“All-Clear” Prediction of Solar Proton Events using Machine Learning and Comparison with Operational Forecasts
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SMD↗
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Results for “operator learning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Detecting Satellite Laser Ranging Station Data and Operational Anomalies with Machine Learning Isolation Forests at NASA's CDDIS
The International Laser Ranging Service (ILRS) is currently composed of 45 active satellite laser ranging (SLR) stations with several more set to join the network over the next several years. Station changes and histories are logged to files, but not always in real time. Sometimes these details are not added until long after changes have been made to the station –on occasion, years later. This in addition to unexpected hardware errors and other system issues that are not immediately detected impact the products generated by analysts. The ILRS Central Bureau (CB) and NASA’s Crustal Dynamics Data Information System (CDDIS) have worked to provide tools for station engineers to use. This includes the creation of station plots which contain temperature and pressure information along with LAser GEOdynamic Satellite (LAGEOS) and LAser RElativity Satellite (LARES) tracking information that enable the monitoring of station performance and todetermine whether the station has undergone any changes. As next steps, the CDDIS is working to enhance these station performance monitoring tools through machine learning. Isolation forest is an unsupervised machine learning algorithm commonly applied to anomaly detection. In this poster, the CDDIS details the steps taken to track anomalies within SLR station performance using isolation forest with LAGEOS and LARES satellite data.
Benjamin P Michael↗
Maximizing Operator Impact: Leveraging Machine Learning to Improve Trending Analysis for the Magnetospheric Multiscale Mission
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Shannon Bull↗
Maximizing Operator Impact: Leveraging Machine Learning to Improve Trending Analysis for the Magnetospheric Multiscale (MMS) Mission
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Shannon Bull↗
Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation
A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.
anomaly detection↗
Digital Learning Network Education Events of NASA's Extreme Environments Mission Operations
NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and web casting. The DLN has created a series of live education videoconferences connecting NASA s Extreme Environment Missions Operations (NEEMO) team to students across the United States. The programs are also extended to students around the world live web casting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO Crewmembers including NASA astronauts, engineers and scientists inform and inspire students about the importance of exploration and share the impact of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration s underwater laboratory. With this program, NASA continues the Agency s tradition of investing in the nation's education programs. It is directly tied to the Agency's major education goal of attracting and retaining students in science, technology, and engineering disciplines. Before connecting with the aquanauts, the students conduct experiments of their own designed to coincide with mission objectives. This paper describes the events that took place in September 2006.
Paul, Heather↗
Collaborating Fuzzy Reinforcement Learning Agents
Earlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.
Berenji, Hamid R.↗
Synthetic Biologic Membrane
The International Space Station (ISS) is a test bed for the technologies that will be used to travel to Mars and beyond. The lessons learned from operating the ISS provide valuable direction to future research and technology development programs. One of the most critical and complicated subsystems on ISS is the life support system. The life support system keeps the crew alive by recycling both air and water. The ISS water recycling system has been operating since 2009 and one of the main lessons learned is that reliability is a key technology performance metric. In the long run reliability is a key cost driver and is a critical factor in insuring crew safety. For long duration missions such as the exploration of Mars, where resupply of spare parts from Earth is difficult if not impossible, reliability is even more important. This presentation will cover research into improving the reliability of ISS systems. It will discuss research into the development of a biomimetic membrane materials that provides self-regeneration capabilities for water recycling systems. It will also cover research into past failures of the ISS water recycling system caused by astronaut bone loss and the build-up of trace contaminates in the cabin.
Biology↗
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations
As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.
Jani, Yashvant↗
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture. These approaches, which include the popular Deep Operator Network (DeepONet), approximate the output functions using a linear combination of learnable basis functions and coefficients that depend on the input functions. We establish a theoretical framework to quantify the neural scaling laws by analyzing its approximation and generalization errors. We articulate the relationship between the approximation and generalization errors of deep operator networks and key factors such as network model size and training data size. Moreover, we address cases where input functions exhibit low-dimensional structures, allowing us to derive tighter error bounds. These results also hold for deep ReLU networks and other similar structures. Our results offer a partial explanation of the neural scaling laws in operator learning and provide a theoretical foundation for their applications.
97 MATHEMATICS AND COMPUTING↗
Lessons Learned from the Stardust Sample Return Mission
These are science and mission design and operations lessons learned from the Stardust Mission, which returned grains from comet Wild-2 and fresh interstellar dust to Earth in 2006 [1]. Science Lessons: Major discoveries of the Stardust Wild-2 samples include the presence of numerous chondrules and CAI in a comet, which requires a much more dynamic early solar system than many had envisaged, and verified predictions made by models requiring outward flow of early solar system solids before the early nebular gas had dissipated [1-3]. No evidence has been found for the presence of live Al-26 in the comet, suggesting late accretion [4]. Carbonates and unusual sulfides were found which potentially require activity of liquid water within the comet, but to only a minor degree at best [5-6]. The presence of abundant thermally-metamorphosed silicates in Wild-2 appears to require assembly from an earlier generation of bodies [7]. The abundance of presolar grains in the Wild-2 samples appears to be below what has been found in most chondritic IDPs and primitive chondrites [1]. The bulk mineralogy of Wild-2 grains does not match the mineralogy from any single other known astromaterial [7], and is also strikingly unlike that inferred from Spitzer Telescope spectra of Comet Temple 1 dust [8]. Amino acids and other fragile organics have been detected among the Wild-2 samples [9], which highlights the critical importance of further developing techniques for organic analysis in small samples, and cleaning outbound spacecraft.
Zolensky, M. E.↗
Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials
Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.
Composite materials↗
Operational Evolution of FTS3: A DevOps Driven Approach to Elastic Operations
The File Transfer Service (FTS3) is a distributed data movement service developed at CERN and widely used to transfer data across the Worldwide LHC Computing Grid (WLCG). At Fermilab, FTS3 supports data transfers for multiple experiments, including Intensity Frontier experiments such as DUNE, enabling reliable data movement between WebDAV endpoints in Europe and the Americas. At CHEP 2021, we reported on the initial containerized deployment of FTS3 on OKD, the community Kubernetes distribution of Red Hat OpenShift. In this work, we present the subsequent evolution of this deployment, focusing on new operational capabilities introduced to improve scalability, robustness, and long-term maintainability. We describe the adoption of more secure and reproducible container build workflows, the integration of DevOps-driven operational practices, and enhancements in monitoring and automation. A key new result is the introduction of horizontal scaling and elastic resource management, allowing FTS3 components to dynamically adapt to workload variations while maintaining service reliability. We also discuss improvements in fault tolerance and operational procedures derived from production experience. Finally, we summarize lessons learned from operating FTS3 as a Kubernetes-native service and outline how these developments have improved the resilience and efficiency of data movement operations at Fermilab.
Munoz Flores, Victor Leopoldo [Fermilab]↗
Real Time Data System (RTDS)
Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.
Muratore, John F.↗
ACTS Ka-Band Earth Stations: Technology, Performance, and Lessons Learned
The Advanced Communications Technology Satellite (ACTS) Project invested heavily in prototype Ka-band satellite ground terminals to conduct an experiments program with the ACTS satellite. The ACTS experiment's program proposed to validate Ka-band satellite and ground station technology. demonstrate future telecommunication services. demonstrate commercial viability and market acceptability of these new services, evaluate system networking and processing technology, and characterize Ka-band propagation effects, including development of techniques to mitigate signal fading. This paper will present a summary of the fixed ground terminals developed by the NASA Glenn Research Center and its industry partners, emphasizing the technology and performance of the terminals (Part 1) and the lessons learned throughout their six year operation including the inclined orbit phase of operations (Full Report). An overview of the Ka-band technology and components developed for the ACTS ground stations is presented. Next. the performance of the ground station technology and its evolution during the ACTS campaign are discussed to illustrate the technical tradeoffs made during the program and highlight technical advances by industry to support the ACTS experiments program and terminal operations. Finally. lessons learned during development and operation of the user terminals are discussed for consideration of commercial adoption into future Ka-band systems. The fixed ground stations used for experiments by government, academic, and commercial entities used reflector based offset-fed antenna systems ranging in size from 0.35m to 3.4m antenna diameter. Gateway earth stations included two systems, referred to as the NASA Ground Station (NGS) and the Link Evaluation Terminal (LET). The NGS provides tracking, telemetry, and control (TT&C) and Time Division Multiple Access (TDMA) network control functions. The LET supports technology verification and high data rate experiments. The ground stations successfully demonstrated many services and applications at Ka-band in three different modes of operation: circuit switched TDMA using the satellite on-board processor, satellite switched SS-TDMA applications using the on-board Microwave Switch Matrix (MSM), and conventional transponder (bent-pipe) operation. Data rates ranged from 4.8 kbps up to 622 Mbps. Experiments included: 1) low rate (4.8- 1 00's kbps) remote data acquisition and control using small earth stations, 2) moderate rate (1-45 Mbps) experiments included full duplex voice and video conferencing and both full duplex and asymmetric data rate protocol and network evaluation using mid-size ground stations, and 3) link characterization experiments and high data rate (155-622 Mbps) terrestrial and satellite interoperability application experiments conducted by a consortium of experimenters using the large transportable ground stations.
Reinhart, Richard C.↗
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↗
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 SDR's 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.
space communication↗
Using AUTORAD for Cassini File Uplinks: Incorporating Automated Commanding into Mission Operations
As the Cassini spacecraft embarked on the Solstice Mission in October 2010, the flight operations team faced a significant challenge in planning and executing the continuing tour of the Saturnian system. Faced with budget cuts that reduced the science and engineering staff by over a third in size, new and streamlined processes had to be developed to allow the Cassini mission to maintain a high level of science data return with a lower amount of available resources while still minimizing the risk. Automation was deemed an important key in enabling mission operations with reduced workforce and the Cassini flight team has made this goal a priority for the Solstice Mission. The operations team learned about a utility called AUTORAD which would give the flight operations team the ability to program selected command files for radiation up to seven days in advance and help minimize the need for off-shift support that could deplete available staffing during the prime shift hours. This paper will describe how AUTORAD is being utilized by the Cassini flight operations team and the processes that were developed or modified to ensure that proper oversight and verification is maintained in the generation and execution of radiated command files.
Goo, Sherwin↗