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As the complexity of both products and systems increases across a wide range of industry sectors, there has been an influx in demand for methods of system organization and optimization. MBSE enhances the ability to obtain, analyze, communicate, and manage data on a comprehensive architecture of a system. In this study, a military combat surveillance scenario is modeled using SysML generating state machine diagrams and activity diagrams using the Magic Model Analyst execution framework plugin. This study seeks to prove the feasibility of an MBSE-enabled framework using SysML to create and simulate a surveillance system that monitors and reports on the health status and performance of an armored fighting vehicle (combat tank) through an Unmanned Ariel Vehicle (UAV). The Magic System of Systems Architect, which actively promotes system development architectural frameworks, was used to construct SysML-compliant models, allowing the creation of intricate model diagrams. The construction of the UAV surveillance scenario emphasized the capability of modifying a diagram feature and ensuring that the alteration is communicated to all linked model diagrams. This study builds on a previously published MBSE-enabled conceptual framework for creating digital twins. The purpose of this research is to test and validate the framework's procedures. Keywords—MBSE, SysML, MBSE framework, UAV, Surveillance 

A thermoelectric energy harvesting device is evaluated to power a bearing health monitoring system. Unlike wayside equipment, the new system is an onboard wireless solution utilizing accelerometer and temperature sensors to assess the bearing condition continuously. The harvesting system consists of two thermoelectric generator modules with aluminium heat sinks, a switching boost converter, a battery management circuit, and a lithium rechargeable battery. The performance of the harvester is validated on an AAR class bearing mounted on a laboratory tester, with load and speed simulating common freight routes of up to 896 miles. The energy harvested varies with operating conditions, and data is presented showing the effect of load and speed. Over a realistic route, the net energy harvested is more than double that needed to indefinitely power a Bluetooth Low Energy sensor. The critical design parameters are the ratio of open-circuit voltage to the temperature difference for the thermoelectric module, and the cold start voltage of the boost converter. 

In this paper, we propose a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Through extensive experiments, we demonstrate that the proposed multiview node embedding is able to perform on par or better than the best of its constituents and provide reliable performance across downstream tasks including node classification and graph reconstruction. Index Terms—multiview learning, node embedding, hybrid tensor decomposition, unsupervised learning 

This paper considers the formation flying of multiple quadrotors with a desired orientation and a leader. In the formation flying control, it is assumed that the desired formation is time-varying and there are the system uncertainty and the information uncertainty. In order to deal with different uncertainties, a backstepping-based approach is proposed for the controller design. In the proposed approach, different types of uncertainties are considered in different steps. By integrating adaptive/robust control results and Laplacian algebraic theory, distributed robust adaptive control laws are proposed such that the formation errors exponentially converge to zero and the attitude of each quadrotor exponentially converges to the desired value. Simulation results show the effectiveness of the proposed algorithms. Keywords: Quadrotor · Distributed control · Cooperative control · Leader–follower control · Formation control 

Consider a multi-aspect tensor dataset which is only observed in multiple complementary aggregated versions, each one at a lower resolution than the highest available one. Recent work [2] has demonstrated that given two such tensors, which have been aggregated in lower resolutions in complementary dimensions, we can pose and solve the disaggregation as an instance of a coupled tensor decomposition. In this work, we are exploring the scenario in which, in addition to the two complementary aggregated views, we also have access to a graph where nodes correspond to samples of the tensor mode that has not been aggregated. Given this graph, we propose a graph-assisted tensor disaggregation method. In our experimental evaluation,we demonstrate that our proposed method performs on par with the state of the art when the rank of the underlying coupled tensor decomposition is low, and significantly outperforms the state of the art in cases where the rank increases, producing more robust and higher-quality disaggregation. 

A digital twin (DT) is an interactive, real-time digital representation of a system or a service utilizing onboard sensor data and Internet of Things (IoT) technology to gain a better insight into the physical world. With the increasing complexity of systems and products across many sectors, there is an increasing demand for complex systems optimization. Digital twins vary in complexity and are used for managing the performance, health, and status of a physical system by virtualizing it. The creation of digital twins enabled by Modelbased Systems Engineering (MBSE) has aided in increasing system interconnectivity and simplifying the system optimization process. More specifically, the combination of MBSE languages, tools, and methods has served as a starting point in developing digital twins. This article discusses how MBSE has previously facilitated the development of digital twins across various domains, emphasizing both the benefits and disadvantages of adopting an MBSE enabled digital twin creation. Further, the article expands on how various levels of digital twins were generated via the use of MBSE. An MBSE enabled conceptual framework for developing digital twins is identified that can be used as a research testbed for developing digital twins and optimizing systems and system of systems. Keywords—MBSE, Digital Twin, Digital Shadow, Digital Model, SysML