Machine Learning-Based Modeling of Rainfall Impact on FMCW Radar Performance
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a machine learning-based modeling framework, designed to quantify the impact of rainfall on Frequency Modulated Continuous Wave (FMCW) radar performance by developing a criterion. the proposed framework encompasses several critical steps, including data collection, data processing, feature selection, performance estimation, and model fitting. Initially, the captured area of radar is divided into multiple units, with each unit sampled on a plane. Six point cloud features are defined to analyze the characteristics of the captured data within each unit. The measured performance is proposed based on the calculated features, which facilitates subsequent machine learning training. The collected data are split into training sets for developing a rainfall criterion and validation sets for testing its performance, with experiment results confirming the effectiveness and precision of the proposed modeling framework.
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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) | 0.000 | 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 it