Corona wind in a system with the pin-to-plane discharge geometry
Bibliographic record
Abstract
Our work dealt with the problems of corona wind and acoustic emission, encountered in pin-to-plane corona generators. Our generator had 19-mm electrode separation, with anode grounded and pin driven by negative voltages from three to fifteen kilovolts. The acoustic signal generated by the corona discharge was received with a microphone (B=10 kHz, at 3 dB). Both the corona generator and microphone were placed in an acoustic anechoic chamber and samples of the acoustic signal for different voltages and corona currents taken. The sampling and on-line analysis of the signal was performed by a signal processing system (Motorola, DSP 56300, r/sub s/=43/spl times/10/sup 3/ s/sup -1/), and a PC. The time domain signal was filtered and acquired in the time domain, then Fourier transformed into the frequency domain, to yield the acoustic signature of the wind, produced by the negative corona. The corona acoustic emission expressed in terms of energy is directly proportional to the intensity of the field, which sustains the corona discharge. The acoustic signal spectrum at low voltages shows a fundamental and a number of harmonic components, superimposed on the spectrum of white noise, with a signal-to-noise ratio of better than one to five. At higher voltages, harmonic components are no longer present, and the acoustic signal energy is emitted in a single spectral line. Although the measurements were performed mostly with a pin-to-plane generator, the results and the developed technique were applied to a cylindrical and to an array generator, with similar results.
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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 | 0.000 | 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) | 0.001 | 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 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".