Analysis of GNSS Interference Impact on Society and Evaluation of Spectrum Protection Strategies
Bibliographic record
Abstract
Global Navigation Satellite System (GNSS) technology is growing fast in our society and new applications are being introduced at an unprecedented pace. The GNSS products provide worldwide and real-time services using precise timing information, positioning and synchronization technologies. Within years, GNSS applications are becoming more accurate and their precision opens doors to a wide range of applications. Nevertheless, these applications are susceptible to disruption in the operation of GNSS receivers when malfunctions, failures or interference occur. This paper’s objective is to make an overall analysis of GNSS failure impact on society and therefore make a review of GNSS spectrum protection strategies. In the first three sections of this analysis, we survey GNSS applications, their importance and their criticality. While questioning the criticality of GNSS applications, we evaluate their impact on main critical infrastructures and particularly the risks of critical dependencies in case of failure or interference. In the last two sections, we investigate GNSS spectrum interference in relation to its effects on crucial infrastructures. We review the principal Radio Frequency Interference (RFI) sources leading to GNSS and satellite communications (SATCOM) spectrum issues. Alongside, we study various ways to mitigate RFI. This process is essential to further develop and standardize mitigation techniques and to ensure GNSS spectrum immunity against RFI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".