Impact of equatorial ionospheric irregularities on GNSS receivers using real and synthetic scintillation signals
Bibliographic record
Abstract
Abstract The impact of L‐band equatorial ionospheric scintillation on Global Navigation Satellite Systems (GNSS) receivers is investigated in this paper using both real and synthetic scintillation data. To this end, various low‐latitude data sets, recorded during the most recent solar maximum, are exploited in post‐processing to develop and verify realistic simulation tools and evaluate GNSS receiver performance. A scintillation simulation model is implemented based on the phase screen formulation of Dr. Charles Rino (1979, 1982, and 2011) which allows oblique signal propagation in an anisotropic propagation medium with multiple irregularity layers (or phase screens) for multiple GNSS frequencies. The observed real scintillation parameters are used to drive GNSS signal simulations. The subsequent simulated GNSS signal time series are verified through comparison with real data for different signal tracking states including the most severe and challenging tracking scenarios. Using both real and synthetic data sets, the impact of scintillation on observation quality and receiver performance is evaluated in terms of probability of loss of phase and frequency lock, as well as the correlation of disturbed L‐band signals transmitted by GNSS satellites on the same transionsopheric path.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".