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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

A scientific operations plan for the large space telescope

The paper describes an LST ground system which is compatible with the operational requirements of the LST. The goal of the approach is to minimize the cost of post launch operations without seriously compromising the quality and total throughput of LST science. Attention is given to cost constraints and guidelines, the telemetry operations processing systems (TELOPS), the image processing facility, ground system planning and data flow, and scientific interfaces.

West, D. K.↗
DOE OSTI2021

Energy Material Network Data Hubs

In early 2015 the United States Department of Energy conceived of a consortium of collaborative bodies based on shared expertise, data, and resources that could be targeted towards the more difficult problems in energy materials research. The concept of virtual laboratories had been envisioned and discussed earlier in the decade in response to the advent of the Materials Genome Initiative and similar scientific thrusts. To be effective, any virtual laboratory needed a robust method for data management, communication, security, data sharing, dissemination, and demonstration to work efficiently and effectively for groups of remote researchers. With the accessibility of new, easily deployed cloud technology and software frameworks, such individual elements could be integrated, and the required collaboration architecture is now possible. The developers have leveraged open-source software frameworks, customized them, and merged them into a platform to enable collaborative energy materials science, regardless of the geographic dispersal of the people and resources. After five years in operations, the systems are demonstratively an effective platform for enabling research within the Energy Material Networks (EMN). This paper will show the design and development of a secured scientific data sharing platform, the ability to customize the system to support diverse workflows, and examples of the enabled research and results connected with some of the Energy Material Networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

A Balanced Model for Exploration of the Terrestrial Planets: Lessons from the Lunar Experience

The Moon is the only extraterrestrial rocky body for which we have a combination of surface-selected samples, high-resolution orbital photography (Lunar Orbiter), manned and robotic surface exploration (Surveyor, Apollo, Luna), and global compositional, mineralogical, and geophysical data (Galileo, Clementine, Lunar Prospector). Beginning in 1998, CAPTEM organized a series of workshops and conference sessions aimed at integrating these diverse data sets. The insights gained by bringing together scientists from the remote-sensing and sample-analysis communities have been singularly rewarding. Not least of these has been the recognition by both groups that having both kinds of data maximizes the scientific return and permits reconciling information from diverse scales and perspectives. The 20-20 hindsight of the Lunar experience thus provides important lessons; learning from mistakes as well as successes, we can derive a sensible scientific program for Mars exploration. In this abstract, we describe examples of key information from (a) in-situ geologic investigation, (b) laboratory analysis of returned samples whose geologic context and location are known, and (c) global remote sensing of mineralogy, composition, and geophysical parameters. We then show the value of integrating these diverse data sets.

Jolliff, B. L.↗
NASA NTRS2004

Viewing ISS Data in Real Time via the Internet

EZStream is a computer program that enables authorized users at diverse terrestrial locations to view, in real time, data generated by scientific payloads aboard the International Space Station (ISS). The only computation/communication resource needed for use of EZStream is a computer equipped with standard Web-browser software and a connection to the Internet. EZStream runs in conjunction with the TReK software, described in a prior NASA Tech Briefs article, that coordinates multiple streams of data for the ground communication system of the ISS. EZStream includes server components that interact with TReK within the ISS ground communication system and client components that reside in the users' remote computers. Once an authorized client has logged in, a server component of EZStream pulls the requested data from a TReK application-program interface and sends the data to the client. Future EZStream enhancements will include (1) extensions that enable the server to receive and process arbitrary data streams on its own and (2) a Web-based graphical-user-interface-building subprogram that enables a client who lacks programming expertise to create customized display Web pages.

Myers, Gerry↗
DOE OSTI2022

Heterogeneous Visualization: A Call for Empirically Driven Technology Advancement and Adoption; Preprint

Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.

heterogeneous visualization↗
NASA NTRS1973

Documentation of the data analysis system for the gamma ray monitor aboard OSO-H

The programming system is presented which was developed to prepare the data from the gamma ray monitor on OSO-7 for scientific analysis. The detector, data, and objectives are described in detail. Programs presented include; FEEDER, PASS-1, CAL1, CAL2, PASS-3, Van Allen Belt Predict Program, Computation Center Plot Routine, and Response Function Programs.

Croteau, S.↗
NASA NTRS1995

The CCD/Transit Instrument (CTI) data-analysis system

The automated software system for archiving, analyzing, and interrogating data from the CCD/Transit Instrument (CTI) is described. The CTI collects up to 450 Mbytes of image-data each clear night in the form of a narrow strip of sky observed in two colors. The large data-volumes and the scientific aims of the project make it imperative that the data are analyzed within the 24-hour period following the observations. To this end a fully automatic and self evaluating software system has been developed. The data are collected from the telescope in real-time and then transported to Tucson for analysis. Verification is performed by visual inspection of random subsets of the data and obvious cosmic rays are detected and removed before permanent archival is made to the optical disc. The analysis phase is performed by a pair of linked algorithms, one operating on the absolute pixel-values and the other on the spatial derivative of the data. In this way both isolated and merged images are reliably detected in a single pass. In order to isolate the latter algorithm from the effects of noise spikes a 3x3 Hanning filter is applied to the raw data before the analysis is run. The algorithms reduce the input pixel-data to a database of measured parameters for each image which has been found. A contrast filter is applied in order to assign a detection-probability to each image and then x-y calibration and intensity calibration are performed using known reference stars in the strip. These are added to as necessary by secondary standards boot-strapped from the CTI data itself. The final stages involve merging the new data into the CTI Master-list and History-list and the automatic comparison of each new detection with a set of pre-defined templates in parameter-space to find interesting objects such as supernovae, quasars and variable stars. Each stage of the processing from verification to interesting image selection is performed under a data-logging system which both controls the pipe-lining of data through the system and records key performance monitor parameters which are built into the software. Furthermore, the data from each stage are stored in databases to facilitate evaluation, and all stages offer the facility to enter keyword-indexed free-format text into the data-logging system. In this way a large measure of certification is built into the system to provide the necessary confidence in the end results.

Cawson, M. G. M.↗
NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗
NASA NTRS2023

Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.

Image coding↗
NASA NTRS2024

Merged and Gridded GPM and Atmospheric River Data Product

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual-frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis-derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high-latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user-friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Marian E Mateling↗
NASA NTRSDate not supplied

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗
DOE OSTI2023

Measuring stellar populations, dust attenuation and ionized gas at kpc scales in 10010 nearby galaxies using the integral field spectroscopy from MaNGA

As one of the three major experiments of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), the Mapping Nearby Galaxies at Apatch Point Observatory (MaNGA) survey has obtained high-quality integral field spectroscopy (IFS) with a resolution of 1–2 kpc for ∼ 10 4 galaxies in the local universe during its six-year operation from July 2014 through August 2020. It is crucial to reliably measure the physical properties of the different components in each spectrum before one can use the data for any scientific study. In the past years we have made lots of efforts to develop a novel technique of full spectral fitting, which estimates a model-independent dust attenuation curve from each spectrum, thus allowing us to break the degeneracy between dust attenuation and stellar population properties when fitting the spectrum with stellar population synthesis models. We have applied our technique to the final data release of MaNGA, and obtained measurements of stellar population properties and emission line parameters, as well as the kinematics and dust attenuation of both stellar and ionized gas components. In this paper we describe our technique and the content and format of our data products. The whole dataset is publicly available in Science Data Bank with the link https://doi.org/10.57760/sciencedb.j00113.00088 .

Physics↗
DOE OSTI2025

Weak Form Scientific Machine Learning: Test Function Construction for System Identification

Weak form Scientific Machine Learning (WSciML) is a recently developed framework for data-driven modeling and scientific discovery. It leverages the weak form of equation error residuals to provide enhanced noise robustness in system identification via convolving model equations with test functions, reformulating the problem to avoid direct differentiation of data. The performance, however, relies on wisely choosing a set of compactly supported test functions. In this work, we mathematically motivate a novel data-driven method for constructing Single-scale-Local reference functions for creating the set of test functions. Our approach numerically approximates the integration error introduced by the quadrature and identifies the support size for which the error is minimal, without requiring access to the model parameter values. Through numerical experiments across various models, noise levels, and temporal resolutions, we demonstrate that the selected supports consistently align with regions of minimal parameter estimation error. We also compare the proposed method against the strategy for constructing Multi-scale-Global (and orthogonal) test functions introduced in our prior work, demonstrating the improved computational efficiency.

FOS: Computer and information sciences↗
NASA NTRS2006

Overview of the Solar-B Mission

The Solar-B mission is a collaboration between the Japan Aerospace Exploration Agency, Institute of Space and Astronautical Science, the National Aeronautics and Space Administration (NASA) and the Particle Physics and Astronomy Research Council (PPARC) of the United Kingdom and the European Space Agency. The principal scientific goals of the mission are to understand the processes of magnetic field generation, transport and ultimate dissipation of solar magnetic fields and how the release of magnetic energy is responsible for the heating and structuring of the chromosphere and corona. The scientific payload consists of three instruments: the Solar Optical Telescope that consists of the Optical Telescope Assembly and the Focal Plane Package (FPP), the X-ray Telescope and the EUV Imaging Spectrometer Each instrument is a result of the combined talents of all the members of the international team and their design and performance is described in separate papers in this session. The instruments are designed to work together as an 'observatory' simultaneously studying the target, at which the spacecraft is pointed, at different levels in the atmosphere. The spacecraft is scheduled for launch in September 2006 from the Uchinoura Space Center into a 600 km circular, sun-synchronous, polar orbit with a nominal elevation of 97.9 degrees. The orbit provides at least two morning and two evening contacts in Japan. Morning contacts are used for recovering quick look science data and the evening contacts for uploading commands. In addition ESA will provide 15 contacts per day from the Norwegian high latitude (78deg 14' N) ground station at Svalbard. The data downloads are transmitted to the ISAS Sirius database. They will be reformatted into FITS files and archived as Level 0 data on the ISAS DARTS system and made available to the scientific community. Scientific operations will be conducted from the IS AS facility located in Sagamihara, Japan. They are separated into planning, implementation and archiving. The planning process involves monthly, weekly and daily planning meetings. All scientific data will be made available after the first six month approximately one week after its collection.

Davis, John M.↗
NASA NTRS1973

Annual ADP planning document

The Category A computer systems at KSC (Al and A2) which perform scientific and business/administrative operations are described. This data division is responsible for scientific requirements supporting Saturn, Atlas/Centaur, Titan/Centaur, Titan III, and Delta vehicles, and includes realtime functions, Apollo-Soyuz Test Project (ASTP), and the Space Shuttle. The work is performed chiefly on the GEL-635 (Al) system located in the Central Instrumentation Facility (CIF). The Al system can perform computations and process data in three modes: (1) real-time critical mode; (2) real-time batch mode; and (3) batch mode. The Division's IBM-360/50 (A2) system, also at the CIF, performs business/administrative data processing such as personnel, procurement, reliability, financial management and payroll, real-time inventory management, GSE accounting, preventive maintenance, and integrated launch vehicle modification status.

Mogilevsky, M.↗
NASA NTRS1983

Analysis of cloud characteristics derived from archived satellite data

The present investigation is concerned with the differing scientific user community requirements for cloud data and the nature of archived satellite data. It is concluded that information on cloud characteristics and cloud cover is of the utmost importance for at least three scientific research areas, viz. numerical weather forecasting, environmental remote sensing, and climatic monitoring and modelling. However, these three user groups require information at different levels of temporal and spatial resolution, and the nature of the comparison between surface and satellite assessment of cloud cover differs among the three groups. Taking into account the diverse requirements of the scientific community and difficulty of a mutually agreed definition of the cloud parameters, a number of recommendations are made. It is suggested that radiance data, not cloud data, should be archived directly from satellite observations, and that the highest spatial and temporal resolutions available should be retained in the archive.

Hughes, N. A.↗
NASA NTRS1980

Heat Capacity Mapping Mission (HCMM) Notification Efforts

To encourage wide use of the Heat Capacity Mapping Mission (HCMM) data, especially among the scientific community, special notifications were prepared to inform them about the data's availability, its form, and the procedures for obtaining them. To achieve the widest distribution to the primary audiences of interest, mailings were made to scientists associated with the OSTA Resource Observation Division programs and to scientific and professional societies and journals. Accompanying the notifications to the societies and journals were samples of the HCMM imagery and a description of the image's predominant characteristics. A follow-up survey was completed to determine the effectiveness of the HCMM notifications.

Source record↗