Hello GFOSS Maintainers and Community, I am Kunal Somani, an engineering student specializing in Robotics and Artificial Intelligence (Thapar Institute) and Data Science (IIT Madras). I am writing to introduce my work as a prospective GSoC 2026 contributor for the ORION CubeSat flatsat testbed, specifically focusing on the AI Payload subsystem. Rather than just drafting a theoretical proposal, I have begun implementing and testing the baseline software architecture to prove the viability of the high-bandwidth telemetry layer. I have submitted two foundational Pull Requests for architectural review: 1. PR #7 (Scaffold Jetson AI environment and Zenoh middleware): Establishes a robust, asynchronous Zenoh bridge for the Jetson environment, utilizing strict Pydantic schemas to validate computer vision telemetry serialization before transmission. Includes an ARM64-targeted Dockerfile for hardware parity. 2. PR #11 (Integrate live YOLOv8 inference pipeline): Replaces simulated payload data with a live, real-time Ultralytics YOLOv8 inference node. It natively captures frames, runs inference, and dynamically maps the tensor outputs into the validated Zenoh telemetry stream. Both PRs have been verified locally with strict linting (ruff) and environment isolation. *Immediate Next Steps:* While waiting for community feedback on the architecture, my next objective is to build out the automated pytest suites to rigorously test the inference node's tensor outputs, before planning the transition to an optimized TensorRT engine. I look forward to your technical feedback on GitHub. Best regards, Kunal GitHub: https://github.com/Kunal-Somani Linkedin: https://www.linkedin.com/in/kunal-somani-227373344/
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