Use of Low-Cost GNSS Receivers for Ionospheric Monitoring
Bibliographic record
Abstract
Abstract Electromagnetic waves propagating through the Earth’s ionosphere are subjected to changes in group and phase velocities, refraction, dispersion, and diffraction. For systems relying on the usage of radio signals affected by the ionosphere, it is of crucial importance to account for these effects. As far as ionospheric monitoring is concerned, high-grade multi-frequency and multi-constellation Global Navigation Satellite System (GNSS) receivers are commonly used. The existing ground-based GNSS receiver networks can provide information on a global scale. Nevertheless, geographic regions still exist where ionospheric monitoring facilities are limited. To improve our knowledge about ionosphere in areas where the existing networks coverage is sparse, an increase in the receivers’ density would be desirable. The cost of the devices has indeed constituted a major obstacle, however, with the significant advance of software-defined radio, the situation is changing through the availability of low-cost solutions. The present work will discuss the performance of the available off-the-shelf low-cost dual-frequency GNSS receivers, testing them in the controlled environment and comparing the derived data with scientific-grade instruments. The accuracy of the collected ionospheric data, such as uncalibrated and calibrated total electron content (TEC) will be analyzed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".