Communications Research and Development at NASA Glenn Research Center
Bibliographic record
Abstract
Over the last several decades, the National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC), formerly Lewis Research Center (LeRC) has performed research and technology development of aeronautic- and space-based communications in support of NASA and the nation. In the 1970s, GRC partnered with the Canadian Department of Communications through the Communications Technology Satellite (CTS) Project, in which GRC researchers were responsible for the development of critical technology components, such as the high-power, traveling-wave tube amplifier (TWTA), thereby pioneering the surge of television channels via satellite. For its efforts, LeRC was awarded an Emmy by the television industry. The decade of the 1980s served as a period for technology development that culminated in the launch of the Advanced Communications Technology Satellite (ACTS) in 1993. The ACTS demonstration of spot beam antenna technology resulted in an overall increase of efficiency in satellite communications. In the latter part of the 1990s and until today, GRC research engineers have continued to conduct research and technology development in multiple domains. The primary focus of this article is to introduce the reader to the long heritage at GRC in communications research and development through the CTS and ACTS projects and delve into specific technology areas following the ACTS Project to today in support of high-data rate communications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".