A Self-Detection Gradient Descent Approach for Semi-Underground LoRa Communication Networks in Smart Irrigation Systems
Bibliographic record
Abstract
Wireless underground sensor networks (WUSNs) have practical applications in domains such as military operations, agriculture, and information science. However, the large attenuation of signals underground has always posed a challenge, especially when it involves dynamic and changing environments. In large irrigation landscapes, the irrigation process is usually required multiple times per day in torrid areas, and the wireless signal is highly attenuated due to soil moisture. Consequently, communication link disconnections may easily happen and highly waste time and energy consumption. Thus it is necessary to have a precise path loss model considering soil moisture to ensure that the path loss does not exceed the allocated link budget. In this work, we present a comprehensive study on the impact of soil moisture on the communication link among underground and aboveground nodes and propose a mathematical long-range (LoRa) path loss model that considers the complex dielectric constant of the soil. Furthermore, we develop a self-detection stochastic gradient descent (SSGD) approach with a distributed clustering sensor network architecture that can self-detect disconnections caused by the irrigation schedule. Based on our case study, it is demonstrated that the SSGD approach is more efficient and reliable than the traditional stochastic gradient descent (SGD) algorithms in high-moisture conditions for smart irrigation applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".