A self-adjusting parametric model for attenuation characteristics of WUSN signal
Bibliographic record
Abstract
Wireless underground sensor networks (WUSNs) are gradually being applied to smart agriculture for soil information collection and monitoring of crop growth environments. WUSN can avoid the inconvenience caused by tillage and other machine operation activities on farmland and obtain multi-level and multi-dimensional parameters in the underground soil environment, which is crucial for soil moisture monitoring of crops. However, WUSN has no universally applicable transmission protocol standards in the field. Therefore, the research of different soil compositions on the placement of wireless sensor network nodes can provide scientific guidance to obtain soil moisture information of agricultural fields, which is important for the development of precision agriculture. In this paper, low-power WUSN nodes were designed, based on the modified Frisian transmission model and the complex refractive index Fresnel model. We proposed an adaptive optimization model and also proposed an improved genetic algorithm that automatically adjusts the fusion parameter according to soil and distance factors, making the prediction of signal attenuation under different soil components more accurate. We used the adaptive optimized model for signal prediction, comparing it with the modified Friis prediction model and the complex refractive index Fresnel prediction model. The results showed that the proposed adaptive optimization model with an automatic parameter is convenient to predict the signal attenuation, and the adaptive optimization model made the prediction error stay really low. To compare with other sensors in the soil environment, the temperature of the distributed fiber-optic temperature sensor was tested, which was predicted by the adaptive model. The result shows that the adaptive model is more favorable to the prediction of signal attenuation in WUSN than distributed fiber-optic temperature sensors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".