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NASA NTRS2001

Aircraft Operations Classification System

Accurate data is important in the aviation planning process. In this project we consider systems for measuring aircraft activity at airports. This would include determining the type of aircraft such as jet, helicopter, single engine, and multiengine propeller. Some of the issues involved in deploying technologies for monitoring aircraft operations are cost, reliability, and accuracy. In addition, the system must be field portable and acceptable at airports. A comparison of technologies was conducted and it was decided that an aircraft monitoring system should be based upon acoustic technology. A multimedia relational database was established for the study. The information contained in the database consists of airport information, runway information, acoustic records, photographic records, a description of the event (takeoff, landing), aircraft type, and environmental information. We extracted features from the time signal and the frequency content of the signal. A multi-layer feed-forward neural network was chosen as the classifier. Training and testing results were obtained. We were able to obtain classification results of over 90 percent for training and testing for takeoff events.

Harlow, Charles↗
NASA NTRS2017

Effects of One Year of Spaceflight on Neurocognitive Function

It is known that spaceflight adversely affects human sensorimotor function. With interests in longer duration deep space missions it is important to understand microgravity dose-response relationships. NASA's One Year Mission project allows for comparison of the effects of one year in space with those seen in more typical six month missions to the International Space Station. In the Neuromapping project we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre- to post-spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad-ranging battery of sensory, motor, and cognitive assessments that are conducted pre-flight, during flight, and post-flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. With the one year mission we had one crewmember participate in all of the same measures pre-, per- and post-flight as in our ongoing study. During this presentation we will provide an overview of the magnitude of changes observed with our brain and behavioral assessments for the one year crewmember in comparison to participants that have completed our six month study to date.

Seidler, R. D.↗
NASA NTRSDate not supplied

Document Classification Techniques for Aviation Letters of Agreement

Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.

ATM↗
NASA NTRSDate not supplied

Document Classification Techniques for Aviation Letters of Agreement

Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.

ATM↗
NASA NTRSDate not supplied

Document Classification Techniques for Aviation Letters of Agreement

Often when working with technical documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques, including Transfer Learning, for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.

Aayushi Batra↗
NASA NTRS2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Rainfall↗
NASA NTRS1990

Where two frontiers meet

Current research projects conducted at the JPL Center for Space Microelectronics Technology are briefly reviewed. These include the development of a new miniature tunneling Goday cell that can be made into arrays. This tunneling IR detector is sensitive to radiation through the infrared and is particularly suited to room temperature measurements of the mid to far infrared. Other developments include ballistic-electron-emission microscopy, a next-generation spacecraft on-board computer using the Direct Memory Access Co-Processor, a 1-sq cm custom chip with 40,000 transistors, new neural network chips, the third-generation hypercube, and a new Hyperswitch Adaptive Message routing VLSI chip.

Kukkonen, Carl↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS2003

NASA Tech Briefs, June 2003

Topics covered include: Nulling Infrared Radiometer for Measuring Temperature; The Ames Power Monitoring System; Hot Films on Ceramic Substrates for Measuring Skin Friction; Probe Without Moving Parts Measures Flow Angle; Detecting Conductive Liquid Leaking from Nonconductive Pipe; Adaptive Suppression of Noise in Voice Communications; High-Performance Solid-State W-Band Power Amplifiers; Microbatteries for Combinatorial Studies of Conventional Lithium-Ion Batteries; Correcting for Beam Aberrations in a Beam-Waveguide Antenna; Advanced Rainbow Solar Photovoltaic Arrays; Metal Side Reflectors for Trapping Light in QWIPs; Software for Collaborative Engineering of Launch Rockets; Software Assists in Extensive Environmental Auditing; Software Supports Distributed Operations via the Internet; Software Estimates Costs of Testing Rocket Engines; yourSky: Custom Sky-Image Mosaics via the Internet; Software for Managing Inventory of Flight Hardware; Lower-Conductivity Thermal-Barrier Coatings; Process for Smoothing an Si Substrate after Etching of SiO2; Flexible Composite-Material Pressure Vessel; Treatment to Destroy Chlorohydrocarbon Liquids in the Ground; Noncircular Cross Sections Could Enhance Mixing in Sprays; Small, Untethered, Mobile Roots for Inspecting Gas Pipes; Paint-Overspray Catcher; Preparation of Regular Specimens for Atom Probes; Inverse Tomo-Lithography for Making Microscopic 3D Parts; Predicting and Preventing Incipient Flameout in Combustors; MEMS-Based Piezoelectric/Electrostatic Inchworm Actuator; Metallized Capillaries as Probes for Raman Spectroscopy; Adaptation of Mesoscale Weather Models to Local Forecasting; Aerodynamic Design using Neural Networks; Combining Multiple Gyroscope Outputs for Increased Accuracy; and Improved Collision-Detection Method for Robotic Manipulator.

Source record↗
NASA NTRS2005

DEVELOPMENT OF AN INFLIGHT COUNTERMEASURE TO MITIGATE POSTFLIGHT GAIT DYSFUNCTION

Following spaceflight crewmembers experience gait and postural instabilities due to inflight adaptive alterations in sensorimotor function. These changes can pose a risk to crew safety if nominal or emergency vehicle egress is required immediately following long-duration spaceflight. At present, no operational countermeasure is available to mitigate postflight locomotor disturbances. Therefore, the goal of this study is to develop an inflight training regimen that facilitates the recovery of locomotor function after long-duration spaceflight. The countermeasure we are developing is based on the concept of variable practice. During this type of training the subject gains experience producing the appropriate adaptive motor behavior under a variety of sensory conditions and response constraints. This countermeasure is built around current ISS treadmill exercise activities. Crewmembers will conduct their nominal inflight treadmill exercise while being exposed to variations in visual flow patterns. These variations will challenge the postural and locomotor systems repeatedly, thereby promoting adaptive reorganization in locomotor behavior. As a result of this training a subject learns to solve a class of motor problems, rather than a specific motor solution to one problem, Le., the subject learns response generalizability or the ability to "learn to learn" under a variety of environmental constraints. We anticipate that this training will accelerate recovery of postural and locomotor function during readaptation to gravitational environments following spaceflight facilitating neural adaptation to unit (Earth) and partial (Mars) gravity after long-duration spaceflight. The study calls for one group of subjects to perform the inflight treadmill training regimen while a control group of subjects performs only the nominal exercise procedures. Locomotor function in both groups is assessed before and after spaceflight using two tests of gait function: The Integrated Treadmill Locomotion Test (ITLT) and the Functional Mobility Test (FMT). The ITLT characterizes alterations in the integrated function of multiple sensorimotor subsystems responsible for the control of locomotion. This test calls for subjects to walk on a motorized treadmill while we assess changes in dynamic postural stability, head-trunk coordination, short-latency head stabilization responses, dynamic visual acuity, lower limb coordination strategies and gait cycle timing. To make these assessments we measure the following parameters while subjects walk on the treadmill: 1) full body 3-dimensional kinematics using a motion capture system (Motion Analysis Corp., Santa Rosa, CA); 2) the shock-wave transmitted from heel-strike to the head using triaxial accelerometers placed on the tibia and head (Entran, Fairfield, NJ); 3) vertical forces using an instrumented treadmill (Kistler Instrument Corp., Amherst, NY); 4) Dynamic visual acuity using Landolt Cs presented on a laptop computer located 4m from the eyes and 5) Gait cycle timing using foot-switches (Motion Lab Systems, Inc., Baton Rouge, LA) attached to the plantar surface of each shoe at the heel and toe. The FMT evaluates a subject's ability to perform challenging locomotor maneuvers similar to those encountered during an egress from a space vehicle. Subjects step over and duck under obstacles along with negotiating a series of pylons set up on a base of 10 cm thick medium density foam. The dependent measures for the FMT are time to complete the course and the number of obstacles touched. To date, we have collected pre and postflight locomotion data from Expeditions 5-9 who will serve as part of the control group for this study. Preliminary results comparing the recovery rates in gait control sub-systems obtained from the ITLT and FMT performance showed two recovery patterns: 1) a concordant recovery trend between gait control parameters and FMT performance indicating a restitution pattern of recovery and 2) gait controecovery that lagged recovery in FMT performance suggesting that improvement in locomotor function was attained through a pattern of substitution. These data suggest that recovery of postflight locomotor function may occur through adaptive mechanisms that lead to either restitution or substitution of function. Understanding the modes of postflight readaptation has implications for countermeasure development and testing and in astronaut postflight rehabilitation.

Bloomberg, J. J.↗
NASA NTRS2005

Development of an Inflight Countermeasure to Mitigate Postflight Gait Dysfunction

Following spaceflight crewmembers experience gait and postural instabilities due to inflight adaptive alterations in sensorimotor function. These changes can pose a risk to crew safety if nominal or emergency vehicle egress is required immediately following long-duration spaceflight. At present, no operational countermeasure is available to mitigate postflight locomotor disturbances. Therefore, the goal of this study is to develop an inflight training regimen that facilitates the recovery of locomotor function after long-duration spaceflight. The countermeasure we are developing is based on the concept of variable practice. During this type of training the subject gains experience producing the appropriate adaptive motor behavior under a variety of sensory conditions and response constraints. This countermeasure is built around current ISS treadmill exercise activities. Crewmembers will conduct their nominal inflight treadmill exercise while being exposed to variations in visual flow patterns. These variations will challenge the postural and locomotor systems repeatedly, thereby promoting adaptive reorganization in locomotor behavior. As a result of this training a subject learns to solve a class of motor problems, rather than a specific motor solution to one problem, Le., the subject learns response generalizability or the ability to "learn to learn" under a variety of environmental constraints. We anticipate that this training will accelerate recovery of postural and locomotor function during readaptation to gravitational environments following spaceflight facilitating neural adaptation to unit (Earth) and partial (Mars) gravity after long-duration spaceflight. The study calls for one group of subjects to perform the inflight treadmill training regimen while a control group of subjects performs only the nominal exercise procedures. Locomotor function in both groups is assessed before and after spaceflight using two tests of gait function: The Integrated Treadmill Locomotion Test (ITLT) and the Functional Mobility Test (FMT). The ITLT characterizes alterations in the integrated function of multiple sensorimotor subsystems responsible for the control of locomotion. This test calls for subjects to walk on a motorized treadmill while we assess changes in dynamic postural stability, head-trunk coordination, short-latency head stabilization responses, dynamic visual acuity, lower limb coordination strategies and gait cycle timing. To make these assessments we measure the following parameters while subjects walk on the treadmill: 1) full body 3-dimensional kinematics using a motion capture system (Motion Analysis Corp., Santa Rosa, CA); 2) the shock-wave transmitted from heel-strike to the head using triaxial accelerometers placed on the tibia and head (Entran, Fairfield, NJ); 3) vertical forces using an instumented treadmill (Kistler Instrument Corp., Amherst, NY); 4) Dynamic visual acuity using Landolt Cs presented on a laptop computer located 4m from the eyes and 5) Gait cycle timing using foot-switches (Motion Lab Systems, Inc., Baton Rouge, LA) attached to the plantar surface of each shoe at the heel and toe. The FMT evaluates s. subject's ability to perform challenging locomotor maneuvers similar to those encountered during an egress from a space vehicle. Subjects step over and duck under obstacles along with negotiating a series of pylons set up on a base of 10 cm thick medium density foam. The dependent measures for the FMT are time to complete the course and the number of obstacles touched. To date, we have collected pre and postflight locomotion data from Expeditions 5-9 who will serve as part of the control group for this study. Preliminary results comparing the recovery rates in gait control sub-systems obtained from the ITLT and FMT performance showed two recovery patterns: 1) a concordant recovery trend between gait control parameters and FMT performance indicating a restitution pattern of recovery and 2) gait controecovery that lagged recovery in FMT performance suggesting that improvement in locomotor function was attained through a pattern of substitution. These data suggest that recovery of postflight locomotor function may occur through adaptive mechanisms that lead to either restitution or substitution of function. Understanding the modes of postflight readaptation has implications for countermeasure development and testing and in astronaut postflight rehabilitation.

Bloomberg, J. J.↗
NASA NTRS2008

Model-Based Fault Tolerant Control

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

Kumar, Aditya↗
NASA NTRS2011

Developing Tests of Visual Dependency

Astronauts develop neural adaptive responses to microgravity during space flight. Consequently these adaptive responses cause maladaptive disturbances in balance and gait function when astronauts return to Earth and are re-exposed to gravity. Current research in the Neuroscience Laboratories at NASA-JSC is focused on understanding how exposure to space flight produces post-flight disturbances in balance and gait control and developing training programs designed to facilitate the rapid recovery of functional mobility after space flight. In concert with these disturbances, astronauts also often report an increase in their visual dependency during space flight. To better understand this phenomenon, studies were conducted with specially designed training programs focusing on visual dependency with the aim to understand and enhance subjects ability to rapidly adapt to novel sensory situations. The Rod and Frame test (RFT) was used first to assess an individual s visual dependency, using a variety of testing techniques. Once assessed, subjects were asked to perform two novel tasks under transformation (both the Pegboard and Cube Construction tasks). Results indicate that head position cues and initial visual test conditions had no effect on an individual s visual dependency scores. Subjects were also able to adapt to the manual tasks after several trials. Individual visual dependency correlated with ability to adapt manual to a novel visual distortion only for the cube task. Subjects with higher visual dependency showed decreased ability to adapt to this task. Ultimately, it was revealed that the RFT may serve as an effective prediction tool to produce individualized adaptability training prescriptions that target the specific sensory profile of each crewmember.

Kindrat, Alexandra N.↗
NASA NTRS2006

Flight Test of an Adaptive Controller and Simulated Failure/Damage on the NASA NF-15B

The method of flight-testing the Intelligent Flight Control System (IFCS) Second Generation (Gen-2) project on the NASA NF-15B is herein described. The Gen-2 project objective includes flight-testing a dynamic inversion controller augmented by a direct adaptive neural network to demonstrate performance improvements in the presence of simulated failure/damage. The Gen-2 objectives as implemented on the NASA NF-15B created challenges for software design, structural loading limitations, and flight test operations. Simulated failure/damage is introduced by modifying control surface commands, therefore requiring structural loads measurements. Flight-testing began with the validation of a structural loads model. Flight-testing of the Gen-2 controller continued, using test maneuvers designed in a sequenced approach. Success would clear the new controller with respect to dynamic response, simulated failure/damage, and with adaptation on and off. A handling qualities evaluation was conducted on the capability of the Gen-2 controller to restore aircraft response in the presence of a simulated failure/damage. Control room monitoring of loads sensors, flight dynamics, and controller adaptation, in addition to postflight data comparison to the simulation, ensured a safe methodology of buildup testing. Flight-testing continued without major incident to accomplish the project objectives, successfully uncovering strengths and weaknesses of the Gen-2 control approach in flight.

Buschbacher, Mark↗
NASA NTRS2009

The Effect of Center of Gravity and Anthropometrics on Human Performance in Simulated Lunar and Mars Gravity

Drawing from the experiences of the Apollo missions, it is evident that the off nominal center of gravity (CG) induced by the portable life support system (PLSS) had significant impact on the locomotion stability of the crew. This in turn is believed to have been a major contributor to the high numbers of falls and high metabolic rates experienced by the crew, and thus significantly hampered the crew s performance. With this in mind, the EVA Physiology, Systems and Performance (EPSP) group at the NASA Johnson Space Center (JSC) has been conducting tests to assess how spacesuit CG location impacts human performance in simulated lunar and Mars gravity. The results acquired to date show correlations between CG location and performance. However, noticeable variations in the performance data have been observed across subjects for fixed CG configurations. Consequently, it was hypothesized that this variability may be attributed to the anthropometrics of the different test subjects. It was further hypothesized that trunk-to-height ratio (THR) may be directly correlated to performance in reduced gravity; i.e. subjects with increased THR may have increased performance. To test this hypothesis, lunar and Mars gravity test data acquired over three years during NASA Neural Buoyancy Lab (NBL) tests and NASA Extreme Environment Missions Operation (NEEMO) missions were analyzed against THR, height, trunk length, and subject body mass/weight. The results of the study supported the hypothesis relating THR and performance, while the other three anthropometric parameters did not provide consistent correlations with performance. This in turn suggests that human performance in reduced gravity may be more dependent on anthropometric proportions than on body segment lengths and mass/weight.

Mulugeta, Lealem↗
NASA NTRS1981

Visually induced self-motion sensation adapts rapidly to left-right reversal of vision

Three experiments were conducted using 15 adult volunteers with no overt oculomotor or vestibular disorders. In all experiments, left-right vision reversal was achieved using prism goggles, which permitted a binocular field of vision subtending approximately 45 deg horizontally and 28 deg vertically. In all experiments, circularvection (CV) was tested before and immediately after a period of exposure to reversed vision. After one to three hours of active movement while wearing vision-reversing goggles, 10 of 15 (stationary) human subjects viewing a moving stripe display experienced a self-rotation illusion in the same direction as seen stripe motion, rather than in the opposite (normal) direction, demonstrating that the central neural pathways that process visual self-rotation cues can undergo rapid adaptive modification.

Oman, C. M.↗
NASA NTRSDate not supplied

Unlocking the Mysteries of the Moon’s Shadowed Regions

The Moon poles host large quantities of water-ice deposits in the permanently shadowed regions (PSRs), which are vital for enabling sustainable human space exploration, making these regions high-priority targets for upcoming Artemis missions [1]. Unfortunately, today, the best available orbital lunar imagery [2, 3] lacks the meter-scale resolution and signal needed to understand the geomorphology and trafficability of PSRs, complicating the planning and execution of future missions seeking to explore PSRs. We have developed an image enhancement tool called HORUS (Hyper-effective nOise Removal Unet Software) [4, 5], designed to enhance LRO Narrow-Angle Camera (NAC) optical low-light imagery of permanently shadowed regions by effectively removing the CCD-related, photon, and other residual noises that corrupt the images. The tool is composed of two deep learning neural networks trained on environmental metadata and real and synthetic imagery, the latter generated by a physical noise model (LROC). We demonstrated that HORUS effectively produces low-noise, high-resolution images (~1.5m/px), achieving a 5 to 10x improvement over existing long-exposure images of PSRs. HORUS allows scientists and engineers to identify geomorphic features (e.g., craters and boulders) in shadowed regions as small as 3 meters across as well as to peek inside of small shadowed regions, for the first time. The tool was deployed and thoroughly validated for NASA's VIPER mission [6], where it was applied to 20 candidate target regions across the lunar South Pole. Additionally, we conducted different approaches to validate the resulting HORUS-processed images. With HORUS denoised images, VIPER scientists can increase their confidence on what surface features (previously unseen) exist in the shadowed regions, helping them plan rover traverses more safely and efficiently (e.g., Fig. 1) In this manuscript, we will describe how VIPER scientists are utilizing HORUS denoised images to extract new information from the terrain and increase their confidence in what surface features exist in the shadowed regions. In combination with other high-resolution images and digital elevation maps, HORUS images are helping the team analyze potential lading and science sites, as well as planning traverses more safely and efficiently (e.g., Fig. 1). Additionally, we will describe how HORUS tool unlocks a broad range of scientific and exploration applications to other Artemis and CPLS missions to the lunar poles, including (but not limited to) geomorphic analysis, change detection, surface hazard detection, and terrain relative navigation.

artificial intelligence↗
NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗