1 citations · 3 across the 11 of their papers we have counts for
12 papers
A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations
Ruben Belo, Marta Guimarães, Cláudia Soares
The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges…
Keep Calm and Avoid Harmful Content: Concept Alignment and Latent Manipulation Towards Safer Answers
Ruben Belo, Marta Guimaraes, Claudia Soares
Large Language Models are susceptible to jailbreak attacks that bypass built-in safety guardrails (e.g., by tricking the model with adversarial prompts). We propose Concept Alignme…
A Proof of Strong Consistency of Maximum Likelihood Estimator for Independent Non-Identically Distributed Data
Ricardo Ferreira, Filipa Valdeira, Marta Guimarães +1
We give a general proof of the strong consistency of the Maximum Likelihood Estimator for the case of independent non-identically distributed (i.n.i.d) data, assuming that the dens…
Satellite Safe Margin: Fast solutions for Conjunction Analysis
Ricardo N. Ferreira, Marta Guimarães, Cláudia Soares
The amount of debris in orbit has increased significantly over the years. With the recent growth of interest in space exploration, conjunction assessment has become a central issue…
Generalizing Trilateration: Approximate Maximum Likelihood Estimator for Initial Orbit Determination in Low-Earth Orbit
Ricardo Ferreira, Filipa Valdeira, Marta Guimarães +1
With the increase in the number of active satellites and space debris in orbit, the problem of initial orbit determination (IOD) becomes increasingly important, demanding a high ac…
One-Shot Initial Orbit Determination in Low-Earth Orbit
Ricardo Ferreira, Marta Guimarães, Filipa Valdeira +1
Due to the importance of satellites for society and the exponential increase in the number of objects in orbit, it is important to accurately determine the state (e.g., position an…
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…
Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models
Marta Guimarães, Cláudia Soares, Chiara Manfletti
The risk of collision between resident space objects has significantly increased in recent years. As a result, spacecraft collision avoidance procedures have become an essential pa…
Probability of Collision of satellites and space debris for short-term encounters: Rederivation and fast-to-compute upper and lower bounds
Ricardo Ferreira, Cláudia Soares, Marta Guimarães
The proliferation of space debris in LEO has become a major concern for the space industry. With the growing interest in space exploration, the prediction of potential collisions b…