The Use of Wireless Sensor Technologies for Condition Monitoring of Modern Aircraft Structures
Bibliographic record
Abstract
Monitoring of the aircraft structures’ during the pre-flight testing is a critical task of the aerospace industry. One of the most promising solutions, not yet widely applied, is continuous monitoring of aircraft structures using wireless sensor network technology. The brief summary of the proposed system is the following: special sensors (devices which detect potential deformations), send signals to the local motes (autonomous computing device equipped with a wireless transmitter). Information from motes is gathered by routers which then transfer the aggregated information to the datacenter. Motes are used for collection and primary processing of the data from sensors, which then sent to the routers, and, ultimately, gateway of corporate network. Applications of corporate network control and define flexible patterns for processing of the information received from sensors. As an example of hardware components for preprocessing and data transmission the system described in this study uses motes and routers from MEMSIC, for measuring the level of deformation - sensors from Tokyo Sokki Kenkyujo. This network structure allows to centralize data collection modes in the process of testing; implement continuous data collection at a defined frequency; process and display data in real-time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".