Characterizing the Statistics of Electromagnetic Interference in Complex Topologies
Bibliographic record
Abstract
The use of statistical methods to describe the electromagnetic interference in electronic systems with complex topologies is considered. The finite difference time domain (FDTD) method is used to generate a large amount of interference data for various systems. These include cavities with apertures as well as partially open enclosures containing conducting objects and a circuit- board with different sized traces. The interfering source is a Gaussian pulse plane wave having a frequency content from DC to well over 8 GHz. Many different attributes of the interference problem are characterized statistically. Probability density functions are fit to various variables in the problem: termination voltages and currents; the magnitude of the electric field intensity; and the different electric field components. It is shown that the statistical nature of the interference depends on the part of the topology which is used to generate the statistics. It is found that standard distributions do a good job of modelling the data.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".