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Developed a data pipeline utilizing a commercial Machine Learning platform to potentially improve the speed in which recommendations are made for continued use of EMU gloves on ISS; demonstrating the use of ISS as a testbed for further, deep space applications.
Obtaining accurate and timely satellite observations is of paramount importance in fields like disaster management, weather diagnoses/forecasting, and Earth Sciences remote sensing. Stored mission data (SMD), from low Earth orbiting (LEO) satellite sensors, provides important observations for these fields and applications, however data access to SMD can be delayed from one and half hours to three hours from the time the observations were made. This data latency poses a significant impact on data product optimal use. We developed a Ground Station Observation Network (GSON) that utilizes commercial ground station as a service (GSaaS) providers to acquire low latency direct broadcast (DB) data from AQUA, SNPP, and JPSS-1 satellites using antennas located in strategic locations around the world. We will discuss techniques to improve the deployment efficiency and code reliability and quality of the GSON framework. Topics include right-sizing and containerization of the code to facilitate integration and adaptation with continuous delivery (CD) pipeline, locating non-code assets in referenceable repositories separated from code, adaptation of pipelines as code and simplification of CD, intersecting with code quality tests and checks as part of the pipeline execution and deployment, and establishing distributed repositories, registries, and system identities in a way that mitigates compromise to the CD pipeline. These techniques enable deployment of processing systems that are both highly specific but also dynamically modifiable. This new class of system allows for a flexible and scalable deployment while avoiding the “black box” issues that can plague large system deployments.
There is a strong demand for the near real time NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) products. As important as real-time information, archived copies of the products form the basis for targeted research focusing on specific events or conditions. To make these SatCORPS products available for downloading, the SatCORPS group has developed a number of tools and technologies to create a hybrid data storage system that leverages the strengths of both cloud and on-premises resources. In this work, we describe the technologies the group uses to marshal disparate data repositories and materialize them into a single searchable overview and give a broad description of the organization of the dataset. As with any implementation, the strengths, weaknesses and constraints surrounding the components establish priorities and provide insight where trade-offs are necessary. We further describe the design and architecture underpinning our hybrid data repository and delivery system.
The integration of cloud services and capabilities across government and commercial sectors has continued to increase over the last several years. Cloud architectures provide on-demand process, storage, and transfer of data as a service without the need to purchase expensive hardware, infrastructure, and their associated maintenance costs. Most cloud environments charge the user for only the resources they use, making cloud implementations highly scalable and easily adjustable to the user’s changing demands.
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