Low energy proton effects on solar cell cover glass materials. Volume 2 - Raw data Final technical report
Raw data from study to determine low energy proton effects on solar cell cover glass materials - Vol. 2
Lillywhite, M. A.↗
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New Rover Conops with High-Performance Onboard Computing: Give Up Raw Data to Reduce Ops Cost and Do More Science
A major portion of time during the tactical operation of Mars rovers is spent for selecting, prioritizing, and coordinating sciences and engineering activities such that they fit within resource constraints, including the downlink data volume, energy, and time. In particular, the downlink data volume constraint is getting particularly tighter in recent missions because modern instruments produce increasingly high data volume while the communication bandwidth is essentially bounded by the law of physics. Tactical operation would be substantially simplified, hence the operation cost could be reduced, if the data volume constraint is relaxed or even removed. In this abstract, we propose a new operation paradigm for achieving this goal. The key observation is that, both in science and engineering applications, the bit size of raw data is typically much greater than the volume of processed information that is needed for scientific or engineering analysis. For example, a full-resolution image from Mastcam-Z, the main science camera on Perseverance, is about 700 kB in volume and we downlinked 29,685 images up to Sol 243, totaling ~20 GB of data. But of course, scientists do not use every pixel of these images; what they really look for in the images are geological features, typically represented by specific geometric configurations or textures. An end product after processing hundreds of Mascam-Z images could be a single geological map summarizing the spatial distribution of the features. For another example, a 100-meter drive of Perseverance produces 7-12 MB of drive telemetry, which records every detail of the rover's motion at 8 Hz, including position, attitude, steering angles, encoder readings, motor currents and many other information. But what the ground engineers eventually pay attention to is the signs of anomaly, such as excessive motor currents or high slip; if a drive is nominal, the vast majority of this data is unused. What if, then, we process the raw data onboard and only downlink the processed data that is relevant to scientific or engineering analyses, such as a list of detected science features (with cropped images) or a list of potential signs of anomaly while driving? A major roadblock for such onboard, high-level information processing has been the onboard computational resource. RAD750, the main onboard computer of Perseverance, is obviously not sufficient for performing complex image or signal processing such as object detection, semantic segmentation, or anomaly detection. Interestingly, RAD750 is not the best processor that Perseverance has; Qualcomm's Snapdragon 801, a modern mobile processor, is on her Heli Base Station, a device for communicating with Mars Helicopter Ingenuity; also, Intel's Atom E3845 processors are on engineering cameras. In the reminder of this paper, we will introduce two particular uses cases of these high-performance co-processors (meaning auxiliary CPU, GPU, or other types of processors that are separate from the main processor that runs the main flight software) for lowering operation cost and accommodating more science activities for a given communication constraint.
Didier, A.↗
A Spatiotemporal Data Cube Approach to Classification of Variable Stars: A Catalog of Candidate Variable Stars from the TESS Full-frame Image Raw Data
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Harry Qiang↗
Raw Data from Study on the Influence of Mixed Mode I-Mode II Loading on Interlaminar Fracture of a Graphite Epoxy Tape Laminate
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James G Ratcliffe↗
Vortex information display system program description manual
A vortex information display system is described which provides flexible control through system-user interaction for collecting wing-tip-trailing vortex data, processing this data in real time, displaying the processed data, storing raw data on magnetic tape, and post processing raw data. The data is received from two asynchronous laser Doppler velocimeters (LDV's) and includes position, velocity, and intensity information. The raw data is written onto magnetic tape for permanent storage and is also processed in real time to locate vortices and plot their positions as a function of time. The interactive capability enables the user to make real time adjustments in processing data and provides a better definition of vortex behavior. Displaying the vortex information in real time produces a feedback capability to the LDV system operator allowing adjustments to be made in the collection of raw data. Both raw data and processing can be continually upgraded during flyby testing to improve vortex behavior studies. The post-analysis capability permits the analyst to perform in-depth studies of test data and to modify vortex behavior models to improve transport predictions.
Conway, R.↗
Near IR Photolysis of HO2NO2: Supplemental Material
MkIV measurements of the volume mixing ratio (VMR) of HO2NO2 at 35 deg N, sunset on Sept. 25, 1993 are given. Measurements of HO2NO2 made between approx. 65 and 70 deg N, sunrise on May 8, 1997 are listed. The uncertainties given are 1 sigma estimates of the measurement precision. Uncertainty in the HO2NO2 line strengths is estimated to be 20%; this is the dominant contribution to the systematic error of the HO2NO2 measurement. Model inputs for the simulations are given. The albedos were obtained from Total Ozone Mapping Spectrometer reflectively data (raw data at ftp://jwocky.gsfc.nasa.gov) for the time and place of observation. Profiles of sulfate aerosol surface area ("Surf. Area") were obtained from monthly, zonal mean profiles measured by SAGE II [Thomason et al., 1997 updated via private communication]. The profile of Be(y) is based on the Wamsley et al. relation with N2O, using MkIV measurements of N20O. All other model inputs given are based on direct MkIV measurements. Finally, we note the latitude of the MkIV tangent point varied considerably during sunrise on May 8, 1997. The simulations shown here were obtained using different latitudes for each altitude.
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Processing Raw HST Data With Up-to-Date Calibration Data
On-the-Fly Reprocessing (OTFR) is a collection of data-processing routines that work within the context of the Hubble Space Telescope (HST) pipeline data-flow system. The purpose served by OTFR is to generate, on demand, scientifically useful data products from raw HST data stored in an archive. First, on the basis of the requested final data products, OTFR retrieves the corresponding sets of raw data from the archives. Next, OTFR processes the raw data sets to remove artifacts and to establish proper header and other template information. Finally, the calibration routines appropriate to the specific data sets are invoked to produce the requested data products, and the data products are released to an archive distribution system for transmission to the requesting party. OTFR offers two notable advantages: (1) Inasmuch as calibrated data occupy about 8 times as much storage space as do raw data, by obviating storage of calibrated data, OTFR reduces the storage capacity needed by the archive; and (2) the calibration routines can be updated to give requesters the benefit of the most recent calibrations.
Miller, Warren↗
Wind information display system user's manual
The Wind Information Display System (WINDS) provides flexible control through system-user interaction for collecting wind shear data, processing this data in real time, displaying the processed data, storing raw data on magnetic tapes, and post-processing raw data. The data are received from two asynchronous laser Doppler velocimeters (LDV's) and include position, velocity and intensity information. The raw data is written onto magnetic tape for permanent storage and is also processed in real time to depict wind velocities in a given spacial region.
Roe, J.↗
DMSP SSJ4 Data Restoration, Classification, and On-Line Data Access
Compress and clean raw data file for permanent storage We have identified various error conditions/types and developed algorithms to get rid of these errors/noises, including the more complicated noise in the newer data sets. (status = 100% complete). Internet access of compacted raw data. It is now possible to access the raw data via our web site, http://www.jhuapl.edu/Aurora/index.html. The software to read and plot the compacted raw data is also available from the same web site. The users can now download the raw data, read, plot, or manipulate the data as they wish on their own computer. The users are able to access the cleaned data sets. Internet access of the color spectrograms. This task has also been completed. It is now possible to access the spectrograms from the web site mentioned above. Improve the particle precipitation region classification. The algorithm for doing this task has been developed and implemented. As a result, the accuracies improved. Now the web site routinely distributes the results of applying the new algorithm to the cleaned data set. Mark the classification region on the spectrograms. The software to mark the classification region in the spectrograms has been completed. This is also available from our web site.
Wing, Simon↗
Querying Semi-Structured Data
The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.
DATA MANAGEMENT↗
Characteristics and processing of fps-16/ jimsphere raw radar data
Error analysis of fps-16/jimsphere raw radar data
De Mandel, R. E.↗
NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation
The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.
Jonathan Dejesus Oribello↗
NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation
The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.
Jonathan D Oribello↗
Availability of Previously Unprocessed ALSEP Raw Instrument Data, Derivative Data, and Metadata Products
In year 2010, 440 original data archival tapes for the Apollo Lunar Science Experiment Package (ALSEP) experiments were found at the Washington National Records Center. These tapes hold raw instrument data received from the Moon for all the ALSEP instruments for the period of April through June 1975. We have recently completed extraction of binary files from these tapes, and we have delivered them to the NASA Space Science Data Cordinated Archive (NSSDCA). We are currently processing the raw data into higher order data products in file formats more readily usable by contemporary researchers. These data products will fill a number of gaps in the current ALSEP data collection at NSSDCA. In addition, we have estabilished a digital, searcheable archive of ALSEP document and metadata as part of the web portal of the Lunar and Planetary Institute. It currently holds approx. 700 documents totaling approx. 40,000 pages
ALSEP↗
A geometric comparison of video camera-captured raster data to vector-parented raster data generated by the X-Y digitizing table
The relative accuracy of a georeferenced raster data set captured by the Megavision 1024XM system using the Videk Megaplus CCD cameras is compared to a georeferenced raster data set generated from vector lines manually digitized through the ELAS software package on a Summagraphics X-Y digitizer table. The study also investigates the amount of time necessary to fully complete the rasterization of the two data sets, evaluating individual areas such as time necessary to generate raw data, time necessary to edit raw data, time necessary to georeference raw data, and accuracy of georeferencing against a norm. Preliminary results exhibit a high level of agreement between areas of the vector-parented data and areas of the captured file data where sufficient control points were chosen. Maps of 1:20,000 scale were digitized into raster files of 5 meter resolution per pixel and overall error in RMS was estimated at less than eight meters. Such approaches offer time and labor-saving advantages as well as increasing the efficiency of project scheduling and enabling the digitization of new types of data.
Swalm, C.↗
ACTS data center
Viewgraphs on ACTS Data Center status report are included. Topics covered include: ACTS Data Center Functions; data flow overview; PPD flow; RAW data flow; data compression; PPD distribution; RAW Data Archival; PPD Audit; and data analysis.
Syed, Ali↗
Skylab Medical Data Center and Archives
The founding of the Skylab medical data center and archives as a central area to house medical data from space flights is described. Skylab program strip charts, various daily reports and summaries, experiment reports and logs, status report on Skylab data quality, raw data digital tapes, processed data microfilm, and other Skylab documents are housed in the data center. In addition, this memorandum describes how the data center acted as a central point for the coordination of preflight and postflight baseline data and how it served as coordinator for all data processing through computation and analysis. Also described is a catalog identifying Skylab medical experiments and all related data currently archived in the data center.
Spross, F. R.↗
Breadboard stellar tracker system test report, volume 2
Complete data from a test program designed to evaluate the performance of a star tracker, a breadboard tracker system, is presented in tabular form. All data presented was normalized to the pixel dimension of 20 micrometers. Data from determination of maximum spatial noise as it applies to the coarse and fine acquisition modes is presented. Pointing accuracy test data, raw pixel data for the track cycle, and data from equipment related tests is also presented.
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