Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Optimization of 5G Coverage Predictions Using a Beamforming Antenna Model and Real Drive Test Measurements

The ability to estimate radio coverage accurately is fundamental for planning and optimizing any wireless network, notably when a new generation, as the 5th Generation (5G), is in an early deployment phase. The knowledge acquired from radio planning of previous generations must be revisited, particularly the used path loss and antennas models, as the 5G propagation is intrinsically distinct. This paper analyses a new beamforming antenna model and distinct path loss models - 3rd Generation Partnership Project (3GPP) and Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications (mmMAGIC) - applying them to evaluate 5G coverage in 3-Dimensional (3D) synthetic and real scenarios, for outdoor and indoor environments. Further, real 5G Drive Tests (DTs) were used to evaluate the 3GPP path loss model accuracy in Urban Macro (UMa) scenarios. For the new antenna model, it is shown that the use of beamforming with multiple vertical beams is advantageous when the Base Station (BS) is placed below the surrounding buildings; in regular UMa surroundings, one vertical beam provides adequate indoor coverage and a maximized outdoor coverage after antenna tilt optimization. The 3GPP path loss model exhibited a Mean Absolute Error (MAE) of 21.05 dB for Line-of-Sight (LoS) and 14.48 dB for Non-Line-of-Sight (NLoS), compared with real measurements. After calibration, the MAE for LoS and NLoS decreased to 5.45 dB and 7.51 dB, respectively. Moreover, the non-calibrated 3GPP path loss model led to overestimations of the 5G coverage and user throughput up to 25% and 163%, respectively, when compared to the calibrated model predictions. The use of Machine Learning (ML) algorithms resulted in path loss MAEs within the range of 4.58 dB to 5.38 dB, for LoS, and within the range of 3.70 dB to 5.96 dB, for NLoS, with the Random Forest (RF) algorithm attaining the lowest error.
Efficient Cross-Modality Graph Reasoning for RGB-Infrared Person Re-Identification

The modality and pose variance between RGB and infrared (IR) images are two key challenges for RGB-IR person re-identification. Existing methods mainly focus on leveraging pixel or feature alignment to handle the intra-class variations and cross-modality discrepancy. However, these methods are hard to keep semantic identity consistency between global and local representation, which the consistency is important for the cross-modality pedestrian re-identification task. In this work, we propose a novel cross-modality graph reasoning method (CGRNet) to globally model and reason over relations between modalities and context, and to keep semantic identity consistency between global and local representation. Specifically, we propose a local modality-similarity module to put the distribution of modality-specific features into a common subspace without losing identity information. Besides, we squeeze the input feature of RGB and IR images into a channel-wise global vector, and through graph reasoning, the identity relationship and modality relationship in each vector are inferred. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-art. The code is available at https://github.com/fegnyujian/CGRNet.
Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.
Linear Array Different Symmetrical Profile Bending Effect on the Input Impedances’ Unbalance and Other Characteristics

The influence of the symmetrical bending of the profile of a linear antenna array along the arc of a circle, parabola, corner on its radiation pattern, efficiency and impedance matrix is studied. Using the example of an 11-element array with different inter-element distances, the range of aperture angles, characterizing the bending, is determined, at which the antenna properties change within the specified small limits. The similarity and difference of the effects for different profile bending lines are demonstrated. The results can be used to synthesize compensation schemes or to assess the need for their development.
Efficiency Gains due to Network Function Sharing in CDN-as-a-Service Slicing Scenarios

The consumption of video contents is currently dominating the traffic observed in ISP networks. The distribution of that content is usually performed leveraging on CDN caches storing and delivering multimedia. The advent of virtualization is bringing attention to the CDN as use case for virtualizing the cache function. In parallel, there is a trend on sharing network infrastructures as a way of reducing deployment costs by ISPs. Then, an interesting scenario emerges when considering the possibility of sharing virtualized cache functions among ISPs sharing a common physical infrastructure, mostly considering that usually those ISPs offer similar content catalogues to final end users. This paper investigates through simulations the potential efficiencies that can be achieved when sharing a virtual cache function if compared to the classical approach of independent virtual caches operated per ISP.
The Primary Actors of Technology Standardization in the Manufacturing Industry

To date, discussions on standards and standards-related market issues have mainly been focused on market influence, such as a company’s performance, trade, and technological innovation. Despite the fact that many studies have been conducted with a focus on technology standards, the ways in which primary actors of standards affect the market are yet to be fully investigated in extant studies. In this study, we investigate the primary actors in technology standardization by carrying out a systematic review and constructing a conceptual framework and concept maps of primary actors. Based on our analysis, we categorize primary actors, according to their roles and timing of engagement in standardization, as follows: Technology producers, standard-setters, regulators, and technology users. We illustrate each actor’s detailed actions, motives, and difficulties with concept maps that are structured based on the SAO/P model, in order to elucidate why primary actors participate in standardization and how they act to achieve their goals or resolve difficulties. Based on our findings, we derive implications in terms of the strategic management of standardization activities in the manufacturing industry.
Increasing the Wireless Power Transfer (WPT) Link Efficiency by High Gain Helices

In this study we compare helix and patch array antenna characteristics for wireless power transfer (WPT). It will be shown that the helices offer higher link efficiency for a similar beam scanning limit compared to patch antenna elements. We provide 12-by-12 element planar array results for both cases at different element spacings. The frequency band of interest is ISM 2.4-2.5GHz and we do the comparisons in this range. It is concluded that helix elements, when used as an array, offer significant improvements for wireless power link efficiency, whilst also offering a reasonable beam scanning range.
Joint Detection and Decoding of Mixed-ADC Large-Scale MIMO Communication Systems With Protograph LDPC Codes

Nowadays, large-scale multiple-input multiple-output (LS-MIMO) with low-resolution analog-to-digital converters (ADCs) is a favorable transmission scheme for 5G and beyond wireless networks to reduce the power consumption of the radio frequency chains and to increase the network capacity. This paper derives the joint message-passing detection and decoding algorithm based on the double-layer graph for LS-MIMO communication systems with mixed-ADCs. The new protograph extrinsic information chart (PEXIT) algorithm is developed to analytically evaluate the performance of protograph low-density parity-check code under various mixed-ADC combinations and LS-MIMO configuration scenarios. The simulation results validate the accuracy of the proposed algorithm. Furthermore, our experiments show that the mixed-ADC system can achieve a significant power gain even when only one received antenna is equipped with high-resolution ADCs. It is observed that 4-bit or 5-bit resolution is an optimal choice for the high-resolution receive antennas. Interestingly, mixed-ADC systems with Ternay-ADCs generally provide significant gains at the cost of the increase in the average resolution by a fraction of a bit. There are specific scenarios where the Ternary-ADC-based system outperforms the 1-bit-ADC based system at the same or lower average resolution. In the particular case of <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula> MIMO configuration where the number of low-resolution antennas is <inline-formula> <tex-math notation="LaTeX">$N_{L} = 12$ </tex-math></inline-formula> and the number of high-resolution antennas is <inline-formula> <tex-math notation="LaTeX">$N_{H} = 4$ </tex-math></inline-formula>, the Ternary-ADC based system can obtain a power gain of about 2 dB at the frame error rate (FER) or bit error rate (BER) level of 10<sup>−5</sup>.
A Robust Method for Estimating the Channel Data Rate in Seamless IEEE 802.11ax Standard Networks

In paper the robust method for estimating the channel data rate was developed for the collective dynamic routing method implementation in seamless IEEE 802.11ax standard networks. Collective dynamic routing method was analyzed with and without the use of a robust method in comparison with static routing by a seamless IEEE 802.11ax standard network segment example. The developed method makes it possible to significantly reduce the dimension of the valid routes set and, consequently, the computational complexity of the collective dynamic routing method implementation with a slight deterioration in its efficiency.
Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective

Fusing information from multiple sensors in aprobabilistic framework can significantly improve the estimation of the system's states, allow for redundancy, and for adaptations in dynamic environments. However, the computation complexity increases with the number of sensors and the dimension of the estimated states. In this letter, we show that techniques used in the field of CSE for distributed estimators on decoupled agents can be applied on local estimators to decouple states from different sensors. Bridging the gap between these domains allows us to propose a novel unified modular multi-sensor fusion strategy for recursive filters that (i) achieves constant maintenance complexity in propagation and private update steps, (ii) supports any-sensor-to-any-sensor observations, (iii) isolates state propagation supporting high rates for propagation sensors, (iv) isolates private sensor updates, and (v) isolates joint updates requiring only the participating sensors’ estimates all while maintaining probabilistic consistency. We port existing CSE strategies into MMSF formulations and compare them as well as a classical MMSF approach against the proposed one in terms of execution time, accuracy, and credibility on real data.
Real-Time Estimation of Multiple Potential Contact Locations and Forces

In this work, we propose a contact point localization and external force estimation algorithm for collaborative robots. In comparison with existing approaches, the proposed algorithm can detect and evaluate multiple solutions and more than 3x faster without loss of accuracy. To achieve real-time performance, a hierarchical robot representation and surface mesh preprocessing was used, allowing us to achieve a 50x speedup in a run-time, compared to checking all contact points. Mesh preprocessing includes two-step clustering in the space of vertices normals vectors and vertices positions. The localization method was tested in a simulated Kinova Jaco 2 and real KUKA iiwa LBR 14 collaborative robots. Our solution allows estimating the contact point on the robot surface with 2.3 cm average accuracy in a more than 600 Hz loop.
Application of Soft-Switching Cell With Inherent Redundancy Properties for Enhancing the Reliability of Boost-Based DC–DC Converters

In this article, the inherent fault-tolerant capability of the soft-switching cell for boost-based dc–dc converters is investigated. The main switch of boost-based dc–dc converters withstands high-temperature stress, which is expected to be failed with a higher probability than other devices. Providing soft-switching condition for the main switch and employing fault-tolerant capability are effective approaches to solve this problem. When a fault occurs for the main switch, the proposed converter can be reconfigured such that the soft-switching cell can change its duty to continue the normal operation of the converter without interruption. Inherent redundancy, low failure rate, high integration, and high efficiency are the specifications of the presented converter. The reliability analyses are presented and the functionality of the active soft-switching cell to provide soft-switching condition and fault-tolerant capability are verified by experimental results.
Robust Service Mapping in Multi-Tenant Clouds

In a multi-tenant cloud, cloud vendors provide services (e.g., elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants’ traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants’ traffic are two critical challenges that affect the tenants’ QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: service node assignment and tenant traffic scheduling. For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ ε , where ε is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.
A New Paradigm in 5G Maximum Power Extrapolation for Human Exposure Assessment: Forcing gNB Traffic Toward the Measurement Equipment

5G base stations usually use different beams to transmit broadcast and user data. Moreover the broadcast beam is always “on air”, whilst the traffic beam is not. This represents a problem in Maximum Power Extrapolation (MPE) procedures for exposure assessment. In fact, currently adopted measurement approaches are based on the mere observation of phenomena. Recently, a different approach for MPE has been proposed by Adda et al., 2020, forcing the traffic toward the measuring position by means of a dedicated User Equipment (UE). Consequently, the measurer loses the “passive” role assumed in the approach usually adopted, and acquires an active role forcing the system under test to assume the most suitable configuration. The use of beam-forcing UEs opens new exciting possibilities, since it makes it possible to take advantage of the UE-specific signals for the estimation for the MPE procedure. The aim of this paper is to explore the potential offered by UE-specific data structures within the MPE considering a real case regarding data acquired on a currently operative 5G base station.
Gamification Elements in E-commerce – A Review

The implementation of gamification is driven by a range of factors, including the environment of the application, the elements involved and the types of users. The best practical method for effective gamification application still remains unclear, making it difficult to determine the most effective elements for an e-commerce website. This paper aims to present the literature review conducted to classify the gamification elements in e-commerce that have been investigated in previous studies which were published within the last four years (20182021). This is done to identify the most appropriate and relevant gamification elements to use in our future study. The findings from previous studies showed that gamification improved positive consumer behaviour in e-commerce, particularly in terms of engagement, and at the same time helped to boost business profitability. Furthermore, previous studies in this field have also found that rewards, badges and leaderboards were the most widely used gamification elements. This study may be used as a foundation for the researchers to build and develop a gamification framework for e-commerce in the future.
Improvement of personal loans granting methods in banks using machine learning methods and approaches in Palestine

For banking organizations, loan approval and risk assessment which is related is a very complex and significant process which is needs a high effort for relevant employee or manager to take a decision, because of manual or traditional methods that used in banks. The banking industry still needs a more precise method of predictive modeling for several problems. In general, for financial institutions and especially for banks forecasting credit defaulters is a hard challenge. The primary role of the current systems is to accept, or sending loan application to a specific level of approval to be studied and it is very difficult to foresee the probability of the borrower for paying the due dues amount without using methods to predict. Machine learning (ML) techniques and the algorithm that belongs to are a very amazing and promising technique in predicting for a large amount of data. Our research proposed to study three machine learning algorithms [1], Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), by using real data collected from Quds Bank with a variables that cover credit restriction and regulator instructions. The algorithm has been implemented to predict the loan approval of customers and the output tested in terms of the predicted accuracy.
HSIL Colposcopy Image Segmentation Using Improved U-Net

The screening and diagnosis of cervical lesions can effectively reduce the risk of cervical cancer. At present, there are few research algorithms and models for colposcopy in cervical lesion image segmentation. If image about colposcopy can be directly used to automatically segment the precancerous lesion area of cervical cancer, it will help doctors to make a faster and more accurate diagnosis, which has important clinical significance. To resolve this conundrum, this paper proposes an improved U-Net network image segmentation method for cervical squamous intraepithelia[1] lesions. Firstly, a sub-coding module is embedded in each layer of the U-Net encoder structure to improve its feature extraction capability. Secondly, dense connection is added to the lowest level convolution operation to solve the problem that the gradient disappears as the network deepens. Compared with U-Net, FCN and SegNet, the precision, Dice coefficient, IOU and Recall evaluation indexes of the model were better in this study, which effectively realized the image segmentation of cervical squamous intraepithelial lesions.
Boron-Doped Diamond MOSFETs With High Output Current and Extrinsic Transconductance

Boron-doped diamond (B-diamond) metal–oxide–semiconductor (MOS) capacitor and MOS field-effect transistor (MOSFET) are fabricated on a flat diamond epitaxial layer. Their electrical properties and thermal stabilities after annealing at 500 °C are investigated. Annealing makes the leakage current density of the B-diamond MOS capacitor increase slightly. There is a larger stretch-out for the capacitance-voltage curve in the depletion region after annealing than that before annealing. The drain-current maxima for the as-fabricated and 500 °C-annealed MOSFETs are obtained as −0.49 and −0.60 mA/mm, respectively. Extrinsic transconductance maxima are 18.7 and $21.4~\mu \text{S}$ /mm, respectively. These obtained values are considerably larger than those obtained by the previously reported B-diamond MOSFETs.
Fast Drivable Area Detection for Autonomous Driving with Deep Learning

Autonomous cars use images of the road to detect drivable areas, identify lanes, objects near the car, and necessary information. This information achieved from the road images are used to make suitable driving decisions for self-driving cars. Drivable area detection is a technique that segments the drivable parts of roads in the image. Modern methods often consider road detection as a pixel by pixel classification task, which is struggling to solve the problem of computational cost and speed. So to increase the speed of performance, we consider the process of drivable area recognition as a row-selection task. In this paper, special rows in the image are selected. Then, the boundaries of the drivable area are detected in these rows. Therefore computational costs reduce significantly. This model is evaluated on the Berkley Deep Drive dataset, and the speed of the process arrives at +300 frames per second, which is faster than previous methods.
On Aggregation and Prediction of Cybersecurity Incident Reports

The study of cybersecurity incidents is an active research field. The purpose of this work is to determine accurate measures of cybersecurity incidents. An effective method to aggregate cybersecurity incident reports is defined to set these measures. As a result we are able to make predictions and, therefore, to deploy security policies. Forecasting time-series of those cybersecurity aggregates is performed based on Koopman’s method and Dynamic Mode Decomposition algorithm. Both techniques have shown to be accurate for a wide variety of dynamical systems ranging from fluid dynamics to social sciences. We have performed some experiments on public databases. We show that the measure of the risk trend can be effectively forecasted.
A 43nW 32kHz Pulsed Injection TCXO with 4.2ppm Accuracy Using ∆Σ Modulated Load Capacitance

This paper presents an ultra-low power temperature-compensated crystal oscillator (TCXO) with a pulsed injection XO driver for IoT applications. Temperature compensation is achieved using a single switched load capacitance, modulated by a ∆ΣM. The ∆ΣM digitizes a piece-wise linear (PWL) approximation of the XO temperature dependence where a coarse 4-bit temperature sensor selects the PWL segment. The proposed 32.768kHz TCXO achieves an accuracy of ±4.2ppm from -20℃ to 85℃ with 3-point trimming and Allan deviation floor of 34 ppb while consuming 43nW.