Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “standardized data”

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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Correlation of Helium Solubility in Liquid Nitrogen

A correlation has been developed for the equilibrium mole fraction of soluble gaseous helium in liquid nitrogen as a function of temperature and pressure. Experimental solubility data was compiled and provided by National Institute of Standards and Technology (NIST). Data from six sources was used to develop a correlation within the range of 0.5 to 9.9 MPa and 72.0 to 119.6 K. The relative standard deviation of the correlation is 6.9 percent.

VanDresar, Neil T.↗
DOE OSTI2025

5-meter Sonic Anemometer Winds Nantucket (Standardized)

This dataset contains three-dimensional wind measurements (u, v, w) at 5 meters, as well as virtual temperature. These measurements are taken from the meteorological tower at Nantucket, with the instrument facing toward the southwest. The data have been standardized into NetCDF format.

17 WIND ENERGY↗
NASA NTRS1993

Standards in the architectural effort

The NASA Scientific and Technical Information (STI) Project Architecture Group was established to build a framework for modernization, provide guidance and direction for standardization and integration efforts, and to develop a set of concepts, interfaces, and entities to specifiy standards. The STI data processing architecture is a set of diagrams and descriptions that characterize the principal functions and services as well as hardware and software components. The use of standards promotes scalability and interchangeability.

Markham, Howard↗
NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗
NASA NTRS1981

RAWINPROC: Computer program for decommutating, interpreting, and interpolating Rawinsonde meteorological balloon sounding data

The FORTRAN computer program RAWINPROC accepts output from NASA Wallops computer program METPASS1; and produces input for NASA computer program 3.0.0700 (ECC-PRD). The three parts together form a software system for the completely automatic reduction of standard RAWINSONDE sounding data. RAWINPROC pre-edits the 0.1-second data, including time-of-day, azimuth, elevation, and sonde-modulated tone frequency, condenses the data according to successive dwells of the tone frequency, decommutates the condensed data into the proper channels (temperature, relative humidity, high and low references), determines the running baroswitch contact number and computes the associated pressure altitudes, and interpolates the data appropriate for input to ACC-PRD.

Staffanson, F. L.↗
DOE OSTI2023

Standardized naming of microbiome samples in Genomes OnLine Database

The power of next-generation sequencing has resulted in an explosive growth in the number of projects aiming to understand the metagenomic diversity of complex microbial environments. The interdisciplinary nature of this microbiome research community, along with the absence of reporting standards for microbiome data and samples, poses a significant challenge for follow-up studies. Commonly used names of metagenomes and metatranscriptomes in public databases currently lack the essential information necessary to accurately describe and classify the underlying samples, which makes a comparative analysis difficult to conduct and often results in misclassified sequences in data repositories. The Genomes OnLine Database (GOLD) (https:// gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute has been at the forefront of addressing this challenge by developing a standardized nomenclature system for naming microbiome samples. GOLD, currently in its twenty-fifth anniversary, continues to enrich the research community with hundreds of thousands of metagenomes and metatranscriptomes with well-curated and easy-to-understand names. Through this manuscript, we describe the overall naming process that can be easily adopted by researchers worldwide. Additionally, we propose the use of this naming system as a best practice for the scientific community to facilitate better interoperability and reusability of microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

On the Ambiguity of Using Isc for Analyzing Suns-Voc Data on Modules

In standard silicon solar modules, cells are strung in series with bypass diodes incorporated to reduce the effects of shading. When all of the strings are partially shaded the measured short circuit current is affected and the Isc of the module will be equal to that of the lowest current cell in the least shadowed string. In bifacial modules the ability to assess losses due to shading is especially important. This paper advocates an alternative approach to converting the raw suns-voc data to pseudo IV data in modules where shading may be affecting the suns-voc curve results.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

Lamour, Julien [Université of Toulouse (France); U↗
NASA NTRS1977

Necessity to adapt land use and land cover classification systems to readily accept radar data

A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

Drake, B.↗
NASA NTRS1996

BATSE Observations of Gamma-Ray Burst Spectra: Low-Energy Behavior of Time-Averaged Spectra - Part 3

We analyze time-averaged spectra from 86 bright gamma-ray bursts from the first 5 years of the Burst And Transient Source Experiment (BATSE) on board the Compton Gamma Ray Observatory to determine whether the lowest energy data are consistent with a standard spectra form fit to the data at all energies. The BATSE Spectroscopy Detectors have the capability to observe photons as low as 5 keV. Using the gamma-ray burst locations obtained with the BATSE Large Area Detectors, the Spectroscopy Detectors' low-energy response can be modeled accurately. This, together with a postlaunch calibration of the lowest energy Spectroscopy Detector discriminator channel, which can lie in the range 5-20 keV, allows spectral deconvolution over a broad energy range, approx. 5 keV to 2 MeV. The additional coverage allows us to search for evidence of excess emission, or for a deficit, below 20 keV. While no burst has a significant (greater than or equal to 3 sigma) deficit relative to a standard spectra model, we find that 12 bursts have excess low-energy emission, ranging between 1.2 and 5.8 times the model flux, that exceeds 5 sigma in significance. This is evidence for an additional low-energy spectral component in at least some bursts, or for deviations from the power-law spectral form typically used to model gamma-ray bursts at energies below 100 keV.

Preece, R. D.↗
NASA NTRS1984

Some methods of computing platform transmitter terminal location estimates

A position estimation algorithm was developed to track a humpback whale tagged with an ARGOS platform after a transmitter deployment failure and the whale's diving behavior precluded standard methods. The algorithm is especially useful where a transmitter location program exists; it determines the classical keplarian elements from the ARGOS spacecraft position vectors included with the probationary file messages. A minimum of three distinct messages are required. Once the spacecraft orbit is determined, the whale is located using standard least squares regression techniques. Experience suggests that in instances where circumstances inherent in the experiment yield message data unsuitable for the standard ARGOS reduction, (message data may be too sparse, span an insufficient period, or include variable-length messages). System ARGOS can still provide much valuable location information if the user is willing to accept the increased location uncertainties.

Hoisington, C. M.↗
DOE OSTI2021

Observation of e + e - → ηψ(2S) at center-of-mass energies from 4.236 to 4.600 GeV

Using a total of 5 25 fb -1 of e + e - collision data with center-of-mass energies from 4.236 to 4.600 GeV, we report the first observation of the processe + e - → ηψ(2S) with a statistical significance of 4.9 standard deviations. The data sets were collected by the BESIII detector operating at the BEPCII storage ring. We measure the yield of events integrated over center-of-mass energies and also present the energy dependence of the measured cross section.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and interkingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Space Station Freedom coupling tasks: An evaluation of their space operational compatibility

The development of the Space Station Freedom tasks that are compatible with both telerobotic as well as extravehicular activity is a necessary redundancy in order to insure successful day to day operation. One task to be routinely performed aboard Freedom will be the changeout of various quick disconnect fluid connectors. In an attempt to resolve these potentially contradictory issues of compatibility, mock-ups of couplings suitable to both extravehicular as well as telerobotic activity were designed and built. An evaluation performed at the Remote Operator Interaction Laboratory at NASA's Johnson Space Center is discussed, which assessed the prototype couplings as well as three standard coupling designs. Data collected during manual and telerobotic manipulation of the couplings indicated that the custom coupling was in fact shown to be faster to operate and generally preferred over the standard coupling designs.

Sampaio, Carlos E.↗
NASA NTRS1994

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

J-ERS-1↗