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This work proposes a methodology for optimizing Solar Chimney Power Plant (SCPP) geometric parameters (chimney height, collector area and chimney radius for fixed values of canopy height) and also presents a framework for a combined techno-economic analysis of an optimized 1 MW SCPP to be implemented in Tozeur, located in the south of Tunisia. A CFD model was used to investigate the influence of the chimney height and radius and the collector radius on the plant's electricity production and these were used in a cost estimation method to obtain an optimized design of the 1 MW SCPP. Subsequently, an economic analysis, based on the Levelized Cost Of Electricity (LCOE) was carried out for the optimized 1 MW SCPP configuration. The optimization procedure yielded an optimum collector radius of 452 m and a corresponding chimney height of 462 m and radius of 5 m to produce 2.1 GWh of electricity per year. For an interest rate of 3%, an inflation rate of 6% and a lifetime of 40 years, the price of electrical energy produced and fed into the national grid is 0.105 TND/kWh. The SCPP would avoid the emission of 480.9 tons/year of CO2 during its 40 year lifetime.

International audience; Tetraselmis sp. strain MD-01, a microalga isolated from the Tunisian marine coast, has been studied for autotrophic lipids production with two stage process. This strategy was conducted for maximum cells production under moderated conditions (2.12 g L−1), followed by cultivation under stressed conditions to induce lipid synthesis. High irradiance (300 μmol m−2 s−1) and nitrogen depletion (S mode) showed better carotenoids accumulation and lower chlorophylls content in microalgae compared to those obtained in biomass cultivated under high irradiance (Sg mode). This two-stage strategy allowed to increase of lipid content in cells cultivated in Sg and S modes from 18% DW (stage I) to 50 and 61.5% DW (stage II), respectively, with an increase of saturated and long chain fatty acids levels. Based on fluorometry analyses, an increase of lipids in Nile-Red stained cells and a decrease of chlorophylls auto-fluorescence under stressed conditions were shown. From spectral data, the lipids: amide I ratio obtained from S mode was 2.17 and 4.45 times higher than that from Sg mode and control conditions, respectively. These results revealed the potential of Tetraselmis strain MD-01 for biodiesel production.

Nowadays, supply chain disruptions caused by COVID-19 pandemic, demand variation, raw material shortage, etc., made the supply chains unable to deal with emerging market problems. Responding to the new requirements has underscored the need to ensure a reconfigurable supply chain in order to survive in this uncertain economic environment. Indeed, the objective of this study is to identify the quantitative factors of each reconfigurability characteristic representing the reconfigurability assessment indicators (modularity, integrability, convertibility, diagnosability, scalability and customization). Based on the literature review, quantitative factors used to assess the degree of reconfigurability in supply chain are determined. These factors allow classifying reconfigurability characteristics according to the degree of their influence on the supply chain structure or supply chain functions. Through this research work, we try to facilitate the assessment of reconfigurability based on its characteristics in order to determine the ability of the supply chain to cope with new emerging disruptions.

Learning from imbalanced training data represents a major challenge that has triggered recent interest from both academia and industry. As far as classification is concerned, it has been observed that several algorithms provide low accuracy when designed out of imbalanced data sets, among which regularized quadratic discriminant analysis (R-QDA) is the most illustrative example. Based on recent asymptotic findings, the study in [2] has brought a better understanding of the reasons behind the excessive sensitivity of R-QDA to data imbalance, which allowed for the development of a novel quadratic based classifier that presents higher robustness to such scenarios. However, the selection of the parameters for this classifier relied on the minimization of the overall classification error rate, which is not considered as a relevant performance metric in extremely imbalanced training data. In this work, we follow a multi-model selection approach for the selection of the parameters of the classifier proposed in [2] . Such an approach involves solving a multi-objective optimization problem, but, contrary to related works, we do not resort to evolutionary algorithms to solve this problem but rather to a solely training data dependent technique based on asymptotic approximations for the classification performances. This allows us to transform the multi-objective optimization problem into a scalar optimization problem. Our proposed approach presents the main advantages of being more accurate and less complex, avoiding the need for computationally expensive cross-validation procedures. Its interest goes beyond the quadratic discriminant analysis, paving the way towards a principled method for the design of classification algorithms in imbalanced data scenarios.

Critics have investigated the relationship between fossil energy and conflicts, incorporating political, military, economic and social factors that have led to these clashes. Nevertheless, to our knowledge, no econometric study has examined the causal relationship between renewable or fossil energy and conflicts such as terrorism in the case of Pakistan. This paper examines the relationship between terrorism, renewable energy and the consumption of fossil energy in Pakistan, taking into consideration several factors such as economic growth and income inequality. This study uses the autoregressive distributed lag (ARDL) testing approach to examine the long-term relationship between the considered variables over the annual period of 1980–2015. The Wald test confirms the existence of long-term relationships between our variables. Granger causality reveals long-term bi-directional causalities between all variables. Although, in the short term there are bidirectional causal links between renewable energy and fossil energy, the study shows that there are short-term unidirectional causalities running from terrorism and fossil energy to GDP, from GDP to renewable energy, and from income inequality to fossil energy. In fact, long-term parameter indicates that fossil energy reduces terrorism and that renewable energy consumption increases fossil energy. In contrast to the long-term parameter, the study reveals that economic growth increases terrorism in Pakistan. Moreover, this project suggests that Pakistan continue to encourage the use of renewable and fossil energies by (or through) intensifying its diplomatic efforts to resolve political and military conflicts around the world, particularly in the Middle East.

PurposeThis research proposes a virtual reality (VR) platform for construction detailing that provides experiential learning in a zero-risk environment. It builds on integrating VR technology as a medium and building information modeling (BIM) as a repository of information and a learning tool.Design/methodology/approachThis work discusses the proposed environment curricular unit prototype design, implementation and validation. The validation of the VR environment was conducted in three phases, namely, piloting, testing (system usability and immersion) and learning gain validation, each of which has its aim and outcomes and has been assessed both qualitatively and quantitatively.FindingsAfter considering the feedback, the VR environment prototype is then validated on the level of learning outcomes, providing the evidence that it would enhance students' engagement, motivation and achievement accordingly. The results indicated 30% learning progress after experiencing the VR environment vs. 13.8% for paper-based studying.Originality/valueIn reference to building construction education, construction site visits provide students with real-life practical experience which are considered an extension for classroom. Nevertheless, it is challenging to integrate construction site visits regularly during the academic semester with respect to the class specific needs. The research at hand adopts integrating VR and BIM in AEC (Architecture, Engineering and Construction) education by proposing a system that can work as a mainstream complementary construction detailing learning method for architecture students. The proposed VR system facilitates a virtual construction site that meets the learning needs where students can explore and build in a real scale environment.

The COVID 19 pandemic, fluctuating demand, market uncertainty and the emergence of new technologies explain the need for a more flexible and agile supply chain. In fact, several important factors should be taken into account in the process of building an adaptive and reconfigurable supply chain. Reconfigurability is used to measure quantitatively the capability of supply chain to change easily their structure and functions. The aim of this work is to evaluate the level of reconfigurability of a supply chain. Quantitative measures of six indicators that characterize reconfigurability are presented in this paper. Then, an index of reconfigurability in supply chain is developed based on The Multi-Attribute Utility Theory in order to choose the most reconfigurable configuration. An illustrative example is also given. From the discussion, it is deduced that the characteristics of the reconfigurable supply chain impacts positively on the degree of reconfigurability.

This letter proposes a uniformly distributed near-field (NF) probing array (NFPA) capable of generating feedback signals suitable for monitoring and improving the performance of a 5G millimeter-wave (mm-wave) beamforming transmitter. The proposed NFPA configuration is carefully designed to ensure comparable coupling ratios between the individual RF chain outputs and the closest NF probes, across a wide frequency range. This maximizes the quality of the feedback signal available to perform array calibration and digital predistortion (DPD) when using realistic 5G modulated signals. A 16-element 27–30-GHz beamforming transmitter is designed and serves as the proof-of-concept prototype. Using a four-element NFPA, the 16 RF chains are calibrated using a 400-MHz modulated signal—achieving less than 0.27 dB and 2.8° root-mean-square (RMS) magnitude and phase calibration errors, respectively. Furthermore, a DPD engine trained using the NFPA-based feedback signal improves the over-the-air transmitted signal’s adjacent channel power ratio (ACPR) and error vector magnitude (EVM) from 30 dB and 8.8% to 39 dB and 2.5%, respectively. The DPD also allowed for an increase of the effective isotropic radiated power (EIRP) from 27.5 to 33 dBm while staying below the maximum EVM of 3.5%.

During the last decade, deep neural networks have shown a great performance in many machine learning tasks such as classification and clustering. One of the most successful networks is the CNN (Convolutional Neural Network), which has been applied in many application domains such as pattern recognition, medical diagnosis, and signal processing. Despite the very interesting performance of CNNs, their architecture design is still so far a major challenge for researchers and practitioners. Several works have been proposed in the literature with the aim to find optimized architectures such as ResNet and VGGNet. Unfortunately, most of these architectures are either manually defined by experts or automatically designed by greedy induction algorithms. Recent works suggest the use of Evolutionary Algorithms (EAs) thanks to their ability to escape locally-optimal architectures. Despite the fact that EAs have shown interesting performance, researchers in this direction have considered the design task as a single-level optimization problem; which represents the main research gap we tackle in this paper. The main contribution behind our work consists in the fact that CNN architecture design has a hierarchical nature and thus could be seen as a Bi-Level Optimization Problem (BLOP) where: (1) the upper level minimizes the network complexity defined by the number of blocks and the number of nodes per block; and (2) the lower level optimizes the convolution block ‘graphs’ topologies by maximizing the classification accuracy. Motivated by the originality of our observation with respect to the state of the art, we frame for the first time the CNN architecture design problem as a BLOP and then solve it using an adapted version of an existing efficient bi-level EA; through the definition of the solution encoding, the fitness function, and the variation operators at each level. The adapted EA is named BLOP-CNN and is assessed on the image classification task using the commonly employed CIFAR-10 and CIFAR-100 benchmark data sets. The analysis of our experimental results show the merits of our proposed method in providing the user with optimized architectures that outperform many recent and prominent architectures coming from the three different approaches, namely: manual design, reinforcement learning-based generation, and evolutionary optimization. Moreover, to show the applicability of our approach, we have conducted a case study on the detection of the COVID-19 using a set of benchmark chest X-ray and Computed Tomography (CT) images.

We examine the coupling of the dynamics of flexible viral nanoparticles to the dynamics of comparably sized polymer chains. Using fluorescence microscopy, we quantify the dynamics of three filamentous viruses, potato virus M (PVM), M13, and pf1, that are suspended in semidilute solutions of partially hydrolyzed polyacrylamide. The dynamics of the viral nanoparticles are approximately diffusive on accessible time and length scales, but the distributions of displacements are non-Gaussian and exhibit increasingly extended tails as the aspect ratio of the viruses or the polymer concentration is increased. The long-time diffusion coefficients do not collapse onto a universal curve based on existing models for rodlike or spherical nanoparticles that are comparably sized to the polymer chains. Instead, the diffusivities appear to collapse as a function of the ratio of the polymer correlation length and a length scale intermediate between the virus radius and length, indicating that the hydrodynamic coupling to the polymer dynamics is affected by the virus anisotropy and flexibility.

The upcoming Internet of Things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber–physical systems. Due to the large scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network intrusion and malware infiltration. Even worse, exploiting the high density of devices and direct wireless connectivity, malware infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic outbreak without being noticed to security administration. A malware outbreak enables adversaries to compromise a large population of devices, which can be exploited to launch versatile cyber and physical malicious attacks. In this context, we utilize spatial firewalls , to safeguard the IoT from malware outbreak. In particular, spatial firewalls are computationally capable devices equipped with state-of-the-art security and anti-malware programs that are spatially deployed across the network to filter the wireless traffic in order to detect and thwart malware propagation. Using tools from percolation theory, we prove that there exists a critical density of spatial firewalls beyond which malware outbreak is impossible. This, in turn, safeguards the IoT from malware epidemics regardless of the infection/treatment rates. To this end, a tractable upper bound for the critical density of spatial firewalls is obtained. Furthermore, we characterize the relative communications ranges of the spatial firewalls and IoT devices to ensure secure network connectivity. The percentage of devices secured by the firewalls is also characterized.

Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

Globalization pressure on organizations calls for new approaches to survive, grow, and compete. An approach is to allow inter-organizational business processes (IOBP) collaboration where each partner would expose its public know-how without putting its private know-how at-risk nor impeding this collaboration from happening. Private processes would be the result of running views over public processes. For a successful IOBP collaboration, constraints like temporal need to be satisfied. Organizations could have different time requirements. This paper presents an approach for generating views over collaborative IOBP along with satisfying the combined temporal constraints that would restrict these collaborative IOBP. The paper also details how the consistency of these constraints would be achieved. The approach is illustrated using an online shopping scenario and demonstrated through a system.

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme.

In this paper, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

Reconfigurable intelligent surface (RIS) based communications have emerged as a new paradigm. This letter proposes a differential reflecting modulation (DRM) scheme for RIS based communication systems. In DRM, information bits are jointly carried by the activation permutations of the reflecting patterns and the phases of the transmitted signals, leading to that DRM can work without any channel state information (CSI) at the transmitter, RIS or receiver. In other words, DRM can release the intricate and resource-consuming channel estimation in the transmission process. Simulation results show that the proposed DRM pays an acceptable SNR penalty compared to non-differential modulation with coherent detection.

Today, social networks have become essential communication tools. The emergence of internal social media platforms and applications in the workplace has created great digital opportunities for empl...

Silicon (Si), a nutrient that currently arousing more and more interest from researchers, particularly in relation to water deficit resistance for durum wheat (Triticum turgidum L. ssp. Durum). The present study aimed to explore the effectiveness of Si seed priming and foliar application on durum wheat performances. Thus, a seed bioassay was conducted to assess the effect of three Si levels (15 and 20 mg/l from Na2SiO3H2O (sodium metasilicate powder extra pure) on the germination of 25 durum wheat genotypes under osmotic stress (150 g/l of PEG6000). Under field conditions in semi-arid environment, we evaluated the Si foliar application potential benefits on the physiological and agro-morphological traits. Results showed that Si application, in particular using 15 mg/l, increased significantly the germination percentage (23.96 and 22.37%) the germination index (24.67 and 25.69%), the length of shoot (23.58 and 21.65%) and roots (22.40 and 20.81%), the seedling fresh weight (35.82% and 27.80%), and the seedling vigor index (41.58% and 38.95%) under non-stressed and stressed conditions, respectively. L6 showed the highest value of germination percentage, shoot and root length, and seedling vigor index for all treatments. In semi-arid conditions, Si foliar application enhanced the relative water content (11.66%) and the chlorophyll index (13.60%). In addition, Si increased spike length (8.47%), seed number/spike (19.01%) and grain yield (19.90%) of all durum wheat genotypes. Differential genotypic responses to Si application were observed at both seedling and adult stages.

High Strength Steel is more used in automotive industry to combine safety improvements and weight reductions of the vehicles. However, these steels raise new challenges for manufacturer, especially for springback and its prediction. Finite Element Analysis is widely used for the prediction of springback but accuracy is strongly depending on numerical as well as physical parameters. Such parameters affecting prediction are for example frictional behavior, constitutive model, loci function. In this paper the sensitivity of friction coefficients and the influence of some material laws on the springback prediction were studied and analyzed in details. Several numerical models are investigated in this work in order to allow the different aspects of materials mechanical behavior to be better described: isotropic work-hardening (Swift, Hockett-Sherby, SHS), isotropic-kinematic hardening, yield loci (Hill 48, Hill 90 and Barlat 91). In this study, we use an industrial example of the deep drawing process: the side sill closing with the material steel TRIP800. All the simulations are carried out with the commercial code Pam-Stamp 2G and comparison with the experimental results are presented.

In this letter, the Meta distributions (MDs) of the secrecy rate in downlink non-orthogonal multiple access (NOMA) systems with randomly located eavesdroppers are investigated. Due to the complexity of calculation, the MDs for NOMA users are approximated by Beta approximation and the analytical expressions for the first two moments of conditional secrecy connection probability of NOMA users are derived, while perfect/imperfect successive interference cancellations on the near user are considered. Finally, the accuracy of the result is verified by Monte Carlo simulations. The results show that the MD can capture the statistics of the secrecy rate in a given secrecy connection probability.

This letter proposes and optimizes a cooperative non-orthogonal multiple-access (Co-NOMA) scheme in the context of multicell visible light communications (VLC) networks to mitigate inter-cell interference. Consider a network where each access-point (AP) serves two users. In each cell, the weak user (cell-edge user) can be served either directly by the VLC AP, or through the strong user that decodes the weak user message and forwards it through the radio-frequency (RF) link. The paper then considers the problem of maximizing the sum-rate under quality-of-service constraints by allocating the powers of the messages and APs, and determining the links serving each weak user. The paper solves this non-convex problem by first finding closed-form solutions of the users’ powers and link selection for fixed APs powers. The APs powers are then iteratively solved in an outer loop. Simulation results show that the proposed scheme improves the sum-rate and fairness as compared to non-orthogonal multiple-access (NOMA) scheme.

In this letter, we investigate a generic model assessing the effect of in-phase/quadrature-phase imbalance (IQI) on an asymmetric dual hop radio frequency/free space optical (RF/FSO) two-way relay (TWR) system in the presence of multiple co-channel interferers (CCIs) at the relay. The fading on the RF and FSO links have been modeled using K-distribution and double generalized Gamma (D-GG) turbulence model, respectively. The impact of non-zero boresight pointing error and type of optical demodulation schemes have been considered on the FSO link. To this end, a closed-form probability density function (PDF) has been derived for the FSO link undergoing D-GG irradiance with non-zero boresight pointing error. Furthermore, the exact and high signal-to-noise (SNR) asymptotic expression for outage probability have been presented. To gain insights into the throughput of the system, approximate and asymptotic expressions for the achievable sum rate (ASR) have been derived. The results show dependency of reliability and throughput offered on IQI at the RF front-end and strength of interference for the considered TWR system., Comment: 13 Pages, 03 Figures

In this letter, we propose a power allocation scheme for relayed orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. The proposed power allocation scheme replies on artificial neural network (ANN) and deep learning to allocate transmit power among various subcarriers at the source and relay nodes. The objective of the power allocation scheme is to minimize the overall transmit power under a set of constraints. Without loss of generality, we assume all subcarriers at source and relay nodes are independently distributed with different statistical distribution parameters. The relay node adopts the fixed-gain amplify-and-forward (FG AF) relaying protocol. We employ the adaptive moment estimation method (Adam) to implement back-propagation learning and simulate the proposed power allocation scheme. The analytical and simulation results show that the proposed power allocation scheme is able to provide comparable performance as the optimal solution but with lower complexity.

We consider the problem of efficient blind parameter estimation in terahertz (THz)-band ultra-massive multiple-input multiple-output (UM-MIMO) systems. UM-MIMO antenna arrays are crucial to overcoming the distance problem in THz communications. Following recent advancements in THz transceiver design, such arrays are intrinsically compact and reconfigurable. We propose dynamically tuning the modulation types, operating frequencies, and indices of transmitting antenna elements for efficient resource utilization in THz UM-MIMO systems. Furthermore, we propose THz-specific signal processing techniques at the receiver side for blindly detecting the transmission parameters. In particular, we propose a low-complexity antenna index, frequency index, modulation mode, and modulation type detectors, and we study the complexity and performance tradeoffs of the proposed schemes.

In many decision making cases, we may have a hierarchical situation between different optimization tasks. For instance, in production scheduling, the evaluation of the tasks assignment to a machine requires the determination of their optimal sequencing on this machine. Such situation is usually modeled as a Bi-Level Optimization Problem (BLOP). The latter consists in optimizing an upper-level (a leader) task, while having a lower-level (a follower) optimization task as a constraint. In this way, the evaluation of any upper-level solution requires finding its corresponding lower-level (near) optimal solution, which makes BLOP resolution very computationally costly. Evolutionary Algorithms (EAs) have proven their strength in solving BLOPs due to their insensitivity to the mathematical features of the objective functions such as non-linearity, non-differentiability, and high dimensionality. Moreover, EAs that are based on approximation techniques have proven their strength in solving BLOPs. Nevertheless, their application has been restricted to the continuous case as most approaches are based on approximating the lower-level optimum using classical mathematical programming and machine learning techniques. Motivated by this observation, we tackle in this paper the discrete case by proposing a Co-Evolutionary Migration-Based Algorithm, called CEMBA, that uses two populations in each level and a migration scheme; with the aim to considerably minimize the number of Function Evaluations (FEs) while ensuring good convergence towards the global optimum of the upper-level. CEMBA has been validated on a set of bi-level combinatorial production-distribution planning benchmark instances. The statistical analysis of the obtained results shows the effectiveness and efficiency of CEMBA when compared to existing state-of-the-art combinatorial bi-level EAs.

This paper proposes the trajectory tracking problem between an autonomous underwater vehicle (AUV) and a mobile surface ship, both equipped with optical communication transceivers. The challenging issue is to maintain stable connectivity between the two autonomous vehicles within an optical communication range. We define a directed optical line-of-sight (LoS) link between the two vehicle systems. The transmitter is mounted on the AUV, while the surface ship is equipped with an optical receiver. However, this optical communication channel needs to preserve a stable transmitter-receiver position to reinforce service quality, which typically includes a bit rate and bit error rates. A cone-shaped beam region of the optical receiver is approximated based on the channel model; then, a minimum bit rate is ensured if the AUV transmitter remains inside of this region. Additionally, we design two control algorithms for the transmitter to drive the AUV to the angle of the maximum achievable data rate and maintain it in the cone-shaped beam region and under an uncertain oceanic environment. Lyapunov function-based analysis that ensures asymptotic stability of the resulting closed-loop tracking error is used to design the proposed Non-linear Proportional and Derivative (NLPD) controller. Numerical simulations are performed using MATLAB/Simulink to show the controllers’ ability to achieve favorable tracking in the presence of the solar background noise within competitive times. Finally, results demonstrate the proposed NLPD controller improves the tracking error performance more than 70% under nominal conditions and 35% with model uncertainties and disturbances compared to the original Proportional and Derivative (PD) strategy.

This paper proposes a new digital predistortion (DPD) scheme for linearizing millimeter-wave hybrid beamforming transmitters using observation receivers with low-bit resolution analog-to-digital converters (ADCs). To train the DPD function required to compensate for the distortions exhibited by a given sub-array (also called main sub-array), an error signal is produced by out-of-phase combining the main sub-array transmitted signal and the one generated by another sub-array (also called auxiliary sub-array) using anti-beamforming modules. The error signal is then frequency down-converted and digitized using a low-bit resolution ADC. Proof-of-concept validation experiments are conducted by applying the proposed DPD system to linearize an off-the-shelf hybrid-beamforming array comprised of four 64-element sub-arrays, operating at 28 GHz and driven with up to 800 MHz orthogonal frequency-division multiplexing modulated signals. Using the proposed DPD scheme, an observation receiver with a 4-bit ADC was sufficient to improve the adjacent channel power ratio by 10 dB and the error vector magnitude was reduced from 5.8% to 1.6%. These results are similar to those obtained using an observation receiver with 16-bit ADC.

In this paper, we consider an unmanned aerial vehicle (UAV) aided mobile relaying system under a buffer constraint. We propose a new relaying protocol employing mixed free-space optical/radio frequency (FSO/RF) communication, i.e., the source-relay and relay-destination links utilize FSO and RF links, respectively, under the buffer constraint at the UAV relay node. Taking the conditions of an imbalance in transmission rate between RF and FSO links into consideration, we study the trajectory optimization problem of buffer-constrained UAV relay node in order to maximize the end-to-end data throughput. Especially, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. We solve the locally optimal trajectory problem of the UAV to maximize the throughput of ground user terminal. As a result, we propose an iterative algorithm that efficiently finds a local optimum solution for the throughput maximization problems. Through this algorithm, we present the resulting trajectories over the the atmospheric condition, the buffer size, and the delay requirement. Also, we show the optimum buffer size and the throughput-delay tradeoff for a given system. Our numerical results validate that the proposed buffer-aided mobile relaying scheme achieves 65.55% throughput gains compared to conventional static relaying scheme.

With the fast-emerging trend of deploying Internet-of-things (IoT) in smart homes and cities, next-generation wireless systems (i.e., beyond fifth-generation (5G) of wireless mobile communications) are expected to connect a plurality of devices, including machines, sensors, tablets, cameras, etc. To this end, the radio frequency (RF) spectrum would fail to fulfill the demands of such enormous data-heavy wireless applications, as operating at the RF spectrum would largely suffer from data congestion and interference. Based on the potential of Visible Light Communications (VLC) to solve the problem of spectrum scarcity and satisfy the high data-rate requirements of beyond 5G systems, this paper focuses on utilizing VLC for IoT applications in indoor environments by means of adequately routing data among VLCbased nodes. Operating at the unlicensed optical band in the visible light region utilizes simple Light-Emitting-Diodes (LEDs), which is a health-friendly communication method. VLC has the advantages of providing ultra-high bandwidth, robustness to electromagnetic interference, and inherent physical security. VLC, however, suffers from severe short communication ranges and line-of-sight (LoS) constraints. This paper proposes overcoming such challenges by means of adopting a transmit diversity scheme that transmits messages over several paths. In order to reduce the receiver outage probability, the paper proposes multiple combination schemes at the receiver side, namely selection combining, maximal-ratio combining, and threshold combining. The simulation results assess the effectiveness of the proposed diversity schemes in terms of signal-to-noise-ratio and outage probability and illustrate the suitability of deploying VLC for next-generation indoor networks. The results show that that maximal-ratio combining outperforms selection combining and threshold combining by approximately 10% and 40%, respectively.

In this paper, we investigate the impact of using improper Gaussian signaling (IGS) scheme on the interweave cognitive radio (CR) paradigm when the cognitive user has access to full or partial channel-state-information (CSI). Throughout this work, we analyze the performance of both the primary user (PU) and secondary user (SU) in terms of instantaneous achievable rate and outage probability, for given sensing and detection capabilities. Then, we optimize the IGS scheme for full and partial CSI scenarios to maximize the achievable rate of the SU while maintaining a minimum quality-of-service (QoS) requirement for the PU. Meanwhile, we derive the corresponding feasibility conditions for the interweave cognitive radio system operation. IGS based systems are proven to provide notable gain over the conventional proper Gaussian signaling, which is considered our benchmark scheme. The IGS scheme is observed to be further beneficial at low SU detection capabilities, strong interference from the cognitive user to the licensed user, and low gain of the PU direct link.

This paper studies the ergodic rate performance of maximum ratio transmission (MRT) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled using the double scattering model. We utilize the minimum-mean-square-error (MMSE) channel estimate for this model, which is used in the design of the MRT precoding. Within this setting, we are interested in deriving tight approximations of the ergodic rate under the assumption that the number of BS antennas $\left ({N}\right)$ , the number of users $\left ({K}\right)$ and that of scatterers $\left ({S}\right)$ grow large with the same pace. These approximations are expressed in simplified closed-form expressions for the special case of multi-keyhole channels. They reveal that unlike the standard Rayleigh channel in which the SINR grows as $\mathcal {O}\left ({\frac {N}{K}}\right)$ , the SINR associated with a multi-keyhole channel scales as $\mathcal {O}\left ({\frac {S}{K}}\right)$ . This particularly shows that the reaped gains of the large-scale MIMO over double scattering channels do not linearly increase with the number of antennas and are limited by the number of scatterers. We further provide simulation results that confirm the close match provided by the asymptotic analysis for moderate system dimensions and provide some useful insights into the interplay between $N$ , $K$ and $S$ .

Airborne base stations (BSs) (carried by drones) have a great potential to enhance the coverage and capacity of 6G cellular networks. However, one of the main challenges facing the deployment of airborne BSs is the limited available energy at the drone, which curtails the flight time. In fact, most current unmanned aerial vehicles (UAVs) can only operate for a maximum of 1 h. The need to frequently visit the ground station (GS) to recharge limits the performance of the UAV-enabled cellular network and leaves the UAV?s coverage area temporarily out of service. In this article, we propose a UAV-enabled cellular network setup based on tethered UAVs (tUAVs). In the proposed setup, the tUAV is connected to a GS through a tether, which provides the tUAV with both energy and data. This enables a flight that can last for days. We describe in detail the components of the proposed system. Furthermore, we list the main advantages of a tUAV-enabled cellular network compared to typical untethered UAVs (uUAVs). Next, we discuss the potential applications and use cases for tUAVs. We also provide Monte Carlo simulations to compare the performance of tUAVs and uUAVs in terms of coverage probability. For instance, for a uUAV that is available 70% of the time (while unavailable because of charging or changing the battery 30% of the time), the simulation results show that tUAVs with a 120-m tether length can provide up to a 30% increase in coverage probability compared to uUAVs. Finally, we discuss the challenges, design considerations, and future research directions to realize the proposed setup.

This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with $M$ antennas communicates with $K$ single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing $N$ passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with $K$. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays., Submitted to IEEE Transactions on Wireless Communications

Terahertz (THz)-band communications are currently being celebrated as a key technology that could fulfill the increasing demands for wireless data traffic in 6G wireless communications. Many challenges, such as high propagation losses and power limitations, which result in short communication distances, have yet to be addressed for this technology to be realized. Ultramassive multiple-input, multiple-output (UMMIMO) antenna systems have emerged as practical means for combatting this distance problem, thereby increasing system capacity. Toward that end, graphene-based nanoantennas have recently been proposed, as they can be individually tuned and collectively controlled in compact UM-MIMO array-of-subarrays (AoSA) architectures. In this article, we present a holistic overview of THz UM-MIMO systems. We assess recent advancements in transceiver design and channel modeling and discuss the major challenges and shortcomings of such designs by deriving the relationships among communication range, array dimensions, and system performance. We further highlight several research advances that could enhance resource allocation at the THz band, including waveform designs, multicarrier configurations, and spatial modulations (SMs). Based on this discussion, we highlight prospective use cases that can bring THz UM-MIMO into reality in the context of sensing, data centers, cell-free systems, and mid-range wireless communications.

As an efficient supplement for terrestrial communications, e.g., fifth-generation communication networks, satellite communication has been proved as a promising choice to realize seamless coverage, due to its inherent superiority over common wireless communication systems, like large-scale coverage area and flexibility of deployment. In this article, a satellite-terrestrial transmission (STT) system including a satellite transmitter (S) and a group of terrestrial receivers (D) is focused on. Considering the randomness of D and employing stochastic geometry, the outage performance and diversity gain of three hybrid automatic repeat request (HARQ) schemes, generalized slotted ALOHA (GSA), repetition time diversity (RTD) and general incremental redundancy (IR), are respectively studied. In detail, the exact closed-form analytical expression for the outage probability (OP) is derived for the GSA scheme, while the approximated expression for the OP of RTD scheme is presented and a useful expression for the OP of IR scheme is derived. Furthermore, the diversity gains of the three considered HARQ schemes are respectively investigated in STT scenarios. Finally, some numerical results are provided to validate the proposed analysis models.

In this work, we investigate the relationship between the reliability and security of a typical two-user downlink non-orthogonal multiple access (NOMA) communication system. The level of successive interference cancellation on NOMA user is considered. Firstly, the impact of various key parameters on transmit signal-to-noise ratio (SNR) of the NOMA users with the reliability outage probability (ROP) constraint is discussed. Taking the minimum of transmit SNR for ROP into account, the secrecy outage performance of the downlink NOMA systems is studied and the analytical expressions of the secrecy outage probability of the NOMA system are derived under two cases of eavesdropping capability. Furthermore, the sum effective secrecy throughput of the NOMA system under two scenarios with considering ROP constraint is derived. Monte Carlo simulations are provided to verify the accuracy of our analysis.

In this paper, we develop the transient performance limits of wireless transmissions for ultra-reliable low-latency communications (URLLC). In particular, we derive the exact statistics of the maximum amount of data that can be delivered during a fixed time duration over fading channels, for both channel state information (CSI) at transmitter side and CSI at receiver side only scenarios. Following a recently proposed data-oriented approach, we then analyze the performance of sample ideal transmission schemes in terms of URLLC support. Selected numerical examples are presented to examine the effect of power adaptation and rateless coding on transient transmission performance over fading channels.

In this paper, a cooperative hybrid visible light communication (VLC)-radio frequency (RF) system with spatially random terminals is considered. Especially, a source node ( $S$ ) with a group of light-emitting diode (LED) lamps transmits information bits to a destination ( $D$ ), which is located out of $S$ ’s coverage area, via a relay node ( $R$ ). $R$ is equipped with a photodetector and an antenna to set up a VLC link between $S$ and $R$ , and a radio frequency (RF) link between $R$ and $D$ , respectively. Meanwhile, an eavesdropper ( $E$ ) equipped with a photodetector and an antenna tries to overhear the information delivery over VLC and RF links. Also, diversity receiving scheme is considered at $E$ , while the same modulation scheme is considered over both VLC and RF links. Furthermore, decode-and-forward scheme is adopted at $R$ to process and forward the received VLC signals. By employing stochastic geometry, we first characterize the probability density function and cumulative distribution function of the received signal-to-noise-ratio over both VLC and RF links, while considering the randomness of the locations of both $R$ , $E$ , and $D$ . Then, the secrecy performance of the target system is studied by deriving the approximated expressions for the secrecy outage probability under various cases. Finally, the proposed analytical models are verified via Monte-Carlo simulations.

Future Internet of Things (IoT) applications demand highly energy-efficient wireless transmission solutions. In this article, we characterize the energy efficiency (EE) of wireless transmission over fading channels from individual transmission session perspective. Specifically, we apply a statistical EE metric to analyze and optimize ideal continuous rate adaptation (CRA) and continuous power adaptation (CPA) transmission schemes. The resulting statistical characterization establishes an EE performance limit for data transmissions over fading channels and facilitates a direct tradeoff analysis between maximal achievable EE and instantaneous latency requirement.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

This paper describes a new single-shear specimen (SSS) and method to characterize the dynamic shear behavior of bulk metals using a traditional Split Hopkinson Pressure Bar (SHPB). By this method, the shear behavior of materials can be tested conveniently over a wide range of strain rates within 105 s−1. This technique was applied to a 304 austenitic stainless steel (ASS) under shear strain rates from 0.001 s−1 to 38700 s−1 at room temperature. Based on finite element (FE) simulations, it was found that the deformation of the specimen shear zone was dominated by shear stress/strain components. Stress state parameters represented by stress triaxiality η and Lode angle parameter θ - were found very close to zero, indicating a deformation mode of simple shear. Besides, an obvious gap existed between the local deformation behavior in the specimen shear zone and the macroscopic stress-strain relations measured by the strain gauges on the SHPB bars. A correction coefficient method was adopted to extract the real shear behavior from the experimentally obtained force-displacement data. Through comparisons between the tested and simulated stress-strain curves, a good agreement was obtained.

The aggregated visible light communication (VLC) and radio frequency (RF) system, which can be viewed as a heterogeneous multi-input-multi-output system, can improve data rate compared to the conventional RF communication systems. In this paper, we first develop optimal power allocation schemes for the aggregated VLC-RF systems for the single and the multi-light-emitting diode scenarios under different dimming control setups. Moreover, we study the energy efficiency maximization problem of the considered system with the minimum rate requirement, transmitted power constraint, and the dimming control consideration which is non-convex. By using the Dinkelbach-type algorithm, we tackle this problem by solving a sequence of convex problems which converges to the global solution. Finally, the effect of critical parameters, such as total power threshold, dimming level, and bandwidths, are revealed by some selected numerical results.

In this correspondence, we consider a mixed radio-frequency/underwater wireless optical communication (RF-UWOC) system with a fixed-gain amplify-and-forward relay, where the RF link experiences a Generalized- $K$ distribution while the UWOC link undergoes a mixture Exponential-Generalized Gamma distribution. In our analysis, new closed-form expressions for the cumulative distribution function (CDF), and probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR) are derived. Capitalizing on these useful statistics, the performance of the proposed mixed RF-UWOC system is investigated in terms of the outage probability, average bit error rate, and average channel capacity. Our analytical expressions are verified via Monte Carlo simulations results, and insightful discussions are presented.

In this letter, a reconfigurable intelligent surface (RIS)-assisted dual-hop visible light communication (VLC)/radio frequency (RF) system in indoor scenarios is proposed, in which the first link is a VLC link while an RIS is deployed in the second RF link. More specifically, a relay first converts the received optical signal into the RF signal blue using either decode-and-forward or amplify-and-forward protocols and then forwards it to the RIS. The RIS alters the electromagnetic response of the impinging waves to provide connectivity between the RF destination and optical source. Based on this model, closed-form expressions for the outage probability and bit error rate (BER) are derived. Also, an asymptotic analysis for the outage probability is presented. Finally, simulation results are provided to verify our analytical results.

This article introduces a unified Hamiltonian formulation for controlling grid-connected direct current microgrid via interconnection and damping assignment–passivity-based control. The direct current microgrid includes hydro–solar–wind hybrid renewable energy systems and battery/supercapacitor hybrid energy storage system. Hybrid renewable energy systems are integrated as a disturbed direct current source, while hybrid energy storage system is integrated via semi-active topology, where the battery is connected to the direct current bus through a bidirectional dc-to-dc converter. The proposed Hamiltonian approach allows us to design a control strategy for the bidirectional dc-to-dc converter and the grid-connected three-phase inverter to (1) reject power disturbances in the direct current bus via the supercapacitor and ensure smooth current in the battery as well as in the grid and (2) assign the system to a desired equilibrium after sharp changes. In the studied article, the stability of the system under the interconnection and damping assignment–passivity-based control controller is investigated. The interconnection and damping assignment–passivity-based control controller is compared with two conventional proportional–integral–based controllers through simulations under the MATLAB/Simulink environment. The main contributions in this article are the unified hybrid modelling for dc-to-dc power converter and grid-connected inverter using port-Hamiltonian approach, external disturbance rejection in the multi-source power system using interconnection and damping assignment–passivity-based control, and the assurance of global asymptotic stability of the closed-loop system.

In this work, a nonlinear dynamic model of an FZG-A10 spur gear was investigated by taking into account for the actual time-varying gear mesh stiffness and the frictional effects between meshing gear teeth to evaluate the influence of the dynamic effects on frictional gear power loss predictions. The equations of motion of the generalized translational-torsional coupled dynamic system derived from Lagrange principle was extended compared to authors’ previous work in order to account for time dependent coefficient of friction and profile errors. The dynamic response of spur gears, computed by an iterative implicit scheme of Newmark, is changed due to the presence of coefficient of friction and profile errors. A dynamic analysis was performed and the influence of frictional effect including tooth shape deviations, in particular, was scrutinized since a time-dependent coefficient of friction is deeply related to the gear surface roughness and all parameters dependent on gears error profiles are introduced in the proposed model. The predicted meshing gear power losses with constant and local friction coefficient were compared. The influence of constant and variable profile errors considered in the local coefficient of friction formulation was also studied and their corresponding root mean square (RMS) power loss was compared to the experimental results. The results using FZG A10 spur gear pairs running under several operating conditions (different loads and speeds) validate the superiority of the proposed model against previous similar models.

Decomposition-based evolutionary algorithms using predefined reference points have shown good performance in many-objective optimization. Unfortunately, almost all experimental studies have focused on problems having regular Pareto fronts (PFs). Recently, it has been shown that the performance of such algorithms is deteriorated when facing irregular PFs, such as degenerate, discontinuous, inverted, strongly convex, and/or strongly concave fronts. The main issue is that the predefined reference points may not all intersect with the PF. Therefore, many researchers have proposed to update the reference points with the aim of adapting them to the discovered Pareto shape. Unfortunately, the adaptive update does not really solve the issue for two main reasons. On the one hand, there is a considerable difficulty to set the time and the frequency of updates. On the other hand, it is not easy to define how to update the search directions for an unknown PF shape. This article proposes to approximate irregular PFs using a set of predefined normal-boundary intersection (NBI) directions. The main motivation behind this article is that when using a set of well-distributed NBI directions, all these directions intersect with the PF regardless of its shape, except for the case of discontinuous and/or degenerate fronts. To handle the latter cases, a simple interaction mechanism between the decision maker (DM) and the algorithm is used. In fact, the DM is asked if the number of NBI directions needs to be increased in some stages of the evolutionary process. If so, the resolution of the NBI directions that intersect the PF is increased to properly cover discontinuous and/or degenerate PFs. Our experimental results on benchmark problems with regular and irregular PFs, having up to fifteen objectives, show the merits of our algorithm when compared to eight of the most representative state-of-the-art algorithms.

This article studies the problem of localization and tracking of a mobile target ship with an autonomous underwater vehicle (AUV). A hybrid acoustic–optical underwater communication solution is proposed, in which the acoustic link is used for the Non-Line-of-Sight (NLoS) localization, and the optical link is for the Line-of-Sight (LoS) transmission. By coordinating these two complementary technologies, it is possible to overcome their respective weaknesses and achieve accurate localization, tracking, and high-rate underwater data transmission. The main challenge for reliable operation is to maintain the AUV over an optical link range while the target dynamics is unknown at all times. Hence, we design an error-based adaptive model predictive control (MPC) and a proportional–derivative (PD) controller incorporating a real-time acoustic localization system to guide the AUV toward the sensor node mounted on the surface ship. We define a connectivity threshold cone with its apex coinciding with the sensor node such that when the underwater vehicle stays inside of this cone, a minimum bit rate is guaranteed. The localization, tracking control, and optical communication scheme is validated through online simulations that integrate a realistic AUV model where the effectiveness of the proposed adaptive MPC and PD controllers is demonstrated.

Underwater optical wireless communication is an emerging field that can provide reliable connectivity for future generation Internet of Underwater Things devices. In this article, we propose a communication system based on single and superposition of Laguerre–Gaussian modes to transfer information and rely on a convolutional neural network for the mode identification in an underwater environment. A 100% recovery fidelity is reported at clear and turbid water. Beyond 90% of identification, accuracy is achieved under different laboratory-emulated underwater turbulence conditions. The practical implementation of the proposed spatial-mode-based communication scheme is further discussed.

This letter investigates the secrecy outage performance of satellite-to-terrestrial downlink transmissions consisting of one legitimate receiver ( ${D}$ ) and one eavesdropper ( ${E}$ ). To reflect the practical application scenarios, it is assumed that ${D}$ and ${E}$ are randomly distributed in the footprint of the satellite. Furthermore, in the considered downlink scenario, ${D}$ and ${E}$ are equipped with multiple antennas, and maximal ratio combining (MRC) technique is adopted at both of them to seek the maximum receiving diversity gain. To reveal the impacts of the randomness of the positions of ${D}$ and ${E}$ , and MRC scheme on the secrecy outage performance of the considered satellite-terrestrial system, the exact and asymptotic closed-form analytical expressions for secrecy outage probability are derived by using stochastic geometry method. Finally, simulation results are given to confirm the accuracy of our proposed analytical models.