GPS-Based Ionospheric Tomography From the Combination of PolSAR and E-CHAIM
Bibliographic record
Abstract
The utilization of Global Positioning System (GPS) for three-dimensional ionospheric electron density reconstruction, i.e., computerized ionospheric tomography (CIT), provides significant importance in investigating the internal structure, variations, and disturbances within the ionosphere. However, the ill-posed problem caused by insufficient observational data or uneven distribution will rely heavily on the selection of initial values, which are typically derived from empirical models with low precision. Aiming at this issue, this paper uses the TEC obtained by the spaceborne full polarization synthetic aperture radar (PolSAR) to correct the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), thus improving the authenticity of the initial value. This study makes full use of the advantages of low frequency full PolSAR in ionospheric sounding, including high precision and resolution, as well as all-day and all-weather operation without a ground receiver. Therefore, the precision of GPS-based tomography can be enhanced, particularly for small-scale anomalies, and it is also simple and easy to achieve. Numerical and measured experiments using GPS, incoherent scatter radar, PolSAR, and E-CHAIM data in Alaska demonstrate that the reconstruction accuracy of the proposed CIT is significantly improved than that of the tomography results using only empirical model. In addition, the effects of PolSAR system errors and voxel size on CIT are analyzed to demonstrate the robustness of the method proposed in this paper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".