Phoning home, with lasers: Optical communications will provide a high-speed connection to Earth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it