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
This paper describes the implementation and experimental results of a software based inertial measurement unit (IMU) signal simulator. The simulated signal generation of inertial sensors is an inverse process of the IMU mechanization (navigation) equations. Compared with using hardware, the IMU simulator (IMUS) saves significant time and money for research work and is very flexible when developing new integration algorithms as it does not impose experimental limitations. Furthermore, the IMUS can be used efficiently when choosing or designing the required hardware characteristics for a given application. It is often difficult to accurately translate IMU errors into state errors (position, velocity, and attitude) which the simulator does with ease. The IMU simulator was developed by the MMSS group, the University of Calgary. It can simulate a variety of sensor errors such as bias instability, random walk, scale factor errors, sensor errors due to thermal drift, gsensitivity, non-orthogonalities, misalignments, and their combinations. The user can also define the output data rate, bandwidth, low pass filter cutoff and so on, based on a variety of vehicle dynamics such as straight line, accelerations, turns, U-turns, bumpy roads, constant velocities, static periods as well as varying attitude, and their combinations for different applications. Meanwhile, the simulator provides GPS position and velocity simulated measurements as an optional function. This tool gives users a fast and effective way for evaluating new inertial sensors using datasheet characteristics provided by the manufacturers or obtained through lab testing. Or, if the IMU characteristics are not known, they can be inferred by generating state errors for given IMU errors, in this way developing the required IMU parameters. The correctness and effectiveness of the IMUS has been verified not only in theory but also in practice by comparing the results from the IMUS to the results from a real hardware IMU using field test data. A real case example for both a simulated and a real hardware MEMS ADI IMU on the IMU/GPS integration level is presented in the paper. It shows that the average position drift during GPS outages using simulator data corresponds well to field test results using the ADI hardware IMU and GPS from a NovAtel OEM4 receiver.
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 | 0.000 | 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