## 2 Related Work  
[PERSON], the author of the book \"cybernetics\", suggested the combination of physical processes, computation, and communication, and he coined the term in 1948 [6]. The term evolved into cyber-physical systems (CPS), which expands the integration from process to complete systems. Its main goal is seamless integration between the practical world and its digital counterpart. In contrast, digital twins (DTs) are focused applications that allow providing high-fidelity virtual models. For this reason, DT can be understood as a focused application of CPS [7]. The origin of the digital twin (DT) concept began in the 1960s, and during this period, NASA developed \"twinning\" methods for its Apollo Space Program. Constant data connection provided telemetry data to an array of 15 simulators. This system could compute many simulated equipment failure scenarios or tested communication failures [8]. Nowadays, it is possible to define a DT as an advanced system that can provide high-fidelity models [9]. The connection between DT and its physical counterpart is crucial to generating high-fidelity models. The two-way data connection enables constant updates, and the main goal is to achieve a higher correlation between simulated and actual data tested in the field. Due to its modularity, digital twin solutions combine many technologies. As an example, Nvidia Omniverse combines telemetry data from sensors with augmented reality (AR) solutions. Digital twin industrial applications are presented in manufacturing, energy, industrial assets, or architecture structures. Intelligent manufacturing solutions (CPS) are currently listed as the top priority in manufacturing of government-level strategies, such as Industrial Internet (US), Industry 4.0 (Germany), or Made in China 2025 (China), reflecting this priority [10]. Early adopters such as BMW, General Electric, Microsoft, or Siemens are developing their digital twin technologies. The logistics sector needs signal monitoring systems to avoid unexpected disruptions [11]. The digital supply chain twin (DSCT) is a digital dynamic simulation model [12]. It has three scopes: network level, site level, and asset level. Smart logistics belongs to the site level, covering warehouses and manufacturing scenarios. IoT solutions in ports [13], smart contracts for tracing supply chain parts [14], or new frameworks for next-generation ports and warehouses are examples of smart logistics applications [15]. The implementation of IoT solutions in ports requires the deployment of a massive number of devices. Therefore, the adoption of massive machine-type communication (mMTC) is required. Data encryption and end-to-end performance are required to test and validate mMTC applications. In this regard, 5G digital twins could perform network simulations for these two specific applications [16]. Digital twin integration with 5G technologies is gaining popularity among the research community. The research efforts from [PERSON] et al. (2021) help to provide a generalized architecture of an IoT smart manufacturing scenario. In this case, the study highlights using digital twins to apply advanced technologies based on 5G wireless communications [17]. According to [PERSON] et al. (2021), \"Many researchers and participators in engineering are not clear which technologies and tools should be used\". Their work provided an extensive summary of enabling technologies for digital twins and established general research directions [18]. The definition of clear research directions is part of the work of [PERSON] et al. (2022), where the authors proposed a framework for digitalized museums. Digitalized museums require combining digital twins, artificial intelligence (AI), and 5G technologies. The implementation of the proposed framework was simulated using MATLAB [19]. According to [PERSON] et al. (2021), \"There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models\".