Phoning home, with lasers: Optical communications will provide a high-speed connection to Earth
Bibliographic record
Abstract
With NASA making serious moves toward a permanent return to the moon, it's natural to wonder whether human settlers-accustomed to high-speed, ubiquitous Internet access-will have to deal with mind-numbingly slow connections once they arrive on the lunar surface. The vast majority of today's satellites and spacecraft have data rates measured in kilobits per second. But long-term lunar residents might not be as satisfied with the skinny bandwidth that, say, the Apollo astronauts contended with. To meet the demands of highdefinition video and data-intensive scientific research, NASA and other space agencies are pushing the radio bands traditionally allocated for space research to their limits. For example, the Orion spacecraft, which will carry astronauts around the moon during NASA's Artemis 2 mission in 2022, will transmit mission-critical information to Earth via an S-band radio at 50 megabits per second. "It's the most complex flight-management system ever flown on a spacecraft," says Jim Schier, the chief architect for NASA's Space Communications and Navigation program. Still, barely 1 Mb/s will be allocated for streaming video from the mission. That's about one-fifth the speed needed to stream a high-definition movie from Netflix. To boost data rates even higher means moving beyond radio and developing optical communications systems that use lasers to beam data across space. In addition to its S-band radio, Orion will carry a laser communications system for sending ultrahigh-definition 4K video back to Earth. And further out, NASA's Gateway will create a long-term laser communications hub linking our planet and its satellite.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.043 |
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".