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Rodney Grubbs

Publications and source records attributed to Rodney Grubbs.

4 records
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Next Generation Big Data Storage for Long Space Missions

This paper presents the results of the HELIOS (Hardened Extremely Long Life In-formation Optical Storage) mission on the International Space Station (ISS) which tested a unique solution for the long-term storage and retrieval of data in space. For this mission Creative Technology (CTech) developed test media—termed WORF (Write Once, Read Forever)—to validate whether this patented technology will survive all critical parameters for harsh space-based environments including microgravity and ionizing radiation. The HELIOS experiment confirmed that the WORF media is impervious to ionizing radiation, microgravity, solar (plasma) eruptions, and the stress from 8 Gs of the launch including extreme temperature expo-sure. The principal results indicate that there has been no discernible degradation of the media after 8 months on the ISS as compared to a control set of media stored on the ground. This data validated the media’s survivability for harsh space environments for long-term and deep space missions. In addition to the space environment, we are confident that WORF technology can be used for data storage where space-related and other long-term or archival integrity is critical such as: geospatial collections from satellites; space weather archives; past, ongoing, and future space mission media and documentation files; the deep space Gate-way program; as well as Big Data applications such as the Vera C. Rubin astronomical observatory (formerly the LSST). WORF technology for the HELIOS experiment uses a proven archival media, redesigned, re-purposed and patented by CTech to store digital data for long periods, measured in decades and possibly centuries. The media stores standing waves embedded in a substrate that capture the precise col-ors or wavelengths projected onto the media. The colors represent numerical data, with each data location storing multiple superimposed wavelengths, which facilitate the storage of multiple data bytes (rather than just zeros and ones); advanced mathematical permutations allow for extremely large data density equal to or greater than contemporary data storage de-vices. These colors cannot fade or degrade over time since the standing waves are physically stabilized (fully oxidized) metallic silver; no dyes are embedded for this storage system, and silver ions resist micro-bacterial and fungal contamination

Rodney Grubbs↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗