Intelligent Weather Systems with Fuzzy Logic Controller for Satellite Networks
Bibliographic record
Abstract
Weather attenuations can have a distorting effect on signal fidelity above 10 GHz that lead to excessive digital transmission error. This loss of signal is commonly referred to as signal attenuation. Signal attenuation impacts QoS in wireless and satellite networks. An intelligent decision support system is therefore necessary for service providers by accurately calculating rain, gaseous, cloud, fog, and scintillation attenuations using predicted signal-weather correlated database in collaboration with ITU-R propagation models combined with gateway, and ground terminal characteristics. The effect becomes a key feature in adjusting and improving satellite signal power, modulation and coding schemes, monitored and controlled altogether by a powerful and efficient intelligent-based attenuation countermeasure system. A three dimensional relationship is proposed among these attenuations with respect to propagation angle and rainfall rate (Harb et al., 2007). The result pilots an enhanced back propagation-learning algorithm that is used to iteratively tune the intelligent controller based on fuzzy logic technique with returned SNR values for activating the weighted Modulation/Codepoint to its optimal values, depending on actual or predicted weather conditions, configuration settings and tolerance/safety margins for SLA commitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".