Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “scale detection”

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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tomography of the gamma-ray sky from cross-correlation with DESI DR2 and unWISE galaxies

We study the origin of extragalactic gamma-ray emission observed by Fermi-LAT, using the cross-correlation of the gamma-ray sky with maps of large-scale structure provided by the DESI and unWISE surveys. Tomographic cross-correlation reveals the bias-weighted redshift distributions of gamma-ray sources. We first illustrate this method by cross-correlating detected gamma-ray point sources with large-scale structure. We find a significant cross-correlation and infer a point source redshift distribution broadly consistent with the distribution of identified optical counterparts previously reported in the literature, as well as a similar linear bias ($b \approx 2$) to massive galaxies that host bright active galactic nuclei. We then study the clustering of the Fermi unresolved gamma-ray background (UGRB), both in auto-correlation and in cross-correlation with large-scale structure. We detect the cross-correlation of the UGRB and LSS at $\sim 10σ$ in total, with highly significant detections from both DESI and unWISE. Our measurements suggest that the redshift distribution of the UGRB is broadly consistent with the redshift distribution of detected point sources. Additionally, we find a relatively weak amplitude for the cross-correlation with large-scale structure at z < 2, suggesting a significant fraction of the UGRB does not come from z < 2 large-scale structure. A natural candidate is contamination of from residual Galactic emission, and our best estimate of the contamination level derived from the UGRB auto-spectrum suggests that the mean bias of UGRB sources is indeed quite similar to the bias of detected Fermi point sources. However, we cannot exclude additional emission from gamma-ray sources at high redshift, z > 2, and we suggest that cross-correlation with tracers at z > 2, including CMB lensing, would be the ideal way to determine the fraction of z > 2 emission.

Krolewski, Alex [Waterloo U., Math. Dept.; Waterlo↗
NASA NTRS1992

Temporal variation in spectral detection thresholds of substrate and vegetation in AVIRIS images

The ability to map changes over large surface areas over time is one of the advantages in using remote sensing as a monitoring tool. Temporal changes in the surface may be gradual, making them difficult to detect in the short-term, and because they commonly occur at the subpixel scale, they may be difficult to detect in the long-term as well. Also, subtle changes may be real or merely an artifact of image noise. It is, therefore, necessary to understand the factors that limit the detection of surface materials in evaluating temporal data. The spectral detectability of vegetation and soil in the 1990 July and October Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of Jasper Ridge, CA was evaluated and compared.

Sabol, Donald E., Jr.↗
NASA NTRS1995

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Ahumada, A. J., Jr.↗
NASA NTRS2021

Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park Across Elevational Gradients

Greenup dates and their responses to elevation and temperature variations across the mountains of Acadia National Park are monitored using remote sensing data, including Landsat 8 surface reflectances (at a 30-m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500-m spatial resolution), during the 2013–2016 growing seasons. The 30-m resolution provides a better scale for studying the phenology variation across elevational gradients than the 500-m resolution, as greenup dates monitored at 30-m scale have better agreement with leaf-out dates recorded in the field alongside the north–south-oriented hiking trails on three of the park’s tallest mountains (466 m, 418 m, and 380 m), and can provide landcover-specific analysis. The spring phenology responses to temperature and elevation vary among different spatial scales. Greenup dates of Acadia National Park monitored at 30-m scale show a weak advancing trend with higher spring temperature, while greenup dates monitored at 500 m show a weak delaying trend. The species mix within landcover at 30-m scale could weaken the advancing trend detected at field observation level. The landcover mix and elevation variation within 500-m scale could alter the spring phenology response to spring temperature variation. Greenup dates monitored at both 30-m and 500-m scales vary among different elevational zones, aspects, landcovers, and years. However, the relationship between greenup dates and elevation is rather weak.

Yan Liu↗
DOE OSTI2018

Potential Source Apportionment and Meteorological Conditions Involved in Airborne 131 I Detections in January/February 2017 in Europe

Traces of particulate radioactive iodine (I-131) were detected in the European atmosphere in January/February 2017. Concentrations of this nuclear fission product were very low, ranging 0.1 to 10 mu Bq m(-3) except at one location in western Russia where they reached up to several mBq m(-3). Detections have been reported continuously over an 8-week period by about 30 monitoring stations. We examine possible emission source apportionments and rank them considering their expected contribution in terms of orders of magnitude from typical routine releases: radiopharmaceutical production units > sewage sludge incinerators > nuclear power plants > spontaneous fission of uranium in soil. Inverse modeling simulations indicate that the widespread detections of I-131 resulted from the combination of multiple source releases. Among them, those from radiopharmaceutical production units remain the most likely. One of them is located in Western Russia and its estimated source term complies with authorized limits. Other existing sources related to I-131 use (medical purposes or sewage sludge incineration) can explain detections on a rather local scale. As an enhancing factor, the prevailing wintertime meteorological situations marked by strong temperature inversions led to poor dispersion conditions that resulted in higher concentrations exceeding usual detection limits in use within the informal Ring of Five (Ro5) monitoring network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Whole-Program Adaptive Error Detection and Mitigation. Final Report

Errors in application state resulting from faults in hardware are an increasing concern on extreme-scale computing systems. Errors that escape detection and lead to silent data corruption are particularly problematic. Detecting errors is an important first step toward fault tolerant program execution. The multi-institutional project addressed a comprehensive approach to error detection and mitigation for scientific applications that combined configurable error detectors, a unified reliability specification, and whole-program detector composition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Cyber security, Tensor decomposition, graph analyt↗
NASA NTRS2009

Lunar Landing Trajectory Design for Onboard Hazard Detection and Avoidance

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing the software and hardware technology needed to support a safe and precise landing for the next generation of lunar missions. ALHAT provides this capability through terrain-relative navigation measurements to enhance global-scale precision, an onboard hazard detection system to select safe landing locations, and an Autonomous Guidance, Navigation, and Control (AGNC) capability to process these measurements and safely direct the vehicle to a landing location. This paper focuses on the key trajectory design issues relevant to providing an onboard Hazard Detection and Avoidance (HDA) capability for the lander. Hazard detection can be accomplished by the crew visually scanning the terrain through a window, a sensor system imaging the terrain, or some combination of both. For ALHAT, this hazard detection activity is provided by a sensor system, which either augments the crew s perception or entirely replaces the crew in the case of a robotic landing. Detecting hazards influences the trajectory design by requiring the proper perspective, range to the landing site, and sufficient time to view the terrain. Following this, the trajectory design must provide additional time to process this information and make a decision about where to safely land. During the final part of the HDA process, the trajectory design must provide sufficient margin to enable a hazard avoidance maneuver. In order to demonstrate the effects of these constraints on the landing trajectory, a tradespace of trajectory designs was created for the initial ALHAT Design Analysis Cycle (ALDAC-1) and each case evaluated with these HDA constraints active. The ALHAT analysis process, described in this paper, narrows down this tradespace and subsequently better defines the trajectory design needed to support onboard HDA. Future ALDACs will enhance this trajectory design by balancing these issues and others in an overall system design process.

Paschall, Steve↗
NASA NTRS1998

The Remote Sensing of Mineral Aerosols and Their Impact on Phytoplankton Productivity using Sea WiFS

The main objective of this proposal was to use SeaWiFs data to study the relationship between aerosols found in aeollan dust and photosynthesis of phytoplankton in open ocean surface waters. This project was a collaborative effort between myself and Dr. Neil Tindale at Texas A&M University and followed on our earlier funded proposal which had been designed as a proof-of-concept study to determine if ocean color sensors such as the Coastal Zone Color Scanner (CZCS) could be used to detect and map large-scale mineral aerosol plumes. Despite the large spatial and temporal gaps inherent in the CZCS data coverage, our results from this initial study indicated that an ocean color sensor could indeed be used to detect aerosols. These encouraging results led us to propose in this proposal the use of SeaWiFS data to study mineral aerosol transport and its impact on phytoplankton production. This proposal orignally intended to make use of SeaWiFS images, but as the launch delay of SeaWiFS dragged on, we had to make do with other satellite data sets. Thus, the focus of this proposal became the CSCS image archive instead. I detail my results and accomplishments with this data set.

Stegmann, Petra M.↗
NASA NTRS1973

Mesoscale history of a small patch of clear air turbulence.

Sequential analyses of data from a network of eight special rawinsonde stations 100 km apart demonstrate the important effects of mesoscale disturbances in a 2.5-hr episode of light-to-moderate clear air turbulence (CAT). The most significant feature was the development of two turbulent fields below and above a 2-km region of a wind minimum which passed over Wallops Island, Va., near 1530 GMT on Feb. 4, 1970. The small scale of these features precludes their detection in the standard 12-hr observation sequence. The rawinsonde network utilized, which incorporates fine-scale details in both space and time, is adequate to provide a very useful current CAT depiction and an excellent source for the preparation of a short-range forecast of CAT.

Boucher, R. J.↗
DOE OSTI2021

Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning

Many self-supervised methods have been proposed with the target of image anomaly detection. These methods often rely on the paradigm of data augmentation with predefined transformations such as flipping, cropping, and rotations. However, it is not straightforward to apply these techniques for non-image data, such as time series or tabular data, while the performance of the existing deep approaches has been under our expectation on tasks beyond images. In this work, we propose a novel active learning (AL) scheme that relied on neural autoregressive flows (NAF) for self-supervised anomaly detection, specifically on small-scale data. Unlike other generative models such as GANs or VAEs, flow-based models allow to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-AL method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The samples with lower likelihoods are selected and further checked by outlier detection using Mahalanobis distance. The augmented samples incorporating with normal samples are used for training a better detector so as to approach decision boundaries. Compared with random transformations, NAF-AL can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple time series and tabular datasets, and a real-world application in advanced manufacturing, with significant improvement on anomaly detection accuracy and robustness over the state-of-the-art.

Zhang, Jiaxin↗
NASA NTRS2020

A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter’s Atmosphere Detected by JunoCam

In the first 20 orbits of the Juno spacecraft around Jupiter, we have identified a variety of wave‐like features in images made by its public‐outreach camera, JunoCam. Because of Juno's unprecedented and repeated proximity to Jupiter's cloud tops during its close approaches, JunoCam has detected more wave structures than any previous surveys. Most of the waves appear in long wave packets, oriented east‐west and populated by narrow wave crests. Spacing between crests were measured as small as ~30 km, shorter than any previously measured. Some waves are associated with atmospheric features, but others are not ostensibly associated with any visible cloud phenomena and thus may be generated by dynamical forcing below the visible cloud tops. Some waves also appear to be converging, and others appear to be overlapping, possibly at different atmospheric levels. Another type of wave has a series of fronts that appear to be radiating outward from the center of a cyclone. Most of these waves appear within 5° of latitude from the equator, but we have detected waves covering planetocentric latitudes between 20°S and 45°N. The great majority of the waves appear in regions associated with prograde motions of the mean zonal flow. Juno was unable to measure the velocity of wave features to diagnose the wave types due to its close and rapid flybys. However, both by our own upper limits on wave motions and by analogy with previous measurements, we expect that the waves JunoCam detected near the equator are inertia‐gravity waves.

Lunar and Planetary Science and Exploration↗
DOE OSTI2021

Detection of a particle shower at the Glashow resonance with IceCube

The Glashow resonance describes the resonant formation of a W - boson during the interaction of a high-energy electron antineutrino with an electron, peaking at an antineutrino energy of 6.3 petaelectronvolts (PeV) in the rest frame of the electron. Whereas this energy scale is out of reach for currently operating and future planned particle accelerators, natural astrophysical phenomena are expected to produce antineutrinos with energies beyond the PeV scale. Here we report the detection by the IceCube neutrino observatory of a cascade of high-energy particles (a particle shower) consistent with being created at the Glashow resonance. A shower with an energy of 6.05 ± 0.72 PeV (determined from Cherenkov radiation in the Antarctic Ice Sheet) was measured. Features consistent with the production of secondary muons in the particle shower indicate the hadronic decay of a resonant W - boson, confirm that the source is astrophysical and provide improved directional localization. The evidence of the Glashow resonance suggests the presence of electron antineutrinos in the astrophysical flux, while also providing further validation of the standard model of particle physics. Its unique signature indicates a method of distinguishing neutrinos from antineutrinos, thus providing a way to identify astronomical accelerators that produce neutrinos via hadronuclear or photohadronic interactions, with or without strong magnetic fields. As such, knowledge of both the flavour (that is, electron, muon or tau neutrinos) and charge (neutrino or antineutrino) will facilitate the advancement of neutrino astronomy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

Detecting Disturbances in a Forest Environment

The interchange between forest and nonforest land and most man-made and natural forest disturbances can be detected on 1:120,000-scale color-infrared film. Bulk multispectral scanner imagery from the Earth Resources Technology Satellite combined and enhanced at a scale of 1:1,000,000 shows major changes in forest and nonforest land-use categories, many timber harvested areas, and some natural disturbances. Late fall to late spring is the best period of the year for detecting forest disturbances. In a study in Georgia, 79 percent of the disturbances in one county were detected on an ERTS color composite for April 1973 with only 12 percent commission error.

Aldrich, R. C.↗
DOE OSTI2022

IceCube at the frontier of macroscopic dark matter direct detection

For a class of macroscopic dark matter models, inelastic scattering of dark matter off a nucleus can generate electromagnetic signatures with GeV-scale energy. The IceCube detector, with its kilometer-scale size, is ideal for directly detecting such inelastic scattering. Based on the slow particle trigger for the DeepCore detector, we perform a detailed signal and background simulation to estimate the discovery potential. For order 1 GeV deposited energy in each interaction, we find that IceCube can probe the dark matter masses up to one gram.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2016

Extreme Water Deficit in Brazil Detected from Space

Extreme droughts have caused significant socioeconomic and environmental damage worldwide. In Brazil, ineffective energy development and water management policies have magnified the impacts of recent severe droughts, which include massive agricultural losses, water supply restrictions, and energy rationing. Spaceborne remote sensing data advance our understanding of the spatiotemporal variability of large-scale droughts and enhance the detection and monitoring of extreme water-related events. In this study, data derived from the Gravity Recovery and Climate Experiment (GRACE) mission are used to detect and quantify an extended major drought over eastern Brazil and provide estimates of impacted areas and region-specific water deficits. Two structural breakpoint detection methods were applied to time series of GRACE-based terrestrial water storage anomalies (TWSA), determining when two abrupt changes occurred. One, in particular, defines the beginning of the current drought. Using TWSA, a water loss rate of 26.1 cmyr21 over southeastern Brazil was detected from 2012 to 2015. Based on analysis of Global Land Data Assimilation System(GLDAS) outputs, the extreme drought is mostly related to lower-than-usual precipitation rates, resulting in high soil moisture depletion and lower-than-usual rates of evapotranspiration. A reduction of 2023 of precipitation over an extended period of 3 years is enough to raise serious water scarcity conditions in the country. Correlations between monthly time series of both grid-based TWSA and ground-based water storage measurements at 16 reservoirs located within southeastern Brazil varied from 0.42 to 0.82. Differences are mainly explained by reservoir sizes and proximity to the drought nucleus.

terrestrial↗
DOE OSTI2014

Probing the active massive black hole candidate in the center of NGC 404 with VLBI

Recently, Nyland et al. argued that the radio emission observed in the center of the dwarf galaxy NGC 404 originates in a low-luminosity active galactic nucleus powered by a massive black hole (MBH, M ≲ 10{sup 6} M{sub ☉}). High-resolution radio detections of MBHs are rare. Here we present sensitive, contemporaneous Chandra X-ray, and very long baseline interferometry (VLBI) radio observations with the European VLBI Network. The source is detected in the X-rays, and shows no long-term variability. If the hard X-ray source is powered by accretion, the apparent low accretion efficiency would be consistent with a black hole (BH) in the hard state. Hard state BHs are known to show radio emission compact on the milliarcsecond scales. However, the central region of NGC 404 is resolved out on 10 mas (0.15-1.5 pc) scales. Our VLBI non-detection of a compact, partially self-absorbed radio core in NGC 404 implies that either the BH mass is smaller than 3{sub −2}{sup +5}×10{sup 5} M{sub ☉}, or the source does not follow the fundamental plane of BH activity relation. An alternative explanation is that the central BH is not in the hard state. The radio emission observed on arcsecond (tens of parsecs) scales may originate in nuclear star formation or extended emission due to AGN activity, although the latter would not be typical considering the structural properties of low-ionization nuclear emission-line region galaxies with confirmed nuclear activity.

79 ASTRONOMY AND ASTROPHYSICS↗