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Dictionary Management System

NASA’s Jet Propulsion Laboratory, Pasadena, California The Dictionary Management System (DMS) is a Web-based tool to develop and store a project command and telemetry dictionary. The dictionary defines a project’s flight/ground interface. In recent years, dictionaries have become very large, making it hard to maintain traditional methods of flat-file storage and manual configuration management. DMS is a centralized solution to this problem that allows a multi-disciplinary team to work together to manage the development, verification, and operational use of the dictionary.

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National Vulnerability Database (NVD) Query Tool

NASA’s Jet Propulsion Laboratory, Pasadena, California Currently, there are no programmatic methods to query the NIST National Vulnerability Database (NVD) without downloading the entire database in XML format, parsing the content, loading the resulting data into a self-hosted database, and then developing an interface for querying the content. This tool takes advantage of the fact that NVD provides an interactive user form for query on the website, and extends it to allow for programmatic queries to generate vulnerability reports.

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Ensemble

Ensemble tools for different missions share a common look and feel, easing the transition of personnel between projects. NASA’s Jet Propulsion Laboratory, Pasadena, California Success in mission operations relies on achieving consensus amongst a wide variety of personnel with diverse backgrounds and education. Previously, NASA missions relied on disparate tools with a variety of inconsistent interfaces. These tools greatly influence how members of the mission communicate with each other, increasing confusion and reducing consensus. Ensemble provides a shared interface that helps scientists and engineers of differing disciplines to collaborate effectively. Within a single tool, Ensemble allows scientists and engineers to efficiently discuss objectives and understand the tradeoffs between exploration and discovery.

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Fast Block Transforms on Large Binary Datasets in the Cloud Using Hadoop Streaming

NASA’s Jet Propulsion Laboratory, Pasadena, California A software framework on top of Hadoop Streaming enables both the processing of binary data in the cloud, and the freedom for the developer to implement his or her mapper and reducer programs in any language, rather than re-implementing existing solutions in Java, or repackaging existing binary data into a text format. Binary data is partitioned into chunks that are kept in a persistent data storage medium. A textual list of filenames for these chunks is piped into a Hadoop Streaming mapper program, which then reads the corresponding files, computes block transforms locally, and writes the results back to persistent data storage. The mapper program is stored on all compute nodes, and the filenames are distributed in parallel across the cluster, so that the workload is evenly distributed and the end-to-end block transform speedup is roughly given by the number of nodes in the cluster.

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Mars Science Laboratory Frame Manager

NASA’s Jet Propulsion Laboratory, Pasadena, California One of the highly desired enhancements to the Mars Exploration Rover (MER) and Phoenix flight missions was the centralized coordinate transform database maintained onboard. Without the database, there are quite a few operations that require cumbersome, error-prone manual calculations on the ground such as pointing a mast camera to an arm tool and driving the rover to the goal defined in a previous site. The Mars Science Laboratory (MSL) Frame Manager flight software implements a centralized frame tree database, which eliminates these cumbersome, error-prone calculations of coordinate entries for commands.

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SIVO-PyD: A Python Distribution for Scientific Computing Visualization

Goddard Space Flight Center, Greenbelt, Maryland SIVO-PyD gathers and automatically installs (in various computing platforms) a collection of Python-related packages for scientific computing and visualization. All of the packages in distribution are accessible within the Python framework. The distribution is self-contained and can be extended with minimal work.

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Formal Validation of Model-Based Fault Management Design Solutions

A number of advantages of modeling fault protection logical design, executing the model, and running a model checker are identified. NASA’s Jet Propulsion Laboratory, Pasadena, California Model-Based System Engineering is becoming widely adopted at JPL and in industry because model-centric systems introduce improved methods of system engineering. As systems with ever-increasing complexity are developed at JPL, model-centric engineering be comes essential for design, test, and validation. Validation of FP designs is historically problematic, with many examples of inadequate resources (people, time, and budget) and/or unexpected problems. Many factors contribute to these issues, but the problem can be traced to a lack of appreciation of system complexity. When considering a system, there are significantly more ways the system can fail (contingency paths) than ways it can succeed (nominal paths). As NASA continues to develop more complex and capable spacecraft, the behavior state space will increase, stressing the ability of teams to properly understand system behavior.

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