Bibliographic record
Abstract
This article presents an extra-large (25 × 50 mm2) flexible printed circuit board (FPCB) mirror to cover both the emitting and receiving lenses of a single-point LiDAR to form a biaxial scanning LiDAR with a compact structure and long measurement distance and low cost. The FPCB mirror is fabricated using the low cost and commercially available FPCB fabrication process. In addition, a widely available single–point LiDAR is used as the measurement unit. This article's novelty lies in the following two points: 1) integrating a large aperture scanning mirror and a single-point LiDAR (low cost and widely available) to construct a biaxial scanning LiDAR; 2) proposed a large aperture FPCB mirror using long existing FPCB process such as to achieve low cost. The scanning LiDAR is designed for applications in factory for robots and automated guided vehicles; navigation. The FPCB mirror consists of a mirror plate, two permanent magnets, and a FPCB structure, which includes two torsion beams, a middle seat, and a Flame retardant 4 (FR4) stiffener frame. Four-layer copper coils are embedded in the FPCB structure, which is fabricated using the low cost commercially available FPCB fabrication process. The mirror plate is diced from a thin silicon mirror plate with gold coating and then attached on top of the middle seat of the FPCB structure. With such large aperture FPCB scanning mirror, and a long measurement distance single-point LiDAR, a compact biaxial scanning LiDAR is constructed. The scanning LiDAR based on the FPCB mirror is constructed and tested. Achieved performances are: the field of view of 60°, measurement distance of 50 m, refresh rate of 20 Hz, 500 points for each scanning frame.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".