Propagation of electromagnetically generated wake fields in inhomogeneous magnetized plasmas

Generation of wake fields by a short electromagnetic pulse in a plasma with an inhomogeneous background magnetic field and density profile is considered, and a wave equation is derived. Transmission and reflection coefficients are calculated in a medium with sharp discontinuities. Particular attention is focused on examples where the longitudinal part of the electromagnetic field is amplified for the transmitted wave. Furthermore, it is noted that the wake field can propagate out of the plasma and thereby provide information about the electron density profile. A method for reconstructing the background density profile from a measured wake field spectrum is proposed and a numerical example is given.Comment: 12 pages in LaTeX style, 11 eps figure

Resonant interaction between gravitational waves, electromagnetic waves and plasma flows

In magnetized plasmas gravitational and electromagnetic waves may interact coherently and exchange energy between themselves and with plasma flows. We derive the wave interaction equations for these processes in the case of waves propagating perpendicular or parallel to the plasma background magnetic field. In the latter case, the electromagnetic waves are taken to be circularly polarized waves of arbitrary amplitude. We allow for a background drift flow of the plasma components which increases the number of possible evolution scenarios. The interaction equations are solved analytically and the characteristic time scales for conversion between gravitational and electromagnetic waves are found. In particular, it is shown that in the presence of a drift flow there are explosive instabilities resulting in the generation of gravitational and electromagnetic waves. Conversely, we show that energetic waves can interact to accelerate particles and thereby \emph{produce} a drift flow. The relevance of these results for astrophysical and cosmological plasmas is discussed.Comment: 12 pages, 1 figure, typos corrected and numerical example adde

Multi-log grasping using reinforcement learning and virtual visual servoing

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves problems of dynamics and path planning, where the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilize a virtual camera to provide an image stream from 3D reconstructed data. We use Cartesian control to simplify domain transfer. Since log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limit on computational resources and time for the challenge of image segmentation, and allows for collecting data in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2--5 logs.Comment: 8 pages, 10 figure

Charged multifluids in general relativity

The exact 1+3 covariant dynamical fluid equations for a multi-component plasma, together with Maxwell's equations are presented in such a way as to make them suitable for a gauge-invariant analysis of linear density and velocity perturbations of the Friedmann-Robertson-Walker model. In the case where the matter is described by a two component plasma where thermal effects are neglected, a mode representing high-frequency plasma oscillations is found in addition to the standard growing and decaying gravitational instability picture. Further applications of these equations are also discussed.Comment: 14 pages (example added), to appear in Class. Quantum Gra

Predictor models for high-performance wheel loading

Autonomous wheel loading involves selecting actions that maximize the total performance over many repetitions. The actions should be well adapted to the current state of the pile and its future states. Selecting the best actions is difficult since the pile states are consequences of previous actions and thus are highly unknown. To aid the selection of actions, this paper investigates data-driven models to predict the loaded mass, time, work, and resulting pile state of a loading action given the initial pile state. Deep neural networks were trained on data using over 10,000 simulations to an accuracy of 91-97,% with the pile state represented either by a heightmap or by its slope and curvature. The net outcome of sequential loading actions is predicted by repeating the model inference at five milliseconds per loading. As errors accumulate during the inferences, long-horizon predictions need to be combined with a physics-based model.Comment: 22 pages, 19 figure