Abstract The rise of online direct‐to‐consumer (DTC) channels expands fresh producers’ distribution networks while intensifying competition with traditional retail channels. Meteorological disasters threaten the DTC channel's reliability, dynamically reshaping the coopetition between fresh producers and retailers. This study develops a theoretical framework to examine strategic interactions within a fresh supply chain, comparing a traditional monopoly model with an integrated distribution system that incorporates both DTC and traditional retail channels. Disruption risks and yield constraints faced by producers are considered. The findings suggest producers should introduce DTC channels during yield contraction but exercise caution in surplus periods. Despite disruption risks, DTC channels pressure retailers to accept higher wholesale prices and increase orders, enhancing the sales revenue of fresh produce with limited yield. However, rising disruption probabilities incentivize retailers to reduce order quantities strategically, leading to a higher risk of stagnation for fresh produce.

ABSTRACT Smart transportation systems use VANETs for real‐time vehicle‐roadside infrastructure communication. These channels enable collision prevention, traffic synchronization, and cooperative driving. Network congestion, signal interference, hardware failures, and malicious assaults may create communication abnormalities in VANET systems due to their open and dynamic nature. The complex and quickly changing patterns of vehicle communication networks make static threshold or statistical anomaly detection methods insufficient. This paper presents a real‐time VANET communication channel monitoring system using AI‐IAD to overcome these constraints. AI‐IAD's hybrid AI architecture models dynamic vehicular communication behavior via adaptive feature fusion and edge‐assisted inference, making it innovative. The system uses multi‐metric learning to identify modest network behavior changes by monitoring packet delivery ratio, communication delay, signal intensity, and channel use. Unlike centralized detection systems, AI‐IAD uses edge‐based processing at roadside units (RSUs) to identify anomalies quickly and reduce network communication. The model also uses traffic‐aware adaptive learning to adapt to vehicle density, mobility patterns, and channel conditions in urban and highway situations. The Vehicular Reference Misbehavior (VeReMi) dataset, a benchmark dataset produced from actual traffic traces and DSRC communication logs, is used to test the proposed AI‐IAD platform. Testing in simulation‐based VANET scenarios shows that the AI‐IAD framework outperforms existing anomaly detection methods with 94.6% detection accuracy, 95.0% precision, and 94.2% recall. The framework's 3.9% false positive rate and 28 ms detection latency provide quick anomaly identification for safety‐critical applications. The system has 92.5% robustness and scalability in large vehicle networks, achieving steady convergence after 500 training epochs. These findings show that the AI‐IAD architecture considerably improves VANET communication channel reliability and security, enhancing intelligent transportation system resilience.

This study introduces a compact, high-performance multiple-input multiple-output (MIMO) antenna engineered for 6G terahertz (THz) communication systems. The antenna is implemented on a polyimide substrate (dielectric constant εr = 3.5, loss tangent tanδ = 0.0027) with dimensions of 405 × 163.75 μm², providing a miniaturized footprint suitable for integrated wireless devices. The antenna exhibits multi-resonance operation at 3.752, 4.204, 4.652, 5.104, 5.548, 6.000, and 6.460 THz, providing corresponding bandwidths of 0.3348, 0.2634, 0.2389, 0.2289, 0.2158, 0.2063, and 0.2096 THz, ensuring wideband coverage suitable for high-data-rate applications. The antenna achieves a peak gain of 13.353 dB, outstanding isolation of − 34.044 dB, and high efficiency of 96.048%, highlighting its suitability for high-data-rate and low-interference 6G communications. Strong diversity performance is demonstrated through an ultra-low envelope correlation coefficient (ECC) of 0.00017856 and a near-ideal diversity gain (DG) of 9.99911, confirming the effectiveness of the proposed design for interference mitigation and channel reliability. CST Microwave Studio (MWS) simulations were employed to generate datasets for supervised regression machine learning to predict antenna gain. Random Forest Regression delivered superior predictive accuracy with MSE = 0.76%, MAE = 5.43%, RMSE = 8.72%, R² = 93.93%, and variance score = 95.12%, closely matching the simulated results. The integration of high-performance multi-band antenna design with regression-based machine learning demonstrates a reliable framework for rapid performance evaluation. The combination of compact geometry, wide multi-band operation, high gain, strong isolation, and machine learning-based predictive modeling positions the proposed antenna as a promising solution for high-data-rate and interference-resilient 6G THz communication networks.

The article is devoted to the development and modeling of software for a dual-loop measurement system with a digital filter designed for precision measurement of substance concentration. The relevance of the topic is due to the increasing requirements for accuracy, noise immunity and speed of measurement channels in modern sensor systems, in particular, in gas analysis. Traditional analog systems often cannot provide the necessary accuracy and flexibility under the influence of disturbing factors and nonlinearities. The subject of the study is the processes of synthesis, discrete approximation and software implementation of digital filtering algorithms for the control loops of a measurement system. The purpose of the study is to develop and substantiate the structure of software that implements algorithms for a digital proportional-integral (PI) filter of the internal speed loop and a fifth-order integrating-differentiating filter of the measurement loop. The research objectives include: synthesis of analog corrective devices; discrete approximation of transfer functions by the bilinear transformation method (Tustin); development of digital filter operation algorithms in the form of difference equations and in vector-matrix form (Discrete State-Space); modeling and verification of the system operation in the MATLAB Simulink environment. The practical significance of the work lies in the creation of software algorithms that significantly improve the accuracy and quality of transient processes of the measurement system. The implementation of the developed software ensures compensation of torque loads, reduction of errors in speed and acceleration, as well as an increase in the overall noise immunity and reliability of the measurement channel. Thus, the study aims to create an effective digital tool for controlling highprecision measurement systems, relevant for instrumentation and process automation.

ABSTRACT This study presents a novel stochastic framework for optimizing hybrid underwater wireless acoustic sensor networks (UWASNs) with mobile gateways. Unlike conventional deterministic connectivity models based solely on signal-to-noise ratio thresholds, the proposed approach introduces a comprehensive network stochastic metric that accounts for packet retransmission probability and channel reliability under Rayleigh fading conditions. The framework systematically analyzes the joint influence of three critical parameter classes on network performance: environmental and modem parameters including carrier frequency, sensor spacing, signal-to-noise ratio, and bandwidth; MAC protocol parameters, specifically the maximum allowable retransmissions and network topology parameters, including reference node placement and cluster configuration. The key innovation lies in deriving analytical reliability curves that enable simultaneous optimization of packet loss ratio and energy consumption across different network scales. Numerical results demonstrate that achieving 95% transmission reliability requires co-design of frequency selection, retransmission limits, and topology configuration rather than isolated parameter tuning.

The article describes a new approach to developing embedded software for LoRa modules based on the ESP32 microcontroller. The main idea behind the work is to create universal firmware with a minimalist architecture and advanced configuration options that ensures reliable peer-to-peer data exchange. The developed system uses a simplified text command format (COMMAND;PARAM=VALUE) instead of JSON, which reduces computational costs and speeds up processing. This simplifies integration into application solutions and increases the efficiency of hardware resource utilization. The firmware integrates a delivery confirmation (ACK) mechanism with retransmission in case of packet loss, which increases the reliability of the communication channel. Additionally, the CONFIG_SYNC command is implemented for automatic synchronization of parameters between nodes, which ensures stability in dynamic conditions. The proposed approach also includes a PING/PING_ACK function, which, in addition to checking connection availability, provides diagnostic characteristics, including RSSI, SNR, TOA, DELAY, and data transfer rate. It is possible to transmit large messages using a packet segmentation and aggregation algorithm that overcomes the hardware limitations of the LoRa SX1276 chip. During the study, the firmware was experimentally tested with variations in key parameters: spreading factor, bandwidth, coding rate, transmission power, and preamble length. The results confirmed the patterns of influence of these parameters on delay, speed, RSSI, and signal-to-noise ratio, which made it possible to form practical recommendations for optimizing the system. The proposed solution combines ease of use, configuration flexibility, and communication quality assessment tools, providing a balance between performance and scalability. Further development involves the integration of artificial intelligence modules, in particular reinforcement learning, for automatic selection of optimal parameters in real time, which opens up prospects for the creation of intelligent self-configuring wireless systems.

Abstract We demonstrate that two independent spin-1/2 Ising-Heisenberg trimer chains provide an effective platform for the quantum teleportation of any entangled two-qubit state through the quantum communication channel formed by two Heisenberg dimers. The reliability of this quantum channel is assessed by comparing the concurrences, which quantify a strength of the bipartite entanglement of the initial input state and the readout output state. Additionally, we rigorously calculate quantities fidelity and average fidelity to evaluate the quality of the teleportation protocol depending on temperature and magnetic field. It is evidenced that the efficiency of quantum teleportation of arbitrary entangled two-qubit state through this quantum communication channel can be significantly enhanced by moderate magnetic fields. This enhancement can be attributed to the magnetic-fielddriven transition from a quantum antiferromagnetic phase to a quantum ferrimagnetic phase, which supports realization of a fully entangled quantum channel suitable for efficient quantum teleportation. The polymeric trimer chains Cu3(P2O6OH)2 are proposed as an experimental resource of this quantum communication channel, which provides an efficient platform for realization of the quantum teleportation up to moderate temperatures 40 K and extremely high magnetic fields 80 T.

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise.

With the acceleration of urbanization and the increasing demand for aesthetics, cable laying is progressively transitioning into urban dense cable channels. The internal environment of these channels is complex, and arbitrary cable laying poses significant threats to normal cable operation. Therefore, this paper proposes a reliability assessment method for hybrid cable laying configurations in urban dense cable channels based on a modified Weibull distribution. Firstly, a Weibull proportional hazards model is constructed by incorporating channel operational risk factors as covariates. Then, Bayesian inference is employed to update the Weibull parameters by integrating expert experience with cable channel O&M data. Subsequently, we select the parameter distribution and reliability evaluation indicators suitable for the operating environment of the cable channel and analyze the influence of the overcrowding rate of cable laying, the operating temperature of the cable, and the distance between cables in the channel on the reliability of the cable channel. Finally, a case study is conducted on a cable channel in a region of the China Southern Power Grid utilizing its actual O&M data to perform a reliability assessment. The effectiveness of the proposed modified Weibull distribution assessment method is validated through model comparison. Furthermore, this study provides differentiated maintenance strategies for specific cables within the channel and proposes a set of highly applicable O&M guidelines.

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals.

The channel reliability function is a crucial tool for characterizing the dependable transmission of messages across communication channels. In many cases, the only upper and lower bounds of this function are known. We investigate the computability of the reliability function and its associated functions, demonstrating that the reliability function is not Turing computable. This also holds true for functions related to the sphere packing bound and the expurgation bound. Additionally, we examine the R∞ function and zero-error feedback capacity, as they are vital in the context of the reliability function. Both the R∞ function and the zero-error feedback capacity are not Banach–Mazur computable.

Cyber-physical systems (CPSs) designed for the Internet of Things (IoT) enhanced security and resource infrastructures to support various applications and services, undetected adversaries in the temporarily connected IoT network impose different user and data privacy threats, this research introduces an Offloading-verified Adversary Detection and Mitigation Scheme (OADMS), this proposed scheme coexists with the IoT communication and CPS security infrastructure for adversary detection, conventional behavior-based adversary detection with partial order adversarial network training validates the infrastructure security support against cyber-attacks. The behavior is analyzed for independent and offloaded service exchanges, reducing communication failures and is recurrently analyzed in the detection process until the service termination, communication metrics of the infrastructure units are used to verify adversary and user channel behavior. The learning process recommendations are exploited to validate the channel's reliability through IoT-sharing platforms, and the performance of the proposed system is assessed using communication latency, failure rate, response ratio, and detection factor. The model achieved an excellent detection accuracy rate of 96.8%. • The article proposes an offloading-verified adversary detection and mitigation scheme using partial order adversarial network training. The adversary impact over the conventional resource exchange and offloading are analyzed using the proposed partial order derivative. • Network training uses adversary impact by identifying the behavior change between observed, actual, and computed values of the illegitimates. This computation helps to secure the allocation, sharing, and availability feasibility of the resources between the users/ nodes. • The proposed scheme is analyzed using different impacting metrics and hyperparameters related to resource attributes such as availability, sharing, and latency, as well as adversary attributes such as behavior change and independent impacts.

Aim: - The efficiency of social media platforms and Sriperumbudur TVstations inrapidlyreaching the target population during product debuts is compared in this study. It seekstodetermine the most effective media, assess its benefits and drawbacks, andofferrecommendations to marketers on how to optimize product launch strategies inSriperumbudur. Materials and methods: - This study, conducted at Saveetha University, aimed to compare the reliability of TV channel and social media advertisements intheSriperumbudur region. A sample size of 384 was determined using a sample calculator, with192 respondents for each group (TV watchers and social media users). Google Forms wereused to collect data, which was then analyzed using Microsoft Excel and SPSSsoftware. Conclusion According to the survey's findings, social media advertisements have a greaterimpact on consumers' decisions to buy, even if TV advertisements are seen as more reliableand trustworthy. The vast majority of respondents acknowledged that advertisements onsocial media have a greater impact on their decisions to buy than advertisements on television. This implies that social media's interactive features and capacity for tailored advertisingarevery successful in increasing customer engagement and conversion. As a result, companiesshould use TV advertisements to increase brand awareness and credibility while alsogivingsocial media advertising top priority as a crucial medium for influencing consumer decisions.

Although millimeter-wave (mmWave) communication systems provide previously unheard-of bandwidth for upcoming wireless networks, they are inevitably vulnerable to channel defects such as fading, blockage, and extreme attenuation. In order to improve the dependability of mmWave communication systems, this research proposes an optimal joint source and channel coding (JSCC) scheme. The suggested approach blends strong channel coding algorithms designed for the particularities of mmWave channels with sophisticated source coding approaches. Results from simulations show significant gains in quality of service (QoS), bit error rate (BER), and dependability in a variety of communication contexts, making the framework a viable option for next-generation wireless systems.

The assessment of maximum exposure levels generated by 5G base stations via the Maximum-Power Extrapolation (MPE) procedure defined in international standards is challenged by the technical traits of next-generation signals. Dynamic resource allocation to multiple users produces highly non-uniform signal frames in both frequency and time, complicating worst-case exposure evaluation. Furthermore, massive MIMO beamforming reduces the reliability of control channel–based measurements, motivating traffic-forcing techniques. The wide frequency span of 5G necessitates measurement protocols applicable to both FR1 and FR2 bands. In this paper, we compare conventional methods–namely, Channel Power (CP) and Zero Span (ZS)–against vector-based techniques like Vector Channel Power (VCP) and PDSCH Power (PP), based on the demodulation of the received signal. Unlike most studies focused on FR1, our measurements target a 5G millimeter-wave MIMO signal under realistic network conditions, including multi-user setups and diverse traffic-forcing scenarios. The results show that decoding-based procedures analyzing the PDSCH traffic channel reliably estimate maximum exposure, even when network operation deviates from ideal scenarios. These findings support integrating PDSCH-based methodologies into future revisions of standards such as IEC 62232.

An optimized task offloading strategy based on deep reinforcement learning combined with channel reliability prediction

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

The development of data transmission systems based on wireless radio communication channels allowed the construction of fundamentally new networks – mesh networks, which are used not only in smart technologies, but are the basis for the construction of cyber-physical and socio-cyber-physical systems (objects of critical infrastructure). The object is the process of ensuring reliable and secure data transmission based on the use of wireless radio communication channels. A mathematical model of information resources protection system functioning is proposed to ensure the signs of immunity and security of the automated data transmission system. To identify threats, a unified classifier and flow state estimation technique are used, which take into account the hybridity and synergy of targeted (mixed) attacks on communication channels. The critical points of the infrastructure elements, as well as the information that circulates and/or is stored, are determined. The assessment of compliance with the regulators’ requirements, both international and state regulatory acts, and the presence and ability of the security system elements to ensure the required level of infrastructure elements protection is taken into account. The proposed approach allows to determine: coefficients of information and internal availability of a wireless radio communication channel, the vector potential of the lagging magnetic field as a result of data transmission work. When evaluating the coefficient of a wireless radio communication channel internal availability, it is proposed to take into account coherent reception of the signal. At the same time, the immunity factor of the wireless radio communication channel is much higher than 1, which provides sufficient protection of information. A technical solution is proposed that will allow the level of confidentiality, integrity, authenticity and reliability of a wireless radio communication channel to approach 100 %The development of data transmission systems based on wireless radio communication channels allowed the construction of fundamentally new networks – mesh networks, which are used not only in smart technologies, but are the basis for the construction of cyber-physical and socio-cyber-physical systems (objects of critical infrastructure). The object is the process of ensuring reliable and secure data transmission based on the use of wireless radio communication channels. A mathematical model of information resources protection system functioning is proposed to ensure the signs of immunity and security of the automated data transmission system. To identify threats, a unified classifier and flow state estimation technique are used, which take into account the hybridity and synergy of targeted (mixed) attacks on communication channels. The critical points of the infrastructure elements, as well as the information that circulates and/or is stored, are determined. The assessment of compliance with the regulators’ requirements, both international and state regulatory acts, and the presence and ability of the security system elements to ensure the required level of infrastructure elements protection is taken into account. The proposed approach allows to determine: coefficients of information and internal availability of a wireless radio communication channel, the vector potential of the lagging magnetic field as a result of data transmission work. When evaluating the coefficient of a wireless radio communication channel internal availability, it is proposed to take into account coherent reception of the signal. At the same time, the immunity factor of the wireless radio communication channel is much higher than 1, which provides sufficient protection of information. A technical solution is proposed that will allow the level of confidentiality, integrity, authenticity and reliability of a wireless radio communication channel to approach 100 %

An adaptive compressive sensing method on hybrid-field channel estimation for a massive MIMO system

The article contains detailed information on the application of the theory of queuing systems (QMS) in the Internet of Things (IoT) networks. The article discusses in detail the mathematical models used to analyze and optimize the provision of services in various systems, including IoT. Various aspects of the use of queue theory in IoT networks are highlighted, such as traffic modeling, data transfer optimization, and the use of stochastic models for more accurate analysis. The study of the characteristics of the communication channel in IoT networks is a key and urgent problem in the context of the rapid development of IoT technologies. With the growing number of connected devices, it becomes critically important to ensure the efficiency and reliability of the communication channel, as well as optimize the use of IoT device resources. This study is aimed at studying and theoretical analysis of the characteristics of the communication channel in IoT using queuing systems. The paper analyzes the features of the communication channel in IoT, examines channel modeling methods, analyzes data transmission delays, evaluates and increases throughput, applies queuing system methods and explores the applications of the results obtained, predicts the development of IoT and makes a final review of scientific work. The Internet of Things (IoT) is a network of interacting devices that use sensors and unique identifiers to exchange information. The widespread use of IoT in smart homes, energy, medicine, logistics and other sectors is accelerating thanks to modern artificial intelligence and machine learning technologies.