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Scalable Gaussian Process Regression

For multimodal data, this method gives higher prediction accuracy than a single approximate model. Ames Research Center, Moffett Field, California Block GP is a Gaussian Process regression framework for multimodal data that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution.

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MBSE-Driven Systems Engineering Visualization Suite

NASA’s Jet Propulsion Laboratory, Pasadena, California There is a need to define the information architecture, ontologies, and patterns that drive the construction and architecture of Model-Based Systems Engineering (MBSE) models, but less clarity is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a wellformed populated model.

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Python Turbulence Detection Algorithm (PyTDA)

Marshall Space Flight Center, Alabama This software, written in the Python programming language, estimates turbulence from Doppler radar data. It ingests radar data using the Department of Energy’s Py-ART open-source radar software toolkit.

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Rigorous Antenna Noise Temperature Calculation Method for International Space Station Visiting Spacecraft

Lyndon B. Johnson Space Center, Houston, Texas The temperature of the orbiting space station’s Sun-facing side could be up to 250 °F (≈120 °C) and will be a significant antenna noise temperature contributor for visiting spacecraft communication and tracking systems during rendezvous. The conventional antenna noise temperature calculation does not take into account the space station reflection effects, and results in an underestimated antenna and system noise temperature. Thus, the visiting spacecraft communication and tracking system performance could be overestimated during rendezvous.

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Recurring Anomaly Detection System (ReADS)

Ames Research Center, Moffett Field, California NASA Engineering & Safety Center (NESC) subject matter experts analyze records in various International Space Station and shuttle databases to identify recurring anomalies. The key problems these experts face in analyzing such database records are:

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Method for Automatic Optimization of Yaw Maneuvers for Orbiting Space Vehicles

Lyndon B. Johnson Space Center, Houston, Texas A new method for optimizing yaw attitude maneuvers on the International Space Station (ISS) was developed. Docking and undocking operations often require 180° yaw rotations, which are the most common large maneuvers on the ISS. When optimized, some large maneuvers, previously performed using thrusters, could be performed using control moment gyroscopes (CMGs) or with significantly reduced thruster firings. The ability to perform a non-propulsive or low-propulsive 180° yaw maneuver on the ISS has been proven through the zero propellant maneuver (ZPM) and the optimal propellant maneuver (OPM). The ZPM and OPM were created by Draper Laboratory using the computational approach. Each maneuver is unique, and can only be calculated on the ground because significant computer resources are needed for the calculations.

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Architecture Framework for Fault Management Assessment and Design

Marshall Space Flight Center, Alabama Modern spacecraft, aircraft, and surface vehicles support complex science missions in harsh environments. These spacecraft and vehicles provide diverse functionality that is deployed on increasingly complex and heterogeneous hardware and mechanical systems, with stringent dependability requirements.

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