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We examined rapid variations in the electron zebra stripe patterns, specifically atL = 1.5, over a three‐month duration, using twin Van Allen Probes within Earth's inner magnetosphere. During geomagnetically quiet intervals, these stripes exhibit a peak‐to‐valley ratio (Δj) ∼1.25 in detrended electron fluxes. However, during geomagnetic storms, they became highly prominent, with Δj > 2.5. The correlation between Δjand net field‐aligned currents (FACs) is observed to be high (0.70). Global magnetohydrodynamic (MHD) simulation results indicate that the westward electric field at midnight at low latitudes in the deep inner magnetosphere correlates well with net FACs. An increase in net FACs could amplify the dawn‐to‐dusk electric field in the deep inner magnetosphere, thereby causing the inward transport of electrons. Given that FACs are linked to the interaction between solar wind and the magnetosphere, our findings emphasize the importance of solar wind‐magnetosphere coupling in the deeper regions of the inner magnetosphere.

 

Although perception is an increasingly dominant portion of the overall computational cost for autonomous systems, only a fraction of the information perceived is likely to be relevant to the current task. To alleviate these perception costs, we develop a novel simultaneous perception–action design framework wherein an agent senses only the task-relevant information. This formulation differs from that of a partially observable Markov decision process, since the agent is free to synthesize not only its policy for action selection but also its belief-dependent observation function. The method enables the agent to balance its perception costs with those incurred by operating in its environment. To obtain a computationally tractable solution, we approximate the value function using a novel method of invariant finite belief sets, wherein the agent acts exclusively on a finite subset of the continuous belief space. We solve the approximate problem through value iteration in which a linear program is solved individually for each belief state in the set, in each iteration. Finally, we prove that the value functions, under an assumption on their structure, converge to their continuous state-space values as the sample density increases. 

This article presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss–Markov process. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as a difference‐of‐convex program and apply the convex‐concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and a model for 2D target tracking by a drone swarm for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity‐promoting, and an effective sensor selection heuristic.