7 citations · 13 across the 13 of their papers we have counts for
13 papers
Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
Francisco Caldas, Cláudia Soares
The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is cruc…
Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape Animation
Stevo Racković, Cláudia Soares, Dušan Jakovetić
In this paper, we present an advanced approach to solving the inverse rig problem in blendshape animation, using high-quality corrective blendshapes. Our algorithm introduces novel…
Predicting the Probability of Collision of a Satellite with Space Debris: A Bayesian Machine Learning Approach
João Simões Catulo, Cláudia Soares, Marta Guimarães
Space is becoming more crowded in Low Earth Orbit due to increased space activity. Such a dense space environment increases the risk of collisions between space objects endangering…
Finding Real-World Orbital Motion Laws from Data
João Funenga, Marta Guimarães, Henrique Costa +1
A novel approach is presented for discovering PDEs that govern the motion of satellites in space. The method is based on SINDy, a data-driven technique capable of identifying the u…
Taxonomy for Resident Space Objects in LEO: A Deep Learning Approach
Marta Guimarães, Cláudia Soares, Chiara Manfletti
The increasing number of RSOs has raised concerns about the risk of collisions and catastrophic incidents for all direct and indirect users of space. To mitigate this issue, it is…
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process
Marta Guimarães, Cláudia Soares, Chiara Manfletti
Current approaches for collision avoidance and space traffic management face many challenges, mainly due to the continuous increase in the number of objects in orbit and the lack o…