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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Multi-frequency multi-polarization processing for spaceborne SAR

The Shuttle Imaging Radar-C (SIR-C) is the third in a series of space shuttle based synthetic aperture radars (SAR) sponsored by the National Aeronautics and Space Administration (NASA). The SIR-C ground data processing system is to process the playbacked SAR signal data into a variety of data products for distribution to the science community. An overview of the end-to-end ground data processing system with emphasis on the unique characteristics involved in the system design is presented. Included in the discussion are science requirements, radar system specifications, input data format specifications, system operations design, data products design, processing algorithm design, hardware architecture design, and software design.

Curlander, John C.↗
NASA NTRS1976

Data management in pattern recognition and image processing systems

Data management considerations are important to any system which handles large volumes of data or where the manipulation of data is technically sophisticated. A particular problem is the introduction of image-formatted files into the mainstream of data processing application. This report describes a comprehensive system for the manipulation of image, tabular, and graphical data sets which involve conversions between the various data types. A key characteristic is the use of image processing technology to accomplish data management tasks. Because of this, the term 'image-based information system' has been adopted.

Zobrist, A. L.↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Thematic Mapper Image Production in the Engineering Checkout Phase

Thematic Mapper data processing during LANDSAT 4's first year was performed on an engineering evaluation basis. Fully corrected products were created for some 282 scenes during this period using software and systems based upon the intended full production systems to become operational following the evaluation period. The engineering systems included substantial software and procedures for assessing spacecraft, instrument and ground processing algorithmic behavior. The data systems are described in terms of performance objectives, processing organization and data flow, quality assurance measurements and achievement of goals. The a priori implementation of TM radiometric and geometric corrections is described. Changes to processing suggested by or implemented on the basis of on-orbit data analysis are discussed. Spacecraft, instrument and algorithmic performance are evaluated.

Fischel, D.↗
DOE OSTI2022

16S and ITS Amplicon Sequencing Fastq files and metadata from PARCHED Panama Tropical Forest soils, 2019-2020,

Model projections predict tropical forests will experience longer periods of drought and more intense precipitation cycles under a changing climate. Such transitions have implications for structure-function relationships within microbial communities. We examine how chronic drying might reshape prokaryotic and fungal communities across four lowland forests in Panama with a wide variation in mean annual precipitation and soil fertility. Four sites were established across a 1000 mm span in mean annual precipitation (2335 to 3300 mm). We expected microbial communities at sites with lower MAP to be less sensitive to chronic drying than sites with higher MAP; while fungal communities to be more resistant to disturbance than prokaryotes. At each location, partial throughfall exclusion structures were established over 10 x 10 m plots to reduce direct precipitation input. Raw demultiplexed sequences (bacteria, archaea, fungal) from soil samples taken from PARCHED Panama Tropical Forest throughfall exclusion experiments. Files that contain 16S are sequences from prokaryotes, ITS indicates sequences from fungi. Compressed fastq files are contained in Field_PARCHED_ITS_fastq_2020.zip, Field_PARCHED_ITS_fastq_2019.zip, Field_PARCHED_16S_fastq_2020.zip, Field_PARCHED_16S_fastq_2019.zip.There are four sites across the isthmus of Panama, each with 4 control and 4 exclusion plots. Throughfall exclusion shelters were built to intercept 50% of throughfall precipitation that hits the soil. Samples were taken on May 2019 and Jan 2020 from 0-10 cm and 10-20 cm depth approximately 9 and 18 months after shelter installation. Metadata and sample IDs for fastq files for 2019 sampling are within Field_PARCHED_Metadata_2019_ITS.csv and Field_PARCHED_Metadata_2019_16S.csv. The metadata for both 16S and ITS fastq files for January 2020 is included in Field_PARCHED_Metadata_2020.csv. No data processing or QA/QC was done on the raw data. Data processing example provided in R notebook file.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1967

Advanced space information systems

Onboard computers for pre-transmission data processing, automation of data acquisition centers, and real time data relay to control centers for advanced space information systems

REAL TIME↗
NASA NTRS1975

AMPS data management concepts

Five typical AMPS experiments were formulated to allow simulation studies to verify data management concepts. Design studies were conducted to analyze these experiments in terms of the applicable procedures, data processing and displaying functions. Design concepts for AMPS data management system are presented which permit both automatic repetitive measurement sequences and experimenter-controlled step-by-step procedures. Extensive use is made of a cathode ray tube display, the experimenters' alphanumeric keyboard, and the computer. The types of computer software required by the system and the possible choices of control and display procedures available to the experimenter are described for several examples. An electromagnetic wave transmission experiment illustrates the methods used to analyze data processing requirements.

Metzelaar, P. N.↗
NASA NTRS1994

The 1994 research and technology report at the Goddard Space Flight Center

The breadth of subject material in this 1994 edition of the Research and Technology Report illustrates the broad scope of activities at the Goddard Space Flight Center. The numerous entries dealing with data processing and visualization show the strong emphasis on data and its interpretation. Reports are presented in the following sections: data processing and visualization; space sciences - high energy astronomy, solar system, and new techniques; earth system science - atmospheres, oceans and ice, solid earth, and soils and vegetation; networks, planning, and information systems - mission scheduling and operations, spacecraft operation and status, software engineering, and infrastructure support; engineering and materials - spacecraft subsystems, launch vehicles, thermal control, new mechanisms, and testing and evaluation; and flight projects.

Soffen, Gerald↗
NASA NTRS2010

A High-Throughput, Adaptive FFT Architecture for FPGA-Based Space-Borne Data Processors

Historically, computationally-intensive data processing for space-borne instruments has heavily relied on ground-based computing resources. But with recent advances in functional densities of Field-Programmable Gate-Arrays (FPGAs), there has been an increasing desire to shift more processing on-board; therefore relaxing the downlink data bandwidth requirements. Fast Fourier Transforms (FFTs) are commonly used building blocks for data processing applications, with a growing need to increase the FFT block size. Many existing FFT architectures have mainly emphasized on low power consumption or resource usage; but as the block size of the FFT grows, the throughput is often compromised first. In addition to power and resource constraints, space-borne digital systems are also limited to a small set of space-qualified memory elements, which typically lag behind the commercially available counterparts in capacity and bandwidth. The bandwidth limitation of the external memory creates a bottleneck for a large, high-throughput FFT design with large block size. In this paper, we present the Multi-Pass Wide Kernel FFT (MPWK-FFT) architecture for a moderately large block size (32K) with considerations to power consumption and resource usage, as well as throughput. We will also show that the architecture can be easily adapted for different FFT block sizes with different throughput and power requirements. The result is completely contained within an FPGA without relying on external memories. Implementation results are summarized.

Nguyen, Kayla↗
NASA NTRS2017

Kepler: A Search for Terrestrial Planets. K2 Handbook

The Kepler spacecraft was repurposed for the K2 mission a year after the failure of the second of Kepler's four reaction wheels in 2013 May. The purpose of this document, the K2 Handbook (K2H), is to describe features of K2 operations, performance, data analysis, and archive products which are common to most K2 campaigns, but different in degree or kind from the corresponding features of the Kepler mission.The K2 Handbook is meant to be read with the following companion documents, which are all publicly available:1. Kepler Instrument Handbook (KSCI-19033) provides information about the design, performance and operational constraints of the instrument and an overview of the types of pixel data that are available.2. Kepler Data Processing Handbook (KSCI-19081) describes how pixels downloaded from the spacecraft are converted by the Kepler Data Processing Pipeline into the data products available at the MAST archive3. Kepler Archive Manual (KDMC-100008) describes the format and content of the data products and how to search for them.4. Kepler Data Characteristics Handbook (KSCI-19040) describes recurring non-astrophysical features of the Kepler data due to instrument signatures, spacecraft events or solar activity and explains how these characteristics are handled by the Kepler pipeline.5. The Ecliptic Plane Input Catalog describes the provenance of the positions and Kepler magnitudes used for target management and aperature photometry.6. K2 Data Release Notes (DRN) are on-line documents available on the K2 science website which describe the data inventory, instrumental signatures and events peculiar to individual observing campaigns.

K2↗
NASA NTRS2019

Exploring Beyond Earth's Atmosphere with Human-Machine Teams

NASA's highly successful Kepler Mission has revolutionized our understanding of the Galaxy. We now know that planets, even Earth-size planets in the habitable zone, are common. With the end of the Kepler Mission we now look to the future with the Transiting Exoplanet Survey Satellite (TESS) which will discover thousands of exoplanets in orbit around the brightest stars in the sky. In a two-year survey, TESS will perform an all-sky search of more than 200,000 stars for temporary drops in brightness caused by planetary transits. With Kepler and TESS, humanity is finally at the verge of studying the masses, sizes, densities, orbits, and atmospheres of a large cohort of small planets, including a sample of rocky worlds in the habitable zones of their host stars which may prove to host life. The massive data sets generated by Kepler and TESS must be meticulously combed for the weakest planetary signals every month. While a daunting and error-prone task for humans, this is an exciting opportunity for the breakthroughs recently seen in machine learning. Specifically, traditional methods for identifying planet transits require extensive data processing pipelines followed by extensive human vetting. This manual process risks loss of information due to the data processing and to inconsistency and biases due to individual human vetters. The latest advancements in machine learning will allow an objective classifier to minimize the losses of information and greatly lessen the burden on the human vetters, in addition to providing assessment of quality and score to each planet candidate, freeing the humans to concentrate on border cases and other more interesting investigations.

Smith, Jeffrey C.↗
NASA NTRS2008

On Certain New Methodology for Reducing Sensor and Readout Electronics Circuitry Noise in Digital Domain

NASA Hubble Space Telescope (HST) and upcoming cosmology science missions carry instruments with multiple focal planes populated with many large sensor detector arrays. These sensors are passively cooled to low temperatures for low-level light (L3) and near-infrared (NIR) signal detection, and the sensor readout electronics circuitry must perform at extremely low noise levels to enable new required science measurements. Because we are at the technological edge of enhanced performance for sensors and readout electronics circuitry, as determined by thermal noise level at given temperature in analog domain, we must find new ways of further compensating for the noise in the signal digital domain. To facilitate this new approach, state-of-the-art sensors are augmented at their array hardware boundaries by non-illuminated reference pixels, which can be used to reduce noise attributed to sensors. There are a few proposed methodologies of processing in the digital domain the information carried by reference pixels, as employed by the Hubble Space Telescope and the James Webb Space Telescope Projects. These methods involve using spatial and temporal statistical parameters derived from boundary reference pixel information to enhance the active (non-reference) pixel signals. To make a step beyond this heritage methodology, we apply the NASA-developed technology known as the Hilbert- Huang Transform Data Processing System (HHT-DPS) for reference pixel information processing and its utilization in reconfigurable hardware on-board a spaceflight instrument or post-processing on the ground. The methodology examines signal processing for a 2-D domain, in which high-variance components of the thermal noise are carried by both active and reference pixels, similar to that in processing of low-voltage differential signals and subtraction of a single analog reference pixel from all active pixels on the sensor. Heritage methods using the aforementioned statistical parameters in the digital domain (such as statistical averaging of the reference pixels themselves) zeroes out the high-variance components, and the counterpart components in the active pixels remain uncorrected. This paper describes how the new methodology was demonstrated through analysis of fast-varying noise components using the Hilbert-Huang Transform Data Processing System tool (HHT-DPS) developed at NASA and the high-level programming language MATLAB (Trademark of MathWorks Inc.), as well as alternative methods for correcting for the high-variance noise component, using an HgCdTe sensor data. The NASA Hubble Space Telescope data post-processing, as well as future deep-space cosmology projects on-board instrument data processing from all the sensor channels, would benefit from this effort.

Kizhner, Semion↗
NASA NTRS1993

Subband/transform functions for image processing

Functions for image data processing written for use with the MATLAB(TM) software package are presented. These functions provide the capability to transform image data with block transformations (such as the Walsh Hadamard) and to produce spatial frequency subbands of the transformed data. Block transforms are equivalent to simple subband systems. The transform coefficients are reordered using a simple permutation to give subbands. The low frequency subband is a low resolution version of the original image, while the higher frequency subbands contain edge information. The transform functions can be cascaded to provide further decomposition into more subbands. If the cascade is applied to all four of the first stage subbands (in the case of a four band decomposition), then a uniform structure of sixteen bands is obtained. If the cascade is applied only to the low frequency subband, an octave structure of seven bands results. Functions for the inverse transforms are also given. These functions can be used for image data compression systems. The transforms do not in themselves produce data compression, but prepare the data for quantization and compression. Sample quantization functions for subbands are also given. A typical compression approach is to subband the image data, quantize it, then use statistical coding (e.g., run-length coding followed by Huffman coding) for compression. Contour plots of image data and subbanded data are shown.

Glover, Daniel↗
NASA NTRS2019

Kepler Data Validation II–Transit Model Fitting and Multiple-Planet Search

This paper discusses the transit model-fitting and multiple-planet search algorithms and performance of the Kepler Science Data Processing Pipeline, developed by the Kepler Science Operations Center (SOC). Threshold crossing events (TCEs), which are transit candidate events, are generated by the Transiting Planet Search (TPS) component of the pipeline and subsequently processed in the data validation (DV) component. The transit model is used in DV to fit TCEs to characterize planetary candidates and to derive parameters that are used in various diagnostic tests to classify them. After the signature associated with the TCE is removed from the light curve of the target star, the residual light curve goes through TPS again to search for additional TCEs. The iterative process of transit model fitting and multiple-planet search continues until no TCE is generated from the residual light curve or an upper limit is reached. The transit model-fitting and multiple-planet search performance of the final release (9.3, 2016January) of the pipeline is demonstrated with the results of the processing of four years (17 quarters) of flight data from the primary Kepler Mission. The transit model-fitting results are accessible from the NASA Exoplanet Archive. The final version of the SOC codebase is available through GitHub.

Threshold crossing events (TCEs↗
NASA NTRS2013

Mars Entry Atmospheric Data System Trajectory Reconstruction Algorithms and Flight Results

The Mars Entry Atmospheric Data System is a part of the Mars Science Laboratory, Entry, Descent, and Landing Instrumentation project. These sensors are a system of seven pressure transducers linked to ports on the entry vehicle forebody to record the pressure distribution during atmospheric entry. These measured surface pressures are used to generate estimates of atmospheric quantities based on modeled surface pressure distributions. Specifically, angle of attack, angle of sideslip, dynamic pressure, Mach number, and freestream atmospheric properties are reconstructed from the measured pressures. Such data allows for the aerodynamics to become decoupled from the assumed atmospheric properties, allowing for enhanced trajectory reconstruction and performance analysis as well as an aerodynamic reconstruction, which has not been possible in past Mars entry reconstructions. This paper provides details of the data processing algorithms that are utilized for this purpose. The data processing algorithms include two approaches that have commonly been utilized in past planetary entry trajectory reconstruction, and a new approach for this application that makes use of the pressure measurements. The paper describes assessments of data quality and preprocessing, and results of the flight data reduction from atmospheric entry, which occurred on August 5th, 2012.

Karlgaard, Christopher D.↗
DOE OSTI2024

Framework for custom event sample augmentations for ATLAS analysis data

For HEP event processing, data is typically stored in column-wise synchronized containers, such as most prominently ROOT’s TTree, which have been used for several decades to store by now over 1 exabyte. These containers can combine row-wise association capabilities needed by most HEP event processing frameworks (e.g. Athena for ATLAS) with column-wise storage, which typically results in better compression and more efficient support for many analysis use-cases. One disadvantage is that these containers, TTree in the HEP use-case, require to contain the same attributes for each entry/row (representing events), which can make extending the list of attributes very costly in storage, even if those are only required for a small subsample of events. Since the initial design, the ATLAS software framework features powerful navigational infrastructure to allow storing custom data extensions for subsamples of events in separate, but synchronized containers. This allows adding event augmentations to ATLAS standard data products (such as DAOD-PHYS or PHYSLITE) avoiding duplication of those core data products, while limiting their size increase. For this functionality, the framework does not rely on any associations made by the I/O technology (i.e. ROOT), however it supports TTree friends and builds the associated index to allow for analysis outside of the ATLAS framework. A prototype based on the Long-Lived Particle search is implemented and preliminary results with this prototype will be presented. At this point, augmented data are stored within the same file as the core data. Storing them in separate files will be investigated in future, as this could provide more flexibility, e.g. certain sites may only want a subset of several augmentations or augmentations can be archived to tape once their analysis is complete.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Plan Position Indicator Hydrometeor Field Statistics (PPIHYD) Evaluation Data Product Version 1.0

The PPIHYD evaluation data product provides distinct hydrometeor field statistics calculated from U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility scanning radar plan position indicator (PPI) scans. These statistics include the equivalent reflectivity factor and Doppler spectral width percentiles, min/max values, and first four moments (mean, standard deviation, skewness, and kurtosis) of distinct hydrometeor features (clustered hydrometeor fields). Statistics also include morphological properties, water content and precipitation rate parameterization-based estimates, and thermodynamic properties interpolated using the Interpolated Sonde value-added product (INTERPSONDE VAP). The data set is organized in tabular form and is accompanied by mask arrays with corresponding indices. This straightforward file structure simplifies scanning radar data processing and renders this data set useful for process understanding and model evaluation studies. This report describes the data set and its processing algorithm and provides some examples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.

54 ENVIRONMENTAL SCIENCES↗