Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-agent OpenAI Gym Environment for Telecom Providers Cooperation

The ever-increasing use of the Internet (streaming, Internet of things, etc.) constantly demands more connectivity, which incentivises telecommunications providers to collaborate by sharing resources to collectively increase the quality of service without deploying more infrastructure. However, to the best of our knowledge, there is no tool for testing and evaluating participation strategies in such collaborations. This article presents a new adaptable framework, based on the OpenAI Gym toolkit, allowing to generate customisable environments for cooperating on radio resources. This framework facilitates the development and comparison of agents (such as reinforcement learning agents) in a generic way. The main goal of the paper is to detail the available functionalities of our framework. We then focus on game theory aspects as multi-player games induced by these environments can be considered as sequential social dilemmas. We show in particular that although each agent has no incentive to remain cooperative at each step of such iterated games, a mutual cooperation provides better outcomes (in other words, Nash Equilibrium is non optimal)
Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

Recently, a huge amount of online consumer reviews (OCRs) is being generated through social media, web contents, and microblogs. This scale of big data cannot be handled by traditional methods. Sentiment analysis (SA) or opinion mining is emerging as a powerful and efficient tool in big data analytics and improving decision making. This research paper introduces a novel method that integrates neutrosophic set (NS) theory into the SA technique and multi-attribute decision making (MADM) to rank the different products based on numerous online reviews. The method consists of two parts: Determining sentiment scores of the online reviews based on the SA technique and ranking alternative products via NS theory. In the first part, the online reviews of the alternative products concerning multiple features are crawled and pre-processed. A neutral lexicon consists of 228 neutral words and phrases is compiled and the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment reasoning is adapted to handle the neutral data. The compiled neutral lexicon, as well as the adapted VADER, are utilized to build a novel adaptation called Neutro-VADER. The Neutro-VADER assigns positive, neutral, and negative sentiment scores to each review concerning the product feature. In this stage, the novel idea is to point out the positive, neutral, and negative sentiment scores as the truth, indeterminacy, and falsity memberships degrees of the neutrosophic number. The overall performance of each alternative concerning each feature based on a neutrosophic number is measured. In the second part, the ranking of alternatives is being evaluated through the simplified neutrosophic number weighted averaging (SNNWA) operator and cosine similarity measure methods. A case study with real datasets (Twitter datasets) is provided to illustrate the application of the proposed method. The results show good performance in handling the neutral data on the SA stage as well as the ranking stage. In the SA stage, findings show that the Neutro-VADER in the proposed method can deal successfully with all types of uncertainties including indeterminacy comparable with the traditional VADER in the other methods. In the ranking stage, the results show a great similarity and consistency while using other ranking methods such as PROMETHEE II, TOPSIS, and TODIM methods.
Complementary Metamaterial based Dual Notch Microwave Sensor

A dual notch sensor is presented based on complementary split ring resonator (CSRR) which is coupled to microstrip transmission line (MTL) for proper excitation. Electromagnetic simulation elucidates the concentration of electric field at the resonance of CSRR. Scattering parameters of the proposed sensor are calculated numerically and used to extract electric permittivity and magnetic permeability of the proposed sensor. Materials under test (MUTs) are placed at the most sensitive part of the sensor for sensitivity analysis. After fabrication the sensor is tested with a measurement error less than 1.5%. The measured results are presented in the form of transcendental equations.
Dual-polarized leaky wave antenna with low cross-polarization based on the mode composite ridged waveguide

the mode composite ridged waveguide is proposed in this paper, it is a composition of substrate integrated waveguide and substrate integrated ridged waveguide with the mode composite and structure-shared characteristic. A Ka-band dual-polarized leaky wave antenna is designed based on the mode composite ridged waveguide, it utilizes the SIW TE10 mode and SIRW TE20 mode to achieve different polarizations within a compact size, the cross-polarization level will be reduced by using this two modes combination.
Perfect Sampling of Multivariate Hawkes Processes

As an extension of self-exciting Hawkes process, the multivariate Hawkes process models counting processes of different types of random events with mutual excitement. In this paper, we present a perfect sampling algorithm that can generate i.i. d. stationary sample paths of multivariate Hawkes process without any transient bias. In addition, we provide an explicit expression of algorithm complexity in model and algorithm parameters and provide numerical schemes to find the optimal parameter set that minimizes the complexity of the perfect sampling algorithm.
A Spline-High Dimensional Model Representation for SRAM Yield Estimation in High Sigma and High Dimensional Scenarios

Traditional Static Random-Access Memory (SRAM) yield estimation through Monte Carlo analysis is an extremely time-consuming process since it runs millions of expensive transistor-level simulations to get the yield results with the specified precision, especially for the large-scale circuits. In this paper, we develop an efficient yield analysis framework by integrating our novel performance metamodel into a state-of-art importance sampling method. The performance meta-model, named Spline-High Dimensional Model Representation (SP-HDMR), is used to substitute the expensive transistor-level simulations in yield estimation. The proposed SP-HDMR model provides a high computationally efficient formula expansion. It uses spline functions as the kernels to describe the various relations between the process parameters and SRAM read access delay. And an adaptive sampling method with sparsity analysis is developed to support SP-HDMR modeling. The experiments on the 40nm SRAM circuits validate the accuracy and the efficiency of the proposed yield analysis framework based on our SP-HDMR model with 1.3X $\sim 5\text{X}$ speedup over the other state-of-art methods within 9% relative error.
A Study of Data Processing for Object Recognition in Scene Image using FRCNN: A Smart Grid Technology

This paper proposes a new learning about the significance of part elements of a scene image with the effort of data processing. A top down tree structure with every node representing an annotation or bounding box having labeled visual features of an object existing in the image is studied in the paper. The images and its object annotations are from a trained dataset and are parsed to obtain the proposed representation. The images from the datasets and their parsed tree representations will be trained using a network called LSTM (Long Short Term Memory) network. The object detection may not be agnostic to the entire content of the image due to being influenced by the image composition and the discovered parts. The attempt has been made to show the object detection as a representation of the objects and their locations, parts of these objects, and the accuracy of the object detection method has been noted to have an efficient record with the implementation of the baseline Fast Region Based Convolutional Neural Network(FRCNN) method. The tested google open images datasets are used and found to have increased object detection record in notably respect to the use of high cost sensors in digital devices.
Impact of Commutation Failure Preventions on HVDC System Based on a Multi-Index Value Set Approach

This paper proposes a systematic method, based on rational approximation and value set approach, to unravel the impact of commutation failure preventions (CFPREVs) on HVDC system dynamics. First, some output variables in HVDC system dynamics are identified as performance indices, it is shown that these indices are rational functions of CFPREVs parameters. Then a numerical approximation method is used to determine the coefficients of the rational functions which describe the analytical relationship between the suggested performance indices and parameters. To help understand the connotation of the obtained expressions, a multi-index value set approach, which exploits the power of geometry and graphics, is described in detail. Finally, the results of the rational approximation and value set approach are reported.
State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Accurate state-of-charge (SOC) and state-of-health (SOH) estimations of batteries are of great significance for electric vehicles. A combined SOC and SOH estimation method for lithium-ion batteries based on a dual extended Kalman filter (EKF) and fractional-order model (FOM) is proposed. A fractional second-order RC model is established and model parameters are identified offline by an adaptive genetic algorithm (AGA). One of the dual filters is used to jointly estimate the SOC and SOH (ohmic internal resistance and capacity), and another is employed to update the model parameters online. Compared with single filter with fixed parameters, the dual filters can obtain more accurate SOC estimation and model voltage prediction. The SOC root-mean square errors (RMSEs) decrease from 6.87%, 8.50% and 7.32% to 0.48%, 0.63% and 0.86% under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST) and the US06 Highway Driving Schedule tests, respectively, and the model voltage RMSEs decrease from 88.6 mV, 79.3 mV and 68.4 mV to 4.9 mV, 5.7 mV and 3.8 mV, respectively at room temperature. The accuracy of the SOH estimation is also verified under these three tests. The convergence and robustness of the proposed method are discussed and verified by using the wrong initial state value and noise analysis.
Applicability of Algorithm Evaluation Metrics for Predictive Maintenance in Production Systems

Algorithm evaluation metrics are used to measure and compare the performance of diagnostic and prognostic algorithms. However, there is no consensus on which of the various metrics are the most suitable for classifiers' performance evaluation. This paper examines the applicability of common evaluation metrics for predictive maintenance applications in production systems. It is intended to clarify (1) which metrics are well suited for this use case, and (2) whether they are sufficient as a sole decision criterion for algorithm selection. Further, (3) the significance of evaluation metrics concerning the practical impact on the production system's performance is investigated. Moreover, we analyze (4) how increasing the production system's complexity affects the correlation between algorithm evaluation metrics and performance. We conducted 960 simulation runs of a flexible flow shop production system to examine the impact of machine breakdowns and different failure distributions with varying confusion matrices. In addition, two different configurations of the production system, which differ in system complexity, were investigated. Eventually, we determined the product-moment correlation coefficient between common evaluation metrics and the production system's performance. The simulation results reveal that metrics that are sensitive to false negative values (FN), like the False Omission Rate (FOR), are very well suited. Nevertheless, a detailed analysis shows that even (FOR) can only provide reliable statements combined with other metrics. We found that as the system becomes more complex, the informative value of the metrics in terms of their impact on the production system's performance decreases. Finally, we propose parameters that could be relevant for developing new metrics considering the current system configuration.
A Review of Multilevel Inverter Topologies in Electric Vehicles: Current Status and Future Trends

Traction inverter, as a critical component in electrified transportation, has been the subject of many research projects in terms of topologies, modulation, and control schemes. Recently, some of the well-known electric vehicle manufacturers have utilized higher-voltage batteries to benefit from lower current, higher power density, and faster charging times. With the ongoing trend toward higher DC-link voltage in electric vehicles, some multilevel structures have been investigated as a feasible and efficient option for replacing the two-level inverters. Higher efficiency, higher power density, better waveform quality, and inherent fault-tolerance are the foremost advantages of multilevel inverters which make them an attractive solution for this application. This paper presents an investigation of the advantages and disadvantages of higher DC-link voltage in traction inverters, as well as a review of the recent research on multilevel inverter topologies for electrified transportation applications. A comparison of multilevel inverters with their two-level counterpart is conducted in terms of efficiency, cost, power density, power quality, reliability, and fault tolerance. Additionally, a comprehensive comparison of different topologies of multilevel inverters is conducted based on the most important criteria in transportation electrification. Future trends and possible research areas are also discussed.
A Deep Learning-based Approach to Line Crossing Prediction for Lane Change Maneuver of Adjacent Target Vehicles

Forecasting the motion of surrounding vehicles is a key issue for autonomous vehicles to assess potential risks and avoid collisions. Among them, the sharp lane change of vehicle in adjacent lane has a greater impact on the ego vehicle. In this paper, we propose a deep learning-based approach to predict the lane change maneuver of adjacent vehicles and quantitatively estimate the position and time to line crossing point (PTLC). In order to distinguish the real lane change from an unintentional drifting between lane boundaries and make accurate prediction of the line crossing point, the features of vehicle kinematics and the driver's driving style as well as the interaction with surrounding vehicle are extracted. Furthermore, a deep neural network is used to process and fuse these features to obtain the probability distribution of PTLC, in which a gated recurrent units (GRU) is adopted as an improvement to robustly learn the historical trajectory of the adjacent target vehicle. Experiments using the data collected from highways show that the proposed method can achieve a reliable prediction of the driver's intention and line crossing point.
A Broadband Metasurface Antenna Based on Staggered Patch Array

A metasurface antenna based on the staggered rectangular patch structure is proposed and analyzed in this paper. The advantages of this antenna are broad bandwidth, miniaturization, low-profile and easy feed. The top of the antenna is composed of a 2×7 rectangular staggered patch array. The height of the substrate is 0. 06λ0 (λ0 is the wavelength at the center frequency of 6.00 GHz in free space). The antenna is fed by the coaxial probe. It exhibits two resonance frequencies within a −10dB operating bandwidth of 22.3%, ranging from 5.26 GHz to 6.60 GHz and the maximum gain of this antenna can be up to 9.94 dBi.
Force restrained control to extend flexibility of trajectory planning

Force restraint in contact-rich tasks with robots is vital in order to avoid breaking objects. Impedance/admittance controls are effective for achieving the restraint; however, they require adjustment of the kinematic impedance, which may induce instability. We proposed a force restrain method while PD controller is used for avoiding unstable robot behavior. This method alters position command in case the robot may give extreme force which leads to damaging objects. This method expands the trajectory planning and motion generation area practically because it can be executed under the condition that the controller autonomously restrains excessive force. To examine the effectiveness of the proposed method, we experimented with contact and detach motion in 1- and 2- DoF translational directions.
Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer’s decision about which algorithm works best for their game.
Near 1 THz Biasless Frequency Tripler Based on Membrane Technology

Schottky diode-based frequency sources are the crucial elements for the room temperature terahertz heterodyne receiver in terahertz imaging and radio astronomy applications. Here, a near 1 THz biasless frequency tripler based on the membrane technology was presented. Linear/Nonlinear co-simulation shows its practicability of full integration up to 1 THz. The power measurement gives 56 uW output power at 930 GHz.
Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Sarcasm is a complicated linguistic term commonly found in e-commerce and social media sites. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilised different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the contextual information in the sarcastic expression. Secondly, many deep learning methods in NLP uses a word embedding learning algorithm as a standard approach for feature vector representation, which ignores the sentiment polarity of the words in the sarcastic expression. This study proposes a context-based feature technique for sarcasm Identification using the deep learning model, BERT model, and conventional machine learning to address the issues mentioned above. Two Twitter and Internet Argument Corpus, version two (IAC-v2) benchmark datasets were utilised for the classification using the three learning models. The first model uses embedding-based representation via deep learning model with bidirectional long short term memory (Bi-LSTM), a variant of Recurrent Neural Network (RNN), by applying Global Vector representation (GloVe) for the construction of word embedding and context learning. The second model is based on Transformer using a pre-trained Bidirectional Encoder representation and Transformer (BERT). In contrast, the third model is based on feature fusion that comprised BERT feature, sentiment related, syntactic, and GloVe embedding feature with conventional machine learning. The effectiveness of this technique is tested with various evaluation experiments. However, the technique’s evaluation on two Twitter benchmark datasets attained 98.5% and 98.0% highest precision, respectively. The IAC-v2 dataset, on the other hand, achieved the highest precision of 81.2%, which shows the significance of the proposed technique over the baseline approaches for sarcasm analysis.
A Compact Low-profile Wideband Loop Antenna for Microwave Thorax Monitoring

In this letter, a compact low-profile wideband loop antenna for microwave thorax monitoring is designed. The antenna with a compact size of 20 mm×20 mm radiates towards the in-body direction. Its simulated bandwidth ranges from 0.23 to 4.98 GHz. The antenna is fabricated and tested with a human subject. Both the simulated and measure data verify its performance.
Mobility Prediction for 5G Core Networks

Mobility is an essential factor in the 5G core network (CN). If the network control plane can predict mobility, the operation will be more efficient and agile with intelligent and proactive decisions. So far, mobility patterns and their prediction models have been extensively studied in the research community with cellular network datasets. Recently, 3GPP initiated the specification of a CN architecture for data analysis and machine learning. In this article, in accordance with this trend, we provide a taxonomy of mobility prediction frameworks in 5G CNs ranging from data collection to model serving, with consideration of the 3GPP architecture and interfaces; and we introduce two key use cases in 5G CNs, where the gains from mobility predictions are evaluated on datasets from live networks. In particular, one of the proposed methods, machine-learning-assisted adaptive paging, reduces signaling overhead by up to 75 percent.
A New OMVDR Adaptive Anti-inference Beamforming Algorithm

Beamforming is a key technology in millimeter-wave communication. By controlling the transmitter and receiver of a millimeter-wave antenna array, the signal-to-noise ratio (SNR) can be effectively improved, and interference and noise can be suppressed. In order to further enhance the anti-interference ability of beamforming, under the condition of minimum variance distortionless response (MVDR) linear constraint, this paper uses the gray wolf optimization (GWO) algorithm numerical search capability to optimize the weights. An optimized minimum variance distortionless response (OMVDR) adaptive beamforming algorithm is presented. In the selection of the fitness function, the idea of separating amplitude and phase is adopted, and the fitness function is set to the weighted sum of the mean square error of the output signal and the input signal, thereby realizing the simultaneous optimization of the weight amplitude and phase. Simulation results show that OMVDR has lower sidelobe level and null and better anti-interference ability than traditional MVDR algorithms and PSO-MVDR.
Application of Digital Beamforming to Software Defined Radio 5G/Radar Systems

This paper presents a flexible phased array system designed at 28 GHz, for Radar and 5G applications, combining the use of software defined radio with GNU radio. The amplitude and phase of the signals that feed the antennas are digitally controlled using an application developed in the GNU Radio. The developed setup uses a USRP N310 to produce Intermediate frequency signals and RF frontend conversion modules combined with a 28 GHz antenna array prototype. The amplitudes of the received signals using beamforming were measured, revealing a good correspondence of the radiation peaks with the desired angle, demonstrating the adequate operation of the system.