Earth-resources technology satellite - NASA Data-Processing Facility
NASA Data Processing Facility for earth resources technology satellite telemetry
Levy, H. H.↗
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The fundamentals of coherent optical data processing. I
Coherent optical data processing principles, discussing lens, diffraction, focused wave front, intensity vs amplitude and spectrum analyzer
Shulman, A. R.↗
The DFVLR main department for central data processing, 1976 - 1983
Data processing, equipment and systems operation, operative and user systems, user services, computer networks and communications, text processing, computer graphics, and high power computers are discussed.
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ATS-F millimeter wave propagation experiment data processing.
Review of the data processing program for the Applications Technology Satellite (ATS-F) scheduled for launch in 1973. The program consists of: (1) short term processing, which utilizes analog data recording and produces amplitude scintillation and time-frequency correlation studies; and (2) long term processing, which utilizes digitally recorded data to provide hourly and daily statistical output functions. The data acquisition and processing program planned for the ATS-F experiment provides a highly automated environment which emphasizes the unique requirements for the characterization of the propagation medium in the frequency bands required for the next generation of space communication systems.
Ippolito, L. J.↗
Remote sensing data processing - Two years ago, today, and two years from today
Certain technical problems arising in the recent past (1975) in the field of the processing of remote sensing data are reviewed including approaches to the analysis of Landsat MSS data and technical difficulties which must be overcome to achieve operational data processing. The current status of remote sensing data processing is then examined with emphasis on such current technical issues as training selection and labeling, sampling schemes and classification and mensuration. Hardware projections are made for the near future (1979) relative to the development of remote sensing data processing.
Holmes, Q. A.↗
On-board data processing for the IRAS telescope system
Onboard data processing has been developed for the Infrared Astronomical Satellite (IRAS) telescope system to effectively utilize the RF bandwidth and minimize the ground-data processing costs. Use of filtering techniques, data deglitching circuits, and data encoding algorithms are combined with a microprocessor-controlled telescope system to achieve a system dynamic range of 100,000 with a noise equivalent flux density of no less than 10 to the minus 19th W/sq cm. Extraneous data, e.g., noise due to radiation hits, will be removed prior to transmission, thereby optimizing ground-data processing for the highest sensitivity and reliability. Simulation results are presented which demonstrate the advantages gained through the use of onboard data processing and illustrate the possible merging of these techniques with an array for the future evolution of 'smart' sensors.
Lum, H.↗
Extension of ERIM multispectral data processing capabilities through improved data handling techniques
The improvement and extension of the capabilities of the Environmental Research Institute of Michigan processing facility in handling multispectral data are discussed. Improvements consisted of implementing hardware modifications which permitted more rapid access to the recorded data through improved numbering and indexing of such data. In addition, techniques are discussed for handling data from sources other than the ERIM M-5 and M-7 scanner systems.
Kriegler, F. J.↗
High-Speed On-Board Data Processing for Science Instruments
A new development of on-board data processing platform has been in progress at NASA Langley Research Center since April, 2012, and the overall review of such work is presented in this paper. The project is called High-Speed On-Board Data Processing for Science Instruments (HOPS) and focuses on a high-speed scalable data processing platform for three particular National Research Council's Decadal Survey missions such as Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS), Aerosol-Cloud-Ecosystems (ACE), and Doppler Aerosol Wind Lidar (DAWN) 3-D Winds. HOPS utilizes advanced general purpose computing with Field Programmable Gate Array (FPGA) based algorithm implementation techniques. The significance of HOPS is to enable high speed on-board data processing for current and future science missions with its reconfigurable and scalable data processing platform. A single HOPS processing board is expected to provide approximately 66 times faster data processing speed for ASCENDS, more than 70% reduction in both power and weight, and about two orders of cost reduction compared to the state-of-the-art (SOA) on-board data processing system. Such benchmark predictions are based on the data when HOPS was originally proposed in August, 2011. The details of these improvement measures are also presented. The two facets of HOPS development are identifying the most computationally intensive algorithm segments of each mission and implementing them in a FPGA-based data processing board. A general introduction of such facets is also the purpose of this paper.
Beyon, Jeffrey Y.↗
Enabling High Performance Reconfigurable Ground Data Processing Systems
The deployment of high data rate instruments onboard satellites with direct broadcast capability demands the implementation of powerful ground data processing systems. Conversely, to enable the widespread usage of direct broadcast data and the generation of subsequent data products, low cost data processing systems must be available. Reconfigurable computers based on Field Programmable Gate Arrays (FPGA) enable the implementation of ground data processing systems that lead to higher performance and lower cost while keeping a high degree of programmability and maintainability. This paper presents the conceptualization of the MODIS Adaptive Level One Accelerator (ALOA), a prototype under construction at NASA's Goddard Space Flight Center. The ALOA is a computer workstation augmented by a reconfigurable computer. The system will calibrate and geographically locate data from the EOS-AM1 MODerate resolution Imaging Spectroradiometer (MODIS) instrument at near real-time data rates.
Hines, Tonjua M.↗
PHYSICAL MEASUREMENTS AND DATA PROCESSING
Instrumentation, calibration and data processing of tiros meteorological satellites
DATA PROCESSING↗
Data processing 1: Advancements in machine analysis of multispectral data
Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.
Swain, P. H.↗
Preliminary design review package for the solar heating and cooling central data processing system
The Central Data Processing System (CDPS) is designed to transform the raw data collected at remote sites into performance evaluation information for assessing the performance of solar heating and cooling systems. Software requirements for the CDPS are described. The programming standards to be used in development, documentation, and maintenance of the software are discussed along with the CDPS operations approach in support of daily data collection and processing.
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High resolution data processing for PFM TELEMETRY.
Data processing of PFM telemetry whereby resolution of frequency measurements is dependent upon SNR, for application to satellite parameters
TELEMETRY↗
Spacelab Data Processing Facility
The Spacelab Data Processing Facility (SDPF) processes, monitors, and accounts for the payload data from Spacelab and other Shuttle missions and forwards relevant data to various user facilities worldwide. The SLDPF is divided into the Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS). The SIPS division demultiplexes, synchronizes, time tags, quality checks, accounts for the data, and formats the data onto tapes. The SOPS division further edits, blocks, formats, and records the data on tape for shipment to users. User experiments must conform to the Spacelab's onboard High Rate Multiplexer (HRM) format for maximum process ability. Audio, analog, instrumentation, high density, experiment data, input/output data, quality control and accounting, and experimental channel tapes along with a variety of spacelab ancillary tapes are provided to the user by SLDPF.
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EGS Collab Experiment 1: Circulation Testing Processed data
This submission includes processed and reduced data for circulation testing that was conducted at the 164' fracture on the 4850 ft level of the Sanford Underground Research Facility. The circulation tests were done to test the flow through the 164' fracture in the EGS Collab Experiment 1 testbed on the 4850 ft level of the Sanford Underground Research Facility in order to validate computer models concerned with flow processes and heat exchange processes in EGS. This data was processed from raw data that is available in the GDR submission linked below: "EGS Collab Circulation Testing Raw Data and Documentation." Python scripts used to process and reduce the data are included. The scripts also serve as "documentation" as well as examples for data processing. More information about the test itself as well as the rationale and process of data processing is available at wiki page linked below.
15 GEOTHERMAL ENERGY↗
LiDAR-Based Point Cloud Data Processing
The presentation includes the procedures of utilizing LiDAR-based point cloud data in WebGL applications, such as capturing data, processing data, importing point cloud into Unity software, and improving application performance. It uses IRC BLD 603 Lab B-12 as an example. It'll be presented at Viz n Learn workshop which is a collaboration series between INL's Applied Visualization Laboratory (AVL) and University of Wyoming (UW)'s Shell 3D Visualization center. Each session will highlight 2 projects from each institution monthly, followed by discussion on areas of collaborative interest and action.
3D↗
Development of nuclear data processing code FRENDY version 2
Nuclear data processing is an important interface between an evaluated nuclear data library and neutronics calculation codes. JAEA has been developed the new nuclear data processing code FRENDY from 2013. FRENDY version 1 generates the ACE files used for the continuous-energy Monte Carlo codes including PHITS, Solomon, Serpent,and MCNP and it was released as the open-source software under the 2-clause BSD license in 2019.After we released FRENDY version 1, many functions, e.g., the multi-group neutron cross-section library generation, the statistical uncertainty quantification of the probability tables for unresolved resonance cross-section, the 'perturbation' of the ACE file for uncertainty quantification, and the modification of the ENDF-6 formatted nuclear data file, have been developed. We released FRENDY version 2 including these functions. The present paper gives an overview of FRENDY and presents features of new functions implemented in FRENDY version 2. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Domain Description Language for Data Processing
We discuss an application of planning to data processing, a planning problem which poses unique challenges for domain description languages. We discuss these challenges and why the current PDDL standard does not meet them. We discuss DPADL (Data Processing Action Description Language), a language for describing planning domains that involve data processing. DPADL is a declarative, object-oriented language that supports constraints and embedded Java code, object creation and copying, explicit inputs and outputs for actions, and metadata descriptions of existing and desired data. DPADL is supported by the IMAGEbot system, which we are using to provide automation for an ecological forecasting application. We compare DPADL to PDDL and discuss changes that could be made to PDDL to make it more suitable for representing planning domains that involve data processing actions.
Golden, Keith↗