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This presentation highlights the JSC (Johnson Space Center)-developed SW (software) products associated with the Core Flight Software (CFS) framework and lists the JSC projects that use them. It also lists the upshots of using the CFS framework in SW development.
Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.
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Tutorial for the Python Prognostics Packages (Being considered for open source)
From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56°N, 30–100°E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).
Remote sensing atmospheric correction methods have primarily focused on imagery over land. However, accurate correction over water is important for monitoring and research of aquatic environments. More research in this area is ongoing, though one of the biggest challenges is enough quality data to develop and validate correction methods. This is especially true for neural network (NN) -based models which have shown promise in this area given enough quality data. To address this deficiency of data, we are leveraging a synthetic dataset produced by a model called SWIPE that uses radiative transfer modeling to simulate the atmospheric effects on water-leaving (WL) reflectance to estimate top-of-atmosphere (TOA) reflectance. This allows us to produce almost unlimited pairs of WL reflectance and corresponding TOA reflectance for model training across a variety of atmospheric conditions. We use two approaches for our atmospheric correction model. One uses a conditional variational autoencoder (VAE) to estimate a single WL reflectance value from a single TOA reflectance value. The second is based on a UNET architecture and estimates an array of WL reflectance values from an array of TOA reflectance values. The goal of the second method is to capture atmospheric effects that occur spatially between values within the array as compared to the first method.
The understanding of the internal structure of the proton and other strongly interacting particles is at the forefront of modern nuclear physics research. Generalized Parton Distribution Functions (GPDs) are a powerful tool to advance the understanding of hadron structure. In addition to the information about the one- dimensional collinear momentum distributions of partons (quarks, anti-quarks, and gluons) known from studies of high energy deep-inelastic reactions, GPDs also carry information on the distribution of partons in the transverse plane, and allow us in this way to access the three-dimensional structure of the nucleon. GPDs can be studied in hard-exclusive reactions and contain also information on the energy-momentum tensor form factors which will allow us to gain insights on quantities like pressure or angular momentum distribution inside the nucleon. The goal of this thesis is to deepen our understanding of the three-dimensional structure of the nucleon. We investigate energy-momentum tensor form factors and densities, and all leading-twist GPDs in the bag model. This quark model provides a consistent theoretical framework to investigate many general concepts that have recently attracted interest, and allows one to study insightful limits like the large-Nc limit, heavy-quark limit or the non-relativistic limit. Another important aspect of this thesis is the discussion of the monopole and quadrupole contributions to the angular momentum density. Finally, the description of pseudoscalar meson production in exclusive processes in terms of GPDs is discussed in the Goloskokov-Kroll model and implemented in the PARTONS framework, a software development project which will provide direct support for experiments at the Jefferson National Lab and the future Electron-Ion Collider.
DDCP protocol software 1.0 This repository contains the C++ implementation of version 1.x of the Distributed Data Communications Protocol (DDCP). DDCP provides request/reply, feature discovery, data transfer, control, interrupt, and transaction support for communicating with accelerator instrumentation over UDP. The standard server port is 65000. The framework is a source dependency for services that communicate directly with DDCP hardware. It is not a deployable service by itself.
Reliable digital instrumentation and control systems (DI&C) are integral for sustaining the continued operation of nuclear power plants. These systems ensure that nuclear reactors operate safely, efficiently, and within regulatory requirements. Yet, the cost of designing and licensing new nuclear DI&C can be prohibitively expensive. Under the U.S. Department of Energy Light Water Reactor Sustainability Program, Idaho National Laboratory has developed a framework for supporting the risk-informed design of DI&C systems by offering methods to support the identification, quantification, and evaluation of risks for various DI&C design architectures. The framework indicates potential software failure modes and provides pathways for quantifying the potential for these software failures, including common cause failures. Using the framework’s systematic approach, challenges for assessing risks within new and existing nuclear DI&C systems can be reduced. Nevertheless, the current framework can be further improved using the convenience of automation. This paper introduces the development of Software for the Hazard Identification and Evaluation of Digital Systems (SHIELDS). SHIELDS is an engineering software package that enables the identification, elimination, and mitigation of potential risks and reduces the burden of deploying reliable DI&C systems. This work introduces plans and techniques to digitize and improve the manual risk assessment modules of the framework. These improvements will save time and increase the repeatability and usability of the framework, making it more accessible to a wider range of users. Ultimately, this introduces SHIELDS and how its modules support efficient development of safe and reliable DI&C systems.
The proposed framework derives a set of quality metrics to provide critical insights into and tracking of grid operations, sensor performance, sensor longevity, and event statistics. Power grid engineers can utilize this information to identify problems with existing sensor locations and problematic power grid assets including generators, transmission lines, load centers, and substations. This information can also be used to identify unexpected/abnormal behavior of power grid components, improve power grid observability, and operational monitoring, and thus enhance real-time decision-making support system. Power grid planners can utilize this information to augment existing sensing architecture with new sensors and improve the observability of the network.
Verification and Validation (V&V) is currently performed during application development for many systems, especially safety-critical and mission- critical systems. The V&V process is intended to discover errors, especially errors related to critical processing, as early as possible during the development process. The system application provides the context under which the software artifacts are validated. This paper describes a framework that extends V&V from an individual application system to a product line of systems that are developed within an architecture-based software engineering environment. This framework includes the activities of traditional application-level V&V, and extends these activities into domain engineering and into the transition between domain engineering and application engineering. The framework includes descriptions of the types of activities to be performed during each of the life-cycle phases, and provides motivation for the activities.
Reuse of products, processes and knowledge will be the key to enable the software industry to achieve the dramatic improvement in productivity and quality required to satisfy the anticipated growing demand. Although experience shows that certain kinds of reuse can be successful, general success has been elusive. A software life-cycle technology which allows broad and extensive reuse could provide the means to achieving the desired order-of-magnitude improvements. The scope of a comprehensive framework for understanding, planning, evaluating and motivating reuse practices and the necessary research activities is outlined. As a first step towards such a framework, a reuse-enabling software evolution environment model is introduced which provides a basis for the effective recording of experience, the generalization and tailoring of experience, the formalization of experience, and the (re-)use of experience.
—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.
The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.
The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.
No abstract available