Quantifying Uncertainty in PSD Parameters Inferred from SAGE III/ISS Extinction Spectra
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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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Abstract not provided.
This report details the results of a three-fold investigation of sensitivity analysis (SA) for machine learning (ML) explainability (MLE): (1) the mathematical assessment of the fidelity of an explanation with respect to a learned ML model, (2) quantifying the trustworthiness of a prediction, and (3) the impact of MLE on the efficiency of end-users through multiple users studies. We focused on the cybersecurity domain as the data is inherently non-intuitive. As ML is being using in an increasing number of domains, including domains where being wrong can elicit high consequences, MLE has been proposed as a means of generating trust in a learned ML models by end users. However, little analysis has been performed to determine if the explanations accurately represent the target model and they themselves should be trusted beyond subjective inspection. Current state-of-the-art MLE techniques only provide a list of important features based on heuristic measures and/or make certain assumptions about the data and the model which are not representative of the real-world data and models. Further, most are designed without considering the usefulness by an end-user in a broader context. To address these issues, we present a notion of explanation fidelity based on Shapley values from cooperative game theory. We find that all of the investigated MLE explainability methods produce explanations that are incongruent with the ML model that is being explained. This is because they make critical assumptions about feature independence and linear feature interactions for computational reasons. We also find that in deployed, explanations are rarely used due to a variety of reason including that there are several other tools which are trusted more than the explanations and there is little incentive to use the explanations. In the cases when the explanations are used, we found that there is the danger that explanations persuade the end users to wrongly accept false positives and false negatives. However, ML model developers and maintainers find the explanations more useful to help ensure that the ML model does not have obvious biases. In light of these findings, we suggest a number of future directions including developing MLE methods that directly model non-linear model interactions and including design principles that take into account the usefulness of explanations to the end user. We also augment explanations with a set of trustworthiness measures that measure geometric aspects of the data to determine if the model output should be trusted.
Abstract not provided.
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Saturn IB program to test spacecraft component reliability
The performance of a nine-ampere-hour, nickel cadmium cell operating in a satellite power system is examined. Data for battery average dissipation, voltage profiles, battery temperatures, and the percent of recharge versus orbit are presented. The occurence of undervoltage and increased recharge/orbit percentages are examined and suggestions are made as to their causes.
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A method of retrieving aerosol size distribution from the measured extinction of solar radiation at wavelengths of 0.45 microns and 1.0 microns has recently been proposed. This method is utilized to obtain latitudinal and altitudinal variations of size distributions of stratospheric aerosols from the Stratospheric Aerosol and Gas Experiment data for March 1979. Small particles are found in the lower stratosphere of the tropical region, and large particles are found at higher altitudes and latitudes in both hemispheres. Results of this study are consistent with the suggestion that the upper troposphere in tropical regions is a source of condensation nuclei in the stratosphere, and they become mature as they move to higher altitudes and latitude.
Satellite and lidar data sets developed over several years of observations are analyzed to detail the macroscopic and microphysical characteristics of polar stratospheric clouds (PSCs). Mappings were made of the sizes, locations, probabilities of occurrence and temperature dependence of the PSCs, and indicated that PSCs are correlated with an extended stratospheric cloud bank in the cold polar vortex region. The bank is bounded by a 188 K isotherm, and the probability of occurrence drops to 50 percent at the 193 K isotherm. Values of 6.3 particles/cu cm and radii averaging 0.0725 micron/particle are calculated, along with an estimated downward velocity of 0.01 m/sec.