Functionality Pre-Encoding: Indirect Learning Technique for Radio Frequency Devices Characterization
Why this work is in the frame
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Bibliographic record
Abstract
Conventional data-driven modeling directly maps the features of a radio frequency (RF) component to its characteristics, leading to redundant representations and prolonged learning times. This work introduces a functionality pre-encoding (FP) approach that leverages Legendre orthogonal polynomial expansions to represent device characteristics with a compact set of coefficients. The device functionality undergoes nonlinear preprocessing to enhance projection accuracy, limiting the dynamic range and capturing response details with minimal coefficients. The proposed method is applied to a septum polarizer (SP) with a 46% fractional bandwidth. Random Forest achieves superior regression accuracy with minimal training time among three models. The entire preprocessing and coefficient extraction require 2 seconds, followed by 2 seconds for training. A validation test case, unseen during training, confirms the model's efficiency and generalization capability in characterizing RF components.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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