GPS L1 phase scintillation using wavelet analysis at high latitude
Bibliographic record
Abstract
Phase scintillation, e.g. as observed from GPS satellites by ground receivers, is generally measured as the standard deviation of the random fluctuations of the phase received over a fixed period of time which is ofteny taken to be 60s. The measured phase scintillation can be an important tool in understanding the ionospheric turbulence producing it, and consequently its effect on GPS positioning. Therefore, it is important to develop reliable ways of quantifying it. A new approach employing a wavelet analysis is implemented in this work to investigate GPS carrier phase fluctuations for different scintillation conditions using GPS data received at high latitudes where the scintillation effect is particularly marked. The phase scintillation obtained using wavelet analysis is also compared with that derived from the standard PSD technique using FFTs. It is concluded that this wavelet approach appears to be a promising method for recording fast variations of phase due to diffraction by ionospheric irregularities. Furthermore, the wavelet analysis, because it can better characterize conditions of non-stationary, can lead to a better understanding of these effects on phase lock loss in GPS receiver PLLs and hence can aid the design of GPS receivers that are more robust to scintillation effects.
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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) | 0.000 | 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 | 0.000 |
| 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 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".