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NASTRAN/DISCOS/SAMSAN DMAP Bridging Program
Design of controller for flexible structure must incorporate flexible body data in format compatible with design and performance evaluation methods. Control designer works with several different programs to obtain all data and capabilities needed. Three programs for design and analysis of flexible structure controllers integrated.
TESS Science Data Products Description Document: EXP-TESS-ARC-ICD-0014 Rev F
This document discusses data product formats that are produced primarily by the TESS Science Processing Operations Center (SPOC) at NASA Ames Research Center. Data products are sent to the TESS Science Operations Center (SOC) at MIT where they are disseminated to the Mikulski Archive for Space Telescopes (MAST) and the TESS Science Office (TSO).
Development of WMS Capabilities to Support NASA Disasters Applications and App Development
During the last year several significant disasters have occurred such as Superstorm Sandy on the East coast of the United States, and Typhoon Bopha in the Phillipines, along with several others. In support of these disasters NASA's Short‐term Prediction Research and Transition (SPoRT) Center delivered various products derived from satellite imagery to help in the assessment of damage and recovery of the affected areas. To better support the decision makers responding to the disasters SPoRT quickly developed several solutions to provide the data using open Geographical Information Service (GIS) formats. Providing the data in open GIS standard formats allowed the end user to easily integrate the data into existing Decision Support Systems (DSS). Both Tile Mapping Service (TMS) and Web Mapping Service (WMS) were leveraged to quickly provide the data to the end‐user. Development of the deliver methodology allowed quick response to rapidly developing disasters and enabled NASA SPoRT to bring science data to decision makers in a successful research to operations transition.
Development of WMS Capabilities to Support NASA Disasters Applications and App Development
During the last year several significant disasters have occurred such as Superstorm Sandy on the East coast of the United States, and Typhoon Bopha in the Phillipines, along with several others. In support of these disasters NASA's Short‐term Prediction Research and Transition (SPoRT) Center delivered various products derived from satellite imagery to help in the assessment of damage and recovery of the affected areas. To better support the decision makers responding to the disasters SPoRT quickly developed several solutions to provide the data using open Geographical Information Service (GIS) formats. Providing the data in open GIS standard formats allowed the end user to easily integrate the data into existing Decision Support Systems (DSS). Both Tile Mapping Service (TMS) and Web Mapping Service (WMS) were leveraged to quickly provide the data to the end‐user. Development of the deliver methodology allowed quick response to rapidly developing disasters and enabled NASA SPoRT to bring science data to decision makers in a successful research to operations transition.
Development of Web Mapping Service Capabilities to Support NASA Disasters Applications / App Development
During the last year several significant disasters have occurred such as Superstorm Sandy on the East coast of the United States, and Typhoon Bopha in the Phillipines, along with several others. In support of these disasters NASA's Short‐term Prediction Research and Transition (SPoRT) Center delivered various products derived from satellite imagery to help in the assessment of damage and recovery of the affected areas. To better support the decision makers responding to the disasters SPoRT quickly developed several solutions to provide the data using open Geographical Information Service (GIS) formats. Providing the data in open GIS standard formats allowed the end user to easily integrate the data into existing Decision Support Systems (DSS). Both Tile Mapping Service (TMS) and Web Mapping Service (WMS) were leveraged to quickly provide the data to the end‐user. Development of the deliver methodology allowed quick response to rapidly developing disasters and enabled NASA SPoRT to bring science data to decision makers in a successful research to operations transition.
Development of Web Mapping Service Capabilities to Support NASA Disasters Applications/App Development
During the last year several significant disasters have occurred such as Superstorm Sandy on the East coast of the United States, and Typhoon Bopha in the Phillipines, along with several others. In support of these disasters NASA's Short‐term Prediction Research and Transition (SPoRT) Center delivered various products derived from satellite imagery to help in the assessment of damage and recovery of the affected areas. To better support the decision makers responding to the disasters SPoRT quickly developed several solutions to provide the data using open Geographical Information Service (GIS) formats. Providing the data in open GIS standard formats allowed the end user to easily integrate the data into existing Decision Support Systems (DSS). Both Tile Mapping Service (TMS) and Web Mapping Service (WMS) were leveraged to quickly provide the data to the end‐user. Development of the deliver methodology allowed quick response to rapidly developing disasters and enabled NASA SPoRT to bring science data to decision makers in a successful research to operations transition.
Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.
Geological Carbon Sequestration Storage Resource Estimates for the Ordovician St. Peter Sandstone, Illinois and Michigan Basins, USA
The Cambro-Ordovician strata of the Midwest of the United States is a primary target for potential geological storage of CO2 in deep saline formations. The objective of this project is to develop a comprehensive evaluation of the Cambro-Ordovician strata in the Illinois and Michigan Basins above the basal Mount Simon Sandstone since the Mount Simon is the subject of other investigations including a demonstration-scale injection at the Illinois Basin Decatur Project. The primary reservoir targets investigated in this study are the middle Ordovician St Peter Sandstone and the late Cambrian to early Ordovician Knox Group carbonates. The topic of this report is a regional-scale evaluation of the geologic storage resource potential of the St Peter Sandstone in both the Illinois and Michigan Basins. Multiple deterministic-based approaches were used in conjunction with the probabilistic-based storage efficiency factors published in the DOE methodology to estimate the carbon storage resource of the formation. Extensive data sets of core analyses and wireline logs were compiled to develop the necessary inputs for volumetric calculations. Results demonstrate how the range in uncertainty of storage resource estimates varies as a function of data availability and quality, and the underlying assumptions used in the different approaches. In the simplest approach, storage resource estimates were calculated from mapping the gross thickness of the formation and applying a single estimate of the effective mean porosity of the formation. Results from this approach led to storage resource estimates ranging from 3.3 to 35.1 Gt in the Michigan Basin, and 1.0 to 11.0 Gt in the Illinois Basin at the P10 and P90 probability level, respectively. The second approach involved consideration of the diagenetic history of the formation throughout the two basins and used depth-dependent functions of porosity to derive a more realistic spatially variable model of porosity rather than applying a single estimate of porosity throughout the entire potential reservoir domains. The second approach resulted in storage resource estimates of 3.0 to 31.6 Gt in the Michigan Basin, and 0.6 to 6.1 Gt in the Illinois Basin. The third approach attempted to account for the local-scale variability in reservoir quality as a function of both porosity and permeability by using core and log analyses to calculate explicitly the net effective porosity at multiple well locations, and interpolate those results throughout the two basins. This approach resulted in storage resource estimates of 10.7 to 34.7 Gt in the Michigan Basin, and 11.2 to 36.4 Gt in the Illinois Basin. A final approach used advanced reservoir characterization as the most sophisticated means to estimating storage resource by defining reservoir properties for multiple facies within the St Peter formation. This approach was limited to the Michigan Basin since the Illinois Basin data set did not have the requisite level of data quality and sampling density to support such an analysis. Results from this approach led to storage resource estimates of 15.4 Gt to 50.1 Gt for the Michigan Basin. The observed variability in results from the four different approaches is evaluated in the context of data and methodological constraints, leading to the conclusion that the storage resource estimates from the first two approaches may be conservative, whereas the net porosity based approaches may over-estimate the resource.
Format for Interchange and Display of 3D Terrain Data
Visible Scalable Terrain (ViSTa) is a software format for production, interchange, and display of three-dimensional (3D) terrain data acquired by stereoscopic cameras of robotic vision systems. ViSTa is designed to support scalability of data, accuracy of displayed terrain images, and optimal utilization of computational resources. In a ViSTa file, an area of terrain is represented, at one or more levels of detail, by coordinates of isolated points and/or vertices of triangles derived from a texture map that, in turn, is derived from original terrain images. Unlike prior terrain-image software formats, ViSTa includes provisions to ensure accuracy of texture coordinates. Whereas many such formats are based on 2.5-dimensional terrain models and impose additional regularity constraints on data, ViSTa is based on a 3D model without regularity constraints. Whereas many prior formats require external data for specifying image-data coordinate systems, ViSTa provides for the inclusion of coordinate-system data within data files. ViSTa admits highspeed loading and display within a Java program. ViSTa is designed to minimize file sizes and maximize compressibility and to support straightforward reduction of resolution to reduce file size for Internet-based distribution.
PoroTomo: BRAD and BRDY GPS Station RINEX Files - December 20, 2014
This dataset provides links to daily RINEX files for two GPS stations at Brady's Hot Springs as of December 20, 2014. The data is formatted as compressed GNSS RINEX observation files and is accessible through CSV files containing metadata such as file size, data type, and publication dates. The dataset includes FTP links for downloading the individual RINEX files and covers data collected through 2012. The CSV files serve as a directory for navigating to the correct FTP locations to access the desired data. Users are advised to cross-reference the filenames and station identifiers (BRAD or BRDY) when downloading files. The files were generated by the UNAVCO GSAC Repository, and all data-related timestamps and units are specified within the CSV structure. Links to additional resources, including the original database for direct downloads and a more recent dataset with data as of March 26, 2015, are also provided.
2016 Renewable Energy Grid Integration Data Book
The 2016 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format. This data book provides an overview of selected key grid integration metrics that represent complex interactions among generation characteristics, market rules, and environmental and safety factors, which may vary by geography, season, and time of day; and presents metrics that either indicate how much variable renewable energy (VRE) is currently being integrated onto the grid, or measure factors that may increase or decrease the challenges associated with integrating VRE generation onto the grid.
Big Data Framework with Machine Learning for D and D Applications - 19108
The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)
SW New Mexico Play Fairway Analysis: Oil Well Formation Tops
Rock formation top picks from oil wells from southwestern New Mexico from scout cards and other sources. There are differing formation tops interpretations for some wells, so for those wells duplicate formation top data are presented in this file.
Generation of Level 3 SMMR and SSM/I Brightness Temperatures for the Period 1978-1999
The NOAA/NASA Pathfinder Program was initially designed to assure that certain key remote sensing data sets of particular significance to global change research were scientifically validated, consistently processed and made readily available to the research community at minimal cost. Through this Program the National Snow and Ice Data Center (NSIDC), University of Colorado has successfully processed, archived and distributed the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) Level 3 (EASE-Grid format) Pathfinder data sets for the period 1978 to 1999. These data are routinely distributed to approximately 150 researchers through various media including CD-ROM, 8 mm tape, ftp and the EOS Information Management System (IMS). At NSIDC these data are currently being applied in the development and validation of algorithms to derive snow water equivalent (NASA NAG5-6636), the mapping of frozen ground and the detection of the onset of melt over ice sheets, sea ice and snow cover. The EASE-Grid format, developed at NSIDC in conjunction with the SMMR-SSM/I Pathfinder project has also been applied to Advanced Very High Resolution Radiometer (AVHRR) and TOVS Pathfinder data, as well as ancillary data such as digital elevation, land cover classification and several in situ data sets. EASE-Grid will also be used for all land products derived from the NASA EOS AMSR-E instrument.
T.Rex
T.Rex is used to explore tabular data sets containing up to ten million records to help rapidly understand a previously unknown data set. Analysis can quickly identify patterns of interest and the records and fields that capture those patterns. T.Rex contains a growing set of deep analytical tools and supports robust export capabilities that selected data can be incorporated into to other specialized tools for further analysis. T.Rex is flexible in ingesting different types and formats of data, allowing the user to interactively experiment and perform trial and error guesses on the structure of the data; and also has a variety of linked visual analytic tools that enable exploration of the data to find relevant content, relationships among content, trends within the content, and capture knowledge about the content. Finally, T.Rex has a rich export capability, to extract relevant subsets of a larger data source, to further analyze their data in other analytic tools.
Drifting Acoustic Measurements around C-Power's SeaRay WEC
The repository contains underwater noise measurements and associated metadata collected around C-Power's SeaRay wave energy converter on July 15, 2024 and July 16, 2024 while it was deployed at the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Measurements were obtained using Drifting Acoustic Instrumentation SYstems (DAISYs). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is a compressed archive (.zip format), containing multiple MATLAB binary data files (.mat format). The structure of each file is included in the repository as a Word document (Data Description MHK-DR.docx). Each file contains time series information for a single DAISY deployment (file naming convention: WETS_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. During these measurements, C-Power's SeaRay was located at approximately 21.48112 N, 157.74451 W.
User's guide to the UTIL-ODRC tape processing program
The UTIL-ODRC computer compatible tape processing program, its input/output requirements, and its interface with the EXEC 8 operating system are described. It is a multipurpose orbital data reduction center (ODRC) tape processing program enabling the user to create either exact duplicate tapes and/or tapes in SINDA/HISTRY format. Input data elements for PRAMPT/FLOPLT and/or BATCH PLOT programs, a temperature summary, and a printed summary can also be produced.