Investigation of the Fresnel Scale From Ionospheric Scintillation Spectra
Bibliographic record
Abstract
Abstract Trans‐ionospheric radio signals recorded on the ground exhibit random amplitude and phase fluctuations attributed to irregularities in the ionospheric electron density. Studying the ground‐based measurements of trans‐ionospheric radio signals can contribute to understanding plasma instability mechanisms leading to the development of ionospheric structures. In this regard, radio signals emitted from satellites, making up the Global Positioning System (GPS), and recorded by the Canadian High Arctic Ionospheric Network (CHAIN) GPS receivers, are analyzed to study the physical signatures of both amplitude and phase fluctuations. The current ionospheric scintillation paradigm posits that amplitude fluctuations arise from diffraction caused by Fresnel scale ionospheric structures, while refraction is responsible for signal phase variations. The amplitude power spectrum profile consistently displays a rollover frequency, which is not equal to the Fresnel frequency under the Taylor hypothesis. Phase screen theory is used to investigate this phenomenon further and identify an empirical relation between the rollover and Fresnel frequencies. Notably, we have found that the rollover frequency is consistently smaller than the Fresnel frequency. Furthermore, the Fresnel frequency extracted from two‐component phase spectra tends to be larger than the rollover frequency. Based on our results, we have concluded that the identified Fresnel frequencies are directly linked to the ionospheric irregularities causing scintillation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".