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Cybercriminals have become an imperative threat because they target the most valuable resource on earth, data. Organizations prepare against cyber attacks by creating Cyber Security Incident Response Teams (CSIRTs) that use various technologies to monitor and detect threats and to help perform forensics on machines and networks. Testing the limits of defense technologies and the skill of a CSIRT can be performed through adversary emulation performed by so-called "red teams". The red team's work is primarily manual and requires high skill. We propose SpecRep, a system to ease the testing of the detection capabilities of defenses in complex, heterogeneous infrastructures. SpecRep uses previously known attack specifications to construct attack scenarios based on attacker objectives instead of the traditional attack graphs or a list of actions. We create a metalanguage to describe objectives to be achieved in an attack together with a compiler that can build multiple attack scenarios that achieve the objectives. We use text processing tools aided by large language models to extract information from freely available white papers and convert them to plausible attack specifications that can then be emulated by SpecRep. We show how our system can emulate attacks against a smart home, a large enterprise, and an industrial control system.

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With the accelerating pace of population aging in China and the implementation of the smart city pilot policy, whether the middle-aged and elderly population can integrate and adapt to this "smart" society has become an urgent problem that needs to be solved. In this context, exploring the impact of smart city pilot policies on the social adaptation health and mental health of middle-aged and elderly people has become a top priority for China to implement a national strategy to actively respond to population aging. Thus, based on panel data from the China Health and Retirement Longitudinal Study (CHARLS) for the years 2011, 2013, and 2015, this study employs the difference-in-differences (DID) method to investigate whether the smart city pilot policy can improve the social adaptive health and mental health of middle-aged and elderly people and to explore in depth the mechanism of its influence. The study finds that compared with non-pilot cities, the social adaptive health and mental health of middle-aged and elderly people in smart cities improve by 0.6% and 2.2%, respectively. The mechanism effect study shows that the smart city pilot policy can improve the mental health of the middle-aged and the elderly through the use of Information and Communication Technology (ICT) and the enhancement of human capital. Furthermore, for the social adaptive health of middle-aged and elderly individuals, the smart city pilot policy can only make improvements through the enhancement of human capital. Heterogeneity analysis shows that the effect of smart city pilot policies on social adaptive health is more pronounced in the middle-aged group than in the elderly group.

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The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy-TOPSIS-Pearson correlation-Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

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China is currently in a new era of an urban transition to a low-carbon economy and digital economic development. Smart cities, as an advanced form of information-based urban development, may be the key to the urban transition to low-carbon emissions. This paper examined the effect of smart city construction (SCC) on urban low-carbon transitions and its transmission mechanisms in China from the dual perspectives of reducing urban total carbon emissions (TCE) and improving urban total-factor carbon emission efficiency (TFCEE). Utilizing a multi-period difference in differences (DID) method, this study was conducted based on panel data of 245 Chinese prefecture-level cities from 2003 to 2021. The results demonstrated that SCC both reduced TCE and enhanced TFCEE. The effects of SCC were stronger in cities with more stringent environmental regulations. SCC achieved the dual effect of reducing TCE and enhancing urban TFCEE by promoting green technological progress and a low-carbon transformation of city residents' lifestyles. Moreover, optimization of the industrial structure was also a transmission mechanism for SCC to improve TFCEE. These conclusions provide an empirical basis for the SCC to empower low-carbon transitions of cities and help countries in different regions to transform the extensive urban development mode and promote urban low-carbon economic development.

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To transform urban areas into smart cities, various technologies-including software, user interfaces, communication networks, and the Internet of Things (IoT)-must tackle complex sustainability and resilience issues. This study aims to investigate the challenges of rapid urban population growth and explore how Information and Communication Technologies (ICT) can be utilized to foster the development of smart cities. Specifically, it seeks to understand how the integration of ICT can contribute to enhancing urban resilience, promoting urban sustainability, and improving citizens' quality of life. The study relied on a literature review, appraisals of fifteen (15) different Smart City software applications and their characteristics (spanning various domains, including data analytics, the Internet of Things (IoT), urban mobility, energy management, and citizen engagement platforms, all related to sustainability and resilience), and thirty (30) case studies cutting across sustainability and resilience. Furthermore, thematic analysis from the case studies was used to evaluate the benefits of smart city applications mapped to the six (6) action areas of Smart City. Based on the findings from case studies and smart city software analysis, rapid urbanisation presents multifaceted challenges like traffic congestion, disaster management, environmental degradation, community engagement, economic disparities, and so on. However, adopting Smart City software applications and aligning with various domains, including data analytics, the Internet of Things (IoT), urban mobility, energy management, and citizen engagement platforms, play pivotal roles in addressing these challenges. Further findings reveal that the benefits of smart city software align with the action areas of smart cities, including Governance, Mobility, Economy, Environment, Living, and People. The research offers practical application of smart city software for Urban designs and planners. It highlights the influence of contextual factors across countries on Smart City effectiveness. The study advances ICT-driven urban transformation, enhancing the quality of life in fast-growing cities.

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The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens' opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study's material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project's implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes.

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This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min-max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%.

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This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents' mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

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Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management.

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Internet of Things (IoT) and 5G communication technologies in smart cities deliver promising services for heterogeneous applications. The application reliability banks on uninterrupted and seamless services experienced by the users. However, the increasing smart city application demands influence the experience reliability through augmented wait times. This article therefore introduces a Coherent Reliability Service Broadcasting Technique (CRSBT) for sustaining constructive application services. This technique incorporates linear regressive and digressive learning for application service improvements and restrictions. Based on the demand, the regressive process verifies the wait time and with the reducing demands, the service broadcast ratio is verified. These two factors are verified post the demand and response through 5G resource allocations and IoT computations. Both the service-oriented features are validated for regressive service broadcast and either of the one is used for digressive response. The coherence between the computations (IoT) and resources (5G) is verified on-demand and linearly. Therefore, the proposed technique is reliable in sustaining service broadcast, less wait time, and maximum flexibility.

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Promoting the formation of the green lifestyle (GL) is a crucial step in achieving comprehensive green transformation of urban economic and social development. The widespread adoption of GL is influenced by various environmental regulations. Previous research mainly focused on the impact of individual policies on GL from the single policy perspective. The mechanisms of the combined effects of policies have not been thoroughly explored, particularly the contributions of each policy during periods of overlap. This paper takes the dual-policy of the New-type Urbanization Policy (NUP) and Smart City Policy (SCP) in China as an example. It employs panel data collected from 271 cities in China during 2007-2019 and establishes a multi-period difference-in-difference model to identify the combined effects of the dual-policy on residents' GL. Additionally, the Shapley value decomposition method is utilized to identify the contribution magnitude of each policy when they act simultaneously. The following conclusions are yielded. Firstly, the combined effects of dual-policy are more effective than a single policy in influencing residents' GL. Secondly, the Shapley value decomposition method reveals that when both policies are simultaneously implemented, SCP contributes a greater weight compared to NUP. Thirdly, the dual-policy can promote residents' adoption of GL through mechanisms such as green technological innovation, public participation in environmental protection, and the agglomeration of tertiary industries. Fourthly, the impact of dual-policy on residents' GL varies across different types and sizes of cities. This study attempts to unseal the "black box" of how the dual-policy influences residents' GL during the green transformation of cities in China, providing theoretical references for relevant urban policies in other countries and contributing to Chinese solutions and experience to global urban green development.

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With the rise of global smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF, comprising three key modules: depthwise separable convolution (DSConv), dual-path attention gate module (DPAG), and feature enhancement module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Additionally, we propose an Internet of Things smart city framework consisting of four main layers: the application domain, the Internet of Things infrastructure layer, the edge layer, and the cloud layer. The proposed algorithm utilizes the Internet of Things infrastructure layer, edge layer, and cloud layer to collect and process data in real-time, achieving faster response times. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

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Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows-Abadi-Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols.

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Rerouting of direct information traffic under the WiMax-1/2 technology control in the case of licensed frequency spectrum overload ensures communication continuity in the smart city's critical infrastructure. The support of such a process in the WiMax-1/2 cluster has its specificity, worthy of analytical formalization. The article presents a mathematical apparatus that allows the average service duration of an information message during its transfer from the terminal to the WiMax-1/2 base station to be estimated. Unlike analogues, the presented concept adequately describes the investigated process for any number of terminals, taking into account both the queuing effect on their side and the functioning of the cumulative query transmission mechanism inherent in WiMax-1/2 technology. Therefore, the proposed mathematical apparatus, describing the process of servicing an information message, takes into account both the average duration accompanied by potential collisions in the process of sending a request for the allocation of communication resources for its transmission to the base station, and the average duration of the information message's stay in the terminal queue. Experimental studies demonstrated the adequacy of the proposed mathematical apparatus for describing the investigated process. The experimental section also formulates the optimization problem of the investigated process resulting from the management of competitive access parameters.

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The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.

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Process algebra is one of the most suitable formal methods to model smart IoT systems for smart cities. Each IoT in the systems can be modeled as a process in algebra. In addition, the nondeterministic behavior of the systems can be predicted by defining probabilities on the choice operations in some algebra, such as PALOMA and PACSR. However, there are no practical mechanisms in algebra either to measure or control uncertainty caused by the nondeterministic behavior in terms of satisfiability of the system requirements. In our previous research, to overcome the limitation, a new process algebra called dTP-Calculus was presented to verify probabilistically the safety and security requirements of smart IoT systems: the nondeterministic behavior of the systems was defined and controlled by the static and dynamic probabilities. However, the approach required a strong assumption to handle the unsatisfied probabilistic requirements: enforcing an optimally arbitrary level of high-performance probability from the continuous range of the probability domain. In the paper, the assumption from the previous research is eliminated by defining the levels of probability from the discrete domain based on the notion of Permissible Process and System Equivalences so that satisfiability is incrementally enforced by both Permissible Process Enhancement in the process level and Permissible System Enhancement in the system level. In this way, the unsatisfied probabilistic requirements can be incrementally enforced with better-performing probabilities in the discrete steps until the final decision for satisfiability can be made. The SAVE tool suite has been developed on the ADOxx meta-modeling platform to demonstrate the effectiveness of the approach with a smart EMS (emergency medical service) system example, which is one of the most practical examples for smart cities. SAVE showed that the approach is very applicable to specify, analyze, verify, and especially, predict and control uncertainty or risks caused by the nondeterministic behavior of smart IoT systems. The approach based on dTP-Calculus and SAVE may be considered one of the most suitable formal methods and tools to model smart IoT systems for smart cities.

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Smart cities are characterized by the integration of various technologies and the use of data to achieve several objectives. These objectives include the creation of efficiencies, boosting economic development, expanding sustainability, and improving the overall quality of life for individuals residing and working within the urban environment. The aim of this study is to analyze the future of smart cities with respect to developing countries, specifically Jordan as the case. This analysis is based on the opinions and feedback from the field experts. In this study, we are tapping into multiple domains of smart cities such as smart governance, education, healthcare, communication, transportation, security, energy, and sustainability. The field experts' consensus was developed with the Delphi method. The Delphi survey comprises eight questions to assess the views about smart city adoption and development with respect to Jordan. The results and findings of this study revealed specific challenges and opportunities in smart city adoption with respect to Jordan. The experts' opinions have validated the study of the 2023 Smart City Index report. They have offered crucial input and future guidance for the adoption of smart cities in Jordan. Additionally, they have indicated which domains of smart cities should be prioritized during the implementation in Jordan.

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Introduction: With the acceleration of urbanization, public health issues have become increasingly prominent in smart city construction, especially in the face of sudden public health crises. A deep research method for public health management based on a 4M perspective (human, machine, materials, methods) is proposed to effectively address these challenges. Methods: The method involves studying the impact of human factors such as population age, gender, and occupation on public health from a human perspective. It incorporates a machine perspective by constructing a public health prediction model using deep neural networks. Additionally, it analyzes resource allocation and process optimization in public health management from the materials and methods perspectives. Results: The experiments demonstrate that the public health prediction model based on deep neural networks achieved a prediction accuracy of 98.6% and a recall rate of 97.5% on the test dataset. In terms of resource allocation and process optimization, reasonable adjustments and optimizations increased the coverage of public health services by 20% and decreased the response time to public health events by 30%. Discussion: This research method has significant benefits for addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, helping smart cities respond more quickly and accurately to potential large-scale public health events in the future. This approach holds important theoretical and practical significance.

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The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.

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The built environment's impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people's travel experience and travel mode choice. The streets' built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device's power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.