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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Ocean Color Optical Property Data Derived from OCTS and POLDER: A Comparison Study

We describe our efforts in studying and comparing the ocean color data derived from the Japanese Ocean Color and Temperature Scanner (OCTS) and the French Polarization and Directionality of the Earth's Reflectances (POLDER). OCTS and POLDER were both on board Japan's Sun-synchronous Advanced Earth Observing Satellite (ADEOS-1) from August 1996 to June 1997, collecting about 10 months of global ocean color data. This provides a unique opportunity for developing methods and strategies for the merging of ocean color data from multiple ocean color sensors. In this paper, we describe our approach in developing consistent data processing algorithms for both OCTS and POLDER and using a common in situ data set to vicariously calibrate the two sensors. Therefore, the OCTS and POLDER-measured radiances are effectively bridged through common in situ measurements. With this approach in processing data from two different sensors, the only differences in the derived products from OCTS and POLDER are the differences inherited from the instrument characteristics. Results show that there are no obvious bias differences between the OCTS and POLDER-derived ocean color products, whereas the differences due to noise, which stem from variations in sensor characteristics, are difficult to correct. It is possible, however, to reduce noise differences with some data averaging schemes. The ocean color data from OCTS and POLDER can therefore be compared and merged in the sense that there is no significant bias between two.

Wang, Menghua↗
NASA NTRS2007

Robotic Vision-Based Localization in an Urban Environment

A system of electronic hardware and software, now undergoing development, automatically estimates the location of a robotic land vehicle in an urban environment using a somewhat imprecise map, which has been generated in advance from aerial imagery. This system does not utilize the Global Positioning System and does not include any odometry, inertial measurement units, or any other sensors except a stereoscopic pair of black-and-white digital video cameras mounted on the vehicle. Of course, the system also includes a computer running software that processes the video image data. The software consists mostly of three components corresponding to the three major image-data-processing functions: Visual Odometry This component automatically tracks point features in the imagery and computes the relative motion of the cameras between sequential image frames. This component incorporates a modified version of a visual-odometry algorithm originally published in 1989. The algorithm selects point features, performs multiresolution area-correlation computations to match the features in stereoscopic images, tracks the features through the sequence of images, and uses the tracking results to estimate the six-degree-of-freedom motion of the camera between consecutive stereoscopic pairs of images (see figure). Urban Feature Detection and Ranging Using the same data as those processed by the visual-odometry component, this component strives to determine the three-dimensional (3D) coordinates of vertical and horizontal lines that are likely to be parts of, or close to, the exterior surfaces of buildings. The basic sequence of processes performed by this component is the following: 1. An edge-detection algorithm is applied, yielding a set of linked lists of edge pixels, a horizontal-gradient image, and a vertical-gradient image. 2. Straight-line segments of edges are extracted from the linked lists generated in step 1. Any straight-line segments longer than an arbitrary threshold (e.g., 30 pixels) are assumed to belong to buildings or other artificial objects. 3. A gradient-filter algorithm is used to test straight-line segments longer than the threshold to determine whether they represent edges of natural or artificial objects. In somewhat oversimplified terms, the test is based on the assumption that the gradient of image intensity varies little along a segment that represents the edge of an artificial object.

Mchenry, Michael↗
NASA NTRS2009

Multi-Mission Automated Task Invocation Subsystem

Multi-Mission Automated Task Invocation Subsystem (MATIS) is software that establishes a distributed data-processing framework for automated generation of instrument data products from a spacecraft mission. Each mission may set up a set of MATIS servers for processing its data products. MATIS embodies lessons learned in experience with prior instrument- data-product-generation software. MATIS is an event-driven workflow manager that interprets project-specific, user-defined rules for managing processes. It executes programs in response to specific events under specific conditions according to the rules. Because requirements of different missions are too diverse to be satisfied by one program, MATIS accommodates plug-in programs. MATIS is flexible in that users can control such processing parameters as how many pipelines to run and on which computing machines to run them. MATIS has a fail-safe capability. At each step, MATIS captures and retains pertinent information needed to complete the step and start the next step. In the event of a restart, this information is retrieved so that processing can be resumed appropriately. At this writing, it is planned to develop a graphical user interface (GUI) for monitoring and controlling a product generation engine in MATIS. The GUI would enable users to schedule multiple processes and manage the data products produced in the processes. Although MATIS was initially designed for instrument data product generation,

Cheng, Cecilia S.↗
NASA NTRS1979

A rocket-borne pulse-height analyzer for energetic particle measurements

The pulse-height analyzer basically resembles a time-sharing multiplexing data-acquisition system which acquires analog data (from energetic particle spectrometers) and converts them into digital code. The PHA simultaneously acquires pulse-height information from the analog signals of the four input channels and sequentially multiplexes the digitized data to a microprocessor. The PHA together with the microprocessor form an on-board real-time data-manipulation system. The system processes data obtained during the rocket flight and reduces the amount of data to be sent back to the ground station. Consequently the data-reduction process for the rocket experiments is speeded up. By using a time-sharing technique, the throughput rate of the microprocessor is increased. Moreover, data from several particle spectrometers are manipulated to share one information channel; consequently, the TM capacity is increased.

Leung, W.↗
DOE OSTI2026

Author Correction: US oil and gas system emissions from nearly one million aerial site measurements

Correction to: Naturehttps://doi.org/10.1038/s41586-024-07117-5 Published online 13 March 2024 In the version of the article initially published, several errors were present and have been corrected in the HTML and PDF versions of the article and Supplementary Information. The main results, conclusions, and our interpretations of the data remain unchanged. See the new Supplementary Information Section S15 for a more detailed description of the errors corrected and the resulting effects on the analysis. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. The following practices may help researchers conducting similar analyses avoid making similar errors: 1, Clear, accessible documentation explaining the interpretation of all columns in data input tables and all internal variables within the model, 2, Simple cross-check calculations computed before and after unit conversions.

Sherwin, Evan D↗
DOE OSTI2025

Euclid I. Overview of the Euclid mission

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg 2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.

cosmology↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2025

ampworks: Battery analysis tools in Python [SWR-25-39]

Ampworks is a collection of tools designed to process experimental battery data with a focus on model-relevant analyses. It currently provides functions for incremental capacity analysis and GITT data processing, helping extract key properties for life and physics-based models (e.g., SPM and P2D). Some tools, like the incremental capacity analysis module, also include graphical user interfaces for ease of use. https://github.com/NREL/ampworks/ https://pypi.org/project/ampworks/

Randall, Corey [National Renewable Energy Laborato↗
DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2026

X-ray Absorption Spectroscopy and Neutron Scattering Data, Mineral Incubation Experiment, Walker Branch Watershed, TN (2020 - 2021)

Iron (Fe) (oxyhydr)oxides are well-recognized contributors to soil carbon (C) storage, but the effects of manganese (Mn) oxides on carbon storage and transformation are relatively unexplored. Here, the relative capacities of Fe and Mn oxides to bind and stabilize soil organic C were directly compared using an in-situ incubation experiment. Quartz sands coated with either poorly crystalline Mn(III/IV) oxides or Fe(III) oxides, or left uncoated, were buried in a temperate forest soil for up to one year. This data package contains processed data outputs of carbon near edge x-ray absorption fine structure spectroscopy (C NEXAFS), iron and manganese x-ray absorption near edge structure spectroscopy (XANES), and small angle neutron scattering (SANS) data collected for initial oxide-coated and uncoated quartz sands and for those buried in the temperate forest soil for 365 days.

Energy (eV)↗
DOE OSTI2026

Corrections to official ENDF/B Evaluation Releases for SCALE Nuclear Data

The official ENDF/B nuclear data evaluation releases in the past two decades have incrementally incorporated more detailed information, new nuclide evaluations, and very often have improved the accuracy of radiation transport codes when modeling shielding, fission reactors, criticality benchmarks, and fusion systems. However, like any large collaborative data compilation, these releases have all included small errors. This report documents the small corrections to ENDF/B releases ENDF/B-VII.1, ENDF/B-VIII.0, and ENDF/B-VIII.1 that have been applied during nuclear data processing to produce data libraries for the SCALE code system.

Brown, Jesse M. [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

SITCOMTN-164: AnaCal Shear Profile of Abell 360 in LSSTComCam Data Preview 1

This technote presents the measurement of the weak-lensing shear profile for the Abell 360 galaxy cluster using LSSTComCam data processed with the AnaCal pipeline. We detail the procedures involved in bright-star masking, source selection, and the estimation of tangential and cross shear around the cluster center. The resulting shear profiles provide key insights into the mass distribution of Abell 360 and demonstrate the capabilities of AnaCal in processing early LSST data, with the tangential shear profile detected at 5σ significance.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Modular space station facilities.

The modular space station will operate as a general purpose laboratory (GPL). In addition, the space station will be able to support many attached or free-flying research and application modules that would be dedicated to specific projects like astronomy or earth observations. The GPL primary functions have been organized into functional laboratories including an electrical/electronics laboratory, a mechanical sciences laboratory, an experiment and test isolation laboratory, a hard data process facility, a data evaluation facility, an optical sciences laboratory, a biomedical and biosciences laboratory, and an experiment/secondary command and control center.

Parker, P. J.↗
NASA NTRS1974

Satellite-tracking and earth-dynamics research programs

Observations and research progress of the Smithsonian Astrophysical Observatory are reported. Satellite tracking networks (ground stations) are discussed and equipment (Baker-Nunn cameras) used to observe the satellites is described. The improvement of the accuracy of a laser ranging system of the ground stations is discussed. Also, research efforts in satellite geodesy (tides, gravity anomalies, plate tectonics) is discussed. The use of data processing for geophysical data is examined, and a data base for the Earth and Ocean Physics Applications Program is proposed. Analytical models of the earth's motion (computerized simulation) are described and the computation (numerical integration and algorithms) of satellite orbits affected by the earth's albedo, using computer techniques, is also considered. Research efforts in the study of the atmosphere are examined (the effect of drag on satellite motion), and models of the atmosphere based on satellite data are described.

Source record↗
NASA NTRS1976

Timeline Analysis Program (TLA-1)

The Timeline Analysis Program (TLA-1) was described. This program is a crew workload analysis computer program that was developed and expanded from previous workload analysis programs, and is designed to be used on the NASA terminal controlled vehicle program. The following information is described: derivation of the input data, processing of the data, and form of the output data. Eight scenarios that were created, programmed, and analyzed as verification of this model were also described.

Miller, K. H.↗
NASA NTRS1976

NASA Global Atmospheric Sampling Program (GASP) data report for tape VL0003

Atmospheric ozone, and related flight and meteorological data for May 1975, obtained during 49 flights of a Pan American World Airways 747, are available as GASP tape VL0003 from the National Climatic Center, Asheville, North Carolina. In addition to the GASP data, tropopause pressure fields obtained from NMC archives for the dates of the GASP flights are included on the data tape. Flight routes and dates, instrumentation, data processing procedures, and data tape specifications are described.

Holdeman, J. D.↗
NASA NTRS1977

NASA Global Atmospheric Sampling Program (GASP) data report for tape VL0005

Atmospheric ozone, water vapor, and related flight and meteorological data were obtained during 214 flights of a United Airlines B-747 and two Pan American World Airways B-747's from March through June 1976. In addition, trichlorofluoromethane data obtained from laboratory analysis of two whole air samples collected in flight are reported. These data are available on GASP tape VL0005 from the National Climatic Center, Asheville, North Carolina. In addition to the GASP data, tropopause pressure fields obtained from NMC archives for the dates of the GASP flights are included on the data tape. Flight routes and dates, instrumentation, data processing procedures, and data tape specifications are described in this report. Selected analyses including ozone and sample bottle data are also presented.

Holdeman, J. D.↗