Arraying Technique for Enhanced Multiplexing of Interferometric Signals (ARTEMIS): An Enabling Technology for Long Range or High Data Rate Microspacecraft Communications
Bibliographic record
Abstract
The Space Flight Laboratory of the University of Toronto Institute for Aerospace Studies has developed a prototype ground station antenna array correlator that offers advantages over previously employed approaches for weak signal communication. By using Orthogonal Frequency Division Multiplexing (OFDM) in the microspacecraft transmission, the array can perform frequency correlation in addition to time correlation (both techniques derive from Very Long Baseline Interferometry) to bring all the signals of the array into alignment. This removes the need for high accuracy local oscillators, such as hydrogen masers, to be used at each antenna to maintain frequency stability. In essence, expensive hardware requirements have been replaced with inexpensive software algorithms, allowing for the construction of low-cost ground station arrays made up of small antennas (eg. 3 m or 6.1 m diameters), perfect for use as a microsatellite ground station with a high data rate link, higher than what is currently possible. Recent hardware prototyping results have confirmed those obtained previously through simulation alone. These new results will be discussed and it will be shown how a small-antenna array ground station could be used to provide a high performance communications link for future microspacecraft missions flying to the Moon and other planets and bodies in the Solar System. The paper will also describe a planned flight demonstration mission currently being arranged through the Space Flight Laboratory’s CanX nanosatellite program.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".