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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

A Monte Carlo Simulation Study of Interference Effect from Multiple HDFS Transmitters above 30 GHz

Commercial operators are now proposing to install thousands of High Density Fixed Services (HDFS) microwave transmitters in large urban centers, such as Los Angeles. These transmitters will share the same frequencies in the Ka band (32 GHz and 37 - 38 GHz) as some Space Research Service (SRS) receiving Earth stations. To face this challenge, Resolution 126 (WRC-97) has requested the International Telecommunications Union-Radiocommunications (ITU-R) to conduct, as a matter of urgency and in time for WRC-99, appropriate studies to determine sharing criteria between stations in the fixed service and stations in other services. The three DSN tracking stations worldwide utilize this frequency band and may become vulnerable to interference from the planned deployments of HDFS transmitters. These HDFS transmitters operate at a relatively strong signal power (up to -60 dBW/Hz). Thus, they will seriously interfere with the sensitive DSN receivers. It has become imperative to accurately predict the impact of HDFS transmitters on NASA's DSN receivers in the Ka band.

Ho, Christian↗
NASA NTRS2016

Evaluating the Impact of Data Placement to Spark and SciDB with an Earth Science Use Case

We investigate the impact of data placement for two Big Data technologies, Spark and SciDB, with a use case from Earth Science where data arrays are multidimensional. Simultaneously, this investigation provides an opportunity to evaluate the performance of the technologies involved. Two datastores, HDFS and Cassandra, are used with Spark for our comparison. It is found that Spark with Cassandra performs better than with HDFS, but SciDB performs better yet than Spark with either datastore. The investigation also underscores the value of having data aligned for the most common analysis scenarios in advance on a shared nothing architecture. Otherwise, repartitioning needs to be carried out on the fly, degrading overall performance.

Spark↗
NASA NTRS2019

Diamondites: Evidence for a Distinct Tectono-Thermal Diamond-Forming Event Beneath the Kaapvaal Craton

The petrogenesis and relationship of diamondite to well-studied monocrystalline and fibrous diamonds are poorly understood yet would potentially reveal new aspects of how diamond-forming fluids are transported through the lithosphere and equilibrate with surrounding silicates. Of twenty-two silicate- and oxide-bearing diamondites investigated, most yielded garnet intergrowths (n = 15) with major element geochemistry (i.e. Ca-Cr) classifying these samples as low-Ca websteritic or eclogitic. The garnet REE patterns fit an equilibrium model suggesting the diamond-forming fluid shares an affinity with high-density fluids (HDF) observed in fibrous diamonds, specifically on the join between the saline–carbonate end- members. The δ13C values for the diamonds range from -5.27 to -22.48 ‰ (V-PDB) with δ18O values for websteritic garnets ranging from +7.6 to +5.9 ‰ (V-SMOW). The combined C-O stable isotope data support a model for a hydrothermally altered and organic carbon-bearing subducted crustal source(s) for the diamond- and garnet-forming media. The nitrogen aggregation states of the diamonds require that diamondite-formation event(s) pre-dates fibrous diamond-formation and post-dates most of the gem monocrystalline diamond-formation events at Orapa. The modelled fluid compositions responsible for the precipitation of diamondites match the fluid-poor and fluid-rich (fibrous) monocrystalline diamonds, where all grow from HDFs within the saline-silicic-carbonatitic ternary system. However, while the nature of the parental fluid(s) share a common lithophile element geochemical affinity, the origin(s) of the saline, silicic, and/or carbonatitic components of these HDFs do not always share a common origin. Therefore, it is wholly conceivable that the diamondites are evidence of a distinct and temporally unconstrained tectono-thermal diamond-forming event beneath the Kaapvaal craton.

S Mikhail↗
NASA NTRS2019

HDF5 Roadmap 2019-2020

In this talk we will give an overview of the new features of the upcoming HDF5 release 1.12.0, and outline the HDF5 roadmap for the next year. We will demonstrate new open source file drivers to access HDF5 files via Amazon Simple Storage Service (Amazon S3) and on Hadoop Distributed File system (HDFS). We will use this presentation to get feedback on the HDF5 roadmap from the ESDIS users and application developers.

Cloud↗
NASA NTRS2000

Ultraviolet Galaxy Counts From STIS Observations of The Hubble Deep Fields

We present galaxy counts in the near and far ultraviolet (NUV and FUV) obtained from Space Telescope Imaging Spectrograph (STIS) observations of portions of the Hubble Deep Field North, (HDFN), the Hubble Deep Field South, (HDFS) and a parallel field near the HDFN. All three fields have deep (AB>29) optical imaging, and we determine magnitudes by taking the ultraviolet flux detected within the limiting optical isophote. An analysis of the UV-optical colors of detected objects, combined with a visual inspection of the UV images, indicates that there are no detectable objects in the UV images which are not also detected in the optical. We measure the detection area and completeness as a function of magnitude by taking the size-magnitude distribution of galaxies in the entire HDFN WFPC2 V+I image, applying the measured UV-optical colors from the detected galaxies, and determining the total area over which each galaxy would have been detected in the UV images. The average area for the simulated galaxies in each UV magnitude bin, (including galaxies which would not be detected at all), provides the effective area and completeness for the bin. We test this procedure with Monte Carlo simulations. The galaxy counts reach to AB=29 in both the NUV and FUV; 1 magnitude fainter than the HDF F30OW counts, and 7 magnitudes fainter than balloon-based counts. We compare our measured counts to various models.

Gardner, J. P.↗
NASA NTRS2013

MERRA/AS: The MERRA Analytic Services Project Interim Report

MERRA AS is a cyberinfrastructure resource that will combine iRODS-based Climate Data Server (CDS) capabilities with Coudera MapReduce to serve MERRA analytic products, store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions, manage storage-side driver, mapper, reducer code sets and realized objects for users, and provide a library of commonly used spatiotemporal operations that can be composed to enable higher-order analyses.

MapReduce↗
NASA NTRSDate not supplied

Simplifying Analysis of Hierarchical HDF5 and NetCDF4 Files with Xarray-Datatree

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. EOSDIS data are stored in formats that are well supported by the Earth Science community. These formats include the Hierarchical Data Format (HDF), with derivative flavors such as HDF-5 and the Network Common Data Format (NetCDF-4). The HDF specification allows for a directory-like hierarchy within a single file, known as "groups". Observational data and associated metadata within a single file can be distributed amongst multiple internal groups, which can also be nested to multiple levels. Working with datasets that have a group hierarchical structure can be difficult because of the nested structure of groups. Widely used packages, such as xarray, have data models that do not accommodate the hierarchical structure within HDF files, requiring users to traverse the file and open different HDF groups as separate, unrelated objects. Xarray-datatree is a Python package developed to solve the difficulty of traversing HDFs with a hierarchical group structure by creating a tree-like hierarchical data structure in xarray. The tree-like structure allows each group to be accessed once a DataTree object is instantiated. The migration of xarray-datatree into the xarray core library will reduce barriers to accessing Earth science data by eliminating the need to understand and traverse the specific hierarchy of a grouped HDF file.

Eni Awowale↗