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The Implementation of Maximum Likelihood Estimation in Space Launch System Vehicle Design
As NASA’s Space Launch System (SLS) approaches first launch, the design has matured to a point where the manufacturing uncertainty has decreased now that many of the components of the launch vehicle have been manufactured and the flight engines have been successfully tested. Prior to this point, a method was required to qualify and capture the impact of the differences between simulation and reality, as well as any uncertainties in the SLS design. Two primary categories of uncertainty arise during the launch vehicle design process. The first represents flight-day uncertainties including dispersions due to winds and temperatures. These are typically examined by performing a Monte Carlo on 6 Degree of Freedom (6-DOF) simulations. The second category of uncertainties represents any manufacturing variations that are present at the individual component level of the launch vehicle design. These variations are constructed using statistical masses and tend to become better understood and refined as the design cycle matures, finally resulting in the launch vehicle as constructed and tested.
The Implementation of Maximum Likelihood Estimation in Space Launch System Vehicle Design
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Increased Likelihood of Appreciable Afternoon Rainfall Over Wetter or Drier Soils Dependent Upon atmospheric Dynamic Influence
The relationship between morning soil moisture and afternoon rainfall persists as an important yet unresolved challenge in land‐atmosphere interaction study, complicated in part by atmospheric influence. Here, we address this relationship by utilizing NASA's satellite soil moisture and precipitation data for the warm season (June–September) of 2015–2019 over Northern Hemisphere land (0–60°N). Raining days are partitioned into low, medium, and high regimes of atmospheric water vapor convergence. Under the low convergence regime, afternoon rainfall is more likely to occur over wetter soils or higher relative humidity; for days with high moisture convergence, occurrence favors drier soils or lower relative humidity. For each regime, afternoon rainfall occurrence favors warmer morning soil or air temperature. These conclusions are not affected by the threshold magnitude utilized to identify afternoon rainfall events by accumulation, but the threshold value does affect the soil moisture (or relative humidity)‐precipitation relationship when convergence regimes are not considered.
Africa Food Security & Agriculture - Predicting the Likelihood of Human-elephant Conflict and Assessing Elephant Habitat Conditions During Extreme Drought and Crop Deficit in the Kavango-Zambezi Area
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Africa Food Security & Agriculture: Predicting the Likelihood of Human-elephant Conflict and Assessing Elephant Habitat Conditions During Extreme Drought and Crop Deficit in the Kavango-Zambezi Area
Human-wildlife conflict is increasingly more common due to human population growth, habitat fragmentation, and changing climatic conditions. This conflict is particularly evident in the Kavango-Zambezi area, where over three million people share the landscape with an abundant megafauna population. As the changing climate continues to exacerbate drought severity and subsequent food availability, conflict between humans and wildlife has become more prevalent. In the Kavango-Zambezi area, conflict between elephants and humans has resulted in crop loss, property damage, and threats to public safety. In order to manage current and future conflict, The Ecoexist Project and Connected Conservation have been working to empower farmers and conserve the natural habitat. This DEVELOP project employed Earth observations to conduct a time series analysis of vegetation health change, elephant movement, and climate conditions, from 2017 to 2020. Data were aggregated into the wet (November through April) and dry (May through October) seasons. The resulting analysis demonstrated the potential to use Landsat 8 Operational Land Imager (OLI), Global Precipitation Measurement’s Integrated Multi-satellite Retrievals for GPM (GPM IMERG), and TerraClimate data to identify potential areas of conflict under increased seasonal variability. An improved understanding of conflict drivers will help support sustainable wildlife conservation and food security in the future.
Human System Risk Communication: The Risk Story, Likelihood and Consequence, and Evidence
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Africa Food Security & Agriculture: Predicting the Likelihood of Human-elephant Conflict and Assessing Elephant Habitat Conditions During Extreme Drought and Crop Deficit in the Kavango-Zambezi Area
Human-wildlife conflict is increasingly more common due to human population growth, habitat fragmentation, and changing climatic conditions. This conflict is particularly evident in the Kavango-Zambezi area, where over three million people share the landscape with an abundant megafauna population. As the changing climate continues to exacerbate drought severity and subsequent food availability, conflict between humans and wildlife has become more prevalent. In the Kavango-Zambezi area, conflict between elephants and humans has resulted in crop loss, property damage, and threats to public safety. In order to manage current and future conflict, The Ecoexist Project and Connected Conservation have been working to empower farmers and conserve the natural habitat. This DEVELOP project employed Earth observations to conduct a time series analysis of vegetation health change, elephant movement, and climate conditions, from 2017 to 2020. Data were aggregated into the wet (November through April) and dry (May through October) seasons. The resulting analysis demonstrated the potential to use Landsat 8 Operational Land Imager (OLI), Global Precipitation Measurement’s Integrated Multi-satellite Retrievals for GPM (GPM IMERG), and TerraClimate data to identify potential areas of conflict under increased seasonal variability. An improved understanding of conflict drivers will help support sustainable wildlife conservation and food security in the future.
Africa Agriculture & Food Security II: Predicting the Likelihood of Human-elephant Conflict and Assessing Patterns in Elephant Movements over Varying Habitat Conditions in the Kavango-Zambezi Area
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Africa Food Security & Agriculture II: Predicting the Likelihood of Human-elephant Conflict and Assessing Patterns in Elephant Movements over Varying Habitat Conditions in the Kavango-Zambezi Area
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Predicting the Likelihood of Human-Elephant Conflict and Assessing Patterns in Elephant Movements Over Varying Habitat Conditions in the Kavango-Zambezi Area
In the Kavango-Zambezi area of southern Africa, three million people live within areas frequently traveled by free-ranging elephants. As the region continues to develop rapidly, urban and agricultural settlements further encroach upon the land that these elephants use. As elephants come into more frequent contact with urban and agricultural areas, human populations face financial loss through crop damage and the potential for injury from direct conflict with elephants. Elephant populations are also at risk of injuries from conflict as well as illness related to the consumption of waste. In order to implement human-elephant conflict mitigation strategies, local conservation groups need to be informed on best practices for coexistence. This project aided The Ecoexist Project and Connected Conservation in understanding the ecological factors that drive elephant movement into human settlements and provided Earth observation data to support conflict management in the future. The team used Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) data to create land use land cover maps and calculate vegetation indices, and used TerraClimate data to analyze drought conditions. These classified maps allowed us to display a time series of human settlement from 1990 to the present and were made explorable alongside other environmental variables in an updated Google Earth Engine (GEE) tool. This project also provided heat maps that show the risk of human-elephant conflict based on historical data of HEC locations. This analysis will provide support for conservation experts in determining best practices for future mitigation and prevention of human-elephant conflict.
Using a Deep Neural Network to Estimate Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters
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Classifying Aircraft using Velocity Data with Support Vector Machines and Likelihood Ratio Tests
This presentation highlights methods to classify aircraft from radar data and investigates how data from flight controllers can be used to train classifiers.
A Generalized Deep Neural Network for Estimating Severe Hail Likelihood from Satellite Infrared Cloud Top Patterns and Microwave Radiances
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Feature Importance of Imager Cloud Products, Microwave Radiometry, and Atmospheric Reanalysis Variables in A Deep Neural Network for Estimating Severe Hail Likelihood
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Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression
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Real-time Likelihood-free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation
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