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Found 7 papers, 2 papers with code
Language Models are Spacecraft Operators
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts.
A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support
In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed.
Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions
With the rapid increase in the number of Anthropogenic Space Objects (ASOs), Low Earth Orbit (LEO) is facing significant congestion, thereby posing challenges to space operators and risking the viability of the space environment for varied uses.
Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management
This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model.
Transformer-based Atmospheric Density Forecasting
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space situational awareness.
DeepVATS: Deep Visual Analytics for Time Series
DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted.
An implementation of the "Guess who?" game using CLIP
CLIP (Contrastive Language-Image Pretraining) is an efficient method for learning computer vision tasks from natural language supervision that has powered a recent breakthrough in deep learning due to its zero-shot transfer capabilities.
