Dynamic analysis of the polar ionosphere using the GPS signal: Toward an optimization of the cutoff scale
Bibliographic record
Abstract
Abstract Using Global Navigation Satellite System observations, such as the amplitude and the phase components of the GPS L1 signal, ionospheric scintillation is characterized and quantified using indices derived from those observables. However, the background electron density of the ionosphere is not stationary, presenting a trend and a nonzero mean, and the GPS motion induces a Doppler shift that will contribute to the nonstationary aspect of the signal; hence, the multiscale nature of the diffracted signal makes it difficult to extract the components of the signal that correspond to scintillation. Constructing scintillation indices from a signal that has a nonscintillation component will lead to erroneous estimation and biased characterization of the scintillation. In this context, we present a technique aiming at retrieving the scintillation components from the raw, transionospheric radio signals. Using wavelet analysis, we define and maximize the entropy of the system, which is composed of two subsystems corresponding to scintillation and nonscintillation contributions. The Tsallis entropy has been considered for the power component, for which a non‐Gaussian behavior has been observed. This entropy is based on a nonextensive approach that introduces a parameter q, quantifying the nonextensivity. On the other hand, the phase presents a Gaussian behavior and is analyzed using the Shannon‐Gibbs entropy. In both cases, the optimum cutoff scale, delimiting the scintillation components, is estimated via the maximization of the entropy, which, as defined here, is a function of the temporal scale. This optimization of the cutoff scale will be key in the construction of an optimum, unbiased index quantifying the ionospheric scintillation using GPS signal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".