Review—Basic and Advanced Inertial Navigation Fluid-Based Technology
Bibliographic record
Abstract
The article reviews most published inertial sensor technologies, including dynamically tuned, optical, MEMS vibratory, mechanical, solid-state, and fluid-based. The working principles of the technologies are elaborated. Also, the advantages and disadvantages of those sensors are laid out. Owing to its excellent overall performance, such as its simple structure, low cost, large measurement range, etc, the current review focuses on the state-of-the-art of fluid-based technology of accelerometers and gyroscopes. The sensing elements of the fluid-based technology that are used in the accelerometer and gyroscope are explained. Moreover, a comparison and analysis of those sensing elements are presented. The comparison shows that the thermal resistor has five orders of magnitude which is the highest dynamic range. However, the porous transducer is higher in bandwidth which is almost 120 Hz. Furthermore, the particle imaging velocimetry gyroscope (PIVG) is reviewed. The PIVG is an innovative technology that is used to measure the angular rate where fluid is used as proof of mass. The review shows that the PIVG is low-cost and almost drift-free. Additionally, compared to commercially available gyroscopes, PIVG provides a superior signal-to-noise ratio (SNR).
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".