GNSS Data Processing Investigations for Characterizing Ionospheric Scintillation
Bibliographic record
Abstract
Ionospheric scintillations are generally characterized via scintillation indices, calculated from amplitude and phase of the received GNSS signals. For many scintillation monitoring and mitigation applications, it is important to determine these indices accurately. Investigating appropriate data algorithms (here, data detrending) in deriving scintillation information for mitigation applications is the focus of this paper. Commonly, most GNSS receivers use a Butterworth filter with a fixed cutoff frequency of 0.1 Hz to remove low frequency trends from the data [Forte and Radicella 2002, Mushini, et al., 2012]. However, as shown in Forte and Radicella, 2002, inherent characteristics of ionospheric effects at different regions require different detrending settings. In this study, four detrending methods and effectiveness of each are examined using real data sets from high latitude and equatorial regions. Based on our results, it is observed that data detrending via wavelet-based filter can result in cleaner (less noisier) signal. In addition, correlation between computed amplitude and phase scintillation indices improves when wavelet filtering is used. Moreover, we present initial considerations for scintillation monitoring and mitigation via exploiting new GNSS signals to determine additional information. To examine real data, IF samples from scintillation events are post-processed using the GSNRx™ software receiver, developed by the Position, Location and Navigation (PLAN) group at the University of Calgary. This software receiver has the capability of processing GPS L1C/A and L2C signals. Scintillation parameters are calculated using the post-correlator in-phase (I) and quadra-phase (Q) components and carrier phase measurements, all obtained from the software receiver.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".