices, • refers to the previous verification report (Yu et al. 2011) for more details about some of the verifications, and • reproduces the test cases and the testers’ reports from the release testing in individual appendices, when possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A General Framework for Progressive Data Compression and Retrieval

In scientific simulations, observations, and experiments, the transfer of data to and from disk and across networks has become a major bottleneck for data analysis and visualization. Compression techniques have been employed to tackle this challenge, but traditional lossy methods often demand conservative error tolerances to meet the numerical accuracy requirements of both anticipated and unknown data analysis tasks. Progressive data compression and retrieval has emerged as a promising solution, where each analysis task dictates its own accuracy needs. However, few analysis algorithms inherently support progressive data processing, and adapting compression techniques, file formats, client/server frameworks, and APIs to support progressivity can be challenging. Here, this paper presents a framework that enables progressive-precision data queries for any data compressor or numerical representation. Our strategy hinges on a multi-component representation that successively reduces the error between the original and compressed field, allowing each field in the progressive sequence to be expressed as a partial sum of components. We have implemented this approach with four established scientific data compressors and assessed its effectiveness using real-world data sets from the SDRBench collection. The results show that our framework competes in accuracy with the standalone compressors it is based upon. Additionally, (de)compression time is proportional to the number of components requested by the user. Finally, our framework allows for fully lossless compression using lossy compressors when a sufficient number of components are employed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

S-193 scatterometer transfer function analysis for data processing

A mathematical model for converting raw data measurements of the S-193 scatterometer into processed values of radar scattering coefficient is presented. The argument is based on an approximation derived from the Radar Equation and actual operating principles of the S-193 Scatterometer hardware. Possible error sources are inaccuracies in transmitted wavelength, range, antenna illumination integrals, and the instrument itself. The dominant source of error in the calculation of scattering coefficent is accuracy of the range. All other ractors with the possible exception of illumination integral are not considered to cause significant error in the calculation of scattering coefficient.

Johnson, L.↗