Rotation of Scanner’s Mirror in MEMS Dimensions with the Use of Lorents Law
Bibliographic record
Abstract
In this project we study a kind of scanner in micro dimensions in which it includes three frames also the third frame is considered as fixed surface and the two others are including two separate coils that electric current enters it and when we expose this electric current in a magnetic field under a specific conditions then with the use of Lorents Force law we can cause the rotation of the frame and mirror’s surface accordingly. Discussed field in this project has a magnitude of between 0.2-4 Tesla in which this magnetic field has no destructive effect on human body also the electric current has a magnitude of between 0.001-0.14 Amper in which the maximum obtained rotation angle is equal to ±54.49724 that in every half cycle with the use of two frames it can be deviated 54.49724 degree from the mirror. This plan with the lower number in coil with a design in smaller dimensions (micrometer) is simulated with a more different design and the result was acceptable so it has been much favored for its medical capabilities. Frames are linked together with Su-8 polymer in which this polymer has been used because of its appropriate softness and bending.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".