Demand for air transportation is expected to continue growing. Within Europe one of the biggest impacts of this traffic growth, is an increase of air travel delay. As it happened during the summer of 2018, where demand from aircraft intending to enter an air sector was not complemented with capacity to safely accommodate it. Incentivised by this event, in this article the problem of predicting a class of measures for demand-capacity balancing, known as Air Traffic Flow and Capacity Management (ATFCM) regulations, is investigated. A Random Forest model was trained on public ATFCM notification messages to predict the amount of ATFCM regulations over different European air sectors for varying prediction horizons. In addition to the predictive model, in this paper a new way to estimate the maximum prediction horizon is proposed. Using the Hurst exponent, the time-scale at which random behaviour is initiated is found. Comparison of the proposed method with the prediction horizon obtained from the largest Lyapunov exponent indicates that the method is a valid technique for estimating the prediction horizon. By extending the prediction horizon of the model, it is found that the proposed method can reasonably estimate the prediction horizon above which prediction accuracy starts to degrade.

Lunar exploration initially started as a race between two superpowers to see who had the highest technical capabilities to send a human onto the Moon’s surface. Until recently, the frequency of Moon missions has been in decline, but in the last couple years there has been an increasing interest in returning. Scientific studies and observations will be performed and will give humanity a better understanding of the solar system and our place in it. As a result there will be a need for a direct communication line between the lunar surface and Earth, such that rovers can be controlled in real-time and immediately relay their data back, independent of their location on the Moon.