Galileo E5 Signal Acquisition using Intermediate Coherent Integration Time
Bibliographic record
Abstract
The acquisition of modern Global Navigation Satellite System (GNSS) signals may be difficult due to the presence of a secondary code. Indeed, short coherent integration times should be used without non-coherent integration, which implies a low sensitivity; or long coherent integration times should be used, requiring synchronisation with the secondary code and thus a full correlation, which implies a significant computational burden, especially for signals with long secondary codes such as the Galileo E5 signal. A third option that lies between the previous two is to perform a partial correlation using less than one secondary code period as input, however this is less efficient in terms of complexity than using an entire secondary code period, and the code's autocorrelation properties are completely changed. The authors recently proposed a method based on combining secondary code correlations, allowing the use of intermediate coherent integration times with the possibility to do non-coherent integrations, and the method was successfully applied to the Global Positioning System (GPS) L5 signal. This paper studies the application of the method to the Galileo E5 signal, compares it with the partial correlation method, and discusses the case where less than one secondary code period is used as an input
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".