Results for “Sensor Metadata” — Page 2

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRS2003

Functional Flight Test Report for Positive Systems' ADAR System 5500 Sensor SN8 Linear

This report describes results of the functional flight test conducted with the Positive Systems' ADAR 5500 sensor system (serial number 8, linear configuration) near Winslow, Arizona on June 30 and July 1, 1999. The in-flight test is one component of the NASA Scientific Data Purchase (SDP) Validation and Verification (V&V) process. It allows to measure characteristics of the entire sensor system affected by both performance of the sensor during a flight and post-flight image processing. The following characteristics were analyzed: changes of dark digital numbers (DN's), radiometric linearity, signal-to-noise ratio (SNR), spatial resolution, and geolocation accuracy. The measured characteristics were compared with the image product specifications defined in the Positive Systems' SDP contract. Dependence of the dark DN's on several factors was analyzed, but no significant correlation was found. However, the observed changes in dark DN's were relatively small, which justifies usage of a constant value in the dark DN subtraction procedure during post-processing. Dependence of measured at-sensor, in-band radiance (in arbitrary units) on measured in-band ground reflectance is very well described by a linear function - The sensor fulfills the linearity requirement. Measured SNR values lower than the contract specifications, but accuracy of that test was possibly affected by non-uniformity of the employed gray-scale panels. The SNR values are generally sufficiently high for most applications. SNR can also be improved during standard flights by using longer exposure times. Full width at half maximum (FWHM) of an edge response derived line spread function was used as a measure of spatial resolution. FWHM was generally smaller than twice the ground sample distance (GSD), in agreement with the contract specifications. Accuracy of the geolocation information, which is provided for the particular images in a metadata file, was found to meet contract requirements as well.

Stanley, Tom↗

NASA NTRSDate not supplied

Opening Historical Airborne Data to Present Day Researchers

For more than 50 years, NASA has flown airborne sensors to carry out research, validate satellite sensors, and test new instrument capabilities. Data collected prior to 2000 are typically analog and difficult to locate and use. The Airborne Data Management Group (ADMG) facilitates rescue of these valuable data to ensure easier discovery, access, and use. But opening historical data comes at a cost of both time and money. Careful decisions are required in assessing the return on investment. - Is there interest in the science community? - Are there government data requirements? - What is the temporal / spatial value of the data? - Can data be transformed to a digital format? - What is cost of transformation? - What time period is needed for rescue? Converting the data to today’s digital storage standards increases value and provides data access. The addition of metadata makes the data easier to search for.

Deborah Smith↗

NASA NTRS2008

Provenance in Data Interoperability for Multi-Sensor Intercomparison

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.

Lynnes, Chris↗

NASA NTRS2022

A Compilation of Global Bio-Optical in Situ Data for Ocean Colour Satellite Applications – Version Three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately.

ocean color↗

NASA NTRSDate not supplied

Towards a Combined Low-Earth Orbit Lightning Climatology: Current Work and Assessing South Atlantic Anomaly Influence

This presentation discusses the current status and ongoing work towards a low-Earth orbit (LEO) lightning climatological product that includes both Lightning Imaging Sensors (LIS) and the Optical Transient Detector (OTD). The project builds upon the work by Cecil et al. (2014) to include the LIS on the International Space Station (ISS), featuring additional inter-instrumental comparisons to more accurately depict the unique capabilities of each instrument. A closer examination of the interference from the South Atlantic Anomaly (SAA) was also conducted on all three LEO sensors. The SAA is a region of the magnetosphere roughly spanning the south-central Atlantic wherein the inner Van Allen radiation belts are closer to Earth than anywhere else, subjecting spacecraft in LEO to significantly higher radiation flux levels. This radiation can produce interference with the LIS and OTD instruments, manifesting as non-lightning luminous events. While these events can be identified easily enough, they can occur in such quantities that they overwhelm the sensors’ processors and effectively ‘blind’ them. This is called the First-In First-Out (FIFO) buffer overflow, and this blinded time reduced the instruments’ view-time as a result. The FIFO overflow and view-time can reliably be used to track the SAA interference (Clark et al. 2024), which allows for the lightning counts attributable to the SAA to be assessed. The impact that the SAA had on the quality of observations from the LEO lightning instrumentation and the significance for the resulting climatological products was investigated. Substantial areas of interference were identified for all three instruments using the view-time and quality flags, each with a unique shape and temporal evolution. The temporal evolution and general region affected are of particular importance when constructing a climatological product, as there are notable reductions to view-time over prolonged periods across a lightning-rich region. However, a temporal element also makes isolating the subsequent impact on the lightning signal significantly more challenging. Lightning counts in this region are highly seasonal and exhibit inter-annual variability, which is compounded by the nature of LEO observations. This study includes preliminary analysis of the impact that the SAA has on the lightning counts given the notable impact shown in the metadata.

Austin G Clark↗

NASA NTRS2017

Astronaut Photography of the Earth: A Long-Term Dataset for Earth Systems Research, Applications, and Education

The NASA Earth observations dataset obtained by humans in orbit using handheld film and digital cameras is freely accessible to the global community through the online searchable database at https://eol.jsc.nasa.gov, and offers a useful compliment to traditional ground-commanded sensor data. The dataset includes imagery from the NASA Mercury (1961) through present-day International Space Station (ISS) programs, and currently totals over 2.6 million individual frames. Geographic coverage of the dataset includes land and oceans areas between approximately 52 degrees North and South latitudes, but is spatially and temporally discontinuous. The photographic dataset includes some significant impediments for immediate research, applied, and educational use: commercial RGB films and camera systems with overlapping bandpasses; use of different focal length lenses, unconstrained look angles, and variable spacecraft altitudes; and no native geolocation information. Such factors led to this dataset being underutilized by the community but recent advances in automated and semi-automated image geolocation, image feature classification, and web-based services are adding new value to the astronaut-acquired imagery. A coupled ground software and on-orbit hardware system for the ISS is in development for planned deployment in mid-2017; this system will capture camera pose information for each astronaut photograph to allow automated, full georegistration of the data. The ground system component of the system is currently in use to fully georeference imagery collected in response to International Disaster Charter activations, and the auto-registration procedures are being applied to the extensive historical database of imagery to add value for research and educational purposes. In parallel, machine learning techniques are being applied to automate feature identification and classification throughout the dataset, in order to build descriptive metadata that will improve search capabilities. It is expected that these value additions will increase interest and use of the dataset by the global community.

Stefanov, William L.↗