Predicción de cobertura en sistemas LMDS/LMCS
Bibliographic record
Abstract
The growing demand for communication services such as voice, data, video, audio, multimedia y/o any combination of these has increased the demand for broadband systems. In this context, the LMDS system (Local Multipoint Distribution Service) and their Canadian version LMCS (Local Multipoint Communication System), which utilize minimum bandwidths of 1.3 GHz, have become in strong candidates to cover this necessity. In this article, an algorithm is developed to determine one of the most important aspects in the planning of systems LMDS/LMCS and of a wireless system in general: coverage system. Additionally, coverage radiuses in the systems LMDS/LMCS for temperate climates and tropical and equatorial regions in analytical way and through the use of the NETDimensionTM software are compared. Finally, the influence of diverse factors is also analyzed in coverage radio of the LMDS/LMCS systems.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".