Synthetic database of space objects encounter events subject to epistemic uncertainty.

The databases included on this article refers to variables and parameters belonging to the Space Traffic Management (STM), Evidence Theory and Machine Learning (ML) fields. They have been used for implementing ML for autonomously predict risk associated to a close encounter between two space (Sanchez and Vasile,  Acta Astronautica, Special Issue for ICSSA2020, In Press [1]). The position of the objected is assumed to be affected by epistemic uncertainty, which has been modeled according to Dempster-Shafer Evidence theory (DSt) [2]. Six datasets are presented. Two ( and , respectively) include samples of space object close encounters subject to epistemic uncertainty on the relative position. Other two databases ( and , respectively) include the values of the Cumulative Plausibility and Belief Curves ( and , respectively) of each sample included in . The remaining databases ( and ), contain the value of the  and  of each sample included in . All of them are synthetic databases created using computer simulation to obtain the results presented in [1].  database is constituted by 9,000 samples and 45 columns and a header, while  is formed by 28,800 samples and 45 columns and a header. These databases come from a set of, respectively, 5 and 14 different families of encounter geometries defined by the range of values that can be assigned to the bounds of the intervals for the uncertain variables, assumed to be affected by epistemic uncertainty, considered to have been provided by two sources of information. The uncertain variables are: the miss distance,  on the impact plane (B plane), the standard deviation of the relative position projected on the B plane, , and the Hard Body Radius of the combined objects, . The dataset is completed with STM related parameters: miss distance and covariance matrix of the uncertain ellipse projected on the B plane enclosing all samples defined by the uncertainty intervals, the Probability of Collision ( ) of this ellipse or the elapsed time to the Time of Closest Approach (); with DSt related parameters: Belief and Plausibility of certain values of ; and the class of the event according to the classification detailed in [1].  and  are constituted by 34 columns and 9000 rows containing the Plausibility and Belief for  values and the corresponding Probabilities of Collision necessary to build the  and CBC of the events in , while  and  are constituted by 34 columns and 28,800 rows containing the Plausibility and Belief for  values and the corresponding Probabilities of Collision values necessary to build the  and CBC of the events in . These databases have a potential usage by the ML community interested in STM as well as for the space community, especially, space operators interested in introduce epistemic uncertainty on collision risk assessment. These databases contribute to build a scarce field such as the databases of encounter events [3].