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NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗

NASA NTRSDate not supplied

Ground Stop Adjuster: A Machine Learning Approach to Improve Air Traffic Management Initiatives

Traffic Management Initiatives (TMIs) play a crucial role in balancing demand and capacity within the U.S. National Airspace System (NAS). In current practice, traffic management coordinators (TMCs) determine and issue TMIs and recent research has explored the use of machine learning tools to aid the TMCs. However, most studies have primarily focused on a particular type of TMI, i.e., Ground Delay Programs (GDPs) due to their higher rate of occurrence and longer duration. This study investigates a machine learning approach for monitoring and adjusting a different type of TMI, i.e., Ground Stop (GS), aiming to assist human decision-makers with accurate, consistent, and timely recommendations. Using data from three major airports in the New York metroplex, we evaluated models that predict GS parameters, such as duration and scope. Our results demonstrate that using data from all airports in the NY metroplex and increasing feature granularity improve the prediction accuracy of the ML models.

Farzan Masrour Shalmani↗

NASA NTRS2000

EPA/NASA/USAF Depainting Effort Concludes

The final report contains strip rate data from all of the methods, lessons learned during processing, metallurgical evaluations of the panels, and summaries of corrosion and hydrogen embrittlement studies. Any changes in surface roughness, fatigue and tensile properties, and crack detectability are noted in the report. No process was singled out above the others, as companies should consider equipment and operational costs when complying with the Aerospace NESHAP (National Emission Standards for Hazardous Air Pollutants) and new OSHA (Occupational Safety and Health Administration) regulations.

Finckenor, Miria M.↗

NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗

NASA NTRS2020

The fate of organic carbon in marine sediments - New insights from recent data and analysis

Organic carbon in marine sediments is a critical component of the global carbon cycle, and its degradation influences a wide range of phenomena, including the magnitude of carbon sequestration over geologic timescales, the recycling of inorganic carbon and nutrients, the dissolution and precipitation of carbonates, the production of methane and the nature of the seafloor biosphere. Although much has been learned about the factors that promote and hinder rates of organic carbon degradation in natural systems, the controls on the distribution of organic carbon in modern and ancient sediments are still not fully understood. In this review, we summarize how recent findings are changing entrenched perspectives on organic matter degradation in marine sediments: a shift from a structurally-based chemical reactivity viewpoint towards an emerging acceptance of the role of the ecosystem in organic matter degradation rates. That is, organic carbon has a range of reactivities determined by not only the nature of the organic compounds, but by the biological, geochemical, and physical attributes of its environment. This shift in mindset has gradually come about due to a greater diversity of sample sites, the molecular revolution in biology, discoveries concerning the extent and limits of life, advances in quantitative modeling, investigations of ocean carbon cycling under a variety of extreme paleo-conditions (e.g. greenhouse environments, euxinic/anoxic oceans), the application of novel analytical techniques and interdisciplinary efforts. Adopting this view across scientific disciplines will enable additional progress in understanding how marine sediments influence the global carbon cycle.

Organic carbon↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

NASA NTRS2023

Psychophysiological Research Methods to Assess Airline Flight Crew Resilient Performance in High-Fidelity Flight Simulation Scenarios

New concepts in aviation system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right. This approach forms a more comprehensive approach to system safety thinking. A need exists for methods to enable a better understanding of human contributions to aviation safety and how they may inform Safety Management Systems (SMS). A high-fidelity 737-800 simulation study was conducted to study how current type-rated commercial airline flight crews anticipate, monitor, respond to, and learn from expected and unexpected disturbances during line operations. A number of dependent measures were collected that included traditional SMS data types, but also non-traditional safety data to include multiple psychophysiological metrics. This paper describes the psychophysiological measures results that evinced the capability of measures to help identify resilient flight crews. Implications for future research and design of future In-time Aviation Safety Management Systems are discussed.

Psychophysiology↗

NASA NTRS2023

Psychophysiological Research Methods to Assess Airline Flight Crew Resilient Performance in High-Fidelity Flight Simulation Scenarios

Psychophysiology↗

NASA NTRS1972

An improved learning decoder

Learning decoder was developed which operates at system data rate without limiting data rate. Decoder is much simpler than those in existence, operates near Shannon's channel capacity, and automatically recovers operation after loss of signal.

Doland, G. D.↗

NASA NTRS1990

Assessment of the pharmacodynamics of intranasal, intravenous and oral scopolamine

Space motion sickness is an important issue in the space medical sciences program. One of the objectives of the ongoing clinical experimental protocol Pharmacokinetics of Intranasal Scopolamine in Normal Subjects is to evaluate the pharmacodynamics of scopolamine using salivary flow rate and pH profiles and cognitive performance tests as pharmacodynamic parameters. Normal volunteers collected saliva and performed the NTI Multiresource Performance Battery tests at designed time intervals to establish control saliva flow rates, salivary pH profiles, and the characteristics of the learning curve for the performance program under normal conditions. In the clinical part of the study, saliva samples and performance test scores are collected from healthy nonsmoking subjects after receiving a single 0.4 mg dose of either intranasal, intravenous, or oral scopolamine.

Tietze, Karen J.↗

NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗

NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗

NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗

NASA NTRS2005

Stable Isotope Characteristics of Jarosite: The Acidic Aqueous History of Mars

The Mars Rover Opportunity found jarosite (Na(+) or K(+))Fe3SO4(OH)6 at the Meridiani Planum site. This mineral forms from the evaporation of an aqueous acidic sulfate brine. Oxygen isotope compositions may characterize formation conditions but subsequent isotope exchange may have occurred between the sulfate and hydroxide of jarosite and water. The rate of oxygen isotope exchange depends on the acidity and temperature of the brine, but it has not been investigated in detail. We performed laboratory experiments to determine the rate of oxygen isotope exchange under varying acidities and temperatures to learn more about this process. Barium sulfate samples were precipitated weekly from acidic sodium sulfate brines. The oxygen isotope composition of the precipitated sulfate was obtained using a Finnigan MAT253 Isotope Ratio Mass-Spectrometer. The results show that water was trapped in barium sulfate during precipitation. Trapped water may exchange with sulfate when exposed to high temperatures, thus changing the isotope composition of sulfate and the observed fractionation factor of oxygen isotope exchange between sulfate and water. The results of our research will contribute to the understanding of oxygen isotope exchange rates between water and sulfate under acidic conditions and provide experimental knowledge for the dehydration of barium sulfate samples.

Earl, Lyndsey D.↗

NASA NTRS2011

Orion Entry Handling Qualities Assessments

The Orion Command Module (CM) is a capsule designed to bring crew back from the International Space Station (ISS), the moon and beyond. The atmospheric entry portion of the flight is deigned to be flown in autopilot mode for nominal situations. However, there exists the possibility for the crew to take over manual control in off-nominal situations. In these instances, the spacecraft must meet specific handling qualities criteria. To address these criteria two separate assessments of the Orion CM s entry Handling Qualities (HQ) were conducted at NASA s Johnson Space Center (JSC) using the Cooper-Harper scale (Cooper & Harper, 1969). These assessments were conducted in the summers of 2008 and 2010 using the Advanced NASA Technology Architecture for Exploration Studies (ANTARES) six degree of freedom, high fidelity Guidance, Navigation, and Control (GN&C) simulation. This paper will address the specifics of the handling qualities criteria, the vehicle configuration, the scenarios flown, the simulation background and setup, crew interfaces and displays, piloting techniques, ratings and crew comments, pre- and post-fight briefings, lessons learned and changes made to improve the overall system performance. The data collection tools, methods, data reduction and output reports will also be discussed. The objective of the 2008 entry HQ assessment was to evaluate the handling qualities of the CM during a lunar skip return. A lunar skip entry case was selected because it was considered the most demanding of all bank control scenarios. Even though skip entry is not planned to be flown manually, it was hypothesized that if a pilot could fly the harder skip entry case, then they could also fly a simpler loads managed or ballistic (constant bank rate command) entry scenario. In addition, with the evaluation set-up of multiple tasks within the entry case, handling qualities ratings collected in the evaluation could be used to assess other scenarios such as the constant bank angle maintenance case. The 2008 entry assessment was divided into two sections (see Figure 1). Entry I was the first, high speed portion of a lunar return and Entry II was the second, lower speed portion of a lunar return, which is similar (but not identical) to a typical ISS return.

Bihari, B.↗

NASA NTRS2020

ISEE System Technical Report

The Immersive Simulations and Engineering Environment (ISEE) Laboratory at Kennedy Space Center is a tool used to assist with operation simulation, for Human Factors operation activities, assisting future missions. The simulations created are important because they ensure that simulated operations are performed for customers, to assist with requirements. Simultaneously, each simulation will allow stakeholders to determine any and all risk associated with the Human Factors involved in a specific assignment. My internship consists of multiple project elements. The first of which, was to assist the Integrations branch chief in developing a growth rate algorithm proof. The integral part of this project, involved learning to powerup and perform the operational procedures of the ISEE lab, capturing and creating characters used in the CAD model virtual environment. In doing so, this led to use the software to model and 3D print screw cap structures for mock-up. The structures will be used to manipulate weight change out used in mock-up design and construction. These structures will also be used in future mock-up models constructed in the lab.

Darryl A J Minor↗

NASA NTRS2016

Lessons Learned while Exploring Cloud-Native Architectures for NASA EOSDIS Applications and Systems

As new, high data rate missions begin collecting data, the NASAs Earth Observing System Data and Information System (EOSDIS) archive is projected to grow roughly 20x to over 300PBs by 2025. To prepare for the dramatic increase in data and enable broad scientific inquiry into larger time series and datasets, NASA has been exploring the impact of applying cloud technologies throughout EOSDIS. In this talk we will provide an overview of NASAs prototyping and lessons learned in applying cloud architectures.

NGAP↗

NASA NTRS2014

Lessons Learned in the First Year Operating Software Defined Radios in Space

Operating three unique software defined radios (SDRs) in a space environment aboard the Space Communications and Navigation (SCaN) Testbed for over one year has provided an opportunity to gather knowledge useful for future missions considering using software defined radios. This paper provides recommendations for the development and use of SDRs, and it considers the details of each SDRs approach to software upgrades and operation. After one year, the SCaN Testbed SDRs have operated for over 1000 hours. During this time, the waveforms launched with the SDR were tested on-orbit to assure that they operated in space at the same performance level as on the ground prior to launch to obtain an initial on-orbit performance baseline. A new waveform for each SDR has been developed, implemented, uploaded to the flight system, and tested in the flight environment. Recommendations for SDR-based missions have been gathered from early development through operations. These recommendations will aid future missions to reduce the cost, schedule, and risk of operating SDRs in a space environment. This paper considers the lessons learned as they apply to SDR pre-launch checkout, purchasing space-rated hardware, flexibility in command and telemetry methods, on-orbit diagnostics, use of engineering models to aid future development, and third-party software. Each SDR implements the SCaN Testbed flight computer command and telemetry interface uniquely, allowing comparisons to be drawn. The paper discusses the lessons learned from these three unique implementations, with suggestions on the preferred approach. Also, results are presented showing that it is important to have full system performance knowledge prior to launch to establish better performance baselines in space, requiring additional test applications to be developed pre-launch. Finally, the paper presents the issues encountered with the operation and implementation of new waveforms on each SDR and proposes recommendations to avoid these issues.

radio equipment↗