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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
NASA NTRS1979

A reexamination of plasma measurements from the Mariner 5 Venus encounter

Mariner 5 plasma data from the Venus encounter have been analyzed with twice the time resolution of the original analysis of Bridge et al. (1967). The velocity distribution function for each spectrum is used to determine more precisely the locations of boundaries and characteristic flow parameters in the interaction region around the planet. A new region is identified in the flow located between magnetosheathlike plasma inside the shock front and an interior low-flux region near the geometrical shadow of the planet. The region is characterized by a wide velocity distribution function and a decrease in ion flux. Using the highest time resolution magnetic field data, it is proposed that rapid magnetic field fluctuations in this region may result in an artificial broadening of the distribution function. It is concluded that very high time resolution is required in future experiments in order to determine the true nature of the plasma in this region.

Shefer, R. E.↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
NASA NTRS1977

Deriving winds for hurricanes using short interval satellite imagery

Results are presented for a study program designed to develop a better means of obtaining wind data needed by hurricane forecasters and to determine the optimum space and time resolution of satellite data used to obtain such winds. The discussion covers cloud tracking with an image display and manipulation system and analysis of IR imagery of Hurricane Eloise. It is shown that greater resolution in time and space of satellite imagery made it possible to derive up to six times as many low-level winds in a hurricane case, to eliminate 'bad winds' caused by mistaking cloud growth for cloud motion, and to remove ambiguities that would have caused difficulty in tracking clouds using only the imagery taken at 30-min intervals. It is suggested to use short-interval imagery (less than 10 min) for wind determination for future research in areas of tropical cyclones.

Gentry, R. C.↗
NASA NTRS2010

Challenges in Transitioning Research Data to Operations: The SPoRT Paradigm

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS and planned NPP, JPSS, and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volume will likely increase by a factor of 5- 10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Jedloved, Gary J.↗
NASA NTRS2013

SPoRT: Transitioning NASA and NOAA Experimental Data to the Operational Weather Community

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS, Suomi NPP, and planned JPSS and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volumes will likely increase by a factor of 5-10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Jedlovec, Gary J.↗
DOE OSTI2022

Classification and Fusion of Two Disparate Data Streams and Nuclear Dissolutions Application

We consider two streams of data or measurements with disparate qualities and time resolutions that need to be classified. The first stream consists of higher quality data at a coarser time resolution, and the other consists of lower quality data at a finer time resolution. We present a fuser-switch method that fuses the set of classifiers of each stream separately and switches between them. We show that this method provides classification decisions at a finer time resolution with superior detection and false alarm probabilities compared to individual classifiers, under the statistical independence and time resolution ratio conditions. When classifiers are trained using machine learning methods, we show that this superior performance is guaranteed with a confidence probability specified by the classifiers' generalization equations. We use these results to provide analytical foundations for previous practical results that achieved significant performance improvements in classifying Pu/Np target dissolution events at a radiochemical processing facility.

Rao, Nageswara↗
NASA NTRS1986

Comparison of solar hard X-ray and UV line and continuum bursts with high time resolution

A comparison of data sets from the UV Spectrometer and Polarimeter and Hard X-ray Burst Spectrometer instruments on SMM has established the close relationship of the impulsive phase hard X-ray and UV continuum and OV line emissions, lending support to the notion that they have a similar origin low in the solar atmosphere. These results severely constrain models that attempt to explain impulsive phase hard X-rays and UV emission; alternative processes of impulsive-phase UV continuum production should accordingly be considered. Attention is given to an electron beam 'hole boring' mechanism and a photoionization radiation transport mechanism.

Orwig, L. E.↗
NASA NTRS1980

Detection of heavy convective precipitation using rapid interval digital radar and satellite data

A study was carried out to investigate the evolution of thunderstorms with short-interval (5 min) geosynchronous digital satellite data and with digital radar data (12 min interval) in order to determine the potential and limitations of using the satellite data to detect regions of heavy convective precipitation. Initial results indicate that satellite-based estimates of thunderstorm updraft intensity are related to storm precipitation rate as indicated in the digital radar data. The conclusions give support to the effort to use satellite data for detection of heavy convective precipitation, but emphasize the need for high time resolution (5 min) data.

Negri, A. J.↗
NASA NTRS2017

First Optical Observations of Interhemispheric Electron Reflections Within Pulsating Aurora

A case study of a pulsating auroral event imaged optically at high time resolution presents direct observational evidence in agreement with the interhemispheric electron bouncing predicted by the Super Thermal Electron Transport model. Pulsation-on times are identified and subsequent equally spaced fainter pulsations are also noted and can be explained by a portion/percentage of the primary precipitating electrons reflecting upward from the ionosphere, traveling to the opposite hemisphere and reflecting upward again. The high time resolution of these data, combined with the short duration of the pulsation-on time (approx. 1 s) and the relatively long spacing between pulsations (approx. 6 to 9 s) made it possible to observe the faint optical pulses caused by the reflected electrons coming from the opposite hemisphere.

thermal↗
NASA NTRSDate not supplied

RadLab and the Environmental Data Application Dashboard: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on the International Space Station (ISS) and multiple spacecraft elsewhere in Earth orbit and in deep space continuously monitor and collect environmental data, transmitting this information back to Earth. These data include ionizing radiation and, on the ISS, CO2, relative humidity levels, and temperature, and are of great importance to space biology research. Ionizing radiation in particular has been established in ground-based experiments as being correlated with increased risk of carcinogenesis and cardiovascular and neurological effects. Looking ahead to future long duration crewed missions beyond low Earth orbit, the ability to study how factors including CO2 levels, light cycle, temperature modulate the response to ionizing radiation and microgravity is essential. To date, access to these data has been fragmented across space agencies, spacecraft, and databases. To address this issue, NASA’s Open Science Data Repository (osdr.nasa.gov) has developed two Web applications: the Environmental Data Application (EDA) and a radiation-specific RadLab. Each consists of an API (application programming interface) and an associated GUI (graphical user interface) that provide single points of access to the data. To date, OSDR has focused on the sensors from payloads and radiation detectors located on the ISS. The Web applications process telemetry information and associated data, such as spacecraft location and orientation, from multiple international databases. The applications’ request syntax enables users to interrogate these data by craft, sensor type, time range, radiation type (galactic cosmic rays, solar particle events, the contribution of the South Atlantic Anomaly), facilitating arbitrary comparisons of original source data at varying time resolutions. The applications provide programmatic access for use in computational pipelines and GUIs for data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗
DOE OSTI2025

CROCUS Air Quality Dataset from the University of Illinois Chicago (UIC), July 2024

This dataset was collected by the measurement system in the Atmosphere, Climate, and Ecosystems (ACE) Lab at the University of Illinois Chicago (UIC) from July 12 to July 31, 2024, as part of the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory.To enhance understanding of urban air quality dynamics in Chicago, and as part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), several instruments were set up to provide continuous measurements of air quality parameters in Chicago during July 2024. These measurements cover both aerosols and gas-phase species. It focuses on particle size distribution (2.5–478 nm) measured by two Scanning Mobility Particle Sizers (SMPS) at a 4-min resolution, total particle number concentrations at a 1-s resolution, and chemical composition from a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS) at a 1-min resolution. Key gas-phase species, including NO, NO₂, SO₂, and O₃, are measured at a 1-min resolution, along with high-resolution NO and dimethyl sulfide (DMS) data from a Chemical Ionization Mass Spectrometer (CIMS). Volatile organic compound (VOC) data for toluene, isoprene, and benzene are provided by a GC-PID with a time resolution of 25 minutes.The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python. Each parameter is stored in an individual dataset, which includes detailed instrument information in the header, as well as the corresponding sample start time and concentration/distribution data for each sample.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Relationship of Interplanetary Shock Micro and Macro Characteristics: A Wind Study

The non-linear least squared MHD fitting technique of Szabo 11 9941 has been recently further refined to provide realistic confidence regions for interplanetary shock normal directions and speeds. Analyzing Wind observed interplanetary shocks from 1995 to 200 1, macro characteristics such as shock strength, Theta Bn and Mach numbers can be compared to the details of shock micro or kinetic structures. The now commonly available very high time resolution (1 1 or 22 vectors/sec) Wind magnetic field data allows the precise characterization of shock kinetic structures, such as the size of the foot, ramp, overshoot and the duration of damped oscillations on either side of the shock. Detailed comparison of the shock micro and macro characteristics will be given. This enables the elucidation of shock kinetic features, relevant for particle energization processes, for observations where high time resolution data is not available. Moreover, establishing a quantitative relationship between the shock micro and macro structures will improve the confidence level of shock fitting techniques during disturbed solar wind conditions.

Szabo, Adam↗
DOE OSTI2025

Comparison of three measurement modalities for 3D characterization of manufactured features and process-induced porosity in titanium alloy additively manufactured parts

Nondestructive characterization of internal features and defects within complex components is vital for many industrial applications, particularly with the advent of additive manufacturing (AM) technologies. However, community understanding of the limitations of nondestructive methods such as X-ray Computed Tomography (CT) can be limited in certain industrial sectors as these may be emergent applications. In this paper, we investigate the limits of X-ray CT measurements and compare extracted data with mechanical polishing serial sectioning (MPSS) and confocal laser scanning microscopy (CLSM). The test object is an additively manufactured titanium alloy disk that contains both process-induced porosity and machined features, including focused ion beam milled features designed to probe the resolution limits of X-ray CT. Results show that each of these characterization techniques has advantages and disadvantages. We compare data acquisition times, spatial resolution, geometric measurement accuracy and defect visualization fidelity across these modalities to establish a practical framework.

Additive manufacturing↗
NASA NTRS1986

Ephemeral active regions and coronal bright points: A solar maximum Mission 2 guest investigator study

A dominate association of coronal bright points (as seen in He wavelength 10830) was confirmed with the approach and subsequent disappearance of opposite polarity magnetic network. While coronal bright points do occur with ephemeral regions, this association is a factor of 2 to 4 less than with sites of disappearing magnetic flux. The intensity variations seen in He I wavelength 10830 are intermittent and often rapid, varying over the 3 minute time resolution of the data; their bright point counterparts in the C IV wavelength 1548 and 20 cm wavelength show similar, though not always coincident time variations. Ejecta are associated with about 1/3 of the dark points and are evident in the C IV and H alpha data. These results support the idea that the anti-correlation of X-ray bright points with the solar cycle can be explained by the correlation of these coronal emission structures with sites of cancelling flux, indicating that, in some cases, the process of magnetic flux removal results in the release of energy. That the intensity variations are rapid and variable suggests that this process works intermittently.

Harvey, K. L.↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
NASA NTRS2012

Restoration and Reexamination of Apollo Lunar Dust Detector Data from Original Telemetry Files

We are recovering the original telemetry (Figure I) from the Apollo Dust, Thermal, Radiation Environment Monitor (DTREM) experiment, more commonly known as the Dust Detector, and producing full time resolution (54 second) data sets for release through the Planetary Data System (PDS). The primary objective of the experiment was to evaluate the effect of dust deposition, temperature, and radiation damage on solar cells on the lunar surface. The monitor was a small box consisting of three solar cells and thermistors mounted on the ALSEP (Apollo Lunar Surface Experiments Package) central station. The Dust Detector was carried on Apollo's 11, 12, 14 and 15. The Apollo 11 DTREM was powered by solar cells and only operated for a few months as planned. The Apollo 12, 14, and 15 detectors operated for 5 to 7 years, returning data every 54 seconds, consisting of voltage outputs from the three solar cells and temperatures measured by the three thermistors. The telemetry was received at ground stations and held on the Apollo Housekeeping (known as "Word 33") tapes. made available to the National Space Science Data Center (NSSDC) by Yosio Nakamura (University of Texas Institute for Geophysics). We have converted selected parts of the telemetry into uncalibrated and calibrated output voltages and temperatures.

McBride, M. J.↗