Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of an Efficient Antenna for Asian Hornet (Vespa Velutina) Tracking Using Insect Telemetry

The Asian hornet (Vespa Velutina) is an invasive predator of wild pollinators in Europe. The only means of containing their fast spread is to destroy their nests, at the invasion front. Radio-telemetry tags have been tested successfully for Asian hornet tracking. However, there are inefficiencies in these tags due to near-field interactions between the hornets and the tags’ antennas. Additionally, there is a need for lighter tags with smaller antennas. Therefore, this work presents a hornet-specific monopole antenna design, operating at 866 MHz. To this aim, the influence of the hornet on the monopole’s radiation performance is characterized, using numerical simulations in which a hornet model, obtained by micro-CT scanning was used. These showed that, when the monopole is in proximity to the hornet and connected to a 50 Ω source, reducing the wire’s length to 98 mm would optimize impedance matching, leading to 0.0004% of the input power to reflect. Moreover, results showed that the near-field interactions between the hornet and the monopole can lower its radiation efficiency by 20%, and its maximum gain by 1.14 dBi.
Intelligent Reflecting Surface-Aided Transmit Diversity and Performance Analysis

Intelligent reflecting surface (IRS) has emerged as a promising technology to enhance wireless communication performance via passive signal reflection. Prior works on IRS have mainly focused on investigating IRS's passive beamforming design to enhance the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to enhance the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open in the literature. In this paper, we study a new IRS-aided communication system with the IRS integrated into its aided access point (AP) to achieve the new function of transmit diversity. Specifically, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any prior CSI of the users. Furthermore, we analyze the performance of IRS-aided transmit diversity in terms of average channel gain and symbol error rate (SER) to get some useful insights. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided transmit diversity over other benchmark schemes.
Enhancing Brain Tumor Classification Through Customization of the Vision Transformer Learning

In the medical diagnosis, the accuracy of decision-making holds immense significance. Healthcare professionals heavily rely on diagnostic results to guide treatment plans, as these decisions have a substantial impact on patients' health outcomes. An incorrect diagnosis can lead to detrimental consequences, including treatment delays or the administration of inappropriate treatments, thereby posing serious harm to the patient's well-being. The utilization of artificial intelligence (AI) in medical image recognition plays a crucial role in assisting various disease diagnosis systems. Improving accuracy of brain tumor classification is an important diagnosis and effective treatment planning. In this paper, we present an approach to improve brain tumor classification accuracy based on vision transformer approach and customizing head classifier and hyperparameter optimization for training. ViT model are a new type of neural network that can achieve state-of-the-art accuracy on image classification tasks. Our learning architecture approach reaches high accuracy with more than 95%. The experimental results demonstrated that the ViT model achieves a higher validation accuracy. This approach also show that the ViT model is more robust to noise and can generalize better to new data.
Impact of Blockers on User Equipment Angular Diversity in THz Microcellular Scenarios

The availability of large bandwidths in the terahertz (THz) band will be a crucial enabler of high data rate applications in next-generation wireless communication systems. The urban microcellular scenario is an essential deployment scenario where the base station (BS) is significantly higher than the user equipment (UE). Under practical operating conditions, moving objects (i.e., blockers) can intermittently obstruct various parts of the BS- UE link. Therefore, in the current paper, we analyze the effect of such blockers. We assume a blockage of the strongest beam pair and investigate the availability and extent of angular diversity, i.e., alternative beampairs that can sustain communication when the strongest is blocked. The analysis uses double-directional channel measurements in urban microcellular scenarios for 145– 146 GHz with BS-UE distances between 18 to 83 m. We relate the communication-system quantities of beam diversity and capacity to the wireless propagation conditions. We show that the SNR loss due to blockage depends on the blocked angular range and the specific location, and we find mean blockage loss to be on the order of 10–20 dB in line-of-sight (LOS) and 5–12 dB in NLOS (non-LOS). This analysis can contribute to the design of intelligent algorithms or devices (e.g., beamforming, intelligent reflective surfaces) to overcome the impact of the blockage.
Extraction of Ambience Sound from Microphone Array Recordings for Spatialisation

It is often desirable to separate main (primary) sound sources and background sound in microphone array recordings where the microphone signals are a combination of these two components, and then render primary and background sounds using different rendering techniques. Considerable research has investigated the extraction of the primary sources from the mixture using source extraction and localisation techniques, to then re-spatialise the extracted signals using the desired spatial audio reproduction format (for example, loudspeaker panning, Ambisonics or binauralisation). Little research, however, has focused on the separation and spatialisation of the ambience (background) component of the mixture, like secondary sources, diffuse field, or reverberation. This component is crucial for an accurate and realistic reconstruction of the overall 3D audio scene. This paper presents a new approach to extracting a spatialised version of the background field from a set of microphone array signals containing the original sound field mixture. The approach utilises an LMS algorithm to perform the separation between the primary sources and background sound. The fundamental theory of this technique is presented and validated with experimental measurements.
A Study on Source Rupture of the 1978 Lan-Hsu, Southeastern Taiwan Earthquake

Seven teleseismic long period P-wave records from World-Wide Standard Seismograph Network (WWSSN) are used to infer the source rupture process of the July 23, 1978 Lanhsu, eastern Taiwan earthquake through the body-wave iterative deconvolution method (Kikuchi and Kanamori, 1982; 1986). Results show that the rupture sequence consists of one main event and one largest subevent near the hypocenter determined by Pezzopane and Wesnousky (1989) and numerous smaller ones at a distance from the hypocenter. Comparison of the spatial distribution of mainshock rupture sequence with aftershocks shows that aftershocks are located in the area which did not break before the occurrence of the above-mentioned events. The total seismic moment of the three largest events is 8.76×10^26 dyne-cm.
Churn Rate Aware Interference Management for Device-to-Device Communications in SDNs

Device-to-device (D2D) communications are employed in cellular networks to improve network coverage and spectral efficiency and reduce latency and power consumption. However, managing the interference between cellular and D2D users is a critical challenge. Recently in 5G cellular networks, the frameworks of software-defined networks (SDNs) and network function virtualization (NFV) are being utilized to implement stochastic algorithms and optimize the network's performance. However, most of these algorithms focus only on improving spectral efficiency and do not care about fairness in terms of serving the number of D2D users. In this work, we present two algorithms to manage the interference so that the churn rate, i.e., the ratio of the number of unserved to the total number of D2D users, can be minimized without affecting the quality of service (QoS) constraint of the cellular users. Using simulations, we corroborate that the proposed churn rate (CR) aware algorithms ensure fairness in allocating resources to the D2D users while maintaining the throughput performance.
Improve Your Aim: A Deep Reinforcement Learning Approach for 5G NR mmWave Beam Refinement

Massive MIMO (mMIMO) technology is considered as a key enabler for 5G and beyond cellular networks, which allows formation of highly directional radiation beams in the millimeter-wave (mmWave) band. Specifically, considering the 5G new radio (NR) standard, a codebook-based approach is used that allows setting the antenna weights, so that both transmission and reception can be achieved in the desired angle. However, when a fixed codebook is used, these angular directions may not be exactly aligned along the optimal path that maximizes the SINR between the transmitter-receiver pair, depending on the granularity of the beam and the codebook size. To address these issues, we propose selection of the analog parameters of the transceiver chain through Deep Reinforcement Learning (DRL). Simulation results show that our approach allows fine-grained beam refinement to the coarse initial estimates of Angle-of-Arrival and Angle-of-Departures in mmWave Frequency Range 2 (FR2) for the 5G NR standard obtained during the a reduced initial beam establishment procedure (P-1). We observe our approach consistently improves the Reference Signal Received Power (RSRP) perceived at the UE side up to 15% while allowing a reduction in the number of Synchronization Signal Blocks (SSBs) up to a factor of $\times 64$ compared to the equivalent number used in P-1 to obtain comparable steering accuracy. Finally, once the trained DRL agent is implemented, it eliminates 100% of control signals needed for the beam refinement procedures, namely P-2 for transmitter beam refinement and P-3 for receiver.
Effect of Polybutylene Succinate Additive in Polylactic Acid Blend Fibers via a Melt-Blown Process

This work aimed to study the influence of the polybutylene succinate (PBS) content on the physical, thermal, mechanical, and chemical properties of the obtained polylactic acid (PLA)/PBS composite fibers. PLA/PBS blend fibers were prepared by a simple melt-blown process capable of yielding nanofibers. Morphological analysis revealed that the fiber size was irregular and discontinuous in length. Including PBS affected the fiber size distribution, and the fibers had a smoother surface with increased amounts of added PBS. Differential scanning calorimetry analysis (DSC) revealed that the crystallization temperature of the PLA sheet (105.8 °C) was decreased with increasing PBS addition levels down to 91.7 °C at 10 wt. % PBS. This suggests that the addition of PBS may affect PLA crystallization, which is consistent with the X-ray diffraction analysis that revealed that the crystallinity of PLA (19.2%) was increased with increasing PBS addition up to 28.1% at 10 wt% PBS. Moreover, adding PBS increased the tensile properties while the % elongation at break was significantly decreased.
Assessing Time Behaviour in Disaster Management by Using Petri Nets and Model Checking

Disaster Management (DM) involves a range of procedures and best practices aimed at minimising the impact of natural or human-made disasters on people, infrastructure, and the environment. Time is a critical factor in DM. An effective and timely response can save lives, prevent further damage to infrastructure and the environment, and facilitate the recovery process. Assessing time behaviour in a DM scenario is a complex task because it depends on many factors like the kind of disaster, resource availability and the situation's complexity. This paper proposes an approach for assessing system behaviour in DM based on an extended version of the Stochastic Reward Nets (SRNs), named SRNs*, as modeling language and the Uppaal Model Checker as a formal analysis tool. The approach is demonstrated through modeling and analysis of a DM scenario tied to emergency response and specifically on the problem of deploying medical resources to provide adequate hospital care.
Pluies extrêmes en Catalogne : influence orographique et caractéristiques synoptiques

EXTREMERAINSTORMS IN CATALONIA.OROGRAPHIC EFFECTANDSYNOPTIC CHARACTERISTICS Rainstorms giving rise to catastrophic Jloods in Catalonia after 1940 are examined, particular attention being paid to the spatial distribution of rainfall as related to the geographical features and to the meteorological conditions involved. Although synoptic-scale situations are rather similar for the most of them, meso-scale and topographical maxima.
Optimizing The Protocol For Extraction Of Bioactive Components From Hibiscus Sabdariffa With Potent Antioxidant Activity

The extract of Hibiscus sabdariffa (HS) also known as roselle from the Malvaceae family is prized for its exceptionally high contents of polyphenols and anthocyanins, the calyces of HS being one of the many sources of natural antioxidants. Considering the numerous health benefits associated with the consumption of such compounds as well its broad application in the food industry, the development of an extraction protocol for such compounds from HS at high yields and antioxidant activity merits scientific relevance. In this study, the optimum conditions to achieve such goals were established by the method of response surface methodology (RSM) using three independent variables: time (30, 165, and 300 min), temperature (50, 70, and 90°C) and ethanol concentration (60, 75 and 90%). A Box-Behnken design (BBD) was utilized to determine the optimum condition that yielded the highest extraction yield and antioxidant activity measured by 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical inhibition activity test. The results showed that under optimum conditions [300 min, 70°C in 90% ethanol concentration and 30 min, 70°C in 90% ethanol concentration], a corresponding 48.44% and 87.93% were obtained for the extraction yield and DPPH activity of HS, respectively; hence verifying the suitability of RSM for optimizing the extraction of HS.
A Review of Leaf Diseases Detection and Classification by Deep Learning

Leaf’s primary function is to produce nutrients through photosynthesis and support the plant’s growth. Leaf diseases caused by bacteria or other pathogens can negatively impact agricultural yields. Immediate and early diagnosis of diseases is vital for plant health. The significant development of deep learning algorithms for leaf disease classification and detection contributed to a solid tool with a robust and reliable accuracy rate. This study presents a comprehensive review of leaf disease research in the literature. It also highlights the gaps that need to be filled as well as the obstacles and problems facing research projects. The total number of papers retrieved from five electronic databases is 256. We analyzed and classified them into seven research questions. The results demonstrate that 63% of the papers are journal articles, 35% are conference papers, and 2% are workshop papers.
A Triple Learner Based Energy Efficient Scheduling for Multi-UAV Assisted Mobile Edge Computing

In this paper, an energy efficient scheduling problem for multiple unmanned aerial vehicle (UAV) assisted mobile edge computing is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also decisions of whether returning to the depot for replenishing energies and updating application placements (due to limited batteries and storage capacities). Aiming to maximize the long-term energy efficiency of all UAVs, i.e., total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs, trajectory planning, energy renewal and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such problem as three coupled multi-agent stochastic games, and then propose a novel triple learner based reinforcement learning approach, integrating a trajectory learner, an energy learner and an application learner, for reaching equilibriums. Simulations evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.
Modeling, Analysis, and Design for Small-Signal Stability in Sequence-Decomposed Grid-Forming Control

Fast and accurate detection of symmetrical components is critical for ride-through of asymmetrical faults in grid-forming (GFM) inverter based resources (IBRs). Sequence-decomposed GFM control enables to emulate the behavior of a synchronous machine by an IBR in both positive- and negative-sequences, where current references are generated separately in each sequence from the extracted symmetrical components of the terminal voltage. Cross-coupled dynamics between the stationary frame components attributed by the symmetrical component extraction (SCE) complicates the analysis and design process and appropriate modeling and analysis method for sequence-decomposed GFM control structures is yet to be reported. In this work, a small-signal model is developed for the analysis and design of such control implementations. It is demonstrated that by virtue of its overall structure, sequence-decomposed GFM control enables simplified analysis eliminating the cross-coupled dynamics characteristic to SCE. Subsequently, comparative analysis is presented between delay based and filter based SCE methods focusing on their impact on small-signal stability. Design guidelines are provided along with supporting experimental evidence using a laboratory inverter prototype. The analysis and experiments demonstrate that delay based sequence component extraction method offers greater stability margins under open-load and loaded GFM operations.
Laser dazzling in the visible region: some issues

Laser dazzling is a threat both to military and civilian operations. It also enables protection capabilities against hostile fire from different kind of weapons. Laser dazzlers can also serve as warning devices in checkpoint and riot control and others. Williamson and McLin has presented a model for eye laser dazzling partly based on the earlier framework for laser safety calculations. We have used this model to investigate dazzling performance for humans under different atmospheric condition such as scattering, extinction and turbulence. We also shortly discuss the influence of optics in front of the eye such as windshields or magnifying optics. The results will be discussed in relation to potential scenarios.
Comprehensive 5G Core Network Slice State Prediction Based on Graph Neural Networks

The ability of predicting state of a Network Slice (NS) is indispensable for the run-time management of NSs for providing proactively adjustment and reconfiguration of the NS to avoid Service Level Agreement (SLA) violation and ameliorate network resource utilization. In the literature, NS state prediction methods neglect the spatio-temporal correlation among NS entities. Moreover, NS state involves both Virtual Network Function (VNF) state and transmission link state in the virtual network of the NS. In this paper, we propose an end-to-end model by integrating Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) for the dynamic NS state prediction. Typically, we apply two types of GNN models, Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN), for aggregating the spatial features for VNFs and transmission links. Then, LSTM is utilized for sequential NS state prediction. Finally, we conducted intensive simulation to validate the effectiveness of the proposed model by comparing to several baselines.
Reinforcement Learning-Based Multi-Domain Network Slice Provisioning

We address the problem of establishing an end-to-end network slice across multiple domains and propose a Reinforcement Learning-based framework that enables multiple domains to collaborate on end-to-end network slicing admission and allocation. The objective is to maximize the long-term revenue of the network operator. We employ a Graph Neural Network (GNN) to capture the topology features as the encoder. The simulation results show that our framework improves the profit of the network operator by up to 15% compared to a greedy algorithm.
Rainfall Forecasting in Burkina Faso Using Bayesian-Wavelet Neural Networks

This work aims to forecast rain locally in Tambarga, Burkina Faso, to be able to fight against a worm inducing the disease called schistosomiasis. The chosen approach relies on a machine-leaning technique called Artificial Neural Networks, which simulates the synapses of a brain, with climatic parameters as inputs, activation functions and outputs in the form of rain prediction. A special case of Neural Networks using Bayesian Computations is used, along with as a transform allowing to capture the changes in climatic conditions, called Wavelet Transform. The precipitation is forecasted in different manners: binary forecast on the presence or absence of rain, linear forecast on the daily and weekly intensity, as well as a rain-class forecast. The most successful predictions have been found to be the binary forecast, as well as the weekly windowed cumulative rain forecast. The daily cumulative rain, as well has the classes forecast have not produced satisfying results, mainly because of the high temporal variability of the observations, as well as the very unequal distribution of observations in the different rain classes. In the end, it has been shown that it is possible to use Bayesian Networks to forecast precipitation in some extent, and that the wavelet transform of the inputs has a positive impact on the accuracy of the prediction.