Dataset corresponding to the publication : Serration Manufacturing Effects on Propeller Trailing Edge Noise Mechanisms, Santamaria et al., JSV, 2026.
Bibliographic record
Abstract
Rotors with trailing edge serrations as presented in Santamaria, Belliot, Sanjosé, Gojon, and Moreau. Serration Manufacturing Effects on Propeller Trailing Edge Noise Mechanisms. Journal of Sound and Vibrations, 2026. Thrust and torque coefficients, Figure of Merit, as well as acoustic autopower signals are provided. CAD files of the serrated and clean rotors, as well as example Matlab code to post-process acoustic results are also given. For aerodynamic results, the thrust and torque coefficients are given as a function of RPM. Fluid density is taken as 1.18kg/m3. For acoustic results, the autopower (Pa2) is given as a function of the frequency (Hz). Autopowers are obtained from fluctuating pressure time signals (16s) by computing the fast Fourier transform (FFT) with a Hanning window applied on 100 segments, using a 50% overlap and a magnitude correction factor, which leads to a frequency resolution of 3.125 Hz. Structure of the acoustic data For each rotation speed, data for 13 microphones located 1.62m away from the rotor axis and at different angles (every 10 degrees) from the rotor plane are provided. Microphone 1 corresponds to +60 degrees (as compared with the rotor plane, where + indicates that it is located in the direction opposite of the flow, as visible in Figure 5 of the corresponding journal paper) Microphone 2 corresponds to +50 degrees ... Microphone 7 corresponds to 0 degrees (the rotor disk plane) ... Microphone 13 corresponds to -60 degrees
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.065 |
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".