M. Abdel-sayed, D. Duclos, G. Faÿ, J. Lacaille, and M. Mougeot, Nmf-based decomposition for anomaly detection applied to vibration analysis, vol.6, pp.73-81, 2016.

J. Allen, Comparison of Time Series and Functional Data Analysis for the Study of Seasonality, 2011.

F. Angiulli and F. Fassetti, Detecting distance-based outliers in streams of data, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp.811-820, 2007.

A. Antoniadis, X. Brossat, J. Cugliari, and J. Poggi, Clustering functional data using wavelets, International Journal of Wavelets, Multiresolution and Information Processing, vol.11, issue.01, p.1350003, 2013.
URL : https://hal.archives-ouvertes.fr/inria-00559115

. Leo-a-aroian, A study of ra fisher's z distribution and the related f distribution, The Annals of Mathematical Statistics, vol.12, issue.4, pp.429-448, 1941.

B. Auder and A. Fischer, Projection-based curve clustering, J. Stat. Comput. Simul, vol.82, issue.8, pp.1145-1168, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00565541

B. Peter and . De-selding, Airbus and oneweb form joint venture to build 900 satellites, 2016.

V. Barnett and T. Lewis, Outliers in statistical data, vol.3, 1994.

C. Barreyre, B. Laurent, J. Loubes, and B. Cabon, Détection d'événements atypiques dans des données fonctionnelles, Les journées de la Statistique, 2016.

Y. Benjamini and Y. Hochberg, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed, Controlling the false discovery rate : a practical and powerful approach to multiple testing, vol.57, pp.289-300, 1995.

G. Boltz, Airbus : le premier satellite haute puissance tout électrique, EUTELSAT 172B, a été lancé par ariane 5. Décryptagéo, 2017.

B. Bonnard, L. Faubourg, and E. Trélat, Mécanique céleste et contrôle des véhicules spatiaux, vol.51, 2005.

D. Bosq, Linear processes in function spaces : theory and applications, vol.149, 2012.

M. Markus, H. Breunig, R. T. Kriegel, J. Ng, and . Sander, LOF : identifying density-based local outliers, ACM sigmod record, vol.29, pp.93-104, 2000.

B. Cadre, B. Pelletier, and P. Pudlo, Estimation of density level sets with a given probability content, Journal of Nonparametric Statistics, vol.25, issue.1, pp.261-272, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00796621

T. Cai, W. Liu, and Y. Xia, Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings, Journal of the American Statistical Association, vol.108, issue.501, pp.265-277, 2013.

B. Candelon and N. Metiu, A distribution-free test for outliers, 2013.

M. Cárdenas-montes, Depth-based outlier detection algorithm, International Conference on Hybrid Artificial Intelligence Systems, pp.122-132, 2014.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection : A survey, ACM computing surveys (CSUR), vol.41, issue.3, p.15, 2009.

M. Ronald-r-coifman and . Victor-wickerhauser, Entropy-based algorithms for best basis selection, IEEE Transactions on information theory, vol.38, issue.2, pp.713-718, 1992.

I. Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), vol.61, 1992.

R. Decourt, Airbus va se doter d'une constellation inédite de satellites d'observation de la terre. Futura sciences, 2016.

J. A. Eustasio-del-barrio, C. Cuesta-albertos, J. M. Matrán, . Rodrí-guez-rodrí, and . Guez, Airbus Defence and Space SAS-"Ce document et les informations qu'il contient sont propriété d'Airbus Defence and Space. Il ne doit pas être divulgué à d'autres fins que celles pour lesquelles il a été remis. Il ne peut être ni reproduit, ni divulgué à des tiers (en tout ou partie) sans l'accord préalable et écrit d'Airbus Defence and Space. Airbus Defence and Space-Tous droits réservés, Ann. Statist, vol.27, issue.4, pp.1230-1239, 1999.

D. Eustasio, J. Barrio, and . Loubes, Central limit theorems for empirical transportation cost in general dimension, 2017.

C. Dimeglio, S. Gallón, J. Loubes, and E. Maza, A robust algorithm for template curve estimation based on manifold embedding, Computational Statistics & Data Analysis, vol.70, pp.373-386, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00834080

X. Ding, Y. Li, A. Belatreche, and L. P. Maguire, An experimental evaluation of novelty detection methods, Neurocomputing, vol.135, pp.313-327, 2014.

C. Désir, S. Bernard, C. Petitjean, and L. Heutte, One class random forests, Pattern Recognition, 2013.

M. Febrero, P. Galeano, and W. González-manteiga, Outlier detection in functional data by depth measures, with application to identify abnormal nox levels, Environmetrics, vol.19, issue.4, pp.331-345, 2008.

F. Ferraty and P. Vieu, Nonparametric functional data analysis : theory and practice, 2006.

S. Fremdt, J. G. Steinebach, L. Horváth, and P. Kokoszka, Testing the equality of covariance operators in functional samples, Scandinavian Journal of Statistics, vol.40, issue.1, pp.138-152, 2013.

M. Fromont, B. Laurent, M. Lerasle, and P. Reynaud-bouret, Kernels based tests with non-asymptotic bootstrap approaches for two-sample problems, COLT, pp.23-24, 2012.

S. Fuertes, G. Picart, J. Tourneret, L. Chaari, A. Ferrari et al., Improving spacecraft health monitoring with automatic anomaly detection techniques, 14th International Conference on Space Operations, p.2430, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01490731

R. Fujimaki, T. Yairi, and K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp.401-410, 2005.

P. Galeano, D. Peña, and . Tsay, Outlier detection in multivariate time series by projection pursuit, Journal of the American Statistical Association, vol.101, issue.474, pp.654-669, 2006.

F. Gamboa, J. Loubes, and E. Maza, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed, c Airbus Defence and Space SAS, vol.1, pp.616-640, 2007.

J. González and A. Muñoz, Representing functional data in reproducing kernel hilbert spaces with application to clustering and classification, Statistics and Econometrics, p.13, 2010.

M. Gupta, J. Gao, C. Charu, J. Aggarwal, and . Han, Outlier detection for temporal data : A survey, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.9, pp.2250-2267, 2014.

S. Ali and . Hadi, Identifying multiple outliers in multivariate data, Journal of the Royal Statistical Society. Series B (Methodological), pp.761-771, 1992.

J. Hamilton, Time series analysis, vol.2, 1994.

M. Douglas and . Hawkins, Identification of outliers, vol.11, 1980.

Z. He, X. Xu, and S. Deng, Discovering cluster-based local outliers, Pattern Recognition Letters, vol.24, issue.9, pp.1641-1650, 2003.

V. Hodges, Designs on space : The lifecycle of a satellite. Catalyst, 2009.

H. Hoffmann, Kernel PCA for novelty detection. Pattern Recognition, 2007.

M. Hubert, J. Peter, P. Rousseeuw, and . Segaert, Multivariate functional outlier detection, Statistical Methods & Applications, vol.24, issue.2, pp.177-202, 2015.

I. Ilea, L. Bombrun, C. Germain, R. Terebes, and M. Borda, Statistical hypothesis test for robust classification on the space of covariance matrices, Image Processing (ICIP), 2015 IEEE International Conference on, pp.271-275, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01228770

J. Jacques and C. Preda, Functional data clustering : a survey, Advances in Data Analysis and Classification, vol.8, issue.3, pp.231-255, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00771030

M. C. Jones, The logistic and the log f distribution. Handbook of the Logistic Distribution, 2006.

M. Edwin, R. Knorr, and . Ng, Finding intensional knowledge of distance-based outliers, VLDB, vol.99, pp.211-222, 1999.

H. Kriegel, P. Kröger, E. Schubert, and A. Zimek, Loop : Local outlier probabilities, Proceedings of the 18th ACM Conference on Information and Knowledge management, 2009.

T. L. Gouic and J. Loubes, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed, Existence and consistency of Wasserstein barycenters. Probability Theory and Related Fields, pp.1-17, 2016.

F. Lehot and J. Clervoy, Histoire de la conquête spatiale, 2017.

F. T. Liu, K. M. Ting, and Z. Zhou, Isolation forest. Data Mining, 2008.
DOI : 10.1109/icdm.2008.17

Y. Regina, J. M. Liu, K. Parelius, and . Singh, Multivariate analysis by data depth : descriptive statistics, graphics and inference,(with discussion and a rejoinder by liu and singh). The annals of statistics, vol.27, pp.783-858, 1999.

E. Lozano and E. Acufia, Parallel algorithms for distance-based and density-based outliers, Fifth IEEE International Conference on, p.4, 2005.
DOI : 10.1109/icdm.2005.116

G. Maral and M. Bousquet, Satellite communications systems : systems, techniques and technology, 2011.

M. Markou and S. Singh, Novelty detection : a review-part 1 : statistical approaches. Signal processing, vol.83, pp.2481-2497, 2003.
DOI : 10.1016/j.sigpro.2003.07.018

URL : http://www.dcs.ex.ac.uk/research/pann/pdf/pann_SS_086.PDF

J. Martínez-heras, A. Donati, G. F. Marcus, F. Kirsch, and . Schmidt, New telemetry monitoring paradigm with novelty detection, SpaceOps 2012 Conference, pp.11-15, 2012.

J. Martínez-heras, A. Donati, B. Sousa, and J. Fischer, Drmust-a data mining approach for anomaly investigation, 12th International Conference on Space Operations, pp.11-15, 2012.

J. Mercer, Functions of positive and negative type and their connection with the theory of integral equations, Philos. Trans. Roy. Soc. London, 1909.

I. Morlini, On the dynamic time warping for computing the dissimilarity between curves. New developments in classification and data analysis, pp.63-70, 2005.
DOI : 10.1007/3-540-27373-5_8

C. Ordoñez, . Martínez, A. Jr-rodríguez-pérez, and . Reyes, Detection of outliers in gps measurements by using functional-data analysis, Journal of Surveying Engineering, vol.137, issue.4, pp.150-155, 2011.

J. Pan, J. Chen, Y. Zi, Y. Li, and Z. He, Monocomponent feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive fourier spectrum segment, Mechanical Systems and Signal Processing, vol.72, pp.160-183, 2016.
DOI : 10.1016/j.ymssp.2015.10.017

A. F. Marco, . Pimentel, L. David-a-clifton, L. Clifton, and . Tarassenko, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed, c Airbus Defence and Space SAS, vol.99, pp.215-249, 2014.

J. Poussin and G. Berger, Eurostar E3000 three-year flight experience and perspective, 25th AIAA International Communications Satellite Systems Conference, p.3124, 2007.
DOI : 10.2514/6.2007-3124

T. Rabenoro, Outils statistiques de traitement d'indicateurs pour le diagnostic et le pronostic des moteurs d'avions, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01225739

. Svetlozar-t-rachev, The monge-kantorovich problem on mass transfer and its applications in stochastics, Teor. Veroyatnost. i Primenen, vol.29, issue.4, pp.625-653, 1984.

A. Ramdas, N. Garcí-a-trillos, and M. Cuturi, On Wasserstein twosample testing and related families of nonparametric tests, Entropy, vol.19, issue.2, p.15, 2017.

J. O. Ramsay and B. W. Silverman, Functional data analysis, Springer Series in Statistics, 2005.

N. Haojie-ren, C. Chen, and . Zou, Projection-based outlier detection in functional data, Biometrika, vol.104, issue.2, pp.411-423, 2017.

P. Joseph, M. Romano, and . Wolf, Stepwise multiple testing as formalized data snooping, Econometrica, vol.73, issue.4, pp.1237-1282, 2005.

E. Roquain, Type I error rate for testing many hypotheses : a survey with proofs, Journal de la Société Francaise de Statistique, vol.152, issue.2, pp.3-38, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00547965

F. S. Schlindwein and D. H. Evans, Autoregressive spectral analysis as an alternative to fast fourier transform analysis of doppler ultrasound signals. Doppler physics and signal processing, 1992.

B. Schölkopf and A. J. Smola, Learning with Kernels, 2000.

B. Schölkopf, A. J. Smola, and K. Müller, Kernel principal component analysis. Artificial Neural Network, 2005.

B. Schölkopf, J. C. Platt, J. C. Shawe-taylor, A. J. Smola, and R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Comput, vol.13, issue.7, pp.1443-1471, 2001.

J. Shawe-taylor and B. Zlicar, Novelty detection with one-class support vector machines. Advances in Statistical Models for Data Analysis, 2015.

R. Rowland, R. Sillito, and . Fisher, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed, c Airbus Defence and Space SAS, vol.27, pp.1025-1044, 2008.

I. Steinwart, On the influence of the kernel on the consistency of support vector machines, Journal of machine learning research, vol.2, pp.67-93, 2001.

S. Subramaniam, T. Palpanas, and D. Papadopoulos, Online outlier detection in sensor data using nonparametric models, Proceedings of the 32nd international conference on Very large data bases, pp.187-198, 2006.

Y. Tao, X. Xiao, and S. Zhou, Mining distance-based outliers from large databases in any metric space, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.394-403, 2006.

T. Tarpey and K. K. Kinateder, Clustering functional data, J. Classification, vol.20, issue.1, pp.93-114, 2003.

A. Thomas, V. Feuillard, and A. Gramfort, Calibration of one-class svm for mv set estimation, Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on, pp.1-9, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01188294

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.

S. Vantini, On the definition of phase and amplitude variability in functional data analysis, Test, vol.21, pp.676-696, 2012.

T. Vatanen, M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen et al., Semi-supervised detection of collective anomalies with an application in high energy particle physics, The 2012 International Joint Conference on, pp.1-8, 2012.

C. Villani, Optimal transport : old and new, vol.338, 2008.

K. Wang and T. Gasser, Alignment of curves by dynamic time warping, Ann. Statist, vol.25, pp.1251-1276, 1997.

R. Wilcox, Kolmogorov-smirnov test. Encyclopedia of biostatistics, 2005.

L. Wu, K. J. Wu, A. Sim, M. Churchill, J. Y. Choi et al., c Airbus Defence and Space SAS-"Ce document et les informations qu'il contient sont propriété d'Airbus Defence and Space. Il ne doit pas être divulgué à d'autres fins que celles pour lesquelles il a été remis. Il ne peut être ni reproduit, ni divulgué à des tiers (en tout ou partie) sans l'accord préalable et écrit d'Airbus Defence and Space. Airbus Defence and Space-Tous droits réservés, IEEE Transactions on Big Data, vol.2, issue.3, pp.262-275, 2016.

. Articles, A. Conférences, B. Barreyre, J. M. Laurent, B. Loubes et al., Boussouf Statistical Methods for Space Application in Space Telemetries, 2018.

?. C. Barreyre, B. Laurent, J. M. Loubes, B. Cabon, and L. , Boussouf Multiple Testing for Outlier Detection in Functional Data, Soumis à IEEE Transactions on Big Data, 2018.

, COMMUNICATIONS ? Détection d'événements atypiques dans les données fonctionnelles Les Journées de la Statistiques, 2016.

?. Multiple, Testing for Outlier Detection in Functional Data Groupe de travail Machine-Learning, 2017.

, ? Multiple Testing for Outlier Detection in Functional Data European Meeting of Statisticians, 2017.

, Detecting Outlier Detection in Multivariate telemetries thanks to Covariance Analysis Big Data from Space, 2017.

, ? Statistical Methods for Outlier Detection in Space Telemetries SpaceOps, 2018.

A. Defence and S. Space, This document and the information it contains are property of Airbus Defence and Space. It shall not be used for any purpose other than those for which it was supplied. It shall not be reproduced or disclosed (in whole or in part) to any third part without Airbus Defence and Space prior written consent, Ce document et les informations qu'il contient sont propriété d'Airbus Defence and Space