Special Coverage

Active Aircraft Pylon Noise Control System
Unmanned Aerial Systems Traffic Management
Method of Bonding Dissimilar Materials
Sonar Inspection Robot System
Applying the Dynamic Inertia Measurement Method to Full-Scale Aerospace Vehicles
Method and Apparatus for Measuring Surface Air Pressure
Fully Premixed, Low-Emission, High-Pressure, Multi-Fuel Burner
Self-Healing Wire Insulation
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Educational Software for OctaSat Nanosatellite Training Kit

Langley Research Center, Hampton, Virginia Nanosatellites are very small satellites that can conduct various space missions using microelectronics, primarily in near-Earth orbits. Aerospace engineering students need to learn about the working principles and control of nanosatellites. However, the cost of an actual nanosatellite system is very expensive and is not suitable for an engineering college-level education. Therefore, this simplified classroom-based simulation kit for nanosatellite was developed, named OctaSat.

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Discovery Through Eigenbasis Modeling of Uninteresting Data

The system learns only what to ignore, reducing the possibility of missing the items of interest. NASA’s Jet Propulsion Laboratory, Pasadena, California When presented with a new data set, a common initial goal is to explore its contents in a discovery mode to find items of interest. However, each user who views the data set may have a different scientific goal in mind, and therefore a different desired prioritization of the items for examination. Further, as the users explore more of the data set, they accumulate concrete examples of what is or is not of interest. The goal of this work was to formalize this iterative approach to understanding large data sets, and instantiate it with methods capable of the necessary adaptation as the system iteratively acquires user feedback.

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Obstacle Avoidance Methods

NASA’s Jet Propulsion Laboratory, Pasadena, California Obstacle avoidance is a difficult problem due to the non-convex state constraints. Therefore, the feasible state space needs to be convexified, or split, into convex regions at which point the search for an optimal solution among those convex regions is done. Methods for obstacle avoidance include two mixed integer linear programming (MILP) methods (obstacle related method and path-related method) and a state-constraint convexification method.

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Control Software for Integrated CW Radar Module

This software controls the behavior of a miniaturized microwave radar module. It controls the hardware, digitizes raw samples from the analog output of the module, and applies DSP (digital signal processing) algorithms to the data stream to reduce the bandwidth and data rate. It also implements an automatic calibration algorithm to adjust the I/Q (in-phase and quadrature) values in the cancellation path to remove a large unchanging signal. The software implements a variety of commands to control the behavior of the system, and provides for synchronization of multiple modules. It encodes the digital data in a format suitable for serial ports.

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An Empirical Metric of Individual Datapoint Utility Given Ample Metadata as Applied to the OCO-2 Flight System

This method constructs new warn levels for metadata-rich data sources. NASA’s Jet Propulsion Laboratory, Pasadena, California Traditionally, quality flags provided a binary yes/no estimation of a datapoint’s utility. However, in modern instrumentation, significant auxiliary information for each datapoint can be obtained. This permits prediction of more than a binary estimate of good or bad data. Further, the physical confounding forces that obscure an observation’s utility are themselves rarely binary, such as the example of clouds with varying thickness from insignificant to entirely opaque. In this method, many different increasingly stringent filters are created allowing less and less data through, while attempting to minimize an error metric. This metric can be compared with select “truth” systems such as ground observations or regions of the Earth where the truth is believed to be predictable and known. For each sounding, the number of these filters that reject the observation in question becomes an estimate of its data quality: larger values mean most filters reject the sounding, while smaller values mean most filters accept the sounding. This integer, ranging from 0 to 19, is called the Warn Level. Instead of a binary yes/no data quality flag, this instead provides a data ordering paradigm with “better” and “worse” data. Warn Levels can be developed for any metadata-rich datasource with a functional error metric to help guide researchers to superior, tunable data filtration.

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Gravitational Compensation Onboard a Comsat

NASA’s Jet Propulsion Laboratory, Pasadena, California This technique for compensating the gravitational attraction experienced by a test-mass freely floating onboard a satellite is new, and solves an important problem that all gravitational wave missions face. Its application to the geostationary Laser Interferometer Space Antenna (gLISA) mission concept addresses and completely solves an important noise source: the gravity-gradient noise.

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AMMOS-PDS Pipeline Service (APPS) — Label Design Tool (LDT)

NASA’s Jet Propulsion Laboratory, Pasadena, California A software program builds PDS4 science product label (metadata) and automatically generates its description as part of the software interface specification (SIS) document. This software allows the mission system engineer to interact programmatically with the PDS4 information model, and retrieve science product metadata information via graphical user interfaces (GUIs). This capability will greatly improve the processes of creating and generating software interface specification documents for science instruments. Given that PDS4 is a newly defined standard, most of the work that is simplified by this software suite is being done manually. This improvement allows the definition and design of PDS4 science data archive models for generating PDS4 compliant labels.

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