A Test Setup for Evaluation of Harmonic Distortions in Precision Inertial Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Steadily improving performance of inertial sensors necessitates significant enhancement of the methods and equipment used for their evaluation. As the nonlinearity of sensors decreases and gets close to that of the exciters, new challenges arise. One of them, addressed in this research, is a superposition of errors caused by the nonlinearity of tested devices with nonlinear distortions of excitation employed for experimental evaluation. This can lead to a cancellation, at least partial, of the effects of both imperfections and underestimation of the actual distortions of the evaluated sensors. We implement and analyze several system architectures and components of applicable motion generation systems from the viewpoint of satisfying the relevant, often conflicting requirements posed by the evaluation of high performance sensors. Robust mechanical integration of the guidance, actuation and measurement functions emerges as a key factor for achieving the needed quality of generated test patterns. We find precision air bearing stages such as ABL1500 series (Aerotech) most suitable for implementing the needed experimental setup. We propose an architecture with two reciprocating stages, implement and evaluate its core components, and illustrate its performance with experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it