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Linear and nonlinear pattern selection in Rayleigh-Benard stability problems

A new algorithm is introduced to compute finite-amplitude states using primitive variables for Rayleigh-Benard convection on relatively coarse meshes. The algorithm is based on a finite-difference matrix-splitting approach that separates all physical and dimensional effects into one-dimensional subsets. The nonlinear pattern selection process for steady convection in an air-filled square cavity with insulated side walls is investigated for Rayleigh numbers up to 20,000. The internalization of disturbances that evolve into coherent patterns is investigated and transient solutions from linear perturbation theory are compared with and contrasted to the full numerical simulations.

Davis, Sanford S.↗

Hardware Implementation of Serially Concatenated PPM Decoder

A prototype decoder for a serially concatenated pulse position modulation (SCPPM) code has been implemented in a field-programmable gate array (FPGA). At the time of this reporting, this is the first known hardware SCPPM decoder. The SCPPM coding scheme, conceived for free-space optical communications with both deep-space and terrestrial applications in mind, is an improvement of several dB over the conventional Reed-Solomon PPM scheme. The design of the FPGA SCPPM decoder is based on a turbo decoding algorithm that requires relatively low computational complexity while delivering error-rate performance within approximately 1 dB of channel capacity. The SCPPM encoder consists of an outer convolutional encoder, an interleaver, an accumulator, and an inner modulation encoder (more precisely, a mapping of bits to PPM symbols). Each code is describable by a trellis (a finite directed graph). The SCPPM decoder consists of an inner soft-in-soft-out (SISO) module, a de-interleaver, an outer SISO module, and an interleaver connected in a loop (see figure). Each SISO module applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm to compute a-posteriori bit log-likelihood ratios (LLRs) from apriori LLRs by traversing the code trellis in forward and backward directions. The SISO modules iteratively refine the LLRs by passing the estimates between one another much like the working of a turbine engine. Extrinsic information (the difference between the a-posteriori and a-priori LLRs) is exchanged rather than the a-posteriori LLRs to minimize undesired feedback. All computations are performed in the logarithmic domain, wherein multiplications are translated into additions, thereby reducing complexity and sensitivity to fixed-point implementation roundoff errors. To lower the required memory for storing channel likelihood data and the amounts of data transfer between the decoder and the receiver, one can discard the majority of channel likelihoods, using only the remainder in operation of the decoder. This is accomplished in the receiver by transmitting only a subset consisting of the likelihoods that correspond to time slots containing the largest numbers of observed photons during each PPM symbol period. The assumed number of observed photons in the remaining time slots is set to the mean of a noise slot. In low background noise, the selection of a small subset in this manner results in only negligible loss. Other features of the decoder design to reduce complexity and increase speed include (1) quantization of metrics in an efficient procedure chosen to incur no more than a small performance loss and (2) the use of the max-star function that allows sum of exponentials to be computed by simple operations that involve only an addition, a subtraction, and a table lookup. Another prominent feature of the design is a provision for access to interleaver and de-interleaver memory in a single clock cycle, eliminating the multiple clock-cycle latency characteristic of prior interleaver and de-interleaver designs.

Moision, Bruce↗

Comparison of temporal resolution selection approaches in energy systems models

Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of abort triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of abort triggers.

Lo, Yunnhon↗

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of Abort Triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of Abort Triggers.

Lo, Yunnhon↗

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.

AI↗

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗

Estimation of atmospheric moisture content from microwave radiometric measurements during CCOPE

A multiple linear regression technique has been applied to retrieve continuous sequences of atmospheric moisture profiles from a set of measured data. In this method the selection of an optimal subset of sensor channels plays a crucial role, in order to reduce the impact of data noise and redundancy. The data were obtained with a 4-channel remote-sensing microwave instrument carried aboard an aircraft. In contrast, most previously reported moisture profile retrievals from microwave radiometry have been used on simulated, discontinuous data. Although the data were obtained over a land surface with only a limited amount of correlative data, the retrievals were quite successful.

Lambrigtsen, B. H.↗

Advanced information processing system: Fault injection study and results

The objective of the AIPS program is to achieve a validated fault tolerant distributed computer system. The goals of the AIPS fault injection study were: (1) to present the fault injection study components addressing the AIPS validation objective; (2) to obtain feedback for fault removal from the design implementation; (3) to obtain statistical data regarding fault detection, isolation, and reconfiguration responses; and (4) to obtain data regarding the effects of faults on system performance. The parameters are described that must be varied to create a comprehensive set of fault injection tests, the subset of test cases selected, the test case measurements, and the test case execution. Both pin level hardware faults using a hardware fault injector and software injected memory mutations were used to test the system. An overview is provided of the hardware fault injector and the associated software used to carry out the experiments. Detailed specifications are given of fault and test results for the I/O Network and the AIPS Fault Tolerant Processor, respectively. The results are summarized and conclusions are given.

Burkhardt, Laura F.↗

Empirical Modeling of Plant Gas Fluxes in Controlled Environments

As humans extend their reach beyond the earth, bioregenerative life support systems must replace the resupply and physical/chemical systems now used. The Controlled Ecological Life Support System (CELSS) will utilize plants to recycle the carbon dioxide (CO2) and excrement produced by humans and return oxygen (O2), purified water and food. CELSS design requires knowledge of gas flux levels for net photosynthesis (PS(sub n)), dark respiration (R(sub d)) and evapotranspiration (ET). Full season gas flux data regarding these processes for wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) from published sources were used to develop empirical models. Univariate models relating crop age (days after planting) and gas flux were fit by simple regression. Models are either high order (5th to 8th) or more complex polynomials whose curves describe crop development characteristics. The models provide good estimates of gas flux maxima, but are of limited utility. To broaden the applicability, data were transformed to dimensionless or correlation formats and, again, fit by regression. Polynomials, similar to those in the initial effort, were selected as the most appropriate models. These models indicate that, within a cultivar, gas flux patterns appear remarkably similar prior to maximum flux, but exhibit considerable variation beyond this point. This suggests that more broadly applicable models of plant gas flux are feasible, but univariate models defining gas flux as a function of crop age are too simplistic. Multivariate models using CO2 and crop age were fit for PS(sub n), and R(sub d) by multiple regression. In each case, the selected model is a subset of a full third order model with all possible interactions. These models are improvements over the univariate models because they incorporate more than the single factor, crop age, as the primary variable governing gas flux. They are still limited, however, by their reliance on the other environmental conditions under which the original data were collected. Three-dimensional plots representing the response surface of each model are included. Suitability of using empirical models to generate engineering design estimates is discussed. Recommendations for the use of more complex multivariate models to increase versatility are included.

Cornett, Jessie David↗

Using X-Ray Computed Tomography to Catalog Rock Fragments in Apollo Drive Tube 73002

Overview: The Apollo missions collected 382 kg of rock, regolith, and core samples from six locations on the nearside of the Moon. Approximately 84% by mass of the Apollo collection remains in pristine condition within the curation facility at Johnson Space Center (i.e., never allocated, continuously stored in dry-N2 purged cabinets, exposure history restricted to Teflon, stainless steel, and Al-metal). Although most Apollo samples have been well characterized, there are several types of samples that have remained wholly or largely unstudied since their return, and/or that have been cu-rated under special conditions, e.g., frozen samples, samples stored in He-purged environment, and previously unopened drive tubes. NASA solicited proposals for the Apollo Next Generation Sample Analysis Pro-gram (ANGSA), and 9 teams were selected to study a subset of the unopened and frozen samples [1]. The first sample opened as part of the ANGSA pro-gram was drive tube 73002. This was originally a ~30 cm long, 4 cm diameter drive tube collected on a land-slide deposit near Lara Crater at the Apollo 17 landing site. It was part of a ~60 cm long double drive tube collected, and the bottom half of the tube (73001) was sealed under vacuum on the Moon [2]. Prior to opening sample 73002, the sample was imaged with a high resolution X-ray Computed Tomography (XCT) scan of the entire tube [3], which provided invaluable information during the dissection process [4]. In addition to the pre-dissection XCT scans, individual >4 mm particles were separated from the 73002 regolith during processing and scanned at high resolution by XCT. Here we pre-sent the initial lithologic classification of 134 individual >4 mm rock fragments separated from the 73002 core during the dissection process. Methodology: Drive tube 73002 was manually dissected in 0.5 cm depth intervals in three passes (Fig. 1) [4,5]. Each interval from pass 1 and 2 was sieved to <1 mm and >1 mm size fractions, and >1 mm particles were further manually subdivided into 1-2, 2-4, 4-10, and >10 mm size fractions. Pass 3 was not sieved, but >10 mm clasts were separated manually. Each 4-10 mm and >10 mm fragment was individually weighed, triply bagged in Teflon, and scanned by XCT. There are 60 rock fragments in Pass 1, 64 rock fragments in Pass 2 (from the 4-10 mm and >10 mm size fractions), and 8 rock fragments from Pass 3 (>10 mm size fraction). Each individually bagged rock fragment was scanned using the 180 kV nano-focus transmission source on the Nikon XTH 320 XCT system at NASA Johnson Space Center [6]. Scanning conditions varied considerably for individual particles, in large part be-cause of the large variation in size (0.008-19.623 g). All scans fell within the following range of scan conditions: 2.8-20.6 um voxel size; 90-155 kV voltage; 18-39 uA current; 1891-3141 projections; and 902-2000 slices. Results and Discussion: The 132 rock fragments from sample 73002 fall into the following general cat-gories: agglutinates (n = 6); basalts (13); impact melts (5); impact melt breccias (IMB; 42); regolith breccias (62); and soil breccias (4); see Figure 2 for representative examples of each lithology. Within most of these broad lithologic groups are recognizable subgroups. For example, a significant portion of regolith breccia fragments contain some agglutinate-like glass (n = 9) or are dilithologic (7) because they contain a single large clast (~50% or more by volume). Subgroups can be based on similarities to previously identified lunar lithologies, such as high-Ti basalts (9) and VLT basalts (4; Fig. 3), or based on commonly seen features within the fragments, such as poikilitic ilmenite IMB (10), ilmenite-lath IMB (12), or vesicular IMB (11) “groups”. Particles in the same “group” are not necessarily intended to be genetically related, but rather identify particles that are similar and that follow-up studies can classify in more detail [7]. Conclusion: Identification of lithologies based on XCT is a powerful tool, but a more absolute classification will sometimes require additional textural information from thin sections (e.g., glassy-matrix regolith vs. impact-melt vs. granulitic breccia) or quantitative mineral compositions (e.g., basalt vs. monomict breccia).

R A Zeigler↗

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗

GeoNEX-SUBSETS: Geostationary Satellite Observations Over International Observing Network Sites

Satellite remote sensing is crucial for monitoring the Earth’s surface and for simulating global carbon and water cycles. Subsets of satellite data from sensors such as MODIS over established observing networks have been valuable for researchers for comparing the ground observed phenomena with satellite observations. Here, we introduce new subsets of the GeoNEX geostationary satellite datasets over locations of several ground observation networks. The NASA Earth Exchange group has been generating geostationary satellite products with our international partners and universities to cover the entire globe. In comparison to polar-orbiting satellite sensors such as MODIS, the new generation geostationary satellites observe target areas at a higher frequency (5-10 minutes), which also significantly increased the data volume. To reduce the burden of downloading and extracting geostationary time series data and provide easy access to the community, we provide subsets of GeoNEX products through our data portal [www.data.nas.nasa.gov]. The ready-to-use subset follows the same format as the MODIS fixed sites subset tools for users who are familiar with MODIS subset data and software. The selected networks include Fluxnet, AERONET, and Phenocam over the conterminous US. We demonstrate the usage of the GeoNEX fixed-site subset data and showcase thir advantages with three example studies. The first example is simulating the diurnal cycle of plant ecophysiology at Fluxnet sites. The high frequency of the GeoNEX time series allows us to run ecological models at sub-hour time steps and directly compare the simulated carbon and water fluxes with half-hourly Fluxnet data. The second example uses the geostationary data to track phenological changes. It highlights the high-frequent observations of geostationary satellites in helping mask cloud covers and capture the quick responses of vegetation to environmental changes. As such, these examples demonstrate the value of ready-to-use GeoNEX subsets data in terrestrial ecophysiology research.

GeoNEX↗

Long-Term Variations of the EOP and ICRF2

We analyzed the time series of the coordinates of the ICRF radio sources. We show that part of the radio sources, including the defining sources, shows a significant apparent motion. The stability of the celestial reference frame is provided by a no-net-rotation condition applied to the defining sources. In our case this condition leads to a rotation of the frame axes with time. We calculated the effect of this rotation on the Earth orientation parameters (EOP). In order to improve the stability of the celestial reference frame we suggest a new method for the selection of the defining sources. The method consists of two criteria: the first one we call cosmological and the second one kinematical. It is shown that a subset of the ICRF sources selected according to cosmological criteria provides the most stable reference frame for the next decade.

Zharov, Vladimir↗

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗

Subsetting of AVHRR and ERBE Data

One of the critical issues of EOSDIS is data subsetting. Often users require data from only a selected region of interest. This paper outlines a few examples of subsetting that have been applied to existing satellite data for earth radiation budget studies.

Christopher, Sundar A.↗

Providing Goal-Based Autonomy for Commanding a Spacecraft

A computer program for use aboard a scientific-exploration spacecraft autonomously selects among goals specified in high-level requests and generates corresponding sequences of low-level commands, understandable by spacecraft systems. (As used here, 'goals' signifies specific scientific observations.) From a dynamic, onboard set of goals that could oversubscribe spacecraft resources, the program selects a non-oversubscribing subset that maximizes a quality metric. In an early version of the program, the requested goals are assumed to have fixed starting times and durations. Goals can conflict by exceeding a limit on either the number of separate goals or the number of overlapping goals making demands on the same resource. The quality metric used in this version is chosen to ensure that a goal will never be replaced by another having lower priority. At any time, goals can be added or removed, or their priorities can be changed, and the 'best' goal will be selected. Once a goal has been selected, the program implements a robust, flexible approach to generation of low-level commands: Rather than generate rigid sequences with fixed starting times, the program specifies flexible sequences that can be altered to accommodate run time variations.

Rabideau, Gregg↗