Experimental Assessment of Stochastic Signals Through the Power Density Method
Bibliographic record
Abstract
The presented methodology allows for the analysis of stochastic signals using an automated near-field measurement system and real-time signal analyzer. In the method below, power density spectra were used to determine random signals once measurement techniques for the near field have been employed to capture stochastic signals. Traditional methods for measurement within the near field identify either the electric (E) or magnetic (H) distributions and, depending on the processing capability of the analyzer used, a description of the time variant signal. It has been observed during the analysis of the measured complex signals that neither the H nor E field distributions have a direct relation to the stochastic field location; as such, a mathematical formula has to be applied to calculate the power density value and position. In the provided method, it is essential that both E and H fields be independently measured in the near field so that the complete complex signal be acquired. Once both fields have been quantified over the same time period and superposition is resolved, the true phase angle can be determined. From the resulting data a Poynting vector can be calculated and post processing algorithms applied to determine the stochastic signals' physical location and power density value. This technique can, through a backscatter analysis, determine if any signal returns to the source, so as to assess if there are any impacts on Signal Integrity or Power Integrity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".