Contribution to develop a generic hybrid technique of satellite system for RFI geolocation
Bibliographic record
Abstract
In the current information age, most of the World-systems are setup, and are communicating wirelessly with each others through the uses of wireless media. Wireless based communications have been accounted as more efficient, faster, cost less and reliable ways to exchange information between the remote stations which may locate in the distance ranges of meters, kilometers and, in any part of the World via satellite transmission. As the satellite communication SATCOM applications and services, e.g., broadcasting and cellular communications and others, are numerous, and rapidly increasing day by day; thus to overcome the required demands to connect the distance located earth stations, number of satellites are in-functional, which, increasing the immersive conjunctions in available radio frequency RF spectrum, generating the issues of radio frequency inference RFI. In this study, we take a step of assessing the interferences sources, using of hybrid geolocation technique, which could happen at the receiver's side, based on the existing most-prominent interference detection scenarios, in SATCOM. While interferences sources were located successfully and accuracy derived, further these signals were characterized, and classification process was performed to identify the belonging interferences type through employed of classification tree method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".