Handling cycle slips in GPS data during ionospheric plasma bubble events
Bibliographic record
Abstract
[1] During disturbed ionospheric conditions such as the occurrence of plasma bubbles, the phase and amplitude of the electromagnetic waves transmitted by GPS satellites undergo rapid fluctuations called scintillation. When this phenomenon is observed, GPS receivers are more prone to signal tracking interruptions, which prevent continuous measurement of the total electron content (TEC) between a satellite and the receiver. In order to improve TEC monitoring, a study was conducted with the goal of reducing the effects of signal tracking interruptions by correcting for “cycle slips,” an integer number of carrier wavelengths not measured by the receiver during a loss of signal lock. In this paper, we review existing cycle-slip correction methods, showing that the characteristics associated with ionospheric plasma bubbles (rapid ionospheric delay fluctuations, data gaps, increased noise, etc.) prevent reliable correction of cycle slips. Then, a reformulation of the “geometry-free” model conventionally used for ionospheric studies with GPS is presented. Geometric information is used to obtain single-frequency estimates of TEC variations during momentary L2 signal interruptions, which also provides instantaneous cycle-slip correction capabilities. The performance of this approach is assessed using data collected on Okinawa Island in Japan during a plasma bubble event that occurred on 23 March 2004. While an improvement in the continuity of TEC time series is obtained, we question the reliability of any cycle-slip correction technique when discontinuities on both GPS legacy frequencies occur simultaneously for more than a few seconds.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".