This paper addresses the problem of detection and identification of sensor attacks in the presence of transient faults. We consider a system with multiple sensors measuring the same physical variable, where some sensors might be under attack and provide malicious values. We consider a setup, in which each sensor provides the controller with an interval of possible values for the true value. While approaches exist for detecting malicious sensor attacks, they are conservative in that they treat attacks and faults in the same way, thus neglecting the fact that sensors may provide faulty measurements at times due to temporary disturbances (e.g., a tunnel for GPS). To address this problem, we propose a transient fault model for each sensor and an algorithm designed to detect and identify attacks in the presence of transient faults. The fault model consists of three aspects: the size of the sensor's interval (1) and an upper bound on the number of errors (2) allowed in a given window size (3). Given such a model for each sensor, the algorithm uses pairwise inconsistencies between sensors to detect and identify attacks. In addition to the algorithm, we provide a framework for selecting a fault model for each sensor based on training data. Finally, we validate the algorithm's performance on real measurement data obtained from an unmanned ground vehicle.

Void space and functionality of the pore surface are important structural factors for proton‐conductive metal–organic frameworks (MOFs) impregnated with conducting media. However, no clear study has compared their priority factors, which need to be considered when designing proton‐conductive MOFs. Herein, we demonstrate the effects of void space and pore‐surface modification on proton conduction in MOFs through the surface‐modified isoreticular MOF‐74(Ni) series [Ni2(dobdc or dobpdc), dobdc=2,5‐dihydroxy‐1,4‐benzenedicarboxylate and dobpdc=4,4′‐dihydroxy‐(1,1′‐biphenyl)‐3,3′‐dicarboxylate]. The MOF with lower porosity with the same surface functionality showed higher proton conductivity than that with higher porosity despite including a smaller amount of conducting medium. Density functional theory calculations suggest that strong hydrogen bonding between molecules of the conducting medium at high porosity is inefficient in inducing high proton conductivity.

We consider the problem of verifying software implementations of linear time-invariant controllers against mathematical specifications. Given a controller specification, multiple correct implementations may exist, each of which uses a different representation of controller state (e.g., due to optimizations in a third-party code generator). To accommodate this variation, we first extract a controller's mathematical model from the implementation via symbolic execution, and then check input-output equivalence between the extracted model and the specification by similarity checking. We show how to automatically verify the correctness of C code controller implementation using the combination of techniques such as symbolic execution, satisfiability solving and convex optimization. Through evaluation using randomly generated controller specifications of realistic size, we demonstrate that the scalability of this approach has significantly improved compared to our own earlier work based on the invariant checking method. Abstract. We consider the problem of verifying software implementations of linear time-invariant controllers against mathematical specifications. Given a controller specification, multiple correct implementations may exist, each of which uses a different representation of controller state (e.g., due to optimizations in a third-party code generator). To accommodate this variation, we first extract a controller's mathematical model from the implementation via symbolic execution, and then check inputoutput equivalence between the extracted model and the specification by similarity checking. We show how to automatically verify the correctness of C code controller implementation using the combination of techniques such as symbolic execution, satisfiability solving and convex optimization. Through evaluation using randomly generated controller specifications of realistic size, we demonstrate that the scalability of this approach has significantly improved compared to our own earlier work based on the invariant checking method. Disciplines Computer Engineering | Computer Sciences

The platform developed in this work can generate user-desired materials which can lead to design of high performance materials for xenon/krypton separation.

We consider the problem of verification of software implementations of linear time-invariant controllers. Commonly, different implementations use different representations of the controller's state, for example due to optimizations in a third-party code generator. To accommodate this variation, we exploit input-output controller specification captured by the controller's transfer function and show how to automatically verify correctness of C code controller implementations using a Frama-C/Why3/Z3 toolchain. Scalability of the approach is evaluated using randomly generated controller specifications of realistic size.

A Mg-IRMOF-74-III structure with azopyridine molecules attached to its unsaturated metal sites is proposed as a new photoresponsive metal–organic framework (MOF) for CO2 capture. Computational simulations indicate that the photochemically induced trans-to-cis transition of the material leads to significant alteration in the CO2 capacity. Specifically, the grand canonical Monte Carlo simulation showed a CO2 adsorption capacity of 89.6 cm3/g at the trans phase, which is higher than any other photoresponsive MOF reported thus far. Moreover, a large desorption capacity of 82.7% can be explained from significant alteration of the pore size distribution that comes from the trans-to-cis transition. Our work is anticipated to provide a blueprint for computational designing of the new photoresponsive MOF prior to the actual experimental synthesis.

Compared to conventional computational screening studies that are limited by the size of database, inverse design has a great potential to facilitate identifying new materials with optimal properties. In this work, we integrate machine learning with genetic algorithm to computationally design metal−organic frameworks (MOFs) for hydrogen storage applications at cryogenic conditions. As such, we identified 6277 MOFs that exceed the current record (37.2 g/L of NPF-200) at operating conditions between 5 and 100 bar at 77 K. MOFs, whose working capacities exceed 40.0 g/L (systembased 2025 DOE target) were also identified, where the highest working capacity obtained from this work was 41.6 g/L, which is higher than any other hypothetical MOFs reported thus far. Furthermore, synthesizability of the top performing structures was assessed by comparing relative stability with their polymorphic structures while taking into account the possibility of interpenetration. We demonstrate that our methodology can successfully design MOFs with both high hydrogen capacity and synthesizability and we anticipate our workflow can be widely applied to various other materials and applications.

The Libra blockchain is designed to store billions of dollars in assets, so the security of code that executes transactions is important. The Libra blockchain has a new language for implementing transactions, called “Move.” This paper describes the Move Prover, an automatic formal verification system for Move. We overview the unique features of the Move language and then describe the architecture of the Prover, including the language for formal specification and the translation to the Boogie intermediate verification language .