CNES organization for station positioning of geostationary satellites

Since 1975, the Toulouse Space Centre (a technical establishment of the French Space Agency, CNES) has successfully brought 15 geostationary satellites on to station. During these 17 years of experience, an organization of human and material resources has been built up that ensures a very high level of reliability in the execution of these station positioning operations. The main characteristics of this organization are a rigourous definition of the roles and responsibilities of each person involved, very detailed operations documentation, and methodical preparation of the operations

Alien Registration- Dulac, Jean P. (Augusta, Kennebec County)

https://digitalmaine.com/alien_docs/18922/thumbnail.jp

Breakthrough in basin modeling using time/space frame

International audienceA new way to model basins that couples the new space-time mathematical framework (defined as the UVT transform) and 3D restoration allows for easy and realistic construction of 4D models. Using models built with the UVT transform, basin modelers will not only include faults and erosional surface properly in all structural settings, but also restore them using a 3D geomechanical finite element engine to model the proper paleo-basin geometries. As the UVT model is being restored, a "hybrid" grid carrying the static and dynamic properties is fully restored. The basin simulation software then takes all the time-dependent geological models and performs its computations on the 4D grid. The goals of basin modeling are to find out whether the oil window was reached, to locate possible traps, and to estimate the volume and quality of hydrocarbons initially generated, migrated, and trapped, as well as to estimate pressure and temperatures in the reservoir (Rudkiewicz et al., 2000). To achieve this, a full subsurface geologic model must be constructed and restored from the source rock to the current topography. Currently, a 4D basin model is a simple horizon based layer cake grid where faults are not represented explicitly as discontinuities. The reconstruction through time is based on the vertical back-stripping of layers. This is limited because the displacement of the blocks along the fault planes cannot be modeled and the material on both sides of reverse faults cannot be represented. The estimation of migration paths across faults can be inaccurate especially in compressive basins or in extensive basins with long offset listric faults.In addition to the limitations in properly representing the model at a given time step, the evolution of the basin as a function of time is done through the simple back-stripping and flattening of layers. So, this paleo-basin geometry can be inaccurate

Residual Reinforcement Learning from Demonstrations

Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal. We extend the residual formulation to learn from visual inputs and sparse rewards using demonstrations. Learning from images, proprioceptive inputs and a sparse task-completion reward relaxes the requirement of accessing full state features, such as object and target positions. In addition, replacing the base controller with a policy learned from demonstrations removes the dependency on a hand-engineered controller in favour of a dataset of demonstrations, which can be provided by non-experts. Our experimental evaluation on simulated manipulation tasks on a 6-DoF UR5 arm and a 28-DoF dexterous hand demonstrates that residual RL from demonstrations is able to generalize to unseen environment conditions more flexibly than either behavioral cloning or RL fine-tuning, and is capable of solving high-dimensional, sparse-reward tasks out of reach for RL from scratch