@inproceedings{stevenson2022_starcon2,

title = {A deep learning-based framework for operational all-vs-all conjunction screening},

author = {Emma Stevenson and Víctor Rodríguez-Fernández and Christophe Taillan and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-11-07},

booktitle = {2nd International Stardust Conference (STARCON-2)},

address = {ESTEC, the Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_amos,

title = {Towards graph-based machine learning for conjunction assessment},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua},

year  = {2022},

date = {2022-09-19},

urldate = {2022-09-19},

booktitle = {2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)},

address = {Maui, Hawaii, USA},

abstract = {In the face of increasing space traffic, the deployment of large constellations, and a growing debris field, identifying potentially catastrophic collisions is an increasingly daunting and computationally challenging task. In this work, we present a novel graph-based machine learning approach for detecting conjunctions between catalogued space objects to aid in this task. Modelling conjunction events as edges between pairs of object nodes, we introduce a graphical representation of the all-vs-all scenario (so-called as it considers conjunction events between all catalogued objects, both active and debris) that is able to profit from recent advancements in Graph Neural Networks, and make a step towards efficient, machine learning based conjunction assessment. For this, we develop a methodology to predict the existence of upcoming conjunction links over a given screening period, which we frame as a graph-to-graph link prediction task, and present some initial findings that demonstrate the learning potential of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9870215,

title = {Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning},

author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho},

doi = {10.1109/CEC55065.2022.9870215},

year  = {2022},

date = {2022-07-18},

urldate = {2022-07-18},

booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1-8},

address = {Padua, Italy},

abstract = {One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_kepassa,

title = {Deep learning for all-vs-all conjunction detection},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {5th Workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KePASSA)},

address = {Logroño, Spain},

abstract = {This paper explores the use of different deep learning techniques for detecting conjunction events in an efficient and accurate way for improved space situational awareness. Framing the problem as a machine learning classification task, we present the performance of different data representations and model architectures on a realistic all-vs-all dataset generated using the CNES BAS3E space surveillance simulation framework, and compare the approaches to operationally used classical filters in screening performance and computational efficiency. Finally, we also investigate a novel methodology for improving the performance and generalisation ability of the models using a pre-trained orbit model, ORBERT, based on self-supervised learning techniques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huertas-garcia_civic-upm_2021,

title = {CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification},

author = {Álvaro Huertas-García and Javier Huertas-Tato and Alejandro Martín and David Camacho},

url = {http://ceur-ws.org/Vol-2936/paper-41.pdf},

year  = {2021},

date = {2021-05-24},

urldate = {2021-05-24},

booktitle = {Conference and Labs of the Evaluation Forum (CLEF) Working Notes},

pages = {520--530},

abstract = {Online Social Networks (OSNs) growth enables and amplifies the quick spread of harmful, manipulative and false information that influence public opinion while sow conflict on social or political issues. Therefore, the development of tools to detect malicious actors and to identify low-credibility information and misinformation sources is a new crucial challenge in the ever-evolving field of Artificial Intelligence. The scope of this paper is to present a Natural Language Processing (NLP) approach that uses Doc2Vec and different state-of-the-art transformer-based models for the CLEF2021 Checkthat! lab Task 3. Through this approach, the results show that it is possible to achieve 41.43% macro-average F1-score in the misinformation detection (Task A) and 67.65% macro-average F1-score in the topic classification (Task B).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2020_iac,

title = {A deep learning approach to space weather proxy forecasting for orbital prediction},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Edmondo Minisci and David Camacho},

url = {http://oa.upm.es/64345/},

year  = {2020},

date = {2020-10-01},

booktitle = {71st International Astronautical Congress (IAC)},

address = {The CyberSpace Edition},

abstract = {The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. This has fundamental implications both in the short term, in the day-to-day management of operational spacecraft, and in the mid-to-long term, in determining satellite orbital lifetime. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hernandez2019ensemble,

title = {An ensemble algorithm based on deep learning for tuberculosis classification},

author = {Alfonso Hernández and Ángel Panizo-LLedot and David Camacho},

doi = {10.1007/978-3-030-33607-3_17},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {International conference on intelligent data engineering and automated learning},

pages = {145--154},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2017evolving,

title = {Evolving deep neural networks architectures for Android malware classification},

author = {Alejandro Martín and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Evolutionary Computation (CEC), 2017 IEEE Congress on},

pages = {1659--1666},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}