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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2020

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Measurement report: Molecular composition, optical properties, and radiative effects of water-soluble organic carbon in snowpack samples from northern Xinjiang, China

Abstract. Water-soluble organic carbon (WSOC) in the cryosphere has an important impact on the biogeochemistry cycling and snow–ice surface energy balance through changes in the surface albedo. This work reports on the chemical characterization of WSOC in 28 representative snowpack samples collected across a regional area of northern Xinjiang, northwestern China. We employed multimodal analytical chemistry techniques to investigate both bulk and molecular-level composition of WSOC and its optical properties, informing the follow-up radiative forcing (RF) modeling estimates. Based on the geographic differences and proximity of emission sources, the snowpack collection sites were grouped as urban/industrial (U), rural/remote (R), and soil-influenced (S) sites, for which average WSOC total mass loadings were measured as 1968 ± 953 ng g−1 (U), 885 ± 328 ng g−1 (R), and 2082 ± 1438 ng g−1 (S), respectively. The S sites showed the higher mass absorption coefficients at 365 nm (MAC365) of 0.94 ± 0.31 m2 g−1 compared to those of U and R sites (0.39 ± 0.11 m2 g−1 and 0.38 ± 0.12 m2 g−1, respectively). Bulk composition of WSOC in the snowpack samples and its basic source apportionment was inferred from the excitation–emission matrices and the parallel factor analysis featuring relative contributions of one protein-like (PRLIS) and two humic-like (HULIS-1 and HULIS-2) components with ratios specific to each of the S, U, and R sites. Additionally, a sample from site 120 showed unique pollutant concentrations and spectroscopic features remarkably different from all other U, R, and S samples. Molecular-level characterization of WSOC using high-resolution mass spectrometry (HRMS) provided further insights into chemical differences among four types of samples (U, R, S, and 120). Specifically, many reduced-sulfur-containing species with high degrees of unsaturation and aromaticity were uniquely identified in U samples, suggesting an anthropogenic source. Aliphatic/protein-like species showed the highest contribution in R samples, indicating their biogenic origin. The WSOC components from S samples showed high oxygenation and saturation levels. A few unique CHON and CHONS compounds with high unsaturation degree and molecular weight were detected in the 120 sample, which might be anthraquinone derivatives from plant debris. Modeling of the WSOC-induced RF values showed warming effects of 0.04 to 0.59 W m−2 among different groups of sites, which contribute up to 16 % of that caused by black carbon (BC), demonstrating the important influences of WSOC on the snow energy budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

Gaytan-Villarreal, Daniel [Carnegie Mellon U.]↗
DOE OSTI2022

Nutrient Restoration of a Large, Impounded, Ultra-Oligotrophic Western River to Recover Declining Native Fishes

Abstract Declines in many fish populations in large, western rivers have been primarily attributed to the anthropogenic reduction of nutrient inputs and subsequent impacts to the food web. The largest known river fertilization program was implemented starting in 2005 on the Kootenai River in northern Idaho to restore resident fisheries. Annual electrofishing surveys were conducted at multiple sites in Idaho and Montana before and during nutrient addition to evaluate assemblage and population-level responses. Although few responses in fish assemblage structure were observed, the addition of liquid ammonium polyphosphate fertilizer (3 μg/L) to the Kootenai River increased fish abundance and biomass over the 20-km stretch of river downstream of the treatment site. Increases were most notable in Largescale Suckers Catostomus macrocheilus, Mountain Whitefish Prosopium williamsoni, and Rainbow Trout Oncorhynchus mykiss populations, although increases in catch and biomass were detected for nearly all fish species. The Kootenai River is approximately 30 times larger in discharge than other rivers that have been experimentally fertilized and provides compelling evidence that the mitigation of nutrient declines in rivers of similar size can result in positive influences on the fish populations where primary and secondary production are limiting growth, survival, and recruitment. However, results from our study also highlight the importance of completing evaluations across varying levels of biological organization (e.g., assemblage and population) and over biologically relevant timeframes.

Hardy, Ryan S.↗
DOE OSTI2020

Proteomic Characterization of Damaged Single Hairs Recovered after an Explosion for Protein-Based Human Identification

Evidence recovery is challenging where an explosion has occurred. Though hair evidence may be sufficiently robust to be recovered at the site, forensic analysis underutilizes the matrix by relying on morphological analysis. Where DNA is compromised, particularly in hair, protein-based human identification presents a promising alternative. Detection of amino acid polymorphisms in hair proteins as genetically variant peptides (GVPs) permits the inference of individualizing single nucleotide polymorphisms for identification. However, an explosive blast may damage hair proteins and compromise GVP identification. This work evaluates effects of an explosive blast on the hair proteome and GVP identification, investigates microscopy as a predictor of proteome profiling success in recovered hairs to improve analysis throughput, and quantifies discriminative power in damaged hairs. The proteomics dataset has been deposited into the ProteomeXchange Consortium (PXD017427). With the exception of degradation in keratins K75, K80, K40, and keratin-associated protein KAP10-11 as markers of hair cuticular damage, corroborated by scanning electron microscopic analysis, minimal hair proteome degradation following explosion allowed successful proteome profiling of single hairs regardless of morphological damage. Finally, GVP identification remained independent of explosion conditions, permitting similar discriminative power between exploded and undamaged hairs. These findings lend greater confidence to GVP analysis in one-inch hairs for forensic identification and provide information about hair protein localization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
NASA NTRS1982

Organic and inorganic interpretations of the Martian UV-IR reflectance spectrum

Viking Lander biology experiment gas chromatography-mass spectrometer analysis has detected no organic molecules above a concentration of parts per billion. The questions considered here include the reason for the lack of organic molecules at the landing sites, whether the sterility of the two sites is representative of the whole of Mars, and whether there may be locations more conducive to organic compound formation and preservation. After simulating the destruction of organic compounds in Mars-like laboratory conditions and examining whether the destructive mechanism might obtain throughout the planet, UV and IR reflectance spectra are re-examined for any evidence of organic molecules and an upper limit is determined for organic carbon content of average Martian soil. It is found that the average Martian soil is poor in inorganics, presents a poor habitat for carbon chemistry-based life forms, and is not likely to preferentially preserve such organisms anywhere on the planet.

Pang, K. D.↗
NASA NTRS1985

Orbit-on-demand vehicles - Mission possible, but one-day turnaround?

The next generation launch-on-demand manned orbital vehicles will ideally be able to perform plane change maneuvers for rendezvous. Plane-changes of, e.g., 11 deg, will require a 3000 fps on-orbit maneuver velocity. Any plane-change capabilities beyond 800 fps rapidly increase the necessary vehicle weight. Sensitivity studies of the payload weight/vehicle weight, the presence or absence of air-breathing engines, high acceleration, etc., indicated that advances are required in the payload stowage pallet, thermal protection system, fault detection electronics, line-replaceable units, and accessibility to realize one-day turnaround. The spacecraft would also need autonomous systems checks and launch from minimum support sites reached by air ferry. A horizontal take-off, two-stage subsonic launch vehicle so far offers the lowest weight, maximum flexibility and minimum support combination of all configurations under consideration.

Talay, T. A.↗
NASA NTRS2000

Soil, Groundwater, Surface Water, and Sediments of Kennedy Space Center, Florida: Background Chemical and Physical Characteristics

This study documented background chemical composition of soils, groundwater, surface; water, and sediments of Kennedy Space Center. Two hundred soil samples were collected, 20 each in 10 soil classes. Fifty-one groundwater wells were installed in 4 subaquifers of the Surficial Aquifer and sampled; there were 24 shallow, 16 intermediate, and 11 deep wells. Forty surface water and sediment samples were collected in major watershed basins. All samples were away from sites of known contamination. Samples were analyzed for organochlorine pesticides, aroclors, chlorinated herbicides, polycyclic aromatic hydrocarbons (PAH), total metals, and other parameters. All aroclors (6) were below detection in all media. Some organochlorine pesticides were detected at very low frequencies in soil, sediment, and surface water. Chlorinated herbicides were detected at very low frequencies in soil and sediments. PAH occurred in low frequencies in soiL, shallow groundwater, surface water, and sediments. Concentrations of some metals differed among soil classes, with subaquifers and depths, and among watershed basins for surface water but not sediments. Most of the variation in metal concentrations was natural, but agriculture had increased Cr, Cu, Mn, and Zn.

Shmalzer, Paul A.↗
NASA NTRS2002

Charged Coupled Device Debris Telescope Observations of the Geosynchronous Orbital Debris Environment - Observing Year: 1998

NASA has been using the charged coupled device (CCD) debris telescope (CDT)--a transportable 32-cm Schmidt telescope located near Cloudcroft, New Mexico-to help characterize the debris environment in geosynchronous Earth orbit (GEO). The CDT is equipped with a SITe 512 x 512 CCD camera whose 24 m2 (12.5 arc sec) pixels produce a 1.7 x 1.7-deg field of view. The CDT system can therefore detect l7th-magnitude objects in a 20-sec integration corresponding to an approx. 0.6-m diameter, 0.20 albedo object at 36,000 km. The telescope pointing and CCD operation are computer controlled to collect data automatically for an entire night. The CDT has collected more than 1500 hrs of data since November 1997. This report describes the collection and analysis of 58 nights (approx. 420 hrs) of data acquired in 1998.

Jarvis, K. S.↗
NASA NTRS2005

Model Development and Testing for THEMIS Controlled Mars Mosaics

As part of our work [1] to develop techniques and procedures to create regional and eventually global THEMIS mosaics of Mars, we are developing algorithms and software to photogrammetrically control THEMIS IR line scanner camera images. We have found from comparison of a limited number of images to MOLA digital image models (DIMs) [2] that the a priori geometry information (i.e. SPICE [3]) for THEMIS images generally allows their relative positions to be specified at the several pixel level (e.g. approx.5 to 13 pixels). However a need for controlled solutions to improve this geometry to the sub-pixel level still exists. Only with such solutions can seamless mosaics be obtained and likely distortion from spacecraft motion during image collection removed at such levels. Past experience has shown clearly that such mosaics are in heavy demand by users for operational and scientific use, and that they are needed over large areas or globally (as opposed to being available only on a limited basis via labor intensive custom mapping projects). Uses include spacecraft navigation, landing site planning and mapping, registration of multiple data types and image sets, registration of multispectral images, registration of images with topographic information, recovery of thermal properties, change detection searches, etc.

Archinal, B. A.↗
NASA NTRS2007

Alaska at the Crossroads of Migration: Space Based Ornithology

Understanding bird migration on a global scale is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Revolutionary advances in remote sensing now provide us with near real-time measurements of atmospheric and land surface conditions at high spatial resolution over entire continents. We use spatially-explicit, individual based bird migration models driven by numerical weather prediction models of atmospheric conditions, dynamic habitat suitability maps derived from remotely sensed land surface conditions, biophysiological models, and biological field data to simulate migration routes, timing, energy budgets, and survival of individual birds and populations. Long-distance migratory birds travel annually between breeding grounds in Alaska and wintering grounds in Latin Amierica. Approximately 25% of these species are potential vectors of Avian Influenza. Alaska is at the crossroads of Asian and New World migratory flyways and is likely to be a point of introduction of Asian H5N1 AI into the western hemisphere. If/when an infected bird is detected, a pressing question will be where was this bird several days ago, and where is it likely to go after it was released from the survey site? Answers to such questions will increase effectiveness of AI surveillance and mitigation measures. From a conservation perspective, Alaska's diverse landscape provides breeding sites for many migrants, and climatic and land surface changes along migratory flyways in the western hemisphere may reduce bird survival and physical condition upon arrival at Alaskan breeding territories, success and migrant populations.

Deppe, Jill↗
NASA NTRS2008

A Self Contained Method for Safe and Precise Lunar Landing

The return of humans to the Moon will require increased capability beyond that of the previous Apollo missions. Longer stay times and a greater flexibility with regards to landing locations are among the many improvements planned. A descent and landing system that can land the vehicle more accurately than Apollo with a greater ability to detect and avoid hazards is essential to the development of a Lunar Outpost, and also for increasing the number of potentially reachable Lunar Sortie locations. This descent and landing system should allow landings in more challenging terrain and provide more flexibility with regards to mission timing and lighting considerations, while maintaining safety as the top priority. The lunar landing system under development by the ALHAT (Autonomous precision Landing and Hazard detection Avoidance Technology) project is addressing this by providing terrain-relative navigation measurements to enhance global-scale precision, an onboard hazard-detection system to select safe landing locations, and an Autonomous GNC (Guidance, Navigation, and Control) capability to process these measurements and safely direct the vehicle to this landing location. This ALHAT landing system will enable safe and precise lunar landings without requiring lunar infrastructure in the form of navigation aids or a priori identified hazard-free landing locations. The safe landing capability provided by ALHAT uses onboard active sensing to detect hazards that are large enough to be a danger to the vehicle but too small to be detected from orbit, given currently planned orbital terrain resolution limits. Algorithms to interpret raw active sensor terrain data and generate hazard maps as well as identify safe sites and recalculate new trajectories to those sites are included as part of the ALHAT System. These improvements to descent and landing will help contribute to repeated safe and precise landings for a wide variety of terrain on the Moon.

Paschall, Stephen C., II↗
NASA NTRS2010

Hematite at Meridiani Planum, Mars, Investigated by Simultaneous Fitting of MER Mossbauer Spectra

The Mars Exploration Rover Opportunity encountered sedimentary outcrop rocks at its landing site. Spherules with diameters in the millimeter range were found to weather from the outcrop rocks. With Opportunity s miniaturised M ssbauer spectrometer MIMOS II, hematite was detected in spherules and in the outcrop matrix [1,2]. Figure 1 shows the target Berry Bowl, where brushed outcrop and an accumulation of spherules could be investigated on sols 46 and 48 of Opportunity s mission. Hematite undergoes a transition from a weakly ferromagnetic above to an antiferromagnetic state below the Morin temperature (T(sub M) approx.265 K for chemically pure, crystalline hematite). The magnetic hyperfine splitting (B(sub hf)) shows a general decrease with increasing temperature and a drop of approx.0.8 T at T(sub M). The quadrupole splitting ((Delta)EQ) changes its sign at T(sub M), with negative values above and positive values below the transition. Crystallinity and particle size influence the magnitude and temperature dependence of the magnetic splitting and the quadrupole splitting [3].

Fleischer, I.↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Rutishauser, David↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully Integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Epp, Chirold D.↗
NASA NTRS2018

Optical Polarisation Variability of Radio-Loud Narrow-Line Seyfert 1 Galaxies: Search for Long Rotations of the Polarisation Plane

This work describes multi-site optical polarimetric observations of several radio-loud Narrow Line Seyfert 1 galaxies. This source class, and the sources in this study, were discovered to be detectable at gamma-ray energies by the Large Area Telescope (LAT) aboard the Fermi satellite. GSFC has played a key role in the development, launch, and operation of Fermi/LAT.

Angelakis, E.↗