@article{nokey,

title = {Benchmarking deep learning approaches for all-vs-all conjunction screening},

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

doi = {https://doi.org/10.1016/j.asr.2023.01.036},

year  = {2023},

date = {2023-01-23},

urldate = {2023-01-23},

journal = {Advances in Space Research},

abstract = {The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{STEVENSON2022595,

title = {A deep learning approach to solar radio flux forecasting},

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

url = {https://www.sciencedirect.com/science/article/pii/S009457652100415X},

doi = {https://doi.org/10.1016/j.actaastro.2021.08.004},

issn = {0094-5765},

year  = {2022},

date = {2022-01-01},

journal = {Acta Astronautica},

volume = {193},

pages = {595-606},

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. 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 = {article}

}

@article{rodriguez20171Analysing,

title = {Analysing temporal performance profiles of UAV operators using time series clustering},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://www.sciencedirect.com/science/article/pii/S0957417416305851},

doi = {http://dx.doi.org/10.1016/j.eswa.2016.10.044},

issn = {0957-4174},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Expert Systems with Applications},

volume = {70},

pages = {103--118},

keywords = {},

pubstate = {published},

tppubtype = {article}

}