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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Use of a multimission system for cost effective support of planetary science data processing

JPL's Multimission Operations Systems Office (MOSO) provides a multimission facility at JPL for processing science instrument data from NASA's planetary missions. This facility, the Multimission Image Processing System (MIPS), is developed and maintained by MOSO to meet requirements that span the NASA family of planetary missions. Although the word 'image' appears in the title, MIPS is used to process instrument data from a variety of science instruments. This paper describes the design of a new system architecture now being implemented within the MIPS to support future planetary mission activities at significantly reduced operations and maintenance cost.

Green, William B.↗
NASA NTRS1975

ASTP experiment support data processing

Activities associated with the generation of ASTP experiment support data in the areas of spacecraft ephemeris and orientation and instrument pointing and field-of-view are documented. It is intended that this document represent a cradle-to-grave chronicle of these activities. To satisfy this intent while facilitating the ready dissemination of information, the document is being published twice. The first publication, scheduled for release prior to ASTP liftoff, includes all preflight phases of the experiment support activity in addition to those appendixes that do not pertain to any mission-specific data. The second publication will provide any required updates to the original documentation and will add all mission-specific data, including documentation of all postflight data processing activities and data archiving information.

Osburn, R. K.↗
NASA NTRS1991

Cooperative processing data bases

Cooperative processing for the 1990's using client-server technology is addressed. The main theme is concepts of downsizing from mainframes and minicomputers to workstations on a local area network (LAN). This document is presented in view graph form.

Hasta, Juzar↗
NASA NTRS2016

TRMM Data Improvement as Part of the GPM Data Processing

NASA has a long standing commitment to the improvement of its mission datasets. Indeed, data reprocessing is always built into the plans, schedule and budget for the mission data processing system. However, in addition to these ongoing mission reprocessing, NASA also supports a final reprocessing of all the data for a mission upon its completion (known as Phase F). TRMM Phase F started with the end of the TRMM mission in June of 2015. This last reprocessing has two overall goals: improvement of the TRMM mission data products; incorporation of the 17+ years of TRMM data into the ongoing NASA/JAXA GPM data processing. The first goal guarantees that the latest algorithms used for precipitation retrievals will also be used in reprocessing the TRMM data. The second goal ensures that as GPM algorithms are improved, the entire TRMM data will always be reprocessed with each GPM reprocessing. In essence TRMM becomes another of the GPM constellation satellites. This paper will concentrate on presenting the improvements to TMI level 1 data including calibration, geolocation, and emissive antenna corrections. It will describe the format changes that will occur how the TMI level 1C product will be intercalibrated using GMI as the reference calibration. It will also provide an overview of changes in the precipitation radar products as well as the combined TMIPR product.

GPM↗
NASA NTRS1974

ERTS operations and data processing

The overall communications and data flow between the ERTS spacecraft and the ground stations and processing centers are generally described. Data from the multispectral scanner and the return beam vidicon are telemetered to a primary ground station where they are demodulated, processed, and recorded. The tapes are then transferred to the NASA Data Processing Facility (NDPF) at Goddard. Housekeeping data are relayed from the prime ground stations to the Operations Control Center at Goddard. Tracking data are processed at the ground stations, and the calculated parameters are transmitted by teletype to the orbit determination group at Goddard. The ERTS orbit has been designed so that the same swaths of the ground coverage pattern viewed during one 18-day coverage cycle are repeated by the swaths viewed on all subsequent cycles. The Operations Control Center is the focal point for all communications with the spacecraft. NDPF is a job-oriented facility which processes and stores all sensor data, and which disseminates large quantities of these data to users in the form of films, computer-compatible tapes, and data collection system data.

Gonzales, L.↗
NASA NTRS2000

Landsat 7 Science Data Processing: A System's Overview

The Landsat Science Data Processing System, developed by NASA for the Landsat 7 Project provides science data handling infrastructure used at the EROS Data Center Landsat 7 Data Handling Facility of the USGS Department of Interior. This paper presents an overview the designs, architectures, and details of the various systems used in the processing of the Landsat 7 Science Data.

Schweiss, Robert↗
NASA NTRS2011

Experiment in Onboard Synthetic Aperture Radar Data Processing

Single event upsets (SEUs) are a threat to any computing system running on hardware that has not been physically radiation hardened. In addition to mandating the use of performance-limited, hardened heritage equipment, prior techniques for dealing with the SEU problem often involved hardware-based error detection and correction (EDAC). With limited computing resources, software- based EDAC, or any more elaborate recovery methods, were often not feasible. Synthetic aperture radars (SARs), when operated in the space environment, are interesting due to their relevance to NASAs objectives, but problematic in the sense of producing prodigious amounts of raw data. Prior implementations of the SAR data processing algorithm have been too slow, too computationally intensive, and require too much application memory for onboard execution to be a realistic option when using the type of heritage processing technology described above. This standard C-language implementation of SAR data processing is distributed over many cores of a Tilera Multicore Processor, and employs novel Radiation Hardening by Software (RHBS) techniques designed to protect the component processes (one per core) and their shared application memory from the sort of SEUs expected in the space environment. The source code includes calls to Tilera APIs, and a specialized Tilera compiler is required to produce a Tilera executable. The compiled application reads input data describing the position and orientation of a radar platform, as well as its radar-burst data, over time and writes out processed data in a form that is useful for analysis of the radar observations.

Holland, Matthew↗
NASA NTRS2003

EP/TOMS Science Data Processing Programmer's Guide. Version 1.9

This EP/TOMS Programmer's Guide provides detailed information concerning the data, software, operations, and maintenance of the EP/TOMS Science Data Processing System. This system was developed for the NASA Goddard Space Flight Center (GSFC), Laboratory for Atmospheres, Atmospheric Chemistry Branch (Code 916) by SSAI. The description of the EP/TOMS Science Data Processing System starts with an overall system perspective (Section 3). A description of all data files ingested, stored, and/or produced follows in Section 4. Detailed descriptions of each major software subsystem are in Section 5. Section 6 presents processing procedures. Section 7 presents maintenance procedures. Procedures for updating this document are included in Section 7.

Source record↗
NASA NTRS1990

Evolution of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight and ground data processing system

The modifications made to both the AVIRIS instrument and the data processing facility since the instrument made its initial flight in the summer of 1987 are described. Historical development of the data system is discussed and attention is given to enhancements to instrument stability and noise performance. AVIRIS data facility objectives include rapid and automated decommutation and archiving of data, the ability to assess the quality of the data and health of the instrument, and the provision of an automated procedure for applying radiometric corrections to the data and providing responsive processing of data requests from investigators. The modifications described have resulted in an overall radiometric stability of better than 10 percent, with stability of only a few percent during a single flight.

Porter, Wallace M.↗
NASA NTRS2017

Mission Use of the SpaceCube Hybrid Data Processing System

The award-winning SpaceCube v2.0 system is a high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. This presentation provides an overview of the design architecture, flexibility, and the advantages of the modular SpaceCube v2.0 high performance data processing system for space applications. The current state of the proven SpaceCube technology is based on 11 years of engineering and operations. Eight systems have been successfully operated in space starting in 2008 with eight more to be delivered for payload integration in 2018 in support of various missions. This presentation will highlight how this multipurpose system is currently being used to solve design challenges of a variety of independent applications. The SpaceCube hardware adapts to new system requirements by allowing for application-unique interface cards that are utilized by reconfiguring the underlying programmable elements on the core processor card. We will show how this system is being used to improve on a heritage NASA GPS technology, enable a cutting-edge LiDAR instrument, and serve as a typical command and data handling (CDH) computer for a space robotics technology demonstration.Finally, this presentation will highlight the use of the SpaceCube v2.0 system on the Restore-L robotic satellite servicing mission. SpaceCube v2.0 is the central avionics responsible for the real-time vision system and autonomous robotic control necessary to find, capture, and service a national asset weather satellite.

Petrick, Dave↗
NASA NTRS1979

Orbiter data reduction complex data processing requirements for the OFT mission evaluation team (level C)

This document addresses requirements for post-test data reduction in support of the Orbital Flight Tests (OFT) mission evaluation team, specifically those which are planned to be implemented in the ODRC (Orbiter Data Reduction Complex). Only those requirements which have been previously baselined by the Data Systems and Analysis Directorate configuration control board are included. This document serves as the control document between Institutional Data Systems Division and the Integration Division for OFT mission evaluation data processing requirements, and shall be the basis for detailed design of ODRC data processing systems.

Source record↗
NASA NTRSDate not supplied

Enabling Open and Interoperable Science: Multi-Omics Data Processing Platform with NASA GeneLab Standardized Bioinformatics Workflows for Space and Earth Research

Multi-omics biological data continues to be generated at an astounding pace. Genomics, transcriptomics, metabolomics, and proteomics, or collectively known as multi-omics data, are used to assess biological functions, and provide invaluable insights into human, animal, plant, and environmental health both on Earth and in Space. Despite the abundance of these valuable data, the need for bioinformatics expertise, particularly as it relates to the niche filed of space biology, and a lack of accessible resources for processing these data limit their usefulness in deriving biological insights. The NASA Open Science Data Repository (OSDR) provides access to omics data from various spaceflight and analog studies. To enhance the accessibility and reusability of these data, GeneLab (part of OSDR) designs and implements standardized, community-driven, open-source bioinformatics workflows to transform raw omics data into standardized processed data. Currently, GeneLab-processed data from hundreds of space studies have been reused for meta-analyses. This has led to new insights and scientific publications that extend beyond the initial research, thereby enriching our understanding of molecular-scale biological responses to the space environment. To make these bioinformatics workflows open and accessible, GeneLab teamed up with DOE-funded initiatives, including the National Microbiome Data Collaborative (NMDC), to create the NASA EDGE [Empowering the Development of Genomics Expertise] Bioinformatics web-based platform. NASA EDGE utilizes shared compute resources to run the GeneLab standardized bioinformatics workflows, which eliminates the need for researchers to have their own high performance computing cluster. The web-based platform makes complicated biological analyses incredibly easy to perform, thus expanding the reach of these analyses to bioinformatics novices, students, and even citizen scientists enabling them to contribute to scientific discoveries and progress. The authors will demonstrate how the NASA EDGE platform can be used to process microbial omics data hosted on OSDR as well as user-generated omics datasets using GeneLab’s standard workflows.

Amanda M. Saravia-Butler↗
NASA NTRS1974

ATS-F/GEOS-C satellite to satellite tracking data processing consideration

The satellite to satellite tracking data unique processing considerations are presented. The strategy to be employed in the application of data processing routines to the ATS-F/GEOS-C satellite to satellite tracking experiment is described. When these considerations are included, studies, analysis and simulations predict an orbital position uncertainty of less than 15 meters and a velocity uncertainty of less than 1.5 millimeters per second.

Bryan, J. W.↗
NASA NTRS2011

Engineering and Scientific Applications: Using MatLab(Registered Trademark) for Data Processing and Visualization

MatLab(TradeMark)(MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many countries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its real strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbox. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using symbolic operations. MatLab in its interpreter programming language form (command interface) is similar with well known programming languages such as C/C++, support data structures and cell arrays to define classes in object oriented programming. As such, MatLab is equipped with most of the essential constructs of a higher programming language. MatLab is packaged with an editor and debugging functionality useful to perform analysis of large MatLab programs and find errors. We believe there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and analysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applications. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientific problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabular format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed.

Sen, Syamal K.↗