Flight Software Development for a CubeSat Application
Bibliographic record
Abstract
This article presents a development of a CubeSat mission software running on an STM32-based on-board computer (OBC). This was conducted under the Canadian CubeSat Project, initiated by the Canadian Space Agency in 2018 to support the development of 15 CubeSats across Canada. The proposed mission software has a multilayered architecture and is divided into five layers from a low layer dedicated to the peripherals to the top layer dedicated to the Mission Applications. The CubeSat protocol (CSP) is used at the communication layer for easing connectivity between subsystems and to communicate with the ground segment. The mission software running on the OBC is built to meet many requirements defined for this satellite, such as version control, classifications, margins, etc. The CubeSat will be able to accomplish two scientific missions related to the study of space weather once the satellite is put into orbit from the International Space Station. An overview of the software running on the OBC is presented, written in C Language, and includes the implementation of the CSP. FreeRTOS used as an operating system for the OBC is also presented. A Command Line Interface was designed for testing purposes to ensure software efficiency and some results are discussed in this article. The flight software consists of three main tasks and subtasks. Of the 1024 kB of flash memory, only 240 kB was used which represents less than 20% of the total memory. The CPU load is 34% for normal, manual, and maintenance modes and 16% for failure modes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".