A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F_10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F_10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data is not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data, the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time series log is carried out, highlighting the benefits of this change of paradigm.