CubeSat-Enabled Free-Space Optics: Joint Data Communication and Fine Beam Tracking
Bibliographic record
Abstract
The integration of CubeSats with Free Space Optical (FSO) links accelerates a major advancement in high-throughput, low-Earth orbit communication systems. However, CubeSats face challenges such as size, weight, and power (SWaP) limitations, as well as vibrations that cause fluctuations in the angle-of-arrival (AoA) of the optical beam at the receiver. These practical challenges make establishing CubeSat-assisted FSO links complicated. To mitigate AoA fluctuations, we expand the receiver's field of view and track the location of the focused beam spot using an array of avalanche photodiodes at the receiver. Initially, we model the optical channel between the transmitter and the detector array. Furthermore, to reduce the computational load of maximum likelihood sequence detection, which is infeasible for CubeSats due to SWaP constraints, we propose a sub-optimal blind sequence data detection approach that relies on the generalized likelihood ratio test (GLRT) criterion. We also utilize combining methods such as equal gain combining (EGC) and maximal ratio combining (MRC) for data detection, benchmarking their performance against the GLRT-based method. Numerical results demonstrate that the proposed low-complexity GLRT-based method outperforms the combining methods, achieving performance close to that of the ideal receiver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".