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The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24–26, which we found to occur before 2040.

In this work we develop a mathematical model to estimate the error for inverse kinematics problem for Gough-Stewart parallel mechanisms. We propose the estimation error method to include manufacture, assembly, backlash, and sensing errors. We provide the error transmission matrices for the length of each leg of the hexapod, which permits evaluation of the accuracy error in the position of each one, given a desired position and orientation of the mobile platform. We also present numerical modelling in order to estimate the accuracy of the methodology herein proposed, for specific attitude operations corresponding to performing a successful ground-LEO nanosatellite optical link. In such a case, we were able to provide the required tolerances for the actuators in order to guarantee an orientation precision requirement of the order of milliradians.

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