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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Dynamics Explorer Science Data Processing System

The Dynamics Explorer project has acquired the ground data processing system from the Atmosphere Explorer project to provide a central computer facility for the data processing, data management and data analysis activities of the investigators. Access to this system is via remote terminals at the investigators' facilities, which provide ready access to the data sets derived from groups of instruments on both spacecraft. The original system has been upgraded with both new hardware and enhanced software systems. These new systems include color and grey scale graphics terminals, an augmentation computer, micrographics facility, a versatile data base with a directory and data management system, and graphics display software packages.

Smith, P. H.↗
DOE OSTI2024

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data Citation Explorer (DCE) v1.0

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data whether or not provenance was clearly indicated. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

Parker, Charles↗
NASA NTRS1993

Initial results from the extreme ultraviolet explorer

Data obtained during the first five months of calibration and science operation of the Extreme Ultraviolet Explorer (EUVE) are presented. Spectra of an extragalactic object were obtained; the object is detectable to wavelenghts longer than 100 A, demonstrating that extragalactic EUV astronomy is possible. Spectra of a hot white dwarf, and a late-type star in quiescence and flaring are shown as examples of the type of spectrographic data obtainable with EUVE. Other objects for which broad band photometric mode data have been obtained and analyzed include an RS CVn star and several late-type stars. The backgrounds in the EUVE detectors are quite low and the character of the diffuse astronomical EUV background has been investigated using these very low rates. Evidence is presented showing that, contrary to previously published reports, EUVE is about three times more sensitive than the English Wide Field Camera in the short wavelength bandpass covered by both instruments. Only limited information has been extracted from the longer bandpasses coered only by EUVE. Nonetheless, the brightest EUV source in the sky, a B star, has been discovered and is detected only in these longer bandpasses.

Bowyer, S.↗
NASA NTRS2020

Recommended Laboratory and Field Studies ahead of Future Mercury Exploration

Data from Mariner 10, MESSENGER, and ground-based telescopic observations have facilitated great advancements towards understanding the geochemistry, geology, internal structure, and space environment (exosphere/magnetosphere) of Mercury. Future exploration efforts by BepiColombo, landed science, and sample return will revolutionize our understanding of this planet. However, to enhance the scientific return of past, present, and future data collection at Mercury, it is imperative that laboratory and field-based studies be conducted over the next decade. This white paper gives examples of laboratory and field studies, including instrument testing, which should take place over the next decade to facilitate the interpretation of data and preparation of future missions to the inner-most planet. By no means are the topics in this paper exhaustive, but rather are meant to demonstrate the immense amount of work that needs to be completed ahead of, or in conjunction with, current and future exploration efforts of Mercury. This work would amplify the benefits from additional data obtained from the planet’s surface to the fullest extent possible, and to aid in decision making of future instrument payloads. We have chosen to focus our inputs here on geochemistry, geology, and space environment related efforts, but we emphasize that extensive research in the areas of geophysics and other geosciences-related disciplines will benefit from additional studies.

Kathleen E. Vander Kaaden↗
NASA NTRS1990

Gaussian windows: A tool for exploring multivariate data

Presented here is a method for interactively exploring a large set of quantitative multivariate data, in order to estimate the shape of the underlying density function. It is assumed that the density function is more or less smooth, but no other specific assumptions are made concerning its structure. The local structure of the data in a given region may be examined by viewing the data through a Gaussian window, whose location and shape are chosen by the user. A Gaussian window is defined by giving each data point a weight based on a multivariate Gaussian function. The weighted sample mean and sample covariance matrix are then computed, using the weights attached to the data points. These quantities are used to compute an estimate of the shape of the density function in the window region. The local structure of the data is described by a method similar to the method of principal components. By taking many such local views of the data, we can form an idea of the structure of the data set. The method is applicable in any number of dimensions. The method can be used to find and describe simple structural features such as peaks, valleys, and saddle points in the density function, and also extended structures in higher dimensions. With some practice, we can apply our geometrical intuition to these structural features in any number of dimensions, so that we can think about and describe the structure of the data. Since the computations involved are relatively simple, the method can easily be implemented on a small computer.

Jaeckel, Louis A.↗
DOE OSTI2023

Methods for rapid identification of anomalous layers in laser powder bed fusion

In situ process monitoring is a key requirement for increased industry acceptance of powder bed Additive Manufacturing. As sensing technologies increase in maturity, attention must also be given to effective data exploration techniques. These data are often high-resolution and multi-modal, with each build consisting of thousands of layers. Here, in this paper, the authors propose two methods enabling users to rapidly identify layers of interest within a build. Both methods leverage results from deep learning based segmentations of in situ powder bed images. The first method is an unsupervised “reverse layer search” algorithm while the second method uses supervised machine learning.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Empirical global model of upper thermosphere winds based on atmosphere and dynamics explorer satellite data

Thermospheric wind data obtained from the Atmosphere Explorer E and Dynamics Explorer 2 satellites have been used to generate an empirical wind model for the upper thermosphere, analogous to the MSIS model for temperature and density, using a limited set of vector spherical harmonics. The model is limited to above approximately 220 km where the data coverage is best and wind variations with height are reduced by viscosity. The data base is not adequate to detect solar cycle (F10.7) effects at this time but does include magnetic activity effects. Mid- and low-latitude data are reproduced quite well by the model and compare favorably with published ground-based results. The polar vortices are present, but not to full detail.

Hedin, A. E.↗
NASA NTRS2007

Exploration Blueprint: Data Book

The material contained in this report was compiled to capture the work performed by the National Aeronautics and Space Administration's (NASA's) Exploration study team in the late 2002 timeframe. The "Exploration Blueprint Data Book" documents the analyses and findings of the 90-day Agency-wide study conducted from September - November 2002. During the summer of 2002, the NASA Deputy Administrator requested that a study be performed with the following objectives: - Develop the rationale for exploration beyond low-Earth orbit - Develop roadmaps for how to accomplish the first steps through humans to Mars - Develop design reference missions as a basis for the roadmaps - Make recommendations on what can be done now to effect this future This planning team, termed the Exploration Blueprint, performed architecture analyses to develop roadmaps for how to accomplish the first steps beyond LEO through the human exploration of Mars. The previous NASA Exploration Team activities laid the foundation and framework for development of NASA's Integrated Space Plan. The reference missions resulting from the analysis performed by the Exploration Blueprint team formed the basis for requirement definition, systems development, technology roadmapping, and risk assessments for future human exploration beyond low-Earth orbit. Emphasis was placed on developing recommendations on what could be done now to effect future exploration activities. The Exploration Blueprint team embraced the "Stepping Stone" approach to exploration where human and robotic activities are conducted through progressive expansion outward beyond low-Earth orbit. Results from this study produced a long-term strategy for exploration with near-term implementation plans, program recommendations, and technology investments. Specific results included the development of a common exploration crew vehicle concept, a unified space nuclear strategy, focused bioastronautics research objectives, and an integrated human and robotic exploration strategy. Recommendations from the Exploration Blueprint included the endorsement of the Nuclear Systems Initiative, augmentation of the bioastronautics research, a focused space transportation program including heavy-lift launch and a common exploration vehicle design for ISS and exploration missions, as well as an integrated human and robotic exploration strategy for Mars.

Bret G Drake↗
NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗
NASA NTRS2017

Exposing the Strategies that Can Reduce the Obstacles: Improving the Science User Experience

It is now well established that pursuing generic solutions to what seem are common problems in Earth science data access and use can often lead to disappointing results for both system developers and the intended users. This presentation focuses on real-world experience of managing a large and complex data system, NASAs Earth Science Data and Information Science System (EOSDIS), whose mission is to serve both broad user communities and those in smaller niche applications of Earth science data and services. In the talk, we focus on our experiences with known data user obstacles characterizing EOSDIS approaches, including various technological techniques, for engaging and bolstering, where possible, user experiences with EOSDIS. For improving how existing and prospective users discover and access NASA data from EOSDIS we introduce our cross-archive tool: Earthdata Search. This new search and order tool further empowers users to quickly access data sets using clever and intuitive features. The Worldview data visualization tool is also discussed highlighting how many users are now performing extensive data exploration without necessarily downloading data. Also, we explore our EOSDIS data discovery and access webinars, data recipes and short tutorials, targeted technical and data publications, user profiles and social media as additional tools and methods used for improving our outreach and communications to a diverse user community. These efforts have paid substantial dividends for our user communities by allowing us to target discipline specific community needs. The desired take-away from this presentation will be an improved understanding of how EOSDIS has approached, and in several instances achieved, removing or lowering the barriers to data access and use. As we look ahead to more complex Earth science missions, EOSDIS will continue to focus on our user communities, both broad and specialized, so that our overall data system can continue to serve the needs of science and applications users.

communications↗
NASA NTRS1985

The AMPTE Charge Composition Explorer science data system

The instrument complement on all three Active Magnetospheric Particle Tracer Explorer (AMPTE) spacecraft is devoted to the conduction of the unified measurements which are required to achieve the scientific aims. A single science data base for each spacecaft has, therefore, been established with the objective to facilitate unified analysis. The data are kept in Science Data Centers in the U.S., West Germany, and the United Kingdom. The Science Data Center dedicated to the Charge Composition Explorer (CCE) is located at the Applied Physics Laboratory of Johns Hopkins University where it was developed. The present paper is concerned with aspects of the computing center's design, development and operations.

Holland, B. B.↗
NASA NTRS2004

Data Management for Mars Exploration Rovers

Data Management for the Mars Exploration Rovers (MER) project is a comprehensive system addressing the needs of development, test, and operations phases of the mission. During development of flight software, including the science software, the data management system can be simulated using any POSIX file system. During testing, the on-board file system can be bit compared with files on the ground to verify proper behavior and end-to-end data flows. During mission operations, end-to-end accountability of data products is supported, from science observation concept to data products within the permanent ground repository. Automated and human-in-the-loop ground tools allow decisions regarding retransmitting, re-prioritizing, and deleting data products to be made using higher level information than is available to a protocol-stack approach such as the CCSDS File Delivery Protocol (CFDP).

Mars missions↗
DOE OSTI2022

The DECam Local Volume Exploration Survey Data Release 2

We present the second public data release (DR2) from the DECam Local Volume Exploration survey (DELVE). DELVE DR2 combines new DECam observations with archival DECam data from the Dark Energy Survey, the DECam Legacy Survey, and other DECam community programs. DELVE DR2 consists of ∼160,000 exposures that cover >21,000 deg$^{2}$ of the high-Galactic-latitude (∣b∣ > 10°) sky in four broadband optical/near-infrared filters (g, r, i, z). DELVE DR2 provides point-source and automatic aperture photometry for ∼2.5 billion astronomical sources with a median 5σ point-source depth of g = 24.3, r = 23.9, i = 23.5, and z = 22.8 mag. A region of ∼17,000 deg$^{2}$ has been imaged in all four filters, providing four-band photometric measurements for ∼618 million astronomical sources. DELVE DR2 covers more than 4 times the area of the previous DELVE data release and contains roughly 5 times as many astronomical objects. DELVE DR2 is publicly available via the NOIRLab Astro Data Lab science platform.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗