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Digital twin has become a critical technical solution for satellite in-orbit service and maintenance. The majority of existing research in this field has focused on constructing system-level digital twins, with various typical applications that primarily describe the operating condition of satellite subsystems. However, achieving a more meticulous portrayal of in-orbit satellites necessitates the inclusion of physical field information, e.g., the temperature field serves as a fundamental basis for characterizing the thermodynamic state and plays a crucial role in precise thermal management of a satellite. The main challenge for realizing thorough state detection and real-time response lies in the cross-level model fusion of system simulation and physical field calculation. To overcome this problem, this paper proposes a Hybrid Digital Twin (HDT) framework that seamlessly integrates the digital twin at the system-level with its physical field-level counterpart, enabling a comprehensive and detailed representation of modeling objects. Specifically, at the physical field-level, deep learning methods are utilized to construct an accurate surrogate model, offering a lightweight, computationally efficient and accelerated execution alternative to the Finite Element Analysis models. At the system-level, multi-domain unified modeling technology is employed to ensure both efficiency and accuracy of coupling simulation. Moreover, a co-simulation approach is developed on the basis of the Functional Mock-up Interface, overcoming the bottleneck of cross-level information interaction and computing time synchronization. The practical utility of the proposed framework is verified by a real-world engineering problem, i.e., satellite temperature field perception and attitude control in the context of the harsh space environment. Whole process engineering experiments demonstrate the effectiveness of the HDT, with mean absolute errors below 1 K for temperature field reconstruction and within 1° for attitude control. [ABSTRACT FROM AUTHOR]

Given the pervasive use of deep learning models across various domains, ensuring model security has emerged as a critical concern. This paper examines backdoor attacks, a form of security threat that compromises model output by poisoning the training data. Our investigation specifically addresses backdoor attacks on object detection models, vital for security-sensitive applications like autonomous driving and smart city systems. Consequently, such attacks on object detection models could pose significant risks to human life and property. Consequently, backdoor attacks on object detection could pose serious threats to human life and property. To elucidate this security risk, we propose and experimentally evaluate five backdoor attack methods for object detection models. The key findings are: (1) Unnecessary Object Generation : a globally embedded trigger creating false objects in the target class; (2) Partial Misclassification : a trigger causing specific class misclassification; (3) Global Misclassification : a trigger reclassifying all objects into the target class; (4) Specific Object Vanishing : a trigger causing non-detection of certain objects; (5) Object Position Shifting : a trigger causing bounding box shifts for a specific class. To assess attack effectiveness, we introduced the Attack Success Rate (ASR), which can surpass 1 in object detection tasks, thus providing a more accurate reflection of the attack impact. Experimental outcomes indicate that the ASR values of these varied backdoor attacks frequently approach or surpass 1, demonstrating our method's capacity to impact multiple objects simultaneously. Additionally, to augment trigger stealth, we introduce Backdoor Attack with Wavelet Embedding (BAWE), which discreetly embeds triggers as image watermarks in training data. This embedding method yields more natural triggers with enhanced stealth. Highly stealthy triggers are less detectable, significantly increasing the likelihood of attack success and efficacy. We have developed a Transformer-based network architecture, diverging from traditional neural network frameworks. Our experiments across various object detection datasets highlight the susceptibility of these models and the high success rate of our approaches. This vulnerability poses significant risks to digital twin systems utilizing object detection technology. Our methodology not only enhances trigger stealth but also suits dense predictive tasks and circumvents current neural network backdoor attack detection methods. The experimental findings expose key challenges in the security of object detection models, particularly when integrated with digital twins, offering new avenues for backdoor attack research and foundational insights for devising defense strategies against these attacks. • We embed watermarks as invisible triggers in training data using wavelet transform. • Our method is suited for dense prediction tasks like object detection. • We use watermarks as triggers and are not limited to standard neural network. [ABSTRACT FROM AUTHOR]

In the Architecture, Engineering, and Construction (AEC) sector, digital twins rely on precise 3D models to convey digital information about physical structures in a virtual space. However, due to the vulnerability to measurement errors in weak-textured regions, 3D point clouds generated by conventional photometric consistency or deep learning-based Multi-View Stereo (MVS) algorithms are often incomplete or inaccurate. Therefore, this paper integrates the consistency constraint across multiple views and the inferential capacity of deep learning to propose a novel approach for refining the missing regions of the depth maps generated by photo-consistency based MVS algorithms. The proposed solution involves a cost volume pyramid-based depth completion (CVP-DC) network with three multi-level pyramid structures, which sequentially estimates and completes depth maps in a coarse-to-fine manner. A dataset that consists of input images and the corresponding depth maps generated by photo-consistency based MVS algorithms, along with output ground truth depth maps, is developed using an open DTU MVS dataset. CVP-DC demonstrates competitive performance when tested on the public DTU MVS dataset, outperforming existing MVS algorithms in terms of both completeness and accuracy. Additionally, experimental studies are conducted utilizing UAV-collected RTK (Real-Time Kinematic) images of an outdoor bridge pier to reconstruct point clouds with absolute scales. Experimental validations demonstrate the effectiveness and applicability of the proposed approach in filling uneven and incomplete depth maps, thereby enhancing the completeness of the generated point clouds. The proposed approach holds promise for establishing precise 3D models for the digital twin of the AEC sector. [ABSTRACT FROM AUTHOR]

The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method. [ABSTRACT FROM AUTHOR]

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities. [ABSTRACT FROM AUTHOR]