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S.T.A.R.S

6 devlogs
19h 30m 2s

A GNN for LEO satellital constelations aimed to reduce data loss and increase efficiency by distributing the traffic against three paths

This project uses AI

Gemini AI: Mainly as a roadmap and assistance in the topics needed to understand to work in the project

GitHub Copilot (GPT-5 model): Used for small bugs and questions, also reviewed PR and made tiny patches

Claude Sonnet 4.5: For fine tunning of logic bugs and bad implementation of certain features

Demo Repository

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lisardowo

Had to add clearer instructions in the usage of the web demo

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lisardowo

Shipped this project!

Hours: 19.03
Cookies: 🍪 325
Multiplier: 17.06 cookies/hr

I built a router for LEO satellital constellations, it uses a DRL Graph neural network to learn the characteristics of a path that tends to fail in order to avoid it. Its supposed to replace the traditional dijkstra-based path finding providing a more stable and reliable way to transfer data, when the model choose a is run it chooses 3 paths and distributes the data to send amongst this three adjusting flow to each one individually based on congestion, bandwith and other factors

It also compresses data (telemetry mostly) and separate it into (n) quantity of files (determined by the compressed size) to ease up a little the traffic

lisardowo

Tried to implement a benchmark (didnt work) it seems that the design of the DRL model is kind of messed so ill ship by now and try to learn more in order to correct the model and gave it proper functionality

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lisardowo

adding a benchmark between dijkstra (commonly used algorithm for path finding) and the DRL model (im going insane)

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lisardowo

Deployed to the web

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lisardowo

Finished the DRL model, made the simulation and a frontend to see the procees (it was more than eight hours but I forget to de log )

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lisardowo

preparing the script for the training of the DRL model

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