non-ITS

AIM: To design and implement an adaptive, AI-driven on-board payload operation scheduler for CENSSAT-1 that optimizes multi-instrument duty cycles and mission science yield based on real-time solar array power generation and orbital energy predictions.

AIM: To design and deploy a specialized Large Language Model (LLM) framework that continuously monitors real-time telemetry and telecommand (TM/TC) streams, automates satellite health status assessment, and performs anomaly analysis for CENSSAT-1. 

AIM: To develop a hybrid physics-based and Machine Learning framework that accurately models, predicts, and quantifies the long-term degradation of satellite solar arrays and high-criticality sensors (such as Fine Sun Sensors and optical detectors). 

AIM: To design, implement, and validate an LLM-assisted Operations Preparation Assistant (OPOS) framework that automates the generation and verification of spacecraft Flight Operations Procedures (FOPs) for CENSSAT-1. 

AIM: To design, optimize, and deploy an on-board Machine Learning framework utilizing space-grade edge-computing hardware (TychoBoB) to process high-frequency sensor streams (such as the 80 GHz Space Debris Radar and optical cameras) for real-time detection, classification, and alert generation of millimeter-sized space debris.