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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.

machine learning↗
NASA NTRS2003

Passive and Active Detection of Clouds: Comparisons between MODIS and GLAS Observations

The Geoscience Laser Altimeter System (GLAS), launched on board the Ice, Cloud and Land Elevation Satellite in January 2003 provides space-borne laser observations of atmospheric layers. GLAS provides opportunities to validate passive observations of the atmosphere for the first time from space with an active optical instrument. Data from the Moderate Resolution Imaging Spectrometer aboard the Aqua satellite is examined along with GLAS observations of cloud layers. In more than three-quarters of the cases, MODIS scene identification from spectral radiances agrees with GLAS. Disagreement between the two platforms is most significant over snow-covered surfaces in the northern hemisphere. Daytime clouds detected by GLAS are also more easily seen in the MODIS data as well, compared to observations made at night. These comparisons illustrate the capabilities of active remote sensing to validate and assess passive measurements, and also to complement them in studies of atmospheric layers.

Mahesh, Ashwin↗
NASA NTRS1979

Martian wind activity detected by a seismometer at Viking lander 2 site

Since the Viking lander 2 seismometer (Anderson et al., 1977) located on top of the lander is extremely sensitive to Martian winds, it can provide a record of Martian wind activity. The seismograph continuously samples the wind-induced lander vibration, and its output complements meteorological data. Some pertinent seismic data are presented along with results of a preliminary spectral analysis of the data. Attention is given to wind speed/seismic noise correlation, and to long-term variation and periodicity. The results indicate that wind variations on Mars are very regular in the summer and winter seasons. Winds are light in summer and exhibit a strong diurnal periodicity, the strongest winds occurring in the early afternoon. Winds become stronger and more irregular in autumn. In all seasons nighttime conditions are usually very quiet. The high wind season correlates with the global dust storm duration. Spring is characterized by very strong though variable wind activity. Other details are also presented.

Nakamura, Y.↗
NASA NTRS2017

Enzymatic Activity Detection via Electrochemistry for Enceladus

Electrochemical detection of biological molecules is a pertinent topic and application in many fields such as medicine, environmental spills, and life detection in space. Proteases, a class of molecules of interest in the search for life, catalyze the hydrolysis of peptides. Trypsin, a specific protease, was chosen to investigate an optimized enzyme detection system using electrochemistry. This study aims at providing the ideal functionalization of an electrode that can reliably detect a signal indicative of an enzymatic reaction from an Enceladus sample.

astrobiolog↗
NASA NTRS1967

Compilation of detection sensitivities in thermal-neutron activation

Detection sensitivities of the chemical elements following thermal-neutron activation have been compiled from the available experimental cross sections and nuclear properties and presented in a concise and usable form. The report also includes the equations and nuclear parameters used in the calculations.

Wahlgren, M. A.↗
NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗
NASA NTRSDate not supplied

A Machine Learning Ready Dataset of Acoustic Power Maps for Detection of Active Region Emergence

The development of an accurate forecast for solar eruptive activity has become increasingly important in order to prevent any potential impact on activities in space and the Earth's environment. It is therefore crucial to detect active regions before they appear on the solar surface and create early warning capabilities for upcoming Space Weather disturbances. In this work, 9TB of solar data (SDO/HMI dopplergrams, magnetograms and continuum intensity maps) involving the emergence of 61 NOAA solar active regions since 2010 were processed using the NASA HECC capabilities. An acoustic power maps time-series dataset was created (for four different frequency ranges and processed to take into account the solar sphere geometric effect ) which can be used for understanding the dynamics of the solar surface and train a variety of ML models. The calculated acoustic power maps carry precursor information associated with the decrease in continuum intensity on the solar surface, verifying older helioseismology research. Our results show that a Long Short-Term Memory (LSTMs) model, with a modest layer depth and the right hyperparameters tuned, when trained on this solar acoustic power maps dataset can predict without false negatives a drop in intensity (associated with the emergence of the active region), up to 18 hours in advance.

SMD↗
NASA NTRS2023

Quantifying Burned Area of Wildfires in the Western United States From Polar-Orbiting and Geostationary Satellite Active-Fire Detections

Background : Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impact. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate. Aims and Methods : In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS)active-fire detections. Hourly time series are created by combining VIIRS estimates with fire radiative power (FRP) estimates from GOES-17 data. Conclusions, Key Results and Implication : We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 and 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.

burned area↗
NASA NTRS1989

Electron Spin Resonance (ESR) detection of active oxygen species and organic phases in Martian soils

The presence of active oxygen species (O(-), O2(-), O3(-)) and other strong oxidants (Fe2O3 and Fe3O4) was invoked in interpretations of the Viking biological experiments and a model was also suggested for Martian surface chemistry. The non-biological interpretations of the biological results gain futher support as no organic compounds were detected in the Viking pyrolysis-gas chromatography mass spectrometer (GCSM) experiments at concentrations as low as 10 ppb. Electron spin resonance (ESR) measures the absorption of microwaves by a paramagnetic and/or ferromagnetic center in the presence of an external field. In many instances, ESR has the advantage of detailed submicroscopic identification of the transient species and/or unstable reaction intermediates in their environments. Since the higly active oxygen species (O(-), O2(-), O3(-), and R-O-O(-)) are all paramagnetic in nature, they can be readily detected in native form by the ESR method. Active oxygen species likely to occur in the Martian surface samples were detected by ESR in UV-irradiated samples containing MgO. A miniaturized ESR spectrometer system can be developed for the Mars Rover Sample Return Mission. The instrument can perform the following in situ Martian samples analyses: detection of active oxygen species; characterization of Martian surface chemistry and photooxidation processes; and searching for organic compounds in the form of free radicals preserved in subsoils, and detection of microfossils with Martian carbonate sediments.

Tsay, Fun-Dow↗
NASA NTRS2008

Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue

We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface sulfur deposits that originate from underlying springs by distinguishing the sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.

autonomy↗
NASA NTRS1987

Photosynthetic action spectra and adaptation to spectral light distribution in a benthic cyanobacterial mat

We studied adaptation to spectral light distribution in undisturbed benthic communities of cyanobacterial mats growing in hypersaline ponds at Guerrero Negro, Baja California, Mexico. Microscale measurements of oxygen photosynthesis and action spectra were performed with microelectrodes; spectral radiance was measured with fiber-optic microprobes. The spatial resolution of all measurements was 0.1 mm, and the spectral resolution was 10 to 15 nm. Light attenuation spectra showed absorption predominantly by chlorophyll a (Chl a) (430 and 670 nm), phycocyanin (620 nm), and carotenoids (440 to 500 nm). Blue light (450 nm) was attenuated 10-fold more strongly than red light (600 nm). The action spectra of the surface film of diatoms accordingly showed activity over the whole spectrum, with maxima for Chl a and carotenoids. The underlying dense Microcoleus population showed almost exclusively activity dependent upon light harvesting by phycobilins at 550 to 660 nm. Maximum activity was at 580 and 650 nm, indicating absorption by phycoerythrin and phycocyanin as well as by allophycocyanin. Very little Chl a-dependent activity could be detected in the cyanobacterial action spectrum, even with additional 600-nm light to excite photosystem II. The depth distribution of photosynthesis showed detectable activity down to a depth of 0.8 to 2.5 mm, where the downwelling radiant flux at 600 nm was reduced to 0.2 to 0.6% of the surface flux.

NASA Discipline Exobiology↗
NASA NTRS1985

The Fibrin slide assay for detecting urokinase activity in human fetal kidney cells

The Fibrin Slide Technique of Hau C. Kwaan and Tage Astrup is discussed. This relatively simple assay involves two steps: the formation of an artificial clot and then the addition of an enzyme (UKOKINASE) to dissolve the clot. The actual dissolving away of the clot is detected by the appearance of holes (lysis zones) in the stained clot. The procedure of Kwaan and Astrup is repeated, along with modifications and suggestions for improvements based on experience with the technique.

Sedor, K.↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
NASA NTRS1995

Immunohistochemical detection of active transforming growth factor-beta in situ using engineered tissue

The biological activity of transforming growth factor-beta 1 (TGF-beta) is governed by dissociation from its latent complex. Immunohistochemical discrimination of active and latent TGF-beta could provide insight into TGF-beta activation in physiological and pathological processes. However, evaluation of immunoreactivity specificity in situ has been hindered by the lack of tissue in which TGF-beta status is known. To provide in situ analysis of antibodies to differentiate between these functional forms, we used xenografts of human tumor cells modified by transfection to overexpress latent TGF-beta or constitutively active TGF-beta. This comparison revealed that, whereas most antibodies did not differentiate between TGF-beta activation status, the immunoreactivity of some antibodies was activation dependent. Two widely used peptide antibodies to the amino-terminus of TGF-beta, LC(1-30) and CC(1-30) showed marked preferential immunoreactivity with active TGF-beta versus latent TGF-beta in cryosections. However, in formalin-fixed, paraffin-embedded tissue, discrimination of active TGF-beta by CC(1-30) was lost and immunoreactivity was distinctly extracellular, as previously reported for this antibody. Similar processing-dependent extracellular localization was found with a neutralizing antibody raised to recombinant TGF-beta. Antigen retrieval recovered cell-associated immunoreactivity of both antibodies. Two antibodies to peptides 78-109 showed mild to moderate preferential immunoreactivity with active TGF-beta only in paraffin sections. LC(1-30) was the only antibody tested that discriminated active from latent TGF-beta in both frozen and paraffin-embedded tissue. Thus, in situ discrimination of active versus latent TGF-beta depends on both the antibody and tissue preparation. We propose that tissues engineered to express a specific form of a given protein provide a physiological setting in which to evaluate antibody reactivity with specific functional forms of a protein.

NASA Discipline Radiation Health↗
NASA NTRS2018

Method and System for Multi-Path Active Defect Detection, Localization and Characterization with Ultrasonic Guided Waves

A method and system of detecting, localizing, and characterizing a defect at one or more spatial points of interest on a structure. The method may include collecting first data in a first state using one or more transducers on the structure, collecting second data in a second state subsequent to the first state, computing a scattered impulse response based on the collected first data and the collected second data, comparing the scattered impulse response with an estimated scattered impulse response corresponding to the case when damage is present at one or more spatial points of interest on the structure, and combining the generated comparison results to detect, localize, and characterize a defect at the one or more spatial points of interest on the structure.

Hall, James Stroman↗