A Reconfigurable GNSS Acquisition Scheme for Time-Frequency Applications
Why this work is in the frame
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Bibliographic record
Abstract
The extreme weakness of global navigation satellite system (GNSS) signals makes them vulnerable to almost every kind of interferences that, without adequate countermeasures, can heavily compromise the receiver performance. An effective solution is represented by time-frequency (TF) analysis that has proved to be able to detect and suppress a wide class of disturbing signals. However, high computational requirements have limited the diffusion of such techniques for GNSS applications. In this paper, we propose an effective solution for the efficient implementation of TF techniques on GNSS receivers. The solution is based on the key observation that the first block of a GNSS receiver, the acquisition stage, implicitly performs a sort of TF analysis. Thus, a slight modification in the traditional acquisition scheme enables the fast and efficient implementation of TF techniques for interference detection. The proposed method is suitable for different types of acquisition scheme and its effectiveness is proved by simulations and examples on real data.
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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 | 0.000 | 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) | 0.000 | 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