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Malgré les injonctions morales des théoriciens et les contraintes de la censure, des dramaturges anglais acclimatent la satura latine et la ménippée sur la scène comique. Ils adoptent des formules dramatiques – la pièce répétée et la revue satirique – qui offrent un cadre structurel et mettent en scène des auteurs dramatiques ou des figures de magistrats, porteurs de la dénonciation. La survivance de ce comique offensif remet en question l’image d’un xviiie siècle anglais sentimental et policé.

This paper presents the GNC concept solution developed for the recovery and landing of Vertical Take-off Vertical Landing (VTVL) launch vehicles in the context of RETALT (RETro-propulsion Assisted Landing Technologies), a European Union Horizon 2020 project with the objective of investigating and developing launch system reusability technologies based on the use of retro-propulsion. The project aims to increase the Technology Readiness Level (TRL) of the recovery technologies up to 5 for structures and mechanisms, Thermal Protection Systems (TPS), Aerodynamics and Aero-thermodynamics, and up to TRL 3 for GNC. One of the great technical challenges related to the reusability of launchers is the recovery Guidance, Navigation and Control (GNC) system, of which DEIMOS Space is in charge for RETALT. In particular, the design of the powered- descent and landing GNC offers a difficult challenge, since it must allow the system to perform a precision landing in a fast- dynamic environment, with extremely limited fuel margins, and with significant unknown dispersions accumulated during prior phases. To tackle this, state-of-the art algorithms based on hybrid Navigation techniques for state estimation, as well as online convex optimization and successive convexification for the design of the guidance GNC sub- function are explored. The Control algorithm operates in distinct modes dependent on the GNC phase and available GNC actuators, and it is based on modern robust control methods in order to provide analytical guarantees over the control performance in the presence of uncertainties and unmodelled dynamics. The proposed GNC solutions were integrated and tested in a high-fidelity simulator and the performance were preliminary assessed, demonstrating the capability to successfully steer the vehicle to the desired landing site.

We derive a general formula for the early time dependence of a phase space distribution evolving according to the kinetic Boltzmann equation. Assuming that the early evolution of the system created in high-energy nuclear collisions can be described by kinetic theory, we calculate the scaling behaviors for the onset of various characteristics of the transverse dynamics. In particular, we show that the scaling behavior of the anisotropic flow coefficients $v_n$ at early times does not depend on the details of the collision kernel or the system composition, while at the same time it differs from the prediction of fluid dynamics., 15 pages. v2: published version

Effectively controlling supermarket refrigeration systems is critical for ensuring the quality of the food we eat. At this moment each supermarket refrigerator uses components such as valves, ejectors, compressors... available at each location. This leads to an obvious reduction in transportation costs and is significantly more environmentally friendly, however arises the problematic of designing a different set of controllers for each installation. This thesis tackles this problem by suggesting a reinforcement learning based approach. A variation of the Q-learning algorithm has been designed and implemented, such implementation has been able to optimally control the aforementioned set of systems. The implementation has been made using a recurrent neural network as policy network and such network is able to forecast the rewards with a mean squared error loss of 0.01. Although several challenges have arisen from this approach, it has been proven that indeed it is possible to control unique systems using reinforcement learning provided that enough training data is acquired.