Preprocessing data collected with the ACTS propagation terminal
Viewgraphs on preprocessing data collected with the ACTS propagation terminal are presented. The steps in ACTS data preprocessing are reviewed.
Gaff, Doug↗
SEARCH · Search NASA
Results for “preprocessed data”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A study to define a scatterometer data preprocessing system Final report
Analysis of scatterometer data preprocessing system
Chapman, R. E.↗
LANDSAT data preprocessing
The effect on LANDSAT data of a Sun angle correction, an intersatellite LANDSAT-2 and LANDSAT-3 data range adjustment, and the atmospheric correction algorithm was evaluated. Fourteen 1978 crop year LACIE sites were used as the site data set. The preprocessing techniques were applied to multispectral scanner channel data and transformed data were plotted and used to analyze the effectiveness of the preprocessing techniques. Ratio transformations effectively reduce the need for preprocessing techniques to be applied directly to the data. Subtractive transformations are more sensitive to Sun angle and atmospheric corrections than ratios. Preprocessing techniques, other than those applied at the Goddard Space Flight Center, should only be applied as an option of the user. While performed on LANDSAT data the study results are also applicable to meteorological satellite data.
Austin, W. W.↗
Application of the robust estimate in SLR data preprocessing
An M-estimator, one kind of a robust estimator, has been used in satellite laser ranging (SLR) data preprocessing. It has been shown that the M-estimator has a 50 percent or more breakdown point.
Tan, De-Tong↗
Software for Preprocessing Data from Rocket-Engine Tests
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC E test-stand complex and utilize the SSC file format. The programs are the following: Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel. QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post-test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot (a free graphing program written by Rick Paris). EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PV-WAVE based plotting software.
Cheng, Chiu-Fu↗
Software for Preprocessing Data From Rocket-Engine Tests
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC "E" test-stand complex and utilize the SSC file format. The programs are the following: 1) Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel; 2) QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot (a free graphing program written by Rick Paris); and 3) EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PVWAVE based plotting software.
Cheng, Chiu-Fu↗
Software for Preprocessing Data From Rocket-Engine Tests
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC E test-stand complex and utilize the SSC file format. The programs are the following: (1) Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel. (2) QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post-test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot. (3) EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PVWAVE based plotting software.
Cheng, Chiu-Fu↗
Developing a Cloud-Based ArcGIS Image Service for TROPOMI Level 2 Data: Preprocessing, Transformation, and Publication
TROPOMI, the Tropospheric Monitoring Instrument aboard the Sentinel-5 Precursor satellite, provides high spatiotemporal resolution atmospheric measurements. It is essential for monitoring air quality, greenhouse gases, and other trace gases. Integrating TROPOMI Level 2 data into an ArcGIS Image Service marks a significant advancement in the accessibility and utility of satellite-derived environmental information for GIS applications. This paper details the methodology for building an ArcGIS Image Service tailored to handle TROPOMI Level 2 data, with a focus on preprocessing, transformation, and cloud-based publication. The workflow utilizes Python and ArcPy for data reformatting, reprojection, and updating, ensuring efficient processing and cloud-based notifications. By establishing a robust pipeline, the ArcGIS Image Service provides real-time access to TROPOMI Level 2 data, enabling users to visualize, analyze, and interpret atmospheric phenomena effectively.
Level 2 Data, ArcGIS, Image Service, ArcPy, OGC↗
A technique for real-time data preprocessing
A processing system is presented that implements simultaneously the efficiency of the special-purpose processor and the total applicability of the general-purpose computer - characteristics commonly thought of as being mutually exclusive. The solution adopted is that of specializing the machine by programming the hardware structure, rather than by adding software systems to it. Data are organized in circulating pages which form a plurality of local dynamic memories for each process. Programs are made up of modules, each describing a transient special-purpose machine. Applications to real-time processing of radar signals are referred to.
Schaffner, M. R.↗
Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques
There are no author-identified significant results in this report.
Landgrebe, D. A.↗
On-board compensation for temporal changes in pushbroom scanner detector arrays
The design and operation of pushbroom and mechanical scanners are reviewed in order to determine differences in data preprocessing. Data preprocessing is categorized into spatial registration and radiometric corrections, as well as into systematic versus nonsystematic corrections. The category of nonsystematic radiometric errors due to temporal changes in detector array properties is emphasized as a subject of special interest and a challenging problem for onboard data preprocessing. Concepts for onboard compensation of temporal changes are presented. In particular one concept which uses dark current as an indicator of temperature change for determining gain and offset corrections is shown to have a number of advantages.
Jobson, D. J.↗
Simple, Script-Based Science Processing Archive
The Simple, Scalable, Script-based Science Processing (S4P) Archive (S4PA) is a disk-based archival system for remote sensing data. It is based on the data-driven framework of S4P and is used for data transfer, data preprocessing, metadata generation, data archive, and data distribution. New data are automatically detected by the system. S4P provides services such as data access control, data subscription, metadata publication, data replication, and data recovery. It comprises scripts that control the data flow. The system detects the availability of data on an FTP (file transfer protocol) server, initiates data transfer, preprocesses data if necessary, and archives it on readily available disk drives with FTP and HTTP (Hypertext Transfer Protocol) access, allowing instantaneous data access. There are options for plug-ins for data preprocessing before storage. Publication of metadata to external applications such as the Earth Observing System Clearinghouse (ECHO) is also supported. S4PA includes a graphical user interface for monitoring the system operation and a tool for deploying the system. To ensure reliability, S4P continuously checks stored data for integrity, Further reliability is provided by tape backups of disks made once a disk partition is full and closed. The system is designed for low maintenance, requiring minimal operator oversight.
Lynnes, Christopher↗
Large Area Crop Inventory Experiment (LACIE). Level 3 baseline; Data Acquisition, Preprocessing and Transmission Subsystem (DAPTS), LANDSAT data requirements, volume 1A, revision A
There are no author-identified significant results in this report.
Source record↗
Large Area Crop Inventory Experiment (LACIE). Level 3 baseline, data Acquisition, Preprocessing and Transmission Subsystem (DAPTS), field data requirements, volume 1B, revision A
There are no author-identified significant results in this report.
Source record↗
Data acquisition, preprocessing and analysis for the Virginia Tech OLYMPUS experiment
Virginia Tech is conducting a slant path propagation experiment using the 12, 20, and 30 GHz OLYMPUS beacons. Beacon signal measurements are made using separate terminals for each frequency. In addition, short baseline diversity measurements are collected through a mobile 20 GHz terminal. Data collection is performed with a custom data acquisition and control system. Raw data are preprocessed to remove equipment biases and discontinuities prior to analysis. Preprocessed data are then statistically analyzed to investigate parameters such as frequency scaling, fade slope and duration, and scintillation intensity.
Remaklus, P. Will↗
Large Area Crop Inventory Experiment (LACIE). Level 3 baseline; Data Acquisition, Preprocessing and Transmission Subsystem (DAPTS), historical agricultural data requirements, volume 1C, revision A
There are no author-identified significant results in this report.
Source record↗
Significant techniques for the processing and interpretation of ERTS-1 data
Preprocessing, classification, and correlation of ERTS-1 data with ground features and analysis of spectral clustered results
Cousin, S. B.↗
Compensation for atmospheric effects in LANDSAT data
Preprocessing algorithms were developed to remove or reduce the variations in multispectral data caused by variations in Sun angle and by changes in the atmospheric aerosol and water vapor levels. The two most significant algorithms developed by using mathematical models to define interrelations between the required multiplicative and additive correction factors so that just a few statistical characteristics of a LANDSAT distribution model would be sufficient to drive the mathematical model and to calculate the preprocessing corrections are examined. These are the atmospheric correction (ATCOR) computer program and the XSTAR haze correction algorithm. Neither the ATCOR nor the XSTAR algorithm provides an explicit compensation for the effects of changing LANDSAT view angle. Development efforts are underway to address this aspect of the preprocessing problem.
Lambeck, P. F.↗