Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “information quality”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Data Fusion With Uncertainty Quantification for Sub-City-Scale Air Quality Assessment and Forecasting

Many information sources can support air quality assessment and forecasting, including atmospheric chemistry model outputs, satellite retrievals of column chemical and aerosol constituents, and surface-based air quality monitoring data from both regulatory and low-cost instruments. Systematic integration of these data sources provides a major opportunity to improve understanding and management of air quality, but also presents technical barriers, especially in resource- and data-constrained settings in the Global South. This presentation describes a data fusion system, currently under development using the Google Earth Engine platform, which aims to integrate the information sources listed above to support comprehensive sub-city-scale assessment and management of air quality. Furthermore, the data fusion framework includes provisions for the quantification of uncertainties in the resulting fused estimates based on the variability of and among the input data sources. These capabilities will allow air quality managers to better understand their local air quality situation, including relative confidence in the fused estimates for different constituents, locations, and times, leading to better informed air quality management decisions. This presentation covers the underlying methodology of the data fusion and uncertainty quantification approaches, provides an update on the status of its implementation, and presents early qualitative and quantitative results.

Carl Malings↗
NASA NTRS1987

Remote Sensing Information Sciences Research Group: Santa Barbara Information Sciences Research Group, year 4

Information Sciences Research Group (ISRG) research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. Particular focus in on the needs of the remote sensing research and application science community which will be served by the Earth Observing System (EOS) and Space Station, including associated polar and co-orbiting platforms. The areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence and both natural and cultural vegetation analysis and modeling research will be expanded.

Estes, John E.↗
NASA NTRSDate not supplied

Satellite-assisted Evaluation of Irrigation Application Efficiency for Lettuce in the Salinas Valley

The Salinas Valley region of California’s Central Coast is a majorU.S. vegetable producer. Lettuce is a leading commodity, grown on about 100,000 acres with annual value near $1.4B (2022 Monterey County Ag Report). Vegetables crops in the area aretypically well watered and fertilized in pursuit of commercial yield and quality standards. Information on irrigation application efficiency, meanwhile, can enhance waterresource sustainability and mitigate impacts of nitrate infiltrationon groundwater quality. The Crop Consumptive Use Fraction (CCUF), previously recommended by the California Department of Water Resources, is a metric that expresses evapotranspiration (ET) of applied water relative to total applied water. More recently under Assembly Bill 1668 addressing water conservation and drought planning, the metric can be used to help quantify application efficiency to support preparation of agricultural water management plans in the state. This study will report CCUF evaluations from several commercial lettuce plantings in the Salinas Valley during the 2022 dry season (late spring-early fall). The evaluations were derived from satellite-based observations of cropET from the OpenETcloud application, and applied water monitored by in-line flowmetersand supplemented by grower reports of irrigation system runtime. The study will also compare observed (satellite-based) ET with modeled ET from the CropManage irrigationand nutrient management decision-support system operated by Cooperative Extension. Additional study in cool-season vegetables is ongoing in 2023.

Satellite-assisted↗
NASA NTRS1988

Information processing deficits in psychiatric populations: Implications for normal workload assessment

In one study, schizophrenics, bipolar manics, and mentally normal individuals were administered a digit recall task. The total performance of schizophrenics looked much like that of a normal processor under a higher load level. The manics' performance was intermediate. Primary performance was particularly poor among the mentally ill subjects. In a second study, three groups in the same populations as in the first study were asked to shadow and recall verbatim eight descriptive text passages. Distraction effects were found for schizophrenics only in the areas of percentage of words correctly shadowed and recall variables; the two areas were not correlated, however. It appears that, for schizophrenics, distraction disrupts the ability to effectively shadow information to a greater extent than it disrupts the ability to encode information for recall. The two studies imply that capacity-carrying abnormalities that affect the quantity but not the quality of information processing can be useful in pointing to information processing of normal humans under high load conditions.

Harvey, Philip D.↗
NASA NTRS2010

Advanced Capabilities for Wind Tunnel Testing in the 21st Century

Wind tunnel testing methods and test technologies for the 21st century using advanced capabilities are presented. These capabilities are necessary to capture more accurate and high quality test results by eliminating the uncertainties in testing and to facilitate verification of computational tools for design. This paper discusses near term developments underway in ground testing capabilities, which will enhance the quality of information of both the test article and airstream flow details. Also discussed is a selection of new capability investments that have been made to accommodate such developments. Examples include advanced experimental methods for measuring the test gas itself; using efficient experiment methodologies, including quality assurance strategies within the test; and increasing test result information density by using extensive optical visualization together with computed flow field results. These points could be made for both major investments in existing tunnel capabilities or for entirely new capabilities.

Kegelman, Jerome T.↗
NASA NTRSDate not supplied

Hemispheric Airborne Measurements of Air Quality (HAMAQ)

Under NASA’s Earth Venture Suborbital program, Hemispheric Airborne Measurements of Air Quality (HAMAQ) will conduct a series of campaigns in 2028 under the Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite instrument. HAMAQ plans include two deployments, including the Mexico City megalopolis and another North American site yet to be selected. The effort will include two aircraft, NASA’s B777 for in situ sampling and G-III for remote sensing. These aircraft will be used to complete a system of integrated observations, combining satellite observations, ground-based monitoring and research observations with air quality modeling. HAMAQ field intensives will serve multiple objectives to include: improving the use of satellite observations in concert with traditional ground monitoring to inform air quality; assessing emissions to better understand their timing and source apportionment; advancing the development of satellite proxies for air quality; and assessing the factors controlling local air quality in each location sampled. Given the long lead time for this campaign, this poster welcomes discussion from the community on strategies and candidate sites for the second deployment. Given the broad applicability of the HAMAQ science objectives and observing strategy, possible partnerships to extend the pursuit of the larger vision of HAMAQ to also sample in Asia and Europe are of interest.

James H Crawford↗
DOE OSTI2024

The Early Data Release of the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
NASA NTRS1998

Asynchronous Transfer Mode Quality-of-Service Testing

In support of satellite-ATM interoperability, researchers at the NASA Lewis Research Center performed asynchronous transfer mode (ATM) quality-of-service experiments using MPEG-2 (ATM application layer 5, AAL5) over ATM over an emulated satellite link. The purpose of these experiments was to determine the free-space link quality necessary to use the ATM protocol to transmit high-quality multimedia information. The experimental results have been submitted to various International Telecommunications Union (ITU) study groups in order to improve and modify current standards and recommendations for the telecommunications industry. Quality-of-service parameters for Class I, stringent class requirements for ITU-T I.356 are currently being debated. The experimental results presented will help to establish these quality-of-service thresholds. This material will also be useful in the development of the ITU-R WP-4B's Draft Preliminary New Recommendation on the Transmission of Asynchronous Transfer Mode Traffic via Satellite (Rec. S.atm).

Ivancic, William D.↗
NASA NTRSDate not supplied

Development of TEMPO Products and Tools to Support Air Quality Management Decisions

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. Our project at the NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on the air quality management community. The initial focus of this project is evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. Our SPoRT viewer has been updated to include visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air quality management stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data in air quality management applications. The major outcome of this project is to support the inclusion of TEMPO data in exceptional event demonstrations by active engagement with stakeholders and ultimately enable more informed air quality management decisions in the future. This talk will provide an update on our project activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

air quality↗
NASA NTRS1972

The ATS F&G systems reliability program.

Assurance of reliability, quality, and proper testing requires a large coordinating effort and a means for connecting the various areas involved. All parts used on the spacecraft are required to meet strict specifications and consequently must be approved by the systems reliability manager. The parts program has access to a computer data bank into which all information concerning nonstandard parts approval requests has been stored. Through the data bank, the system collects and distributes timely information concerning quality and reliability to all departments that may be concerned.

Doyle, H.↗
NASA NTRS2022

Satellites Quantify the Spatial Extent of Cyanobacterial Blooms Across the United States at Multiple Scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Harmful Algal Blooms↗
NASA NTRS2022

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snowfree imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Harmful algal blooms↗
NASA NTRS2007

An IEEE 1451.1 Architecture for ISHM Applications

The IEEE 1451.1 Standard for a Smart Transducer Interface defines a common network information model for connecting and managing smart elements in control and data acquisition networks using network-capable application processors (NCAPs). The Standard is a network-neutral design model that is easily ported across operating systems and physical networks for implementing complex acquisition and control applications by simply plugging in the appropriate network level drivers. To simplify configuration and tracking of transducer and actuator details, the family of 1451 standards defines a Transducer Electronic Data Sheet (TEDS) that is associated with each physical element. The TEDS contains all of the pertinent information about the physical operations of a transducer (such as operating regions, calibration tables, and manufacturer information), which the NCAP uses to configure the system to support a specific transducer. The Integrated Systems Health Management (ISHM) group at NASA's John C. Stennis Space Center (SSC) has been developing an ISHM architecture that utilizes IEEE 1451.1 as the primary configuration and data acquisition mechanism for managing and collecting information from a network of distributed intelligent sensing elements. This work has involved collaboration with other NASA centers, universities and aerospace industries to develop IEEE 1451.1 compliant sensors and interfaces tailored to support health assessment of complex systems. This paper and presentation describe the development and implementation of an interface for the configuration, management and communication of data, information and knowledge generated by a distributed system of IEEE 1451.1 intelligent elements monitoring a rocket engine test system. In this context, an intelligent element is defined as one incorporating support for the IEEE 1451.x standards and additional ISHM functions. Our implementation supports real-time collection of both measurement data (raw ADC counts and converted engineering units) and health statistics produced by each intelligent element. The handling of configuration, calibration and health information is automated by using the TEDS in combination with other electronic data sheets extensions to convey health parameters. By integrating the IEEE 1451.1 Standard for a Smart Transducer Interface with ISHM technologies, each element within a complex system becomes a highly flexible computation engine capable of self-validation and performing other measures of the quality of information it is producing.

Morris, Jon A.↗
DOE OSTI2020

System-Level Optimization to Improve Biofuel Potential via Genetic Engineering and Hydrothermal Liquefaction

The economic success of biofuels and bioproducts depends on system-level optimization including biomass production and conversion. Hydrothermal liquefaction (HTL) can convert wet biomass such as microalgae into a biofuel intermediate (BFI) under elevated temperatures and pressure. An understanding of the impacts of biomass composition on BFI yield and quality can inform genetic engineering strategies in the improvement of biochemical composition for biofuel production. In this work, wild type cyanobacterium Synechocystis sp. PCC 6803 biomass was doped with various common cellular storage compounds in lab-scale HTL experiments. Doping with glycogen or polyhydroxybutyrate (PHB) significantly reduced BFI yields, while doping with triglycerides (TAG) or medium chain-length polyhydroxyalkanoate (mcl-PHA) increased BFI yield and quality. In light of these observations, a genetically engineered Synechocystis strain deficient in glycogen biosynthesis was cultivated to produce biomass for HTL, leading to a 17% increase in BFI yield. In addition, we built a multiphase component additivity (MCA) model that can predict BFI yield and quality with PHAs in the biomass. This work demonstrates an effective strategy to integrate strain development with downstream biomass conversion to maximize biofuel yield, with lessons applicable to microalgae as well as other biomass.

09 BIOMASS FUELS↗
NASA NTRS1998

MPEG-2 Over Asynchronous Transfer Mode (ATM) Over Satellite Quality of Service (QoS) Experiments: Laboratory Tests

Asynchronous transfer mode (ATM) quality of service (QoS) experiments were performed using MPEG-2 (ATM application layer 5, AAL5) over ATM over an emulated satellite link. The purpose of these experiments was to determine the free-space link quality necessary to transmit high-quality multimedia information by using the ATM protocol. The detailed test plan and test configuration are described herein as are the test results. MPEG-2 transport streams were baselined in an errored environment, followed by a series of tests using, MPEG-2 over ATM. Errors were created both digitally as well as in an IF link by using a satellite modem and commercial gaussian noise test set for two different MPEG-2 decoder implementations. The results show that ITU-T Recommendation 1.356 Class 1, stringent ATM applications will require better link quality than currently specified; in particular, cell loss ratios of better than 1.0 x 10(exp -8) and cell error ratios of better than 1.0 x 10(exp -7) are needed. These tests were conducted at the NASA Lewis Research Center in support of satellite-ATM interoperability research.

Ivancic, William D.↗
NASA NTRS2001

Sensing, Spectra and Scaling: What's in Store for Land Observations

Bill Pecora's 1960's vision of the future, using spacecraft-based sensors for mapping the environment and exploring for resources, is being implemented today. New technology has produced better sensors in space such as the Landsat Thematic Mapper (TM) and SPOT, and creative researchers are continuing to find new applications. However, with existing sensors, and those intended for launch in this century, the potential for extracting information from the land surface is far from being exploited. The most recent technology development is imaging spectrometry, the acquisition of images in hundreds of contiguous spectral bands, such that for any pixel a complete reflectance spectrum can be acquired. Experience with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has shown that, with proper attention paid to absolute calibration, it is possible to acquire apparent surface reflectance to 5% accuracy without any ground-based measurement. The data reduction incorporates in educated guess of the aerosol scattering, development of a precipitable water vapor map from the data and mapping of cirrus clouds in the 1.38 micrometer band. This is not possible with TM. The pixel size in images of the earth plays and important role in the type and quality of information that can be derived. Less understood is the coupling between spatial and spectral resolution in a sensor. Recent work has shown that in processing the data to derive the relative abundance of materials in a pixel, also known is unmixing, the pixel size is an important parameter. A variance in the relative abundance of materials among the pixels is necessary to be able to derive the endmembers or pure material constituent spectra. In most cases, the 1 km pixel size for the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is too large to meet the variance criterion. A pointable high spatial and spectral resolution imaging spectrometer in orbit will be necessary to make the major next step in our understanding of the solid earth surface and its changing face.

Goetz, Alexander F. H.↗
NASA NTRS2021

Oklahoma Health and Air Quality: Mapping Air Quality Using NASA Earth Observations to Investigate Recent Increases in Ozone Concentrations

Tropospheric ozone (O3) is formed by anthropogenic pollutants interacting with sunlight and is considered harmful to human health in high concentrations. In the summer of 2018, the Oklahoma Department of Environmental Quality (DEQ) measured unexpected spikes in O3 in Seiling, Oklahoma, with concentrations exceeding those measured in bustling Oklahoma City and Tulsa. The DEQ tracks air quality using ground monitors and does not utilize Earth observation data in its monitoring or analysis. This project used remotely sensed data to investigate these2018 air quality anomalies, identifying possible causes. We analyzed atmospheric data from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-5P Tropospheric Ozone Monitoring Instrument (TROPOMI) in conjunction with ground-based measurements of tropospheric ozone (O3), nitrogen dioxide (NO2), methane (CH4), carbon monoxide (CO), formaldehyde (HCHO) and aerosol optical depth (AOD). We compared Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model simulations and Earth observation visualizations to pinpoint ozone spike causes. We also generated models to identify contributing factors to variations in ground ozone concentrations in our study area. The results point to a variety of ozone spike causes, primarily from outside of the state, and support the placement of additionalNO2, O3, and CO monitors to the southeast of Seiling. These analyses can help guide the placement of future monitors in the ground monitoring network and inform air quality regulations in Oklahoma.

Carolina Rosales↗