Impulse Train Test Accuracy versus Resources for Evaluation of Supraharmonics Assessment Methods
Bibliographic record
Abstract
This paper shows that FPGA resources should not be the primary concern in developing a supraharmonic instrument for field measurement. Instead, the coherence of the results with impulse trains should be the primary target, as described in the CISPR16-1-1 standard. An FPGA resource analysis and simulations were performed to demonstrate this. For the implementation test, the Digital CISPR (D-CISPR) and the Light-Quasi-Peak (Light-QP) methods were implemented on FPGA. The two methods necessitated a low level of resources and required the same FPGA. For the simulations, the D-CISPR, the Light-QP and the proposed Numerical-Heterodyne methods were studied with the impulse train test and two examples of real-world signals. It was noted that the results were similar for the Numerical-Heterodyne and D-CISPR, closely matching the CISPR impulse train test targets, but were different for the Light-QP. A real-world example of an industrial plant shows the impact of the impulse train test behaviour. When a sharp transition is made in the signal, the Numerical-Heterodyne and D-CISPR methods have an overshoot while the light-QP does not, yielding a difference of 20% on the maximal measurements. Whether such an overshoot is desirable or not is discussed and should be the main criterion to select the best supraharmonic measurement method.
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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 | 0.004 | 0.001 |
| 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 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".