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NASA NTRS1982

The next generation microwave sounder for weather satellites

Microwave sounders are multichannel microwave radiometers for measuring atmospheric temperature (or humidity) profiles. These devices are also known as microwave spectrometers because a sounder typically has a number of channels measuring the spectral shape of an absorption line. Each channel of the sounder responds essentially to a given layer of the atmosphere. The characteristics of currently employed microwave sounders on satellites are briefly examined, and new features expected in connection with the introduction of the next generation sounders are discussed. There will probably be an increase in the number of oxygen channels with the objective to extend the weighting function height coverage into the stratosphere and to improve the temperature retrieval accuracy. Other features are to be related to better surface resolution, auxiliary channels, and water vapor sounding.

Shiue, J. C.↗

NASA NTRSDate not supplied

Satellite orbit predictor

An analog aid to determine satellite coverage of Emergency Locator Transmitters Emergency Position Indicating Radio Beacon (ELT/EPIRB) distress incidence is discussed. The satellite orbit predictor is a graphical aid for determining the relationship between the satellite orbit, antenna coverage of the spacecraft and coverage of the Local User Terminal. The predictor allows the user to quickly visualize if a selected position will probably be detected and is composed of a base map and a satellite track overlay for each satellite.A table of equator crossings for each satellite is included.

Friedman, Morton l.↗

DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Characterizing Peatland Microtopography Using Gradient and Microform-Based Approaches

Abstract Peatlands represent an important component of the global carbon cycle, storing 180–621 Gt of carbon (C). Small-scale spatial variations in elevation, frequently referred to as microtopography, influence ecological processes associated with the peatland C cycle, including Sphagnum photosynthesis and methane flux. Microtopography can be characterized with measures of topographic variability and by using conceptual classes (microforms) linked to function: most commonly hummocks and hollows. However, the criteria used to define these conceptual classes are often poorly described, if at all, and vary between studies. Such inconsistencies compel development of explicit quantitative methods to classify microforms. Furthermore, gradient-based characterizations that describe spatial variability without the use of microforms are lacking in the literature. Therefore, the objectives of this study were to (1) calculate peatland microtopographical elevation gradients and measures of spatial variability, (2) develop three microform classification methods intended for specific purposes, and (3) evaluate and contrast classification methods. Our results suggest that at spatial scales much larger than microforms, elevation distributions are unimodal and are well approximated with parametric probability density functions. Results from classifications were variable between methods and years and exhibited significant differences in mean hollow areal coverages of a raised ombrotrophic bog. Our results suggest that the conceptualization and classification of microforms can significantly influence microtopographic structural metrics. The three explicit methods for microform classification described here may be used and built upon for future applications.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Dynamics and Origin of Extra-solar Planetary Systems and Microlensing Detection of Extra-solar Planets

We compare a space-based microlensing search for planets, with a ground based microlensing search originally proposed by D. Tytler (Beichman, et al. 1996). Perturbations of microlensing light curves when the lens star has a planetary companion are sought by one wide angle survey telescope and an array of three or four followup narrow angle telescopes distributed in longitude that follow events with high precision, high time resolution photometry. Alternative ground based programs are considered briefly. With the four 2 meter telescopes distributed in longitude in the southern hemisphere in the Tytler proposal, observational constraints on a ground-based search for planets during microlensing events toward the center of the galaxy are severe. Probably less than 100 events could be monitored per year with high precision, high time resolution photometry with only about 42% coverage on the average regardless of how many events were discovered by the survey telescope. Statistics for the occurrence and properties for Jupiter-mass planets would be meaningful but relatively meager four years after the program was started, and meaningful statistics for Earth-mass planets would be non existent. In contrast, the 14,500 events in a proposed 4 year space based program (GEST = Galactic Exoplanet Survey Telescope) would yield very sound statistics on the occurrence, masses and separations of Jupiter-mass planets, and significant constraints on similar properties for Earth-mass planets. The significance of the Jupiter statistics would be to establish the frequency of planetary systems like our own, where terrestrial planets could exist inside the orbits of the giants.

Peale, S. J.↗

NASA NTRS1999

Characteristics of Thunderstorms and Lightning Flashes Which Produce Mesospheric Transient Luminous Events

A six year record of optical observations of lightning-induced mesospheric transient luminous events (TLEs) is available from the Yucca Ridge Field Station (YRFS) near Ft. Collins, CO. Climatological analyses reveal sprites and elves occur in a variety of convective storm types, but principally mesoscale convective systems (MCSs) and squall lines. Severe supercell storms rarely produce TLEs, except during their dissipating stage. Few TLEs are observed during storms with radar echo areas <7,500 sq km. Above this size there is a modest correlation with radar areal coverage. A typical High Plains storm produces 45 TLEs over a 143 interval. Sprites and most elves are associated with +CGs. The probability of a TLE increases with peak current. In six storms, 5.1% of +CGs produced TLEs, the number increasing to 32% of +CGs with >75 kA and 52% of +CGs with >100 kA peak current.

Lyons, W. A.↗

DOE OSTI2022

Projected network performance for multiple isotopes using next-generation xenon monitoring systems

Since about 2000 (Bowyer et al., 1998), radioxenon monitoring systems have been under development and testing for the verification of the Comprehensive Nuclear Test-Ban Treaty (CTBT). Operation of the systems since then has resulted in development of a next-generation of systems that are nearly ready for operational deployment. By 2010, the need to screen out civilian sources was well known (Auer et al., 2010; Saey, 2009), and isotopic ratio approaches were soon considered (Kalinowski et al., 2014) to identify specific sources. New generation systems are expected to improve the ability to verify the absence of nuclear tests by using isotopic ratios when multiple isotopes are detected. In this work, thousands of releases were simulated to compute the global detection probability of 131m Xe, 133m Xe, 133 Xe, and 135 Xe at 39 noble gas systems in the International Monitoring System (IMS) for both current and next-generation systems. Three release scenarios are defined at 1 h, 1 d, and 10 d past a 1 kt TNT equivalent 235U explosion event. Multiple cases using from one part in a million to the complete release of the xenon isotopic activity are evaluated for each scenario. Coverage maps and global integrals comparing current and next-generation monitoring systems are presented showing that next-generation noble gas systems will create measurable improvements in the IMS. The global detection probability for 133 Xe is shown to be strong in all scenarios, but only modestly improved by next-generation equipment. However, the detection probability for 131m Xe and 133m Xe increased to about 50% in different scenarios, providing a second detectable isotope for many events. As anticipated from shorter sampling intervals, the expected number of detecting samples roughly doubled and the expected number of detecting stations rose by approximately 50% for all release scenarios. Thus, it might be anticipated that future events would consist of multiple 133 Xe detections and one or more second isotope detections. In conclusion, signals of this nature should increase detection confidence, tighten release location estimates, improve rejection of civilian signals, and lessen the impacts from individual systems being offline for maintenance or repair reasons.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Systematic planning of moving target defence for maximising detection effectiveness against false data injection attacks in smart grid

Abstract Moving target defence (MTD) has been gaining traction to thwart false data injection attacks against state estimation (SE) in the power grid. MTD actively perturbs the reactance of transmission lines equipped with distributed flexible AC transmission system (D‐FACTS) devices to falsify the attacker's knowledge about the system configuration. However, the existing literature has not systematically studied what influences the detection effectiveness of MTD and how it can be improved based on the topology analysis. These problems are tackled here from the perspective of an MTD plan in which the D‐FACTS placement is determined. We first exploit the relation between the rank of the composite matrix and the detecting effectiveness. Then, we rigorously derive upper and lower bounds on the attack detecting probability of MTDs with a given rank of the composite matrix. Furthermore, we analyse existing planning methods and highlight the importance of bus coverage by D‐FACTS devices. To improve the detection effectiveness, we propose a novel graph theory–based planning algorithm to retain the maximum rank of the composite matrix while covering all necessary buses. Comparative results on multiple systems show the high detecting effectiveness of the proposed algorithm in both DC‐ and AC‐SE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Abstract. In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

Mahesh, Ankur↗

NASA NTRS1986

Identifying deforestation in Brazil using multiresolution satellite data

The use of multiresolution satellite data to monitor deforestation on a continental/subcontinental scale is examined. MSS, local area coverage (LAC), global area coverage (GAC), and GOES data were applied to the study of deforestation in Rondonia, Brazil; the characteristics of these sensors are described. The probability thresholding and vegetation-index thresholding procedures used to process the data in order to differentiate between forest from nonforest are analyzed. The LAC, GAC, and GOES data are compared to MSS data. It is observed that GOES data is not useful for monitoring colonization projects due to excessive noise in the data; the GAC data is only applicable in large areas of contiguous forest clearing; the MSS data when available is applicable as a ground data reference source for differentiating cleared areas from primary; the LAC data are capable of delineating colonization clearings; and the probability thresholding procedure differentiates forest from nonforest more accurately than the vegetation-index procedure. The data reveal that the LAC data combined with the probability threshold procedure provide the best data-source/classification-procedure combination.

Nelson, R.↗

NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗

NASA NTRS1995

A LEO concept for millimeter wave satellite communication

A conceptual 60 satellite LEO constellation for millimeter wave communication is discussed. It could be launched in segments, with the first 30 satellites providing high elevation angles for all time in the Northern latitudes between Miami and Thule. The second set of 30 satellites would complete the worldwide coverage with emphasis on high ground elevation angles in the densely populated temperate zones. Full earth searches for all time are used to generate probability density functions for elevation angle. The density functions are used to derive optimum frequencies for random elevation systems. The 55 degree average elevation angle and 14 degree standard deviation are seen to be acceptable for 0.997 rain availability in Washington, DC for the 40 to 47 GHz region. The 40 to 47 GHz region is nearly optimum, if 0.99 rain availability is acceptable.

Jackson, A. H.↗

NASA NTRS2010

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

Russell, Carl↗

DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1987

Optimal checkpointing of real-time tasks

Analytical models for the design and evaluation of checkpointing of real-time tasks are developed. First, the execution of a real-time task is modeled under a common assumption of perfect coverage of on-line detection mechanisms (which is termed a basic model). Then, the model is generalized (to an extended model) to include more realistic cases, i.e., imperfect coverages of on-line detection mechanisms and acceptance tests. Finally, an optimal placement of checkpoints is determined to minimize the mean task execution time while the probability of an unreliable result (or lack of confidence) is kept below a specified level. In the basic model, it is shown that equidistant intercheckpoint intervals are optimal, whereas this is not necessarily true in the extended model. An algorithm for calculating the optimal number of checkpoints and intercheckpoint intervals is presented with some numerical examples for the extended model.

Shin, Kang G.↗

NASA NTRS2002

Outbursts In Symbiotic Binaries (FUSE 2000)

During the past year, we made good progress on analysis of FUSE observations of the symbiotic binary Z And. For background, Z And is a binary system composed of a red giant and a hot component of unknown status. The orbital period is roughly 750 days. The hot component undergoes large-scale eruptions every 10-20 yr. An outburst began several years ago, triggering this FUSE opportunity. First, we obtained an excellent set of ground-based optical data in support, of the FUSE observations. We used FAST, a high throughput low resolution spectrograph on the 1.5-m telescope at Mt. Hopkins, Arizona. A 300 g/ mm grating blazed at 4750 A, a 3 in. slit, and a thinned Loral 512 x 2688 CCD gave us spectra covering 3800-7500 A at a resolution of 6 A. The wavelength solution for each spectrum has a probable error of +/- 0.5 A or better. Most of the resulting spectra have moderate signal-to-noise, S/.N approx. greater than 30 per pixel. The time coverage for these spectra is excellent. Typically, we acquired spectra every 1-2 nights during dark runs at Mt. Hopkins. These data cover most of the rise and all of the decline of the recent outburst. The spectra show a wealth of emission lines, including H I, He I, He II, [Fe V11], and the Raman scattering bands at 6830 A and 7088 A. The Raman bands and other high ionization features vary considerably throughout the outburst. These features will enable us to correlate variations in the FUSE spectra with variations in the optical spectra. Second, we began an analysis of FUSE spectra of Z And. We have carefully examined the spectra, identifying real features and defects. We have identified and measured fluxes for all strong emission lines, including the O VI doublet at 1032 A and 1038 A. These and several other strong emission lines display pronounced P Cygni absorption components indicative of outgrowing gas. We will attempt to correlate these velocities with similar profiles observed on optical spectra. The line velocities - together with line variations - will yield physical parameters for the expanding shell of gas in the outer atmosphere of the hot component. We also worked on several diagnostic tools, including upgrades to photoionization programs developed by the PI and others. We plan to use these tools to derive electron densities and temperatures front intercombination and forbidden lines observed on optical and FUSE spectra. Preliminary results indicate a large electron density, n(sub e) is greater than or = 10(exp 10)/cc and a modest electron temperature, T(sub e) approx. 20,000 K. We see no evidence for shocked gas as observed in some other symbiotics. However, we have yet to include several important lines of [Fe VII] and [Ne V] in the analysis. Inclusion of these lines will yield an improved estimate of the electron temperature in the gas. Finally, we have one additional FUSE spectrum planned for acquisition during this cycle. These data will provide important information concerning the state of the system farther along in its decline. Once we have this spectrum in hand, we plan to complete our analysis and publish our results.

Kenyon, Scott J.↗

NASA NTRS2004

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the shortest route updates the edges in its path.

Alena, Richard I.↗