Bibliographic record
Abstract
The paper presents a practical Doppler estimator to be used in underwater acoustic (UWA) channels. The estimation is performed using OFDM pilot signals. The OFDM pilot signals are passed through a Doppler channel that adds the above effects to the signal. The receiver then performs Doppler estimation on the signal. The estimator finds an estimate for the Doppler scaling factor (also called the mach number) using the centroid of the energy spectral density (ESD) of the received signal. A suitable domain (“window of estimation”) for each pilot is chosen to avoid pilot ambiguity resulting from large Doppler shifts. This leads to a necessary condition for the estimator's accuracy that must be satisfied by the designer. After estimating the mach number, Doppler compensation is performed in a two-step process. First the received signal is resampled using the mach number estimate to undo the signal dilation/compression. The second step is to compensate for the residual Doppler shift that persists by back-shifting the frequency spectrum of the pilots. Due to the high accuracy of the estimator, the pilot positions are recovered exactly. A Simulink model that uses the proposed estimator and compensator is presented, and the results show how the estimator performs in a typical underwater channel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".