Enhancement of CN tower lightning current derivative signals using a Modified Power Spectral Subtraction method
Bibliographic record
Abstract
The CN tower has been a source of lightning current data for the past 15 years. Since the CN tower is a transmission tower and it is not unexpected that the recorded lightning current signals be corrupted with different kinds of noise. The existence of noise makes it difficult to extract the return-stroke current waveform parameters from the measured waveforms, which are important for developing lightning protection systems. In this paper, a modified power spectral subtraction (MSS) method has been developed in order to de-noise the lighting return-stroke current derivative signals measured at the tower. In order to evaluate the proposed de-noising technique, the derivative of Heidler function is used to model the measured return-stroke current derivative signal. The measured current derivative signal is simulated using the Heidler derivative model and by artificially corrupting it with noise signals measured at the tower in the absence of lightning. The proposed MSS method is applied to denoise the simulated current derivative signal and the resultant waveform is compared with the Heidler derivative model, thus enabling the accurate evaluation of the proposed method. The results of the evaluation show a substantial improvement in the signal peak-to-noise peak ratio (SPNPR) of up to 32 dB depending on the level of the noise signal added to the Heidler derivative function. Furthermore, 95.7%-98.5% recovery of the peak of the original Heidler derivative function was obtained. For further evaluation of the proposed MSS method, the conventional spectral subtraction (SS) method is applied for de-noising the same simulated current derivative signals, which produced a substantially lower SPNPR of up to 16 dB with a peak recovery of 93.3%-97.5% of the original Heidler derivative model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".