Search NASA⌕ Search

SEARCH · Search NASA

Results for “data formats”

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.

At least 217 records · Page 12

Independent Component Analysis for Preprocessing Optical Signals in Support of Multi-User Communication

In an effort to construct an optical transceiver for supporting multi-point communication, researchers developed large field-of-view FSO transceivers with wide apertures for facilitating rapid acquisition and optical signal tracking. The design is constructed from fiber-bundle(s) intelligently arranged to provide multiple optical pathways within a single transceiver, with each pathway limited to a particular optical directionality. Because designs with wide apertures are susceptible to receiving several optical signals simultaneously from multiple transmitters, accurately decoding individual signals and achieving true multi-user communication can be difficult. The work detailed in this paper investigated the use of blind source separation techniques, mainly independent component analysis (ICA), to estimate transmitted signals from their observed, combined mixtures sans information. Effects of signal power, format, data rate, wavelength, and turbulence severity on signal separation, as well as signal demodulation accuracy was also analyzed using experimental optical signals and corresponding mixtures. Signal generators, laser sources, turbulence emulator, beam profiler, photodiodes, and oscilloscopes comprised the experiment setup. Optical signals with varying power, format, and wavelength were generated and propagated through turbulence with varying degree severity, and finally received by the fiber-bundle-based optical transceiver. Results demonstrate that an ICA technique can accurately process the combined signals into corresponding individual components. The investigation further identified signal characteristics (e.g., power levels, format, data rate) and turbulence severity under which ICA is unable to accurately perform signal separations.

Optics↗

"One-Stop Shopping" for Ocean Remote-Sensing and Model Data

OurOcean Portal 2.0 (http:// ourocean.jpl.nasa.gov) is a software system designed to enable users to easily gain access to ocean observation data, both remote-sensing and in-situ, configure and run an Ocean Model with observation data assimilated on a remote computer, and visualize both the observation data and the model outputs. At present, the observation data and models focus on the California coastal regions and Prince William Sound in Alaska. This system can be used to perform both real-time and retrospective analyses of remote-sensing data and model outputs. OurOcean Portal 2.0 incorporates state-of-the-art information technologies (IT) such as MySQL database, Java Web Server (Apache/Tomcat), Live Access Server (LAS), interactive graphics with Java Applet at the Client site and MatLab/GMT at the server site, and distributed computing. OurOcean currently serves over 20 real-time or historical ocean data products. The data are served in pre-generated plots or their native data format. For some of the datasets, users can choose different plotting parameters and produce customized graphics. OurOcean also serves 3D Ocean Model outputs generated by ROMS (Regional Ocean Model System) using LAS. The Live Access Server (LAS) software, developed by the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA), is a configurable Web-server program designed to provide flexible access to geo-referenced scientific data. The model output can be views as plots in horizontal slices, depth profiles or time sequences, or can be downloaded as raw data in different data formats, such as NetCDF, ASCII, Binary, etc. The interactive visualization is provided by graphic software, Ferret, also developed by PMEL. In addition, OurOcean allows users with minimal computing resources to configure and run an Ocean Model with data assimilation on a remote computer. Users may select the forcing input, the data to be assimilated, the simulation period, and the output variables and submit the model to run on a backend parallel computer. When the run is complete, the output will be added to the LAS server for

Li, P. Peggy↗

Functionality of the Python Packages for the HERMES Mission

The Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) is a set of four instruments that will fly on the Lunar Gateway, an orbital outpost which will support Artemis lunar operations. HERMES will focus on understanding the causes of space-weather variability as driven by the Sun and modulated by the magnetosphere. In this talk, we will discuss the open source approach of the HERMES Science Operation Center (SOC) team being implemented in a number of Python packages which all work together. We will describe the core package which contains Python interfaces for the loading, calibrating, plotting, validating, and saving of measurement data through Common Data Format (CDF) files making use of pycdf provided by spacepy. Each instrument also has a Python package developed using a package template and will provide specific calibration and processing functionality to each instrument. The packages make extensive use of the scientific Python ecosystem and maintain compatibility with PyHC standards. The abstraction of intricate, high heritage data formats, such as CDF files, in Python enables easier analysis and opens doors for greater participation in heliophysics science.

hermes↗

Direct I/O for RNTuple Columnar Data

RNTuple is the new columnar data format designed as the successor to ROOT’s TTree format. It allows to make use of modern hardware capabilities and is expected to be used in production by the LHC experiments during the HL-LHC. In this paper, we discuss the usage of Direct I/O to fully exploit modern SSDs, especially in the context of the recent addition of parallel RNTuple writing. We describe the alignment requirements imposed by Direct I/O and approaches to meet them for columnar data formats. Finally, we discuss performance results for both writing and reading, in synthetic benchmarks as well as real-world applications.

Hahnfeld, Jonas [CERN; Goethe U., Frankfurt (main)↗

Accessing and Understanding MODIS Data

The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.

Leptoukh, Gregory↗

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗

STILGAR Seismic Array Data

Two three-component dense seismic arrays were deployed above an active limestone mine in Pleasant Gap, Pennsylvania; one in the fall and one in the spring of 2023. Each campaign lasted approximately four weeks and included 80 to 100 seismic stations with stations spaced between 100 and 400 meters apart. Each campaign also included several small dense seismic arrays. Two types of seismic stations were deployed including FairfieldNodal ZLand 3C All-in-One seismometers and Geospace 3C GS-ONE geophone paired with GSX3 and GSX4 dataloggers. This dataset includes ~ 4 TB of three-component continuously recorded seismic data in the miniseed format data from both campaigns as well as a readme file for the data structure.

58 GEOSCIENCES↗

Hestia SW-IFL Onroad Fossil Fuel Carbon Dioxide (FFCO2) product: Road segment-level annual FFCO2 emissions across Arizona (2017-2022), version 1.1

The SW-IFL onroad fossil fuel carbon dioxide (FFCO2) emissions data product represents CO2 emissions from the combustion of fossil fuels by motor vehicles (e.g., passenger cars, trucks, buses, motorcycles) traveling on designated roadways. The emissions are represented geographically on each road segment within the state of Arizona spanning the 2017 to 2022 time period. This data product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions data are provided in CSV (input data, ONR_FFCO2_AZ_county.csv) and GeoPackage form (output polyline objects - about 786,000 road segments, XXXX_AZ_v1.1.gpkg) designated by road class (interstates, arterials, collectors, local). The metadata file (Metadata_SW-IFL_Onroad_annualFFCO2_v1.1.docx) provides details about attributes and data formats. The GeoPackage emissions data are provided separately for local roads and nonlocal roads (interstates, arterials, collectors). The method file (Methods_SW-IFL_Onroad_annualFFCO2_v1.1.docx) describes the data processing flow and data sources. Update on 2024-04-17: Updates were made to both the input emission data file (.csv) and output segment-level emission file (.gpkg). There was an update in county-level emission input data (ONR_FFCO2_AZ_county.csv) and the entire road segments were reprocessed to reflect this update.Update on 2024-04-29: Update was made to one output segment-level emission file (Nonlocal_AZ_v1.0.gpkg). There was an error in the AADT values and the data were reprocessed to reflect this update.Update on 2024-10-22: Temporal coverage was extended to include 2022. VMT values were recalculated using new AADT data and the entire road segments were reprocessed to reflect these updates.

54 ENVIRONMENTAL SCIENCES↗

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗

A cometary and interplanetary dust experiment on the Vega spacecraft missions to Halley's Comet

The dust counter and mass analyzer on board both the Vega 1 and Vega 2 spacecraft scheduled to encounter Halley's Comet are discussed. The operational modes, data formats, telemetry modes and data acquisition, and scientific measurements planned for this experiment both at encounter and during interplanetary travel are described. The initial postlaunch calibration and experiment status are summarized in an appendix.

Perkins, M. A.↗

ACE: A distributed system to manage large data archives

Competitive pressures in the oil and gas industry are requiring a much tighter integration of technical data into E and P business processes. The development of new systems to accommodate this business need must comprehend the significant numbers of large, complex data objects which the industry generates. The life cycle of the data objects is a four phase progression from data acquisition, to data processing, through data interpretation, and ending finally with data archival. In order to implement a cost effect system which provides an efficient conversion from data to information and allows effective use of this information, an organization must consider the technical data management requirements in all four phases. A set of technical issues which may differ in each phase must be addressed to insure an overall successful development strategy. The technical issues include standardized data formats and media for data acquisition, data management during processing, plus networks, applications software, and GUI's for interpretation of the processed data. Mass storage hardware and software is required to provide cost effective storage and retrieval during the latter three stages as well as long term archival. Mobil Oil Corporation's Exploration and Producing Technical Center (MEPTEC) has addressed the technical and cost issues of designing, building, and implementing an Advanced Computing Environment (ACE) to support the petroleum E and P function, which is critical to the corporation's continued success. Mobile views ACE as a cost effective solution which can give Mobile a competitive edge as well as a viable technical solution.

Daily, Mike I.↗

Reduction of ETS-VI Laser Communication Equipment Optical-Downlink Telemetry Collected During GOLD

Free-space laser communications experiments were conducted between the laser communication equipment (LCE) on board the Japanese Engineering Test Satellite VI (ETS-VI) and the ground station located at the Table Mountain Facility (TMF) during late 1995 and early 1996. This article describes the on-line data reduction process used to decode LCE telemetry (called E2) downlinked on the optical carrier during the Ground/Orbiter Lasercomm Demonstration (GOLD) experiments. The LCE has the capability of transmitting real-time sensor and status information at 128 kbps by modulating the onboard diode laser. The optical downlink was detected on the ground, bit synchronized, and the resulting data stream stored on a data recorder. The recorded data were subsequently decoded by on-line data processing that included cross-correlation of the known telemetry data format and the downlink data stream. Signals obtained from the processing can be useful not only in evaluating the characteristics of the LCE but also in understanding uplink and downlink signal quality.

Toyoshima, M.↗

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis.

Leptoukh, G.↗

Exploring NASA and ESA Atmospheric Data Using GIOVANNI, the Online Visualization and Analysis Tool

Giovanni, the NASA Goddard online visualization and analysis tool (http://giovanni.gsfc.nasa.gov) allows users explore various atmospheric phenomena without learning remote sensing data formats and downloading voluminous data. Using NASA MODIS (Terra and Aqua) and ESA MERIS (ENVISAT) aerosol data as an example, we demonstrate Giovanni usage for online multi-sensor remote sensing data comparison and analysis.

Leptoukh, Gregory↗

Urban-scale Energy Modeling: Scaling Beyond Tax Assessor Data

In an attempt to attain building-specific characteristics for urban-scale building energy models, county-specific tax assessors’ data is often an initial data source. This data source can contain valuable information such as year built, area, height, HVAC type, and roof/wall descriptions.We will show examples of 2,000 fields from Hamilton County in Tennessee with examples of many fields which are not relevant to urban-scale building energy modeling, are incorrect compared to other data sources, and highlight some lessons learned working with such a data source.There are currently 3,142 counties in the United States, each with their own data format, field definitions, and data access policy. As urban-scale involves city-scale analysis potentially covering multiple counties and matures toward state- or nation-scale analysis, county-by-county approaches are not scalable. While there are efforts to unify these datasets, there is an increasing proliferation of data and algorithms that can cover wider areas and provide more accurate inputs for urban-scale models. This paper summarizes computer vision of imagery, cartographic layers, building type assessment, and model generation used to achieve scalable detection and analysis of buildings.

New, Joshua↗

Spacelab uplink/downlink data flow and formats

The results of an analysis of the Spacelab (SL) data uplink/downlink structure and those data system elements associated with the support of this data flow are presented. Specific objectives of this report are to present the results of the following analyses: (1) operations of the SL high rate multiplexer, including format structure, data rates, format combinations, format switching, etc.; (2) operations of SL data recorders to include the definition of modes, data rates and forms; (3) operations of the high rate demultiplexer as described above; (4) potential experiment data formats defining formatting parameters to be considered in decommutation analysis; (5) SL computer input/output (I/O) decommutation channels, including the definition of structure, quantity and use of this I/O data; (6) detailed requirements of the data quality monitoring philosophy for this function.

Kandefer, F.↗