Bibliographic record
Abstract
Using the parameters provided by the project Telesat LEO, this thesis addresses the Doppler frequency shift encountered when an earth terminal station receives signals from a LEO satellite.To achieve frequency synchronization, there is a necessity to construct a Costas loop to demodulate the data in the receiver.However, since the frequency shift produced by the Doppler phenomenon is very large, the challenge becomes achieving and speeding up the acquisition time of the Costas loop.Based on the theoretical foundations of the PLL, this thesis reviews the theory of BPSK and QPSK Costas loops.Then the thesis creates a new way of achieving fast synchronization for a BPSK Costas loop by adding a quadri-correlator in one of the Costas loop arms.To further optimize the synchronization, the combination of a traditional BPSK Costas loop and a BPSK Costas loop with a quadri-correlator is then introduced, which guarantees both stability and rapidity of acquisition.The construction of a QPSK Costas loop is significantly different from that of a BPSK Costas loop.The thesis then shows that the addition of a quadri-correlator cannot be used in a QPSK Costas loop, but that the synchronization time can still be reduced by changing the parameters in the loop filter.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".