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With the rapid increase in the number of various terminal devices in distribution systems, the important impact of communication networks on power supply reliability in cyber–physical distribution systems (CPDSs) is becoming increasingly prominent. The traditional wired communication method makes it difficult to meet the demand in some scenarios due to high cost and construction difficulty. For this reason, this study evaluated the reliability of power distribution cyber–physical systems, considering fifth-generation (5G) mobile communication technology wireless access conditions. First, a reliability model of a communication channel in a 5G communication network was established, and the indirect impact of information system failure on the power distribution system was analyzed. Secondly, a reliability assessment method based on the improved least path method was devised by combining switch action failure, self-healing processing, and load transfer. The simulation results show that 5G communication technology has significant advantages in improving system reliability, and the proposed method is closer to the development direction of future power distribution systems, which provides an effective reference basis for the optimized operation of power distribution cyber–physical systems. [ABSTRACT FROM AUTHOR]

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities. [ABSTRACT FROM AUTHOR]

This paper addresses semiotic elements of ritual in human encounter. The notion of an essential ritual presence in the existential/communicative connection of persons has been established in the work of Langer, Gadamer, and Jakobson. Yet, as Richard Lanigan maintains, vital aspects of Jakobson's model of communication are typically missed in the application of his work, a consequence of which is that social science no longer differentiates between "communication" and "information". As such, everything perceived as meaningful is reducible to "message", and thus, effective communication means merely "finding the right message". The regression to our current cybernetic world, along with the intellectual paradigms enabling it, was never a foregone conclusion, but the entrenchment of social science in information theory makes it clear that an epistemological commitment to a semiotic phenomenology of communicative existence—and the visibility of ritual life—may well be our only way out. [ABSTRACT FROM AUTHOR]

Given how quickly artificial intelligence technology is developing, federated learning (FL) has emerged to enable effective model training while protecting data privacy. However, when using homomorphic encryption (HE) techniques for privacy protection, FL faces challenges related to the integrity of HE ciphertexts. In the HE-based privacy-preserving FL framework, the public disclosure of the public key and the homomorphic additive property of the HE algorithm pose serious threats to the integrity of the ciphertext of FL's aggregated results. For the first time, this paper employs covert communication by embedding the hash value of the aggregated result ciphertext received by the client into the ciphertext of local model parameters using the lossless homomorphic additive property of the Paillier algorithm. When the server receives the ciphertext of the local model parameters, it can extract and verify the hash value to determine whether the ciphertext of the FL's aggregated results has been tampered with. We also used chaotic sequences to select the embedding positions, further enhancing the concealment of the scheme. The experimental findings demonstrate that the suggested plan passed the Welch's t-test, the K–L divergence test, and the K–S test. These findings confirm that ciphertexts containing covert information are statistically indistinguishable from normal ciphertexts, thereby affirming the proposed scheme's effectiveness in safeguarding the integrity of the FL's aggregated ciphertext results. The channel capacity of this scheme can reach up to 512 bits per round, which is higher compared to other FL-based covert channels. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the scope of Internet of Things (IoT). There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems a bit low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. In this study, to the best of the authors' knowledge, it is addressed for the first time in the literature that better performance is achieved for the OTFS waveform transmission in a selective decode-and-forward (SDF) cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source, a traffic sign/relay and a smart vehicle/destination moving at a constant speed, an end-to-end BER expression is derived. SNR-BER analysis is performed with this SDF-OTFS scheme and it is shown that a superior BER performance is achieved compared to the traditional point-to-point single-input single-output (SISO)-OTFS structure. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a decentralized machine learning approach whereby each device is allowed to train local models, eliminating the requirement for centralized data collecting and ensuring data privacy. Unlike typical typical centralized machine learning, collaborative model training in FL involves aggregating updates from various devices without sending raw data. This ensures data privacy and security while collecting a collective learning from distributed data sources. These devices in FL models exhibit high efficacy in terms of privacy protection, scalability, and robustness, which is contingent upon the success of communication and collaboration. This paper explore the various topologies of both decentralized or centralized in the context of FL. In this respect, we investigated and explored in detail the evaluation of four widly used end-to-end FL frameworks: FedML, Flower, Flute, and PySyft. We specifically focused on vertical and horizontal FL systems using a logistic regression model that aggregated by the FedAvg algorithm. specifically, we conducted experiments on two images datasets, MNIST and Fashion-MNIST, to evaluate their efficiency and performance. Our paper provides initial findings on how to effectively combine horizontal and vertical solutions to address common difficulties, such as managing model synchronization and communication overhead. Our research indicates the trade-offs that exist in the performance of several simulation frameworks for federated learning. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance. [ABSTRACT FROM AUTHOR]

Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes. [ABSTRACT FROM AUTHOR]

Simple Summary: The success and the satisfaction of clinicians in practice relies heavily on how consultations, communication, and relationships are built with clients. Very knowledgeable and competent veterinarians might not be able to provide their services if the client declines their services based on misperceptions or lack of understanding, potentially resulting in lack of treatments of pets in need. The Calgary–Cambridge Guide for consultations and communication is an important tool taught in most veterinary colleges to help future clinicians improve their communication skills, ultimately benefiting the veterinary profession, clients, and the welfare of their pets. In this commentary we describe challenges in applying these guidelines in multilingual and multicultural scenarios such as Hong Kong. In these scenarios clinicians and clients often need an interpreter, adding complexity to the interaction and communication. Non-verbal communication, where body language plays an important role in showing expressions, empathy, and concerns is not effective or is altered if there is not even eye contact or a translator is not accurately interpreting all these emotions. This commentary analyses the challenges encountered by veterinarians during consultations in multicultural and multilingual centres. The Calgary–Cambridge Guide is a widely recognised framework for teaching communication skills to healthcare professionals that has become a cornerstone of communication training programs in medicine and other healthcare fields. In the context of veterinary medicine, its integration into communication training programs has become an asset improving communication, education, interaction, and quality of service, enhancing the veterinary–client–patient relationship (VCPR). In veterinary medicine, however, a more challenging consultation dynamic involves the veterinarian, the owner, and the animal. The addition of a veterinary assistant that acts as an interpreter or translator is common in Hong Kong where the native language (Cantonese) coexists with English when consultations are led by non-native language speakers. This addition converts this commonly dyadic model into a triadic communication model. The addition of an assistant interpreter influences the way consultations are conducted, how information is conveyed, and how interpersonal cues and empathy are delivered. In this report we depict challenges applying the Calgary–Cambridge Guide in multicultural and multilingual veterinary medical centres in Hong Kong and highlight the role of veterinary supporting staff in these scenarios, specifically veterinary assistant interpreters. [ABSTRACT FROM AUTHOR]

The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health-specific LLM-based robots in terms of multi-modal communication through human–robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field. [ABSTRACT FROM AUTHOR]

Today, AI is primarily narrow, meaning that each model or agent can only perform one task or a narrow range of tasks. However, systems with broad capabilities can be built by connecting multiple narrow AIs. Connecting various AI agents in an open, multi-organizational environment requires a new communication model. Here, we develop a multi-layered ontology-based communication framework. Ontology concepts provide semantic definitions for the agents' inputs and outputs, enabling them to dynamically identify communication requirements and build processing pipelines. Critical is that the ontology concepts are stored on a decentralized storage medium, allowing fast reading and writing. The multi-layered design offers flexibility by dividing a monolithic ontology model into semantic layers, allowing for the optimization of read and write latencies. We investigate the impact of this optimization by benchmarking experiments on three decentralized storage mediums—IPFS, Tendermint Cosmos, and Hyperledger Fabric—across a wide range of configurations. The increased read-write speeds allow AI agents to communicate efficiently in a decentralized environment utilizing ontology principles, making it easier for AI to be used widely in various applications. [ABSTRACT FROM AUTHOR]

This paper presents a geometry-based stochastic model (GBSM) for a reconfigurable intelligent surface (RIS)-assisted multiple-input–multiple-output (MIMO) system tailored to high-speed rail environments, addressing energy leakage at space lattice points caused by the time-dependent reception direction and differing antenna array resolutions. Initially, this study explores the spatial domain feature map and spreading antenna lobe characteristics from various RIS array configurations to reshape the polarization distribution and facilitate a virtual antenna array. Subsequently, the time-dependent reception direction and position are derived by integrating the dynamics of train operations. Finally, the received signal is aligned with the polarization direction to assess the signal reception efficiency and finalize the model output. Research findings indicate that the number of RIS elements, spacing between RIS elements, and mobile relay (MR) movement characteristics significantly influence the performance of the system. Compared to existing models, the proposed model proficiently captures the effects of time-dependent receiver angle properties and RIS array configuration. [ABSTRACT FROM AUTHOR]

Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributed estimation method, a scalable suboptimal distributed consensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users' real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users' privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch's t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem. [ABSTRACT FROM AUTHOR]

A company's cyber defenses are based on a secure infrastructure and risk-aware behavior by employees. With rising cyber threats and normative training efforts showing limited impact, raising cyber risk awareness is emerging as a challenging effort. The review of the extant literature on awareness diagnosis shows interdisciplinary but mainly theoretical approaches to understanding attitudes and influencing risk behavior. We propose and test a novel methodology to combine and operationalize two tools, deep metaphor interviews and the IDEA risk communication model, to apply them for the first time in the context of behavioral cyber vulnerabilities. The results show a link between diagnosed attitudes and effective risk behavior in a real-life organizational setting, indicating the potential for an expanded diagnostic effort. We propose to develop a broader diagnostic and intervention set to improve cyber awareness and a toolkit to support the business practice of cyber risk management. [ABSTRACT FROM AUTHOR]

In the progress of research in the field of semantic communication, most efforts have been focused on optimizing the signal-to-noise ratio (SNR), while the design and optimization of the bit rate required for transmission have been relatively neglected. To address this issue, this study introduces an innovative low-bit-rate image semantic communication system model, which aims to reconstruct images through the exchange of semantic information rather than traditional symbol transmission. This model employs an image feature extraction and optimization reconstruction framework, achieving visually satisfactory and semantically consistent reconstruction performance at extremely low bit rates (below 0.03 bits per pixel (bpp)). Unlike previous methods that used pixel accuracy as the standard for distortion measurement, this research introduces multiple perceptual metrics to train and evaluate the proposed image semantic encoding model, aligning more closely with the fundamental purpose of semantic communication. Experimental results demonstrate that, compared to WebP, JPEG, and deep learning-based image codecs, our model not only provides a more visually pleasing reconstruction effect but also significantly reduces the required bit rate while maintaining semantic consistency. [ABSTRACT FROM AUTHOR]

In addressing the critical role of emotional context in patient–clinician conversations, this study conducted a comprehensive sentiment analysis using BERT, RoBERTa, GPT-2, and XLNet. Our dataset includes 185 h of Greek conversations focused on hematologic malignancies. The methodology involved data collection, data annotation, model training, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and specificity. BERT outperformed the other methods across all sentiment categories, demonstrating its effectiveness in capturing the emotional context in clinical interactions. RoBERTa showed a strong performance, particularly in identifying neutral sentiments. GPT-2 showed promising results in neutral sentiments but exhibited a lower precision and recall for negatives. XLNet showed a moderate performance, with variations across categories. Overall, our findings highlight the complexities of sentiment analysis in clinical contexts, especially in underrepresented languages like Greek. These insights highlight the potential of advanced deep-learning models in enhancing communication and patient care in healthcare settings. The integration of sentiment analysis in healthcare could provide insights into the emotional states of patients, resulting in more effective and empathetic patient support. Our study aims to address the gap and limitations of sentiment analysis in a Greek clinical context, an area where resources are scarce and its application remains underexplored. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) in recent years, many IoT devices use communication systems to transmit data. Data packets are inevitably at risk of tampering during data transmission, which can lead to information errors and damage during communication. Since IoT terminals are often operated under human supervision, it is essential to improve and ensure the transmission of information, avoid data tampering, and ensure the accuracy of packet transmission. This research successfully improves the message transmission method of IoT communication and the communication model by combining it with blockchain architecture. Combining communication protocols with blockchain serial connections eliminates the need to control operating and managing processes. By replacing message transmission with coverage, the reliability of data transmission in the IoT communication system is improved, and the flexibility of the data transmission process is enhanced. Through practical verification through experimentation, the study successfully improved the incapability of effectively constructing a cross-coded message transmission mode in the Message Queuing Telemetry Transport (MQTT) communication protocol, removed a single layer of encryption rule, and created an encryption mode capable of providing complex arrangement organization. [ABSTRACT FROM AUTHOR]

The traditional Internet has many security problems. It is difficult to guarantee the authenticity, integrity, and synchronization of message transmission, and it lacks a message-traceability mechanism, which is caused by its performance-oriented design. To address these problems, this paper proposes a memorable communication method based on cryptographic accumulators. In this method, both parties in the communication can verify the message data sent and received arbitrarily by virtue of the memory value. As long as a simple memory value comparison is performed, the strong consistency of all message data can be ensured. This method has the security advantages of synchronization, verification, traceability, and non-tamperability, as well as the performance advantages brought by batch signature and verification. In this paper, the memorable communication model, the memory function, and the memorable communication process are designed, and theoretical analysis shows that the memorable communication method has synchronization and traceability and can realize batch signature and authentication. In addition, a chain-key can be constructed based on a memory value to achieve key per-packet updating. Comparative analysis shows the transmission efficiency, traceability efficiency, and security performance of the memorable communication method. [ABSTRACT FROM AUTHOR]

Numerous applications of the Internet of Things (IoT) feature an event recognition behavior where the established Shannon capacity is not authorized to be the central performance measure. Instead, the identification capacity for such systems is considered to be an alternative metric, and has been developed in the literature. In this paper, we develop deterministic K-identification (DKI) for the binary symmetric channel (BSC) with and without a Hamming weight constraint imposed on the codewords. This channel may be of use for IoT in the context of smart system technologies, where sophisticated communication models can be reduced to a BSC for the aim of studying basic information theoretical properties. We derive inner and outer bounds on the DKI capacity of the BSC when the size of the goal message set K may grow in the codeword length n. As a major observation, we find that, for deterministic encoding, assuming that K grows exponentially in n, i.e., K = 2 n κ , where κ is the identification goal rate, then the number of messages that can be accurately identified grows exponentially in n, i.e., 2 n R , where R is the DKI coding rate. Furthermore, the established inner and outer bound regions reflects impact of the input constraint (Hamming weight) and the channel statistics, i.e., the cross-over probability. [ABSTRACT FROM AUTHOR]

This paper mainly studies the deployment of hybrid sensors on highways. By constructing the deployment location constraint model, the overall accuracy of sensor deployment can be maximized. In addition, in order to meet the needs of intelligent transportation, the consideration of traffic data communication is added to the work. The highway under study is first divided into several units, and the combination type of sensors is used to represent the possible layout of two adjacent sensors. Then, a 0–1 optimization model reflecting the interaction between the sensor position and the server position is established. Then, a two-step search algorithm is proposed to find the optimal solution of the model and determine the deployment scheme with maximum accuracy. Finally, an example is given to verify the method. The results show that there are significant differences between uniform unit-deployment schemes and non-uniform unit-deployment schemes. Through the sensitivity analysis of each factor, the influence of budget and communication radius on the deployment plan is proven. In addition, the ramp length can also have a negative impact on the target value. [ABSTRACT FROM AUTHOR]

Underwater wireless optical communication (UWOC) is an emerging technology with wide-ranging applications in marine exploration, offshore industries, environmental monitoring, and underwater robotics. In order to investigate the application of UWOC in environments of different water quality, this study establishes a model of an optical communication channel and analyzes the impact of water quality on communication range. Our experimental design is employed to validate the effectiveness of the model and analyze the sources of model errors. Furthermore, this research introduces the concept of an "effective communication space" for underwater optical wireless communication and constructs an experimental platform to test the effective communication space under various water quality conditions. In addition, the application methods and workflow of wireless optical communication on underwater mobile platforms are discussed, and field tests are conducted in a practical lake environment to verify the application value of the effective communication space. This research offers valuable guidance for advancing the study and engineering applications of UWOC technology. [ABSTRACT FROM AUTHOR]

Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems. [ABSTRACT FROM AUTHOR]

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure. [ABSTRACT FROM AUTHOR]

Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender's social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender's organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories. [ABSTRACT FROM AUTHOR]

Virtual coupling (VC) technology has received much attention because of its significant advantages in enhancing the railway transport capacity; it achieves efficient train coupling operation through advanced communication technology. However, due to the uncertainty of the operating environment, a stable and effective control system is the key enabler for realization. In this paper, an event-triggered distributed model predictive control (ET-DMPC) method is proposed for the cooperative tracking control of virtual coupling trains (VCTS), considering resource limitations and multiple constraints. Firstly, a distributed model predictive control (DMPC) framework is designed. Based on the established VCTS dynamics model of the dual-leader communication topology, a distributed optimization objective function and safety constraints containing state information of the neighboring train system are constructed. Secondly, due to the limitations of communication and computational resources, the event triggering (ET) mechanism is further introduced, and an ET-DMPC method suitable for VCTS is proposed. The trigger condition of each unit train is designed on the premise of guaranteeing system stability, under which the system can guarantee the input-state stability (ISS), and the recursive feasibility of the system is proven via theoretical analysis. Finally, the VCTS composed of four CRH380A unit trains is used as the control object for simulation experiments, and through two sets of experimental simulation analysis, the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Holographic communication is considered one of the typical scenarios in the 6G era. Studies have shown that the light field display is considered the most effective naked-eye 3D display method in the 6G era. Despite this, there are still many issues worthy of study. Since there are currently no experience-evaluation standards for holographic communications proposed worldwide, this also causes a lot of research work to remain at the design level and vision. To truly realize the holographic communication scenario, it is necessary to systematically evaluate the light field display technology. The level of user experience determines the value of the holographic communication scenario. This requires quantifying the user's experience level and mapping it to the technical parameters of the light field display image. However, there is still room for improvement in related research. This paper proposes a model based on semi-supervised learning, which takes light field image data of various scenes as input and uses three experience scores of comfort, space, and realism as output to complete the subjective experience evaluation of light field images. Compared with evaluation methods that focus on the quality of the image itself, this article focuses more on the effect on human experience. Compared with existing work, this paper makes improvements in two respects: feature engineering and training strategies. In terms of feature selection, the convolutional neural network is used to extract image content features, and the image quality parameter-extraction module is used to extract image property features. The two are spliced as the input of the classifier; in terms of the training strategy, pseudo-labels and dynamic thresholds are used for training. The final experimental results show that on the MPI-LFA data set, the comfort dimension's classification accuracy is 80.21, the spatial dimension's classification accuracy is 83.12, and the realism dimension's classification accuracy is 81.88. [ABSTRACT FROM AUTHOR]

Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, as well as linear regression- and random forest-based predictive models that infer the expected signal loss from its frequency, measurement point locations, and body parameters. The results demonstrate a close correspondence between the amplitude-frequency response (AFR) predicted by the electrical circuit models and the experimental data gathered from volunteers. The accuracy of our predictive models was assessed by using their root mean square errors (RMSE), ranging from 1.5 to 7 dB depending on the signal frequency within the 0.05 to 20 MHz range. These results allow researchers and engineers to simulate and forecast the expected signal loss within BCC systems during their design phase. [ABSTRACT FROM AUTHOR]

Uplink communication across the water–air interface holds great potential for offshore oil surveys and military applications. Among the various methods available for implementing uplink communication, translational acoustic-RF (TARF) communication stands out due to its ability to withstand wave interference and exhibit low absorption losses. However, the physical processes underlying such systems are currently under-researched, and channel models for evaluating its communication performance indicators are lacking. Herein, we propose a signal-to-noise ratio (SNR) channel model for evaluating the performance metrics of an uplink communication system combining acoustic and millimeter waves for the first time and validate the accuracy of the proposed model through experiments. Specifically, in the process of model construction, the physical process of the communication system was deeply studied, and the corrections of multipath effects, box vibrations, and second-order nonlinear coefficients of the amplitude of the water surface were realized. The water-to-air cross-medium communication system was built, and communication experiments were conducted to validate the feasibility of combining acoustic and millimeter wave communication. This research provides a theoretical and experimental foundation for the design and evaluation of TARF communication systems, providing valuable guidance for enhancing the system's performance metrics and promising an innovative approach for modern seaborne communication. [ABSTRACT FROM AUTHOR]

With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside a vehicle, and it cannot evaluate whether reduced radio receiver sensitivity, antenna isolation, and communication distance satisfy the system requirements for vehicular communication, thus making it unsuitable for digital communication systems. With the development of remote sensing technology, high-precision digital maps are easy to acquire and thus widely used. In this work, a modified five-level evaluation model based on digital maps is proposed, where digital maps are employed to support receiver sensitivity, antenna isolation, and communication performance evaluation. Through remote sensing technology and digital maps, a terrain profile is obtained, and a more accurate vehicle communication propagation model is established. In the experiment, an actual armored vehicular communication system example is applied to verify the performance of the proposed five-level evaluation model. Compared with the free-space propagation model, the error of the actual power received by the receiver is reduced by 0.97%, and the error of the communication distance where the sensitivity of the receiver is reduced by more than the system EMC threshold is reduced by 16.78%. The calculated antenna isolation degree is basically consistent with the actual measurement data. The model is able to evaluate the electromagnetic compatibility of an armored vehicular communication system more quickly, accurately, and comprehensively compared to previous evaluation models. [ABSTRACT FROM AUTHOR]

Distributed hypothesis testing (DHT) has emerged as a significant research area, but the information-theoretic optimality of coding strategies is often typically hard to address. This paper studies the DHT problems under the type-based setting, which is requested from the popular federated learning methods. Specifically, two communication models are considered: (i) DHT problem over noiseless channels, where each node observes i.i.d. samples and sends a one-dimensional statistic of observed samples to the decision center for decision making; and (ii) DHT problem over AWGN channels, where the distributed nodes are restricted to transmit functions of the empirical distributions of the observed data sequences due to practical computational constraints. For both of these problems, we present the optimal error exponent by providing both the achievability and converse results. In addition, we offer corresponding coding strategies and decision rules. Our results not only offer coding guidance for distributed systems, but also have the potential to be applied to more complex problems, enhancing the understanding and application of DHT in various domains. [ABSTRACT FROM AUTHOR]

This study investigates the impact of generative AI systems like ChatGPT on semi-structured decision-making, specifically in evaluating undergraduate dissertations. We propose using Davis' technology acceptance model (TAM) and Schulz von Thun's four-sides communication model to understand human–AI interaction and necessary adaptations for acceptance in dissertation grading. Utilizing an inductive research design, we conducted ten interviews with respondents having varying levels of AI and management expertise, employing four escalating-consequence scenarios mirroring higher education dissertation grading. In all scenarios, the AI functioned as a sender, based on the four-sides model. Findings reveal that technology acceptance for human–AI interaction is adaptive but requires modifications, particularly regarding AI's transparency. Testing the four-sides model showed support for three sides, with the appeal side receiving negative feedback for AI acceptance as a sender. Respondents struggled to accept the idea of AI, suggesting a grading decision through an appeal. Consequently, transparency about AI's role emerged as vital. When AI supports instructors transparently, acceptance levels are higher. These results encourage further research on AI as a receiver and the impartiality of AI decision-making without instructor influence. This study emphasizes communication modes in learning-ecosystems, especially in semi-structured decision-making situations with AI as a sender, while highlighting the potential to enhance AI-based decision-making acceptance. [ABSTRACT FROM AUTHOR]

This paper proposes a joint optimization mechanism for packet length and power in the scenario of covert short packet D2D communication, so as to effectively improve the communication performance between D2D pairs subject to the covert constraint. Specifically, we construct a short-packet D2D communication model assisted by covert communication, and further propose the notion of effective covert throughput (ECT) to quantitatively characterize the trade-off between the covertness and reliability of IoT state monitoring information transmission. Secondly, in the constructed communication scenario, we analyze the detection error probability of the warden and clarify that the existing equal power transmission of pilot and data signals can minimize the detection performance of the warden. However, this strategy is achieved by compromising the transmission performance of the system, which means that the ECT of D2D pair may not be optimal. Thirdly, we aim to maximize the ECT of D2D pair and construct a joint optimization problem for pilot transmission power, data transmission power, and packet length. Furthermore, a joint optimization algorithm based on the 2D search is adopted to obtain the optimal solution of the established optimization problem. Simulation results demonstrated that the transmission performance of the joint optimization algorithm is better than that of the scheme of the equal power scheme on the premise of ensuring the covertness. [ABSTRACT FROM AUTHOR]

Digital substations, also referred to as modern power grid substations, utilize the IEC 61850 station and process bus in conjunction with IP-based communication. This includes communication with switch yard equipment within the substation as well as the dispatch center. IEC 61850 is a global standard developed to standardize power grid communications, covering multiple communication needs related to modern power grid substations or digital substations. Unlike the legacy communication standards, IEC 60870-5-104 and DNP3, IEC 61850 is specifically designed for IP-based communication. It comprises several communication models and supports real-time communication by introducing the process bus to replace traditional peer-to-peer communication with standard network communication between substation equipment and the switch yard. The process bus, especially Sampled Measured Values (SMV) communication, in modern power grid substations relies on extremely accurate and synchronized time to prevent equipment damage, maintain power grid system balance, and ensure safety. In IEC 61850, time synchronization is provided by the Precision Time Protocol (PTP). This paper discusses the significance and challenges of time synchronization in IEC 61850 substations, particularly those associated with PTP. It presents the results of a controlled experiment that subjects time synchronization and PTP to cyber-attacks and discusses the potential consequences of such attacks. The paper also provides recommendations for potential mitigation strategies. The contribution of this paper is to provide insights and recommendations for enhancing the security of IEC 61850-based substations against cyber-attacks targeting time synchronization. The paper also explores the potential consequences of cyber-attacks and provides recommendations for potential mitigation strategies. [ABSTRACT FROM AUTHOR]

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study. [ABSTRACT FROM AUTHOR]

In this paper, we consider a variant of dominating set problem, i.e., the total dominating set problem. Given an undirected graph G = (V , E) , a subset of vertices T ⊆ V is called a total dominating set if every vertex in V is adjacent to at least one vertex in T. Based on LP relaxation techniques, this paper gives a distributed approximation algorithm for the total dominating set problem in general graphs. The presented algorithm obtains a fractional total dominating set that is, at most, k (1 + Δ 1 k) Δ 1 k times the size of the optimal solution to this problem, where k is a positive integer and Δ is the maximum degree of G. The running time of this algorithm is constant communication rounds under the assumption of a synchronous communication model. [ABSTRACT FROM AUTHOR]

The study examined whether the 3R (reframe, prioritize, and reform) communication model intervention can impact parents' and adolescents' HPV vaccination acceptability. We used face-to-face methods to recruit participants from three local churches in the Ashanti Region of Ghana. Participants completed pre- and post-intervention assessments based on the validated Theory of Planned Behavior survey. We organized two face-to-face presentations for parents and adolescents separately for parents (n = 85) and adolescents (n = 85). Participants' post-intervention vs. pre-intervention scores for attitude (mean = 35.46 ± SD = 5.46 vs. mean = 23.42 ± SD = 8.63), knowledge (M = 28.48 ± SD = 5.14 vs. M = 16.56 ± SD = 7.19), confidence (M = 8.96 ± SD = 3.43 vs. M = 6.17 ± SD = 2.84), and intention for vaccine acceptance (M = 4.73 ± SD = 1.78 vs. M = 3.29 ± SD = 1.87) increased significantly (p < 0.001). The intervention showed that for every one-unit increase in the participants' self-confidence and attitude scores, the odds of the HPV vaccination acceptability increased by 22% (95% CI: 10–36) and 6% (95% CI: 0.1–12), respectively. Intention for vaccine acceptance, F (1167) = 6.89, and attitude toward vaccination, F (1167) = 19.87, were significantly higher among parents than adolescents (p < 0.001), after controlling for the baseline scores. These findings suggest that the intervention targeting parents' and adolescents' attitudes and knowledge has the potential to increase HPV vaccination acceptance in Ghana. [ABSTRACT FROM AUTHOR]

G3-powerline communication (G3-PLC) is a robust communication protocol originally developed for smart metering in low-voltage power distribution networks. Modeling G3-PLC modems is an essential task to investigate electromagnetic compatibility (EMC) issues related to the coexistence of the PLC signal with the high-frequency noise affecting low-voltage networks, mainly due to the presence of power converters and non-linear loads. Since detailed information on the modem internal architecture is usually not available to the end-user, this work investigates the possibility of developing behavioral (black-box) models of G3-PLC modems, whose parameters can be estimated starting from measurements carried out at the modem output ports. To this end, suitable test benches are set up and used for model-parameter extraction as well as for validation purposes. Experiments have proven that an equivalent representation involving non-ideal voltage sources (i.e., in terms of extended Thevenin/Norton equivalent circuits) is no longer feasible for the transmitting modem, since the presence of a closed-loop control system invalidates the linearity assumption. Hence, while the receiving modem is still modeled through an impedance matrix (since it behaves as a linear device), an alternative representation is proposed for the transmitting modem, which resorts to the use of two ideal voltage sources in accordance with the substitution theorem. Experimental results prove that the proposed modeling strategy leads to satisfactory predictions of the currents propagating on the PLC system in the frequency interval of interest. Hence, it could be used in combination with high-frequency models of the other components in the network to investigate EMC and the coexistence of the PLC signal with the high-frequency noise generated by power converters. [ABSTRACT FROM AUTHOR]

Three-dimensional human pose estimation is a hot research topic in the field of computer vision. In recent years, significant progress has been made in estimating 3D human pose from monocular video, but there is still much room for improvement in this task owing to the issues of self-occlusion and depth ambiguity. Some previous work has addressed the above problems by investigating spatio-temporal relationships and has made great progress. Based on this, we further explored the spatio-temporal relationship and propose a new method, called STFormer. Our whole framework consists of two main stages: (1) extract features independently from the temporal and spatial domains; (2) modeling the communication of information across domains. The temporal dependencies were injected into the spatial domain to dynamically modify the spatial structure relationships between joints. Then, the results were used to refine the temporal features. After the preceding steps, both spatial and temporal features were strengthened, and the estimated final pose will be more precise. We conducted substantial experiments on a well-known dataset (Human3.6), and the results indicated that STFormer outperformed recent methods with an input of nine frames. Compared to PoseFormer, the performance of our method reduced the MPJPE by 2.1%. Furthermore, we performed numerous ablation studies to analyze and prove the validity of the various constituent modules of STFormer. [ABSTRACT FROM AUTHOR]

The rapid development of wearable technology has facilitated the collection and sharing of health data, allowing patients to benefit from caretakers and medical research. However, these personal health data often contain sensitive information and it is typically not known in advance with whom the information will be shared. Therefore, messages must be encrypted and shared while adhering to the decoupled communication model. This paper presents NACDA, a secure many-to-many data-sharing service on the Named Data Network (NDN). NACDA uses Identity-Based Encryption with Wildcard Key Derivation (WKD-IBE) to allow naming-based access control, enabling data subjects to share data securely and flexibly regardless of the data processor. In addition, NACDA supplements a decentralized authorization mechanism with blockchain to ensure data subjects' data ownership and enforce access policies. We developed an NDN-based prototype and performed a security analysis to demonstrate NACDA's feasibility. [ABSTRACT FROM AUTHOR]

In recent years, federated GBDTs have gradually replaced traditional GBDTs, and become the focus of academic research. They are used to solve the task of structured data mining. Aiming at the problems of information leakage, insufficient model accuracy and high communication cost in the existing schemes of horizontal federated GBDTs, this paper proposes an algorithm of gradient boosting decision trees based on horizontal federated learning, that is, secure and efficient FL for GBDTs (SeFB). The algorithm uses locality sensitive hashing (LSH) to build a tree by collecting similar information of instances without exposing the original data of participants. In the stage of updating the tree, the algorithm aggregates the local gradients of all data participants and calculates the global leaf weights, so as to improve the accuracy of the model and reduce the communication cost. Finally, the experimental analysis shows that the algorithm can protect the privacy of the original data, and the communication cost is low. At the same time, the performance of the unbalanced binary data set is evaluated. The results show that SeFB algorithm compared with the existing schemes of horizontal federated GBDTs, the accuracy is improved by 2.53% on average. [ABSTRACT FROM AUTHOR]

As the Internet communication model changes from host-centric to content-centric, information-centric networking (ICN) as a new network architecture has received increasing attention. There are often multiple replicas of content in ICN, and how to reasonably utilize the characteristics of multiple replicas to further improve user experience is an important issue. In this paper, we propose a replica-selection algorithm, called the transmission completion time estimation (TCTE) algorithm. TCTE maintains the state of replica nodes in the domain with passive measurements in a limited domain of an enhanced name resolution system (ENRS), then estimates the transmission completion time of different replica nodes and selects the smallest one. When no replica is found in the ENRS domain, the nearest-replica algorithm will be used, so TCTE will not increase the traffic in the core network. Experiments show that TCTE not only effectively improves the user's download rate and edge node throughput, reduces download rate fluctuations, reduces user download delay, and improves fairness, but also has universal applicability. [ABSTRACT FROM AUTHOR]

The study on the numerical model of communication processes implemented in RFID systems, in which textile electronic (RFIDtex) tags are used, is presented in the paper. The efficiency analysis covers the case of dynamic identification of a large amount of RFIDtex tags that are located in a spatial interrogation zone of a typical Internet of Textile Things (IoTT) application. Simulations carried out in order to verify the efficiency of the identification process are confirmed by measurements on the dedicated laboratory stand. Since the application of the experiment is located in the area of a maintenance-free store to detect and distinguish textile products, particular attention is paid to reconstruction of conditions and object arrangements that are typical for this type of space. The model and experiment are developed on the basis of RFIDtex transponders that are restricted under the patent claim PL231291. The obtained results prove that within the scope of the assumed number of RFIDtex transponders, the user has great freedom in choosing the parameters of the communication protocol. [ABSTRACT FROM AUTHOR]

In this paper, first, a broad overview of existing covert channel communication-based security attacks is provided. Such covert channels establish a communication link between two entities that are not authorized to share data. The secret data is encoded into different forms of signals, such as delay, temperature, or hard drive location. These signals and information are then decoded by the receiver to retrieve the secret data, thereby mitigating some of the existing security measures. The important steps of covert channel attacks are described, such as data encoding, communication protocol, data decoding, and models to estimate communication bandwidth and bit error rate. Countermeasures against covert channels and existing covert channel detection techniques are also summarized. In the second part of the paper, the implications of such attacks for emerging packaging technologies, such as 2.5D/3D integration are discussed. Several covert channel threat models for 2.5D/3D ICs are also proposed. [ABSTRACT FROM AUTHOR]

Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless communication systems. However, because of the problem of a high peak-to-average power ratio (PAPR), OFDM symbols can be distorted at the MIMO OFDM transmitter. It degrades the signal detection and channel estimation performance at the MIMO OFDM receiver. In this paper, three deep neural network (DNN) models are proposed to solve the problem of non-linear distortions introduced by the power amplifier (PA) of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 × 2 MIMO OFDM and 4 × 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map the signals at the receiver. Proposed model type II uses the DNN model to learn and filter out the channel noises at the receiver. Proposed model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust bit error rate (BER) performances of the proposed receivers are achieved through the software and hardware implementation results. In addition, we have also implemented appropriate hardware architectures for the proposed DNN models using special techniques, such as quantization and pipeline to check the feasibility in practice, which recent studies have not done. Our hardware architectures are successfully designed and implemented on the Virtex 7 vc709 FPGA board. [ABSTRACT FROM AUTHOR]

Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is playing a pivotal role in 5G technology and has the potential to be useful in future developments beyond 5G. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of individual user data rate (IDR) still remained an unexplored area. Previously, it has been shown that reconfigurable intelligent surfaces (RIS) can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between access point-to-RIS-to-user device. Due to this point-to-point communication model, optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster using a geometric progression-based power allocation method such that IDR reaches its upper bound. These observations are also verified using the recently published magic matrix-based power allocation method. There are two observations from this study: (i) the IDR is better in the case of the TDMA-RIS-NOMA downlink system than using downlink NOMA alone and (ii) irrespective of the number of meta-atoms and total cluster power, the upper bound of IDR cannot be increased beyond a certain limit for all users except the highest channel gain user. Because of the restricted upper bound for IDR, we suggest that the RIS-assisted downlink TDMA-NOMA system is more suitable for IoT applications, where minimum IDR can also suffice [ABSTRACT FROM AUTHOR]

Reliable wireless communications are crucial for ensuring workers' safety in underground tunnels and mines. Visible light communications (VLC) have been proposed as auxiliary systems for short-range wireless communications in underground environments due to their seamless availability, immunity to electromagnetic interference, and illumination capabilities. Although multiple VLC channel models have been proposed for underground mines (UM) so far, none of these models have considered the wavelength dependence of the underground mining VLC channel (UM-VLC). In this paper, we propose a single-input, single-output (SISO), wavelength-dependent UM-VLC channel model considering the wavelength dependence of the light source, reflections, light scattering, and the attenuation due to dust and the photodetector. Since wavelength dependence allows us to model VLC systems more accurately with color-based modulation, such as color-shift keying (CSK), we also propose a wavelength-dependent CSK-based UM-VLC channel model. We define a simulation scenario in an underground mine roadway and calculate the received power, channel impulse response (CIR), signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), root mean square (RMS) delay, and bit error rate (BER). For comparison, we also calculate these parameters for a monochromatic state-of-the-art UM-VLC channel and use it as a reference channel. We find that the inclusion of wavelength-dependency in CSK-based UM-VLC systems plays a significant role in their performance, introducing color distortion that the color calibration algorithm defined in the IEEE 802.15.7 VLC standard finds harder to revert than the linear color distortion induced by monochromatic CSK channels. [ABSTRACT FROM AUTHOR]

Network on chip (NoC) is the main solution to the communication bandwidth of a multi-processor system on chip (MPSoC). NoC also brings more route requirements and is highly prone to errors caused by crosstalk. Crosstalk has become a major design problem in deep-submicron NoC communication design. Hence, a crosstalk error model and corresponding reliable system with error correction code (ECC) are required to make NoC communication reliable. In this paper, a reliability system evaluation model (RSE) of NoC communication with analysis from backend to frontend has been proposed. In the backend, a crosstalk error rate model (CER) is established with a three-wire RLC coupling model and timing constraints. The CER is used to establish functional relations between interconnect spacing, length and signal frequency, and test system reliability. In the frontend, a reliability system performance model (RSP) is established with a CER, reliability method cost and bandwidth. The RSE summarizes the frontend and backend model. In order to verify the RSE model, we propose a reliability system with a hybrid automatic repeat request technique (RSHARQ). Simulation demonstrates that the CER model is close to real circuit design. Through the CER and RSP model, the performance of RSHARQ could be simulated. [ABSTRACT FROM AUTHOR]

The dielectric resonator antenna (DRA) can be modeled as a series and parallel combination of electrical networks consisting of a resistor (R), inductor (L), and capacitor (C) to address peculiar challenges in antennas suitable for application in emerging wireless communication systems for higher frequency range. In this paper, a multi-stacked DRA has been proposed. The performance and characteristic features of the DRA have been analyzed by deriving the mathematical formulations for dynamic impedance, input impedance, admittance, bandwidth, and quality factor for fundamental and high-order resonant modes. Specifically, the performance of the projected multi-stacked DRA was analyzed in MATLAB and a high-frequency structure simulator (HFSS). Generally, results indicate that variation in the permittivity of substrates, such as high and low, can potentially increase and decrease the quality factor, respectively. In particular, the impedance, radiation fields and power flow have been demonstrated using the proposed multi-stacked electrical network of R, L, and C components coupled with a suitable transformer. Overall, the proposed multi-stacked DRA network shows an improved quality factor and selectivity, and bandwidth is reduced reasonably. The multi-stacked DRA network would find useful applications in radio frequency wireless communication systems. Additionally, for enhancing the impedance, BW of DRA a multi-stacked DRA is proposed by the use of ground-plane techniques with slots, dual-segment, and stacked DRA. The performance of multi-stacked DRA is improved by a factor of 10% as compared to existing models in terms of better flexibility, moderate gain, compact size, bandwidth, quality factor, resonant frequency, frequency impedance at the resonance frequency, and the radiation pattern with Terahertz frequency range. [ABSTRACT FROM AUTHOR]