Investigation of Aircrew Noise Exposure Due to The Use of The Intercom system Onboard The RCAF CH-149 Helicopter
Bibliographic record
Abstract
The National Research Council (NRC) Flight Research Laboratory (FRL) conducted a series of intercom signal voltage measurements on several CH-149 aircraft at RCAF Comox CFB in March 2019. The purpose of this test campaign and reporting was to support the Department of National Defence (DND) in investigating several issues related to the intercom system such as: multiple types of squelches reported by aircrew as well as to quantify the aircrew noise exposure levels due to the use of the CH-149 intercom system. Spectral analysis of the CH-149 intercom voice signal indicated the presence of high squelch noise levels and multiple tonal frequency peaks. Different types of squelch were analyzed and similarities as well as differences were discussed. Further analysis of the voice communication signals showed that the CH-149 intercom system was the source of significant noise amplitude levels during squelch events. The CH-149 intercom system introduced an averaged A-weighted OSPL at the aircrew ear entrance location of 96.5 dB(A) during regular flight communications. During squelch events, aircrew were exposed to an averaged A-weighted OSPL at the aircrew ear entrance location of up to 112.7 dB(A). Combining the helicopter cabin background noise and intercom communication noise will provide a more realistic estimate of CH-149 aircrew noise exposure during in-flight missions. Based on this investigation, several types of squelch noise events were recorded and evaluated for the CH-149 helicopter intercom voice communication. In particular it was concluded that proper selection and use of hearing protection devices as well as mitigation of the squelch events were important factors to alleviate the auditory risk for aircrew and improve speech intelligibility during flight missions on-board the CH-149.
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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 | 0.001 | 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".