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Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder-based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches. 

Following the discovery of topological insulators, there has been a renewed interest in superconducting systems that have strong spin-orbit (SO) coupling. Here we address the fundamental question of how the spin properties of a otherwise spin-singlet superconducting ground state evolve with increasing SO impurity density. We have mapped out the Zeeman critical field phase diagram of superconducting Al films that were deposited over random Pb cluster arrays of varying density. These phase diagrams give a direct measure of the Fermi liquid spin renormalization, as well as the spin orbit scattering rate. We find that the spin renormalization is a linear function of the average Pb cluster -to- cluster separation and that this dependency can be used to tune the spin susceptibility of the Al over a surprisingly wide range from 0.8χ₀to 4.0χ₀, whereχ₀is the non-interacting Pauli susceptibility.

 

Large impact‐melt pockets in shergottites contain both Martian regolith components and sulfide/sulfite bleb clusters that yield high sulfur concentrations locally compared to bulk shergottites. The regolith may be the source of excess sulfur in the shergottite melt pockets. To explore whether shock and release of secondary Fe‐sulfates trapped in host rock voids is a plausible mechanism to generate the shergottite sulfur bleb clusters, we carried out shock recovery experiments on an analog mixture of ferric sulfate and Columbia River basalt at peak pressures of 21 and 31 GPa. The recovered products from the 31 GPa experiment show mixtures of Fe‐sulfide and Fe‐sulfite blebs similar to the sulfur‐rich bleb clusters found in shergottite impact melts. The 21 GPa experiment did not yield such blebs. The collapse of porosity and local high‐strain shear heating in the 31 GPa experiment presumably created high‐temperature hotspots (~2000 °C) sufficient to reduce Fe³⁺to Fe²⁺and to decompose sulfate to sulfite, followed by concomitant reduction to sulfide during pressure release. Our results suggest that similar processes might have transpired during shock production of sulfur‐rich bleb clusters in shergottite impact melts. It is possible that very small CO presence in our experiments could have catalyzed the reduction process. We plan to repeat the experiments without CO.