<i>C</i> / <i>N</i> <sub>0</sub> estimation: design criteria and reliability analysis under global navigation satellite system (GNSS) weak signal scenarios
Why this work is in the frame
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Bibliographic record
Abstract
This study provides a comprehensive theoretical analysis of a modified maximum likelihood signal-to-noise ratio (SNR) estimator and quantifies the minimum coherent integration time required to achieve a predefined level of accuracy. The SNR estimator is derived under the assumptions of perfect frequency synchronisation, data bit aiding and constant signal phase during the observation window. The probability density function (pdf) of the SNR estimator in logarithmic units is derived and used to quantify the bias and error bounds associated with the considered SNR estimator. The minimum coherent integration time is determined by requiring a desired level of accuracy with a given probability level, that is the integration time is chosen in order to make the SNR estimate lie in a predefined confidence interval. Theoretical results have been validated using GNSS software and hardware simulations. The agreement between theoretical and experimental results supports the validity of the developed theory.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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