Over the past few years YouTube has became a popular site for video broadcasting and earning money by publishing various different skills in the form of videos. For some people it has become a main source to earn money. Getting the videos trending among the viewers is one of the major tasks which each and every content creator wants. Popularity of any video and its reach to the audience is completely based on YouTube's Recommendation algorithm. This document is a dataset descriptor for the dataset collected over the time span of about 45 days during the Israel-Hamas War.

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. We suggest using spectrum sensing and integrating UAV-based networks to enhance efficiency of the LSA system. This research employs two methods to solve the problem. We first propose a novel greedy algorithm, named market share-based weighted greedy algorithm (MSWGA) to achieve better fairness compared to the traditional auction methods and as the second approach, we exploit deep reinforcement learning (DRL) algorithms, to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

Full-duplex Cognitive Radio (FD-CR) technology has the potential to significantly improve the spectral efficiency of next-generation wireless systems. However, residual self-interference (RSI), unavoidable in FD systems, represents a colored noise, particularly at mm-wave frequencies. This fact affects blind Spectrum Sensing (SS) algorithms, preventing them from maintaining a constant false alarm rate (CFAR). This causes a power-throughput trade-off resulting in significant performance degradation. Detection in colored noise has been addressed in the literature through time-domain whitening aided by offline training. However, this method is ineffective in the presence of time-varying self-interference (SI). Our paper proposes an adaptive filter-based whitening approach to allow a blind SS to retain the CFAR property in mobile scenarios without the need for offline training. Focusing on low-complexity adaptive filtering, we analytically demonstrate that both the Least Mean Squares (LMS) and the Recursive Least Squares Lattice (RLSL) filters enable the sphericity test to achieve the CFAR property in typical FD-CR scenarios. We highlight the advantages of RLSL over LMS, including faster convergence, superior tracking, and modularity, making it suitable for effective implementation, as well as for efficient low-complexity SI cancellation. Numerical results confirm superior performance in high RSI power scenarios compared to offline solutions and LMS.

In this paper, we present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance and power consumption of our system with measurements. We show that our system achieves acceptable data rates, is very space and energy efficient with an average power consumption of 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work.

Marine radar is widely employed in ocean monitoring systems. However, the presence of sea clutter and noise significantly hampers the detection performance of marine radar. In this paper, we propose an unsupervised-to-supervised sea clutter suppression and denoise framework. The unsupervised stage provides sufficiently high-quality pseudo-labels to the supervised stage, effectively addressing the challenge of obtaining clean labels in current supervised deep learning-based (DL-based) sea clutter suppression methods. The experiments demonstrate that our proposed method exhibits excellent adaptability in both single-target and multi-target scenarios, as well as under various pulse quantities, effectively suppressing sea clutter and noise in low signal-to-clutter ratio (SCR) and signal-to-noise ratio (SNR). In addition, we also verified that the proposed method does not lose clean signals at high SCR and SNR. To validate the widespread applicability of our unsupervised stage, we integrate it into other supervised DL-based clutter suppression methods, and the experiments show a noticeable improvement in the performance of these methods. To further verify the effectiveness of the proposed framework, we conduct validation on downstream applications of radar target detection. Our code will be released after possible acceptance.

In a world where traditional authentication systems are constrained, the introduction of voice-based authentication provides a viable option. This cutting-edge biometric security method uses unique vocal traits including pitch, tone, and speech patterns to create a unique voiceprint. Its sophisticated and dependable verification procedure, when combined with voice-activated services and secure access systems, not only solves au-thentication issues but also improves user experience overall. Our suggested method provides a thorough blueprint for developing a lightweight voice authentication system that is based on text. This system leverages an effective encoder model to convert voice spectrograms, making deployment easy even on edge devices with limited resources. Various dimensionality reduction techniques are explored to obtain optimal voice embeddings that capture speaker uniqueness while minimizing model complexity. A key novelty is the application of model compression techniques including lightweight architectures and Siamese Networks to obtain highly condensed voice embeddings for each user, reducing storage and infrastructure costs compared to traditional voice authentication methods. The proposed lightweight spectrogram embeddings leverage language-agnostic acoustic features, enabling language-independent speaker verification. Additionally, dimensionality reduction applied during voice registration allows the capturing of discriminative voice characteristics in a low-dimensional compact feature space. This significantly cuts down the storage requirements and infrastructure costs per user compared to standard voice biometric approaches, while retaining competitive verification performance. The architecture is optimized for responsiveness by leveraging lightweight frameworks. The proposed system delivers competitive voice authentication capabilities while minimizing memory, computational, and energy footprints. This makes the system useful for integration into smart devices and paves the way for ubiquitous voice biometrics.

We compute the weight distribution of R(4, 9) by combining the approach described in D. V. Sarwate's Ph.D. thesis from 1973 with knowledge on the affine equivalence classification of Boolean functions. To solve this problem posed, e.g., in the MacWilliams and Sloane book [12, p. 447], we apply a refined approach based on the classification of Boolean quartic forms in eight variables due to Ph. Langevin and G. Leander, and recent results on the classification of the quotient space R(4, 7)/R(2, 7) due to V. Gillot and Ph. Langevin.

Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking which could be detrimental to business operations. Consequently, the detection of these URLs is of crucial importance however, current Machine Learning (ML) models are susceptible to backdoor attacks. These attacks involve manipulating a small percentage of training data labels, such as Label Flipping (LF), which changes benign labels to malicious ones and vice versa. This manipulation results in misclassification and leads to incorrect model behavior. Therefore, integrating defense mechanisms into the architecture of ML models becomes an imperative consideration to fortify against potential attacks. The focus of this study is on backdoor attacks in the context of URL detection using ensemble trees. By illuminating the motivations behind such attacks, highlighting the roles of attackers, and emphasizing the critical importance of effective defense strategies, this paper contributes to the ongoing efforts to fortify ML models against adversarial threats within the ML domain in network security. We propose an innovative alarm system that detects the presence of poisoned labels and a defense mechanism designed to uncover the original class labels with the aim of mitigating backdoor attacks on ensemble tree classifiers. We conducted a case study using the Alexa and Phishing Site URL datasets and showed that LF attacks can be addressed using our proposed defense mechanism. Our experimental results prove that the LF attack achieved an Attack Success Rate (ASR) between 50-65% within 2-5%, and the innovative defense method successfully detected poisoned labels with an accuracy of up to 100%.

Molecular communication is a communication paradigm inspired by biological systems, where chemical signals are used to encode and transmit information. MoSK (Molecule Shift Keying) is proposed as a modulation technique that utilizes different types of signaling molecules to encode digital information. MoSK mimics natural molecular communication by leveraging the diversity of signaling molecules and their specific roles. Experimental testbeds have been developed using mi-crofluidic systems, droplet-based platforms, and biological cells to validate MoSK. A prototype platform for MoSK implementation is presented, including a transmitter with infusion and selection valves, and a fluorescence-based receiver. The receiver detects and decodes fluorescence signals emitted by Graphene Quantum Dots (GQDs), which are water-soluble and fluorescent molecular messengers. The fluorescence signals of blue-GQDs and cyan-GQDs are acquired by the receiver, and the performance of the system is evaluated in terms of synchronization, detection threshold, and symbol recognition using Principal Component Analysis (PCA). The results demonstrate the successful detection and recognition of different symbols, even at lower concentrations. PCA proves to be an efficient method for qualitative recognition of molecular messengers in MoSK-based molecular communication systems.

This work presents the complete design process of a 3D fully-metallic reflectarray. The structure is composed of two unit cells with resonators in z direction that are phase-shifted 180 • and distributed in the x-y direction. Here will be shown how those unit cells have been designed and how we determine their distribution. Furthermore, a tolerance analysis is carried out with the aim of obtaining a prototype that is robust to manufacturing imperfections. Lastly, that protoype is measured to verify the results.

This letter considers a reconfigurable intelligent surface (RIS)-aided indoor visible light communication system, where a mirror array-based RIS is deployed to assist the communication from a light-emitting diode (LED) to multiple user terminals (UTs). We aim to maximize the sum-rate in an entire serving period by jointly optimizing the orientation of the RIS reflecting unit, the time fraction for the UT, and the transmit power at the LED, subject to the communication and illumination intensity requirements. To solve this high-dimensional non-convex problem, we transform it as a constrained Markov decision process. Then, a soft actor-critic (SAC)-based deep reinforcement learning algorithm is proposed with the goal of maximizing both the average reward and the expected policy entropy. Simulation results prove the effectiveness of the proposed SAC-based joint optimization design in improving the sum-rate and long-term average reward.

The bit error rate (BER) analysis of non-orthogonal multiple access (NOMA) has been widely considered in the literature with the assumptions of perfect and imperfect successive interference cancellation (SIC). For both cases, exact closed-form formulas were derived under various channel models, number of users, and modulation orders. However, all the analysis reported overlooked the transformations that affect the probability density function (PDF) of additive white Gaussian noise (AWGN) after the SIC process. Therefore, the signal model after the SIC process is generally inaccurate, which makes the analysis just approximations rather than exact because the noise after SIC is not Gaussian anymore. Therefore, this letter derives the exact noise PDF after the SIC process and evaluates its impact on the BER analysis. The analytical results obtained show that the noise PDF after SIC should be modeled as a truncated Gaussian mixture. Moreover, the PDF after successful and unsuccessful SIC should be modeled differently.

Mobile artificial intelligence-generated content (AIGC) is an innovative technology that can support the evolution and updating processes of virtual twins (VTs) in human digital twin (HDT) systems. With a reliable and efficient automatic information generation process, the requirement for a timely physical-to-virtual synchronization in HDT can be satisfied. While such an AIGC-enabled HDT system can facilitate modelling high fidelity VTs, generating rare disease data and providing timely customized services, it may suffer from poor understanding of contexts, lack of creativity, and various security and privacy concerns. In this paper, we propose a new framework, which integrates federated learning (FL) and semantic communication (SemCom) to enhance performance in such a system while improving accuracy and convergence properties. Such an integrated FL-enhanced SemCom (FLeS) solution, however, comes with its own challenges. First, we present a holistic architectural framework for the proposed FLeS paradigm for mobile AIGC-enabled HDT systems and discuss the associated design requirements and challenges. We later present some key technologies necessary to realize such a solution before elaborating on some technical issues to suggest future directions. We believe that this article will open up new research opportunities and motivate new research efforts toward incorporating FLeS techniques for mobile AIGC, especially in emerging mobile services such as HDT.

This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (VT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-VT synchronization while the VT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the VT-experienced delay and the PT-VT communication time without compromising the specific requirements of HDT.

To facilitate intelligent vehicles in making informed decisions and plans, the precise and efficient prediction of vehicle trajectories is imperative. However, a vehicle's future trajectory is not solely determined by its own historical path; it is also influenced by neighboring vehicles (NVs). Hence, understanding the interactions between vehicles is crucial for trajectory prediction. Additionally, the computational challenges posed by long sequence time-series forecasting (LSTF) add complexity to trajectory prediction tasks. This paper introduces a novel network, named Sparse Attention Graph Convolution Network (SAGCN), designed to comprehensively consider the trajectory interaction details of multiple vehicles, optimizing the LSTF for the target vehicle (TV). Specifically, grounded in real-world driving scenarios and vehicle interaction nuances, a multi-vehicle topology graph is formulated to amalgamate the historical trajectories of the TV and the interaction trajectories of NVs. The SAGCN network employs the Graph Convolutional Network (GCN) to assimilate and analyze diverse features within the multi-vehicle topology graph, subsequently computing the future trajectory of the vehicle through a sparse attention mechanism. The proposed method is validated and evaluated using natural datasets. The results demonstrate that, in comparison to state-of-the-art methods, the SAGCN network presented attains exceptional prediction accuracy and satisfactory time efficiency when predicting the trajectories of TV in LSTF.

In today's data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.

Network slicing offers the potential to enhance service satisfaction and optimize resource utilization, particularly in scenarios where radio resources are limited. Subslicing has been shown to improve the slice performance. In this paper, the monitor-analyze-plan-execute-knowledge (MAPE-K)-type management closed control loop (MCCL) is implemented for slice performance improvement by subslicing. The subslicing can improve slice performance if the slice performance depends on the size of slice bandwidth part (BWP). For Plan function, the classifier neural network was trained to decide whether the subslice should be split, merged or not changed by their performance. The training data contains slice performance data of all possible subslice sizes. For Execute function, the subslice splitting algorithm was proposed, which clusters UEs by their block error ratio (BLER) and allocates bandwidth proportionally to group requested sum rate and group BLER. A realistic 5G new radio (NR) band serving a set of user equipments (UE) of different values of their BLER and requested rates was a setup of radio access network (RAN) slice simulated using MATLAB R2021b. Subslicing has reduced bandwidth utilization, and slice BLER while increased slice goodput (application-level throughput). Proposed subslice splitting algorithm when UEs are clustered by their achieved BLER, then the slice BLER reduces additional 20% and slice goodput increases up to additional 9% compared to no subslicing when UEs are clustered by their requested rates. This effect was larger for the uplink. In runtime scenarios for poor-BLER UEs the smaller subslices improve slice utilization and BLER, while larger subslices improve goodput.

6G presents new opportunities to enrich the cellular ecosystem by introducing battery-less Zero-energy Internet of Things (ZE-IoT) devices, thus unleashing an era of massive, sustainable, and smart connectivity. This explains the increased interest in ZE-IoT in academia and industry. The road to a 6G future empowered by ZE-IoT entails cohesive efforts in the realm of standardization, academic research, and industrial trials, which are synergistic with the anticipated market demand and the dominant technology direction. In this article, we provide a holistic view of a 6G ZE-IoT future informed by the ongoing standardization activities in the 3rd generation partnership project (3GPP) for ZE-IoT, the role of the emerging technology trends such as digital twins and artificial intelligence, and the technical challenges in integrating ZE-IoT into the cellular ecosystem. Finally, we present some recent research results to address some of the discussed challenges.  

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless interaction between devices. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fall short in addressing these concerns due to the unique characteristics of IoT networks such as heterogeneity, scalability, and resource constraints. To address these challenges, this survey paper first explores the intersection of quantum computing, federated learning, and 6G wireless networks as a novel approach to enhancing IoT security and privacy. In order to enable several secure intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among a vast array of IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, key performance metrics, and summarizing the vital insights, challenges, and trends in the realm of securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks, suggesting potential integration with 7G, and summarizing the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

In this letter, we analyze the performance of a downlink non-orthogonal multiple access (NOMA) network that uses co-phasing at an intelligent reflecting surface (IRS) to communicate to a near-user (NU) and a far-user (FU) concurrently. Both users combine the direct and reflected signals. Considering cross-channel interference (CI) and a finite number of phase levels at the IRS, we derive closed-form expressions for the outage probability. Closed form expressions are also presented for throughput, energy efficiency (EE) and optimal power allocation. A low complexity procedure is suggested to determine the optimum partitioning so as to maximize the FU throughput while ensuring a desired throughput at the NU. The proposed IRS-NOMA (IN) system outperforms conventional NOMA (CN) and orthogonal multiple access (OMA) systems in terms of throughput and EE, and permits a broader range of NOMA power allocations. Simulations validate the derived expressions.

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the operational efficiency of shipping fleets. The findings of this study support the advancement of PdM methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering collaboration opportunities among them.

Growing concerns about the environmental impact of digital activities is leading companies and developers to consider more actively Green IT and best practices to conceive websites in a more eco-friendly way. This study investigates the landscape of web analytic tools in the context of eco-conception. A selection of currently available tools have been compared and assessed through a custom web page embodying eco-friendly and non-eco-friendly practices. The tools reported widely disparate ecological evaluation for the sampled website, thus revealing their limitations in terms of effective gauging. This research emphasizes substantial shortcomings in assessing video content, a major factor of internet environmental impact. It demonstrates the pressing need for enhanced methodologies within web analytic tools to comprehensively evaluate websites' environmental footprints.

Frame coalescing is a well-established technique which manages the low power idle (LPI) mode supported by energy efficient Ethernet (EEE) interfaces. Generally, this technique enables EEE interfaces to remain in the LPI mode for a certain amount of time upon the arrival of the first frame (timer-based coalescing), or until a predefined amount of traffic accumulates in the transmission buffer (size-based coalescing). In this paper, we propose a novel open-loop dynamic coalescing technique that is based on model predictive control (MPC) and queuing theory. In contrast with the conventional timer-based coalescing, the proposed method enables the update of the timer parameter repeatedly throughout the duration of the LPI mode of a single coalescing cycle by taking into account the arrival instant and size of the frames waiting in the buffer. Two different methods, namely MPC-mean and MPC-tail, are proposed which attempt to minimize the energy consumption of the Ethernet link under constraints on mean and tail of the queue waiting time, respectively. The effectiveness of the proposed dynamic MPC-based coalescing algorithms are validated using simulations with synthetic and actual traffic traces.