Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data enhancement”

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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Empowering Critical Infrastructure Communication with Secure 5G Private Networks

With the transformational New Radio- Unlicensed (NR-U), 5G network can be operated with unlicensed and shared spectrum. Private 5G networks without any licensed bands, which are both highly expensive and usually available to only large commercial wireless providers, can now be used for a whole range of new applications including smart factories, warehouses, connected cars and drones. 5G’s support of a) massive machine type communication (mMTC) for a large number of connected devices with b) ultra-reliable low latency communication (URLLC) capability when needed, and c) up to 20 times higher data rate with enhanced mobile broadband (eMBB) than previously available, enables new and powerful capabilities in a wireless network. While these capabilities are transformational, necessary security and reliability requirements have to be satisfied when used in critical infrastructure such as factories, power plants, water systems, ports, and other industrial facilities. 5G standards have introduced significant security improvement over 4G/LTE as well as mitigations for new attack surfaces created by changes in the 5G network. This talk will discuss these security improvements and whether they meet the security properties required for mission critical communication over wireless.

5G↗
DOE OSTI2021

Towards high data-rate diffusive molecular communications: A review on performance enhancement strategies

Diffusive molecular communications (DiMC) have recently gained attention as a candidate for nano- to micro- and macro-scale communications due to its simplicity and energy efficiency. As signal propagation is solely enabled by Brownian motion mechanics, DiMC faces severe inter-symbol interference (ISI), which limits reliable and high data-rate communications. Herein, recent literature on DiMC performance enhancement strategies is surveyed; key research directions are identified. Signaling design and associated design constraints are presented. Here, studies on fundamental information theoretic limits of DiMC channel are reviewed. Classical and novel transceiver designs are discussed with an emphasis on methods for ISI mitigation and performance-complexity tradeoffs. Key parameter estimation strategies such as synchronization and channel estimation are considered in conjunction with asynchronous and timing error robust receiver methods. Finally, source and channel coding in the context of DiMC is presented.

42 ENGINEERING↗
NASA NTRS2000

Subsetting and Formatting Landsat-7 LOR ETM+ and Data Products

The Landsat-7 Processing System (LPS) processes Landsat-7 Enhanced Thematic Mapper (ETM+) instrument data into large, contiguous segments called "subintervals" and stores them in Level OR (LOR) data files. The LPS processed subinterval products must be subsetted and reformatted before the Level I processing systems can ingest them. The initial full subintervals produced by the LPS are stored mainly in HDF Earth Observing System (HDF-EOS) format which is an extension to the Hierarchical Data Format (HDF). The final LOR products are stored in native HDF format. Primarily the EOS Core System (ECS) and alternately the DAAC Emergency System (DES) subset the subinterval data for the operational Landsat-7 data processing systems. The HDF and HDF-EOS application programming interfaces (APIs) can be used for extensive data subsetting and data reorganization. A stand-alone subsetter tool has been developed which is based on some of the DES code. This tool makes use of the HDF and HDFEOS APIs to perform Landsat-7 LOR product subsetting and demonstrates how HDF and HDFEOS can be used for creating various configurations of full LOR products. How these APIs can be used to efficiently subset, format, and organize Landsat-7 LOR data as demonstrated by the subsetter tool and the DES is discussed.

Reid, Michael R.↗
NASA NTRS1983

The effects of solar incidence angle over digital processing of LANDSAT data

A technique to extract the topography modulation component from digital data is described. The enhancement process is based on the fact that the pixel contains two types of information: (1) reflectance variation due to the target; (2) reflectance variation due to the topography. In order to enhance the signal variation due to topography, the technique recommends the extraction from original LANDSAT data of the component resulting from target reflectance. Considering that the role of topographic modulation over the pixel information will vary with solar incidence angle, the results of this technique of digital processing will differ from one season to another, mainly in highly dissected topography. In this context, the effects of solar incidence angle over the topographic modulation technique were evaluated. Two sets of MSS/LANDSAT data, with solar elevation angles varying from 22 to 41 deg were selected to implement the digital processing at the Image-100 System. A secondary watershed (Rio Bocaina) draining into Rio Paraiba do Sul (Sao Paulo State) was selected as a test site. The results showed that the technique used was more appropriate to MSS data acquired under higher Sun elevation angles. Topographic modulation components applied to low Sun elevation angles lessens rather than enhances topography.

Parada, N. D. J.↗
DOE OSTI2020

Enhanced Hanford Low-Activity Waste Glass Property Data Development (Phase 3)

This work was performed for the U.S. Department of Energy (DOE) Office of River Protection (ORP) to provide expert evaluation and experimental work in support of the River Protection Project vitrification technology development1. The long-term objective of this work is to expand the property-composition database for Hanford site low-activity waste (LAW) glasses and property-composition models to cover the balance of the mission for the Hanford Waste Treatment and Immobilization Plant (WTP). When this effort is complete, enhanced LAW glass property-composition models will be developed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1994

Gravity model improvement using the DORIS tracking system on the SPOT 2 satellite

A high-precision radiometric satellite tracking system, Doppler Orbitography and Radio-positioning Integrated by Satellite system (DORIS), has recently been developed by the French space agency, Centre National d'Etudes Spatiales (CNES). DORIS was designed to provide tracking support for missions such as the joint United States/French TOPEX/Poseidon. As part of the flight testing process, a DORIS package was flown on the French SPOT 2 satellite. A substantial quantity of geodetic quality tracking data was obtained on SPOT 2 from an extensive international DORIS tracking network. These data were analyzed to assess their accuracy and to evaluate the gravitational modeling enhancements provided by these data in combination with the Goddard Earth Model-T3 (GEM-T3) gravitational model. These observations have noise levels of 0.4 to 0.5 mm/s, with few residual systematic effects. Although the SPOT 2 satellite experiences high atmospheric drag forces, the precision and global coverage of the DORIS tracking data have enabled more extensive orbit parameterization to mitigate these effects. As a result, the SPOT 2 orbital errors have been reduced to an estimated radial accuracy in the 10-20 cm RMS range. The addition of these data, which encompass many regions heretofore lacking in precision satellite tracking, has significantly improved GEM-T3 and allowed greatly improved orbit accuracies for Sun-synchronous satellites like SPOT 2 (such as ERS 1 and EOS). Comparison of the ensuing gravity model with other contemporary fields (GRIM-4C2, TEG2B, and OSU91A) provides a means to assess the current state of knowledge of the Earth's gravity field. Thus, the DORIS experiment on SPOT 2 has provided a strong basis for evaluating this new orbit tracking technology and has demonstrated the important contribution of the DORIS network to the success of the TOPEX/Poseidon mission.

Nerem, R. S.↗
NASA NTRS2011

Bit of History and Some Lessons Learned in Using NASA Remote Sensing Data in Public Health Applications

The NASA Applied Sciences Program's public health initiative began in 2004 to illustratethe potential benefits for using remote sensing in public health applications. Objectives/Purpose: The CDC initiated a st udy with NASA through the National Center for Environmental Health (NCEH) to establish a pilot effort to use remote sensing data as part of its Environmental Public Health Tracking Network (EPHTN). As a consequence, the NCEH and NASA developed a project called HELIX-Atlanta (Health and Environment Linkage for Information Exchange) to demonstrate a process for developing a local environmental public health tracking and surveillance network that integrates non-infectious health and environment systems for the Atlanta metropolitan area. Methods: As an ongo ing, systematic integration, analysis and interpretation of data, an EPHTN focuses on: 1 -- environmental hazards; 2 -- human exposure to environmental hazards; and 3 -- health effects potentially related to exposure to environmental hazards. To satisfy the definition of a surveillance system the data must be disseminated to plan, implement, and evaluate environmental public health action. Results: A close working r elationship developed with NCEH where information was exchanged to assist in the development of an EPHTN that incorporated NASA remote sensing data into a surveillance network for disseminating public health tracking information to users. This project?s success provided NASA with the opportunity to work with other public health entities such as the University of Mississippi Medical Center, the University of New Mexico and the University of Arizona. Conclusions: HELIX-Atlanta became a functioning part of the national EPHTN for tracking environmental hazards and exposure, particularly as related to air quality over Atlanta. Learning Objectives: 1 -- remote sensing data can be integral to an EPHTN; 2 -- public tracking objectives can be enhanced through remote sensing data; 3 -- NASA's involvement in public health applications can have wider benefits in the future.

Quattrochi, Dale A.↗
NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗
NASA NTRS2022

Research Campaign: Advanced Manufacturing in Space: Properties, Structure and Simulations to Pioneer New Applications

This topic underpins synthesis of, and in many cases manufacturing of, value-added materials via liquid phase processing. Materials and processes range from cast metals and optical glasses through exploration-relevant technologies such as additive manufacturing, casting and in-situ resource utilization to support human space exploration. A recurring theme is the need for accurate thermophysical property and atomic structure data for relevant materials and the exploitation of reduced convection/sedimentation in microgravity to enhance the quality of data and to develop new and transformative technologies for materials processing.

Michael SanSoucie↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗
NASA NTRS2020

Assimilating GCOM-W1 AMSR2 and TRMM TMI Radiance Data in GEOS Analysis and Reanalysis

The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) observed the Earth in lower latitudes between 1997 - 2015. Its conical-scan radiometer has nine channels and measured microwave radiances between 10 and 89 GHz. These data provide information on atmospheric temperature, humidity, clouds, precipitation, as well as sea surface temperature. Radiance data from other microwave radiometers such as Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) onboard various Defense Meteorological Satellite Program (DMSP) satellites are assimilated in clear-sky conditions in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. The GMAO's Hybrid 4D-EnVar-based Atmospheric Data Assimilation System (ADAS) is enhanced with an all-sky microwave radiance data assimilation capability in the real-time GEOS-Forward Processing (FP) system. Currently, the FP system assimilates Global Precipitation Measurement (GPM) microwave imager (GMI) radiance data utilizing this all-sky capability, and is being extended to use more all-sky data from other microwave radiometers. In this presentation, we will focus on impacts of all-sky TMI radiance data on GEOS analyses of atmospheric moisture, precipitation and other fields, and discuss their applications for future GEOS reanalyses.

Jin, Jianjun↗
NASA NTRS1985

Enhanced rock discrimination using Landsat-5 Thematic Mapper (TM) data

The application of TM data to rock discrimination is discussed. Sixteen specific terrains derived from geologic maps are examined on TM images of the Arabian shield obtained on Apr. 14, 1984; visual enhancement procedures are applied to the images. The rock types observed in the test site are described; the major sedimentary formations in the test area are laterite and sandstone. The data reveal that the layered rocks in the outcrop consist of a variety of metamorphosed volcanics, metamorphosed sediments, and amphibolite, and the intrusive complex is composed of several classes of mafic and acidic rocks.

Blodget, H. W.↗
DOE OSTI2020

Solving differential equations using deep neural networks

Recent work on solving partial differential equations (PDEs) with deep neural networks (DNNs) is presented. The paper reviews and extends some of these methods while carefully analyzing a fundamental feature in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to the compressible Euler equations is discussed and analyzed. This analysis includes a comparison of a DNN-based approach with conventional finite element and finite volume methods, and demonstrates that the DNN is competitive in terms of degrees of freedom required for a given accuracy. Further, the DNN-based approach is extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is a priori insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms that are then inferred via supervised training with synthetic experimental data. The resulting DNN framework for PDEs enables straightforward system prototyping and natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of an entire parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗