Arraying Technique for Enhanced Multiplexing of Interferometric Signals (ARTEMIS): An Enabling Technology for Long Range or High Data Rate Microspacecraft Communications
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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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