Proposed Encoding/Decoding Algorithm to Recover Satellite Uncompleted Received Frames
Bibliographic record
Abstract
In some cases, a communication session between a satellite and a ground station can be interrupted during the frame reception. Despite the fact that strong FEC (Forward Error Correction) codes such as turbo codes are used to protect frames from errors they are unable to recover the data in the case when the received frame is incomplete. As a result, incomplete frames are lost even if they contain critical mission data. In this paper, we propose a modification to turbo encoding and decoding algorithms which provides an ability to recover incompletely received data frames. The proposed modification causes a negligible rate loss and only a slight increase in encoder and decoder complexity. The proposed algorithm is based on segmentation of the information frame into sub-frames and calculation and insertion of terminations bits after each sub-frame during the encoding process. The inserted termination bits help the ground station decoder to recover the incomplete frames. This modification can be used in any turbo coding/decoding system since it can be realized as a slight adjustment to the maximum a posteriori probability (MAP) decoding algorithm (standard turbo decoder) and hence it does not require any additional hardware. The proposed algorithm has been tested on Consultative Committee for Space Data Systems (CCSDS) turbo codes of rates 1/2 and 1/3 with information frame length of 1784 bits. For incomplete frames which are 1700 bits long the modified decoder provides a 20dB performance gain over the standard turbo encoding/decoding algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".