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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Electronic device and method for compressing video data

A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.

Tohlen, Michael Aaron↗
DOE OSTI2023

Electronic device and method for compressing video data

A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.

Tohlen, Michael Aaron↗
DOE OSTI2025

House Advantage or House of Cards? Stacking the Deck for Data Videos Leads to Null Results: Preprint

Videos are becoming a ubiquitous means of sharing information on social media platforms. In response, data videos - short clips combining visualization with dynamic storytelling, audio descriptions, and spatial referencing - have gained popularity for communicating data. These affordances suggest that data videos might communicate data patterns, trends, and concepts more effectively than static visualizations, enhancing comprehension. However, existing research has not systematically tested this claim. To address this gap, we conducted three controlled studies to measure comprehension differences between data videos and static visualizations. Despite leveraging visual cues and audio explanations, no data video led to significantly better comprehension than an analogous static visualization. Our results suggest data videos are not categorically better and that future research should examine the tradeoffs between their engagement benefits and costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from Yosemite National Park, Water Year 2024

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed at 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are intended to support hydrologic modeling efforts to better resolve the fate of mountain water, and are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. A time-lapse camera at one site captures valley-scale seasonal snow cover variability.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into a short video stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Camera / Raw Data

The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data contain video data of the wind turbine blades during operation as well as while parked. Since the blades had a coning angle, the blades are only in frame of the video camera when the blades are in a pitched to run configuration.

17 WIND ENERGY↗
DOE OSTI2023

Apparatus and method for agricultural data collection and agricultural operations

Aspects of the subject disclosure may include, for example, obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the camera is attached to a mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from encoder(s), wherein the encoder(s) are attached to the robot; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem. Additional embodiments are disclosed.

Chowdhary, Girish↗
DOE OSTI2023

Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.

Erdagi, Ismet Goksad↗
DOE OSTI2026

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

So you think you can track?

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.

42 ENGINEERING↗
DOE OSTI2020

Data, Photographs, Videos, and Information for the Niwot Ridge Subalpine Forest (US-NR1) AmeriFlux site

This data package contains data and information about the operation of the Niwot Ridge Subalpine Forest AmeriFlux site (US-NR1) between Nov 1998 to the present (2020). This data archive supplements the primary 30-min data storage for the US-NR1 data (i.e., https://doi.org/10.17190/AMF/1246088) by providing the following: (i) five-minute statistics (means, variances, covariances) of all data measured by the data system between Nov 1998 and September 2020 in netCDF format, (ii) CSV data files saved within the memory of the CR23X data loggers (as well as an archive of the data logger programs), (iii) an archive of previous 30-min ASCII data versions of the US-NR1 AmeriFlux data and information related to each data release (a replica of what can be found at http://urquell.colorado.edu/data_ameriflux/), (iv) a web calendar (in HTML format) documenting activity at the site (a replica of http://urquell.colorado.edu/calendar/), (v) photos (over 15,000) and video taken at the site between years 2001 and present day (2020), and (vi) several auxiliary datasets, primary related to trees near the site, soil moisture and soil temperature, and subcanopy radiation data. The data package is setup so that the web calendar, photos, and electronic logbook can be easily accessed on a local computer using a web browser. The provided data files are in either netCDF, CSV, ASCII, or MATLAB format. To obtain a better understanding about the archive, please start by reading the PDF: README_ESS_DIVE_USNR1_readme_first.pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Addressing Issues with Working Memory in Video Object Segmentation

Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initial image-mask pair and past image-masks as a working memory buffer. While the current state of the art models perform very well on clean video data, their reliance on a working memory of previous frames leaves room for error. Affinity-based algorithms include the inductive bias that there is temporal continuity between consecutive frames. To account for inconsistent camera views of the desired object, working memory models need an algorithmic modification that regulates the memory updates and avoid writing irrelevant frames into working memory. A simple algorithmic change is proposed that can be applied to any existing working memory-based VOS model to improve performance on inconsistent views, such as sudden camera cuts, frame interjections, and extreme context changes. The resulting model performances show significant improvement on video data with these frame interjections over the same model without the algorithmic addition. Our contribution is a simple decision function that determines whether working memory should be updated based on the detection of sudden, extreme changes and the assumption that the object is no longer in frame. By implementing algorithmic changes, such as this, we can increase the real-world applicability of current VOS models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Longwave infrared (6.6–11.4 µm) dual-comb spectroscopy with 240,000 comb-mode-resolved data points at video rate

Using sub-3-cycle pulses from mode-locked Cr:ZnS lasers at λ ≈ 2.4 µm as a driving source, we performed high-resolution dual-frequency-comb spectroscopy in the longwave infrared (LWIR) range. A duo of highly coherent broadband (6.6–11.4 µm) frequency combs were produced via intrapulse difference frequency generation in zinc germanium phosphide (ZGP) crystals. Fast (up to 0.1 s per spectrum) acquisition of 240,000 comb-mode-resolved data points, spaced by 80 MHz and referenced to a Rb clock, was demonstrated, resulting in metrology grade molecular spectra of N 2 O (nitrous oxide) and CH 3 OH (methane). The key to high-speed massive spectral data acquisition was low intensity and phase noise of the LWIR combs and high (7.5%) downconversion efficiency, resulting in a LWIR power of 300 mW for each comb.

Vasilyev, Sergey (ORCID:0000000225203547)↗
DOE OSTI2022

Overview and How-to Tutorial Videos for Using NEWTS Data

Overview and How-to Tutorial Videos for Using NEWTS Data Video 1: Overview of NEWTS Database Video 2: How-to tutorial for EPA Flue Gas Desulfurization (FGD) Effluent NEWTS dataset Video 3: How-to tutorial for USGS Produced Waters NEWTS dataset Video 4: How-to tutorial for EPA Ash NEWTS dataset Video 5: How-to tutorial for Quillinan, et al 2018 DOE Geothermal Technology Office REE dataset Video 6: Tutorial video on navigating the NEWTS Dashboard, with an overview of NEWTS and navigating between the NEWTS Dashboard and Datasets (https://netl-doe.maps.arcgis.com/apps/dashboards/a5fa4192f7c6478dab3d6180d9c30b84) Video 7: Additional tutorial video on navigating the NEWTS Dashboard and investigating specific data points in the Dashboard and Datasets Video 8: Re-record of recent webinar giving an overview of the NEWTS Database and Dashboard, including interacting with the NEWTS Dashboard, locating specific data points, and finding the relevant streams in the NEWTS Database and datasets on EDX. Includes overview of the datasets, case studies, and steps for taking stream data from the database and modeling stream data in OLI Studio and Geochemist's Workbench. Note: Video 3 tutorial is also applicable to the USGS Brackish Water NEWTS dataset.

Aqueous Chemistry↗
DOE OSTI2026

On Road Testing Data

This dataset provides the following on road testing data: - Videos - In-vehicle dash camera videos during different testing scenarios. - Signal controller data - NTCIP log data and processed signal timing data from the corresponding signal controllers - Vehicle data - Vehicle data recorded during the testing, including GNSS, communication, CAN signals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗