Optimized design of energy efficiency of agricultural small base station in Aksu region based on RF power amplifier
Bibliographic record
Abstract
The purpose of this study is to solve the energy efficiency problem of small agricultural base stations, propose an optimal design scheme based on RF power amplification, and verify its effectiveness through simulation experiments.In order to achieve the research purpose, this paper first defines the objectives and principles of energy efficiency optimization design, and puts forward the energy efficiency optimization technology based on RF power amplification.On this basis, a complete set of energy efficiency optimization design scheme for small agricultural base stations is designed.And by building a simulation platform, set the parameters close to reality, and simulate the operation state of the base station in different scenarios.The simulation results show that the stability of the algorithm in this paper is considerable under different loads.Even if the load is large, the stability of this method can reach above 89%.The proposed energy efficiency optimization scheme can significantly reduce the energy consumption of the base station and improve the overall energy efficiency performance under different load and interference conditions.This result proves the effectiveness and superiority of the scheme and provides strong support for practical application.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".