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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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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Probing Large-scale Coherence between Spitzer IR and Chandra X-Ray Source-subtracted Cosmic Backgrounds

We present new measurements of the large-scale clustering component of the cross-power spectra of the source-subtracted Spitzer -IRAC cosmic infrared background and Chandra -ACIS cosmic X-ray background surface brightness fluctuations Our investigation uses data from the Chandra Deep Field South, Hubble Deep Field North, Extended Groth Strip/AEGIS field, and UDS/SXDF surveys, comprising 1160 Spitzer hours and ∼12 Ms of Chandra data collected over a total area of 0.3 deg{sup 2}. We report the first (>5 σ ) detection of a cross-power signal on large angular scales >20″ between [0.5–2] keV and the 3.6 and 4.5 μ m bands, at ∼5 σ and 6.3 σ significance, respectively. The correlation with harder X-ray bands is marginally significant. Comparing the new observations with existing models for the contribution of the known unmasked source population at z < 7, we find an excess of about an order of magnitude at 5 σ confidence. We discuss possible interpretations for the origin of this excess in terms of the contribution from accreting early black holes (BHs), including both direct collapse BHs and primordial BHs, as well as from scattering in the interstellar medium and intra-halo light.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

A Multi-Center Space Data System Prototype Based on CCSDS Standards

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing Radio Frequency (RF) propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. However, the maturity level of this protocol stack is insufficient for mission inclusion at this time. This Space Data System prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of this protocol set. In order to reduce costs, future missions can take advantage of these standard protocols, which will result in increased interoperability between control centers. This prototype demonstrates these capabilities by implementing a realistic space data system in which telemetry is published to control center applications at the Jet Propulsion Lab (JPL), the Marshall Space Flight Center (MSFC), and the Johnson Space Center (JSC). Reverse publishing paths for commanding from each control center are also implemented. The target vehicle consists of realistic flight computer hardware running Core Flight Software (CFS) in the integrated Power, Avionics, and Power (iPAS) Pathfinder Lab at JSC. This prototype demonstrates a potential upgrade path for future Deep Space Network (DSN) modification, in which the automatic error recovery and communication gap compensation capabilities of DTN would be exploited. In addition, SM&C provides architectural flexibility by allowing new service providers and consumers to be added efficiently anywhere in the network using the common interface provided by SM&C's Message Abstraction Layer (MAL). In FY 2015, this space data system was enhanced by adding telerobotic operations capability provided by the Robot API Delegate (RAPID) family of protocols developed at NASA. RAPID is one of several candidates for consideration and inclusion in a new international standard being developed by the CCSDS Telerobotic Operations Working Group. Software gateways for the purpose of interfacing RAPID messages with the existing SM&C based infrastructure were developed. Telerobotic monitor, control, and bridge applications were written in the RAPID framework, which were then tailored to the NAO telerobotic test article hardware, a product of Aldebaran Robotics.

Rich, Thomas M.↗
DOE OSTI2018

Deep learning tackles cloud detection

To better understand Earth systems, atmospheric scientists use cloud data collected by a remote sensing tool called lidar. These data are an important part of global forecasting, but analyzing lidar images by hand is time-consuming. Researchers at Pacific Northwest National Laboratory teamed up to see if a deep learning model could process lidar data better than current automatic detection techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Hawthorne Nevada Deep Direct-Use Feasibility Study - Navy Geothermal Program Office General Data

The objective of this project is to use a multi-disciplinary, three-tiered approach to assess the geothermal resource and determine the feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the various town and county facilities in Hawthorne, Nevada. This assessment directly targets a geothermal resource recently characterized by the Navy Geothermal Program Office (GPO) as part of a focused exploration and development campaign. This data set contains: Excel file of the temperature at depth logs; Zip file containing shapefiles of gravity magnitude and base data for the Hawthorne Geothermal area; Word doc files containing HWAD geothermal report, figures and local stratigraphy; Zip folder containing PDF files of relevant HWAD document; Zip folder containing JPEGs of the John Oldow maps; Zip folder containing MXDs, xls, and dbf files of the Hawthorne Geothermal area and figures; Zip folder containing Hawthorne Geothermal data from Epsilon

15 GEOTHERMAL ENERGY↗
NASA NTRS1986

Total ionospheric electron content calibration using SERIES GPS satellite data

The current status of the Deep Space Network advanced systems research into ionospheric calibration techniques, based on Global Positioning System (GPS) data is described. A GPS-based calibration system is planned to replace the currently used Faraday rotation method by 1989. The SERIES receiver system determines the differential group delay of signals transmitted at two different carrier frequencies. This differential delay includes an ionospheric component and a GPS transmitter offset. The transmitter offsets are different for each GPS satellite. Tests were conducted to assess the effect of the offsets on the ionospheric calibration accuracy. From the obtained data, the total electron content and GPS transmitter offsets are calculated by a least squares estimation method employing a local model of total ionospheric electron content. The end product is an estimation of the total ionospheric content for an arbitrary line-of-sight direction. For the presented polynomial fitting technique, the systematic error due to mismodeling is estimated to be approximately 6 x 10 to the 16th power el/sq m, while the formal error is approximately 2 x 10 to the 16th power el/sq m. The final goal is an error of 3 x 10 to the 16th power el/sq m (approximately 0.7 ns at 2.3 GHz).

Lanyi, G.↗
NASA NTRS1997

Joint US-Japan Observations with the Infrared Space Observatory (ISO): Deep Surveys and Observations of High-Z Objects

Several important milestones were passed during the past year of our ISO observing program: (1) Our first ISO data were successfully obtained. ISOCAM data were taken for our primary deep field target in the 'Lockman Hole'. Thirteen hours of integration (taken over 4 contiguous orbits) were obtained in the LW2 filter of a 3 ft x 3 ft region centered on the position of minimum HI column density in the Lockman Hole. The data were obtained in microscanning mode. This is the deepest integration attempted to date (by almost a factor of 4 in time) with ISOCAM. (2) The deep survey data obtained for the Lockman Hole were received by the Japanese P.I. (Yoshi Taniguchi) in early December, 1996 (following release of the improved pipeline formatted data from Vilspa), and a copy was forwarded to Hawaii shortly thereafter. These data were processed independently by the Japan and Hawaii groups during the latter part of December 1996, and early January, 1997. The Hawaii group made use of the U.S. ISO data center at IPAC/Caltech in Pasadena to carry out their data reduction, while the Japanese group used a copy of the ISOCAM data analysis package made available to them through an agreement with the head of the ISOCAM team, Catherine Cesarsky. (3) Results of our LW2 Deep Survey in the Lockman Hole were first reported at the ISO Workshop "Taking ISO to the Limits: Exploring the Faintest Sources in the Infrared" held at the ISO Science Operations Center in Villafranca, Spain (VILSPA) on 3-4 February, 1997. Yoshi Taniguchi gave an invited presentation summarizing the results of the U.S.-Japan team, and Dave Sanders gave an invited talk summarizing the results of the Workshop at the conclusion of the two day meeting. The text of the talks by Taniguchi and Sanders are included in the printed Workshop Proceedings, and are published in full on the Web. By several independent accounts, the U.S.-Japan Deep Survey results were one of the highlights of the Workshop; these data showed conclusively that the ISOCAM S/N continues to decrease as the square root of time for periods as long as 13 hours.

Sanders, David B.↗
DOE OSTI2022

Super-Resolution Approaches in Three-Dimensions for Classification and Screening of Commercial-Off-The-Shelf Components

X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

47 OTHER INSTRUMENTATION↗
NASA NTRS1987

Radio science with Voyager 2 at Uranus - Results on masses and densities of the planet and five principal satellites

Phase-coherent Doppler data generated by the Deep Space Network with the radio communication system during the Voyager 2 encounter with Uranus in January 1986, optical navigation data generated by the Voyager Navigation Team with the Voyager 2 imaging system, and ground-based astrometric data obtained over an 8-yr period are compiled and analyzed to determine the masses and densities of Uranus and its principal satellites. The data-analysis procedures are explained in detail, and the results are presented in tables and graphs. The mean density of Uranus is found to be 1.285 + or - 0.001 g/cu cm, whereas the mean uncompressed mass of all five satellites is 1.48 + or - 0.06 g/cu cm, or 0.10 g/cu cm above the density expected for a homogeneous solar mix of rock, H2O and NH3 ice, and CH4 as clathrate hydrate. This difference is tentatively attributed to the presence of 15 mass percent of pure graphite, which would provide the thermal conductivity required to keep the satellites cold and undifferentiated.

Anderson, J. D.↗
NASA NTRS1988

Is there a twenty year wave in the diurnal anisotropy of cosmic rays?

Data obtained with the Deep River neutron monitor and the underground vertical muon telescope at Embudo for the period 1965-1979 are analyzed. The data show that for 1957-1970 the diurnal anisotropy is unidirectional. During 1971-1979, the diurnal anisotropy consists of two components. One is in the east-west direction and the other is the radial component, with direction along 12:00 LT. The latter attains a maximum amplitude in 1976. No evidence is found for the existence of the twenty year wave in the diurnal anisotropy of cosmic rays.

Ahluwalia, H. S.↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
DOE OSTI2021

Reweighting the Sivers function with jet data from STAR

The reweighting procedure that using Bayesian statistics incorporates the information contained in a new data set, without the need of re-fitting, is applied to the quark Sivers function extracted from Semi-Inclusive Deep Inelastic Scattering (SIDIS) data. We exploit the recently published single spin asymmetry data for the inclusive jet production in polarized pp collisions from the STAR Collaboration at RHIC, which cover a much wider x region compared to SIDIS measurements. The reweighting method is extended to the case of asymmetric errors and the results show a remarkable improvement of the knowledge of the quark Sivers function.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Spin Orbit Correlations and the Structure of the Nucleon

Extensive experimental measurements of spin and azimuthal asymmetries in various processes have stimulated theoretical interest and progress in the studies of the nucleon structure. The interpretation of experimental data in terms of parton distribution functions, generalized to describe transverse momentum and spatial parton distributions, is one of the main remaining challenges of modern nuclear physics. These new parton distribution and fragmentation functions encode the motion and the position of partons and are often referred to as three-dimensional distributions describing the three-dimensional (3D) structure of the nucleon. Understanding of the production mechanism and performing phenomenological studies compatible with factorization theorems using minimal model assumptions are goals of the analysis of the experimental data. HERMES and COMPASS Collaborations and experiments at Jefferson Lab have collected a wealth of polarized and unpolarized Semi-Inclusive Deep Inelastic Scattering (SIDIS) data. These data play a crucial role in current understanding of nucleon spin phenomena as they cover a broad kinematical range. The Jefferson Lab 12 GeV upgrade data on polarized and unpolarized SIDIS will have a remarkably higher precision at large parton fractional momentum x compared to the existing data. We argue that both experimental and phenomenological communities will benefit from the development of a comprehensive extraction framework that will facilitate the extraction of the 3D nucleon structure, help understand various assumptions in extraction and data analysis, help to insure the model independence of the experimental data and validate the extracted functions. In this paper we present the latest developments in the field of the spin asymmetries with emphasis on observables beyond the leading twist in SIDIS, indispensable for studies of the complex 3D nucleon structure, and discuss different components involved in precision extraction of the 3D partonic distribution and fragmentation functions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

An Overview of the Usefulness of Machine Learning Techniques on Network Packet Data

Understanding the health and behavior of a computer network allows for better network efficiency and security. We present an overview of various machine learning techniques for classifying network packet data via packet metadata. While some classical machine learning approaches achieve reasonable results, the most accurate classification can be achieved with deep learning. On the four data sets studied herein, a basic deep learning model achieved at or near 100\% classification accuracy. We also propose a method for determining variable importance as a means for potential transfer learning applications to classifying yet unseen network packet data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Stripped Elliptical Galaxies as Probes of ICM Physics. III. Deep Chandra Observations of NGC 4552: Measuring the Viscosity of the Intracluster Medium

We present results from a deep (200 ks) Chandra observation of the early-type galaxy NGC 4552 (M89), which is falling into the Virgo cluster. Previous shallower X-ray observations of this galaxy showed a remnant gas core, a tail to the South of the galaxy, and twin “horns” attached to the northern edge of the gas core. In our deeper data, we detect a diffuse, low surface brightness extension to the previously known tail, and measure the temperature structure within the tail. We combine the deep Chandra data with archival XMM-Newton observations to put a strong upper limit on the diffuse emission of the tail out to a large distance (10× the radius of the remnant core) from the galaxy center. In our two previous papers, we presented the results of hydrodynamical simulations of ram pressure stripping specifically for M89 falling into the Virgo cluster and investigated the effect of intracluster medium (ICM) viscosity. In this paper, we compare our deep data with our specifically tailored simulations and conclude that the observed morphology of the stripped tail in NGC 4552 is most similar to the inviscid models. We conclude that, to the extent the transport processes can be simply modeled as a hydrodynamic viscosity, the ICM viscosity is negligible. More generally, any micro-scale description of the transport processes in the high- β plasma of the cluster ICM must be consistent with the efficient mixing observed in the stripped tail on macroscopic scales.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1976

A history of the deep space network

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

Corliss, W. R.↗