Miracle-dz/UniFAR-FTUs huggingface dataset:
QoE-DASH: DASH QoE Performance Evaluation Tool for Edge-Cache and Recommendation

The converging ecosystem provided by Multi-access Edge Computing (MEC) has motivated novel DASH video streaming provisioning scenarios involving the joint coordination of different mechanisms for caching, communication and control. Given the complexity of designing such mechanisms, it is important to provide the research community with open-source tools that support the assessment of their feasibility, specially in real-world environment. Current network emulators still require a significant programming effort to meeting this need. To fill this gap, a new DASH emulator called QoE-DASH is presented in this work. QoE-DASH builds upon goDASH to evaluate the QoE of users consuming DASH content, taking into account network properties, user preferences, and context information. To demonstrate the capabilities of QoE-DASH, we exercise different functionalities of our tool and present a case study where two joint caching and recommendation models, proposed in the literature, are evaluated and their effects on user QoE are depicted using state-of-the-art QoE metrics.
Low-PAPR DFRC MIMO-OFDM Waveform Design for Integrated Sensing and Communications

In this paper, we explore a multiple-input multiple- output (MIMO) system with orthogonal frequency division multiplexing (OFDM) transmissions and study the low peak- to-average power ratio (PAPR) MIMO-OFDM waveform design for integrated sensing and communications (ISAC). This is done by leveraging a weighted objective function on both communication and radar performance metrics under power and PAPR constraints. The formulated optimization problem can be equivalently transformed into several sub-problems which can be parallelly solved by the semi-definite relaxation (SDR) method and the optimal rank-1 solution can be obtained in general. The feasibility, effectiveness, and flexibility of the proposed low-PAPR MIMO-OFDM waveform design method are demonstrated by a range of simulations on communication sum rate, symbol error rate as well as radar beampattern and detection probability.
Contention-free Scheduling of Periodic Traffic Sources in WiGig: Simulation Framework and Performance Analysis

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum, thus making it possible to wirelessly approach several emerging use cases, such as eXtended Reality (XR), telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service (QoS), that only contention-free scheduling algorithms can guarantee. In this paper, we propose a framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3 (ns-3), a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the trade-offs between contention-based and contention-free access strategies.
Exploring the Limits of Biological Complexity Amenable to Studies by Incoherent Neutron Spectroscopy

The wavelengths of neutrons available at neutron scattering facilities are comparable with intra- and inter-molecular distances, while their energies are comparable with molecular vibrational energies, making such neutrons highly suitable for studies of molecular-level dynamics. The unmistakable trend in neutron spectroscopy has been towards measurements of systems of greater complexity. Several decades of studies of dynamics using neutron scattering have witnessed a progression from measurements of solids to liquids to protein complexes and biomembranes, which may exhibit properties characteristic of both solids and liquids. Over the last two decades, the frontier of complexity amenable to neutron spectroscopy studies has reached the level of cells. Considering this a baseline for neutron spectroscopy of systems of the utmost biological complexity, we briefly review what has been learned to date from neutron scattering studies at the cellular level and then discuss in more detail the recent strides into neutron spectroscopy of tissues and whole multicellular organisms.
Online DGA Sensor Calibration Using GANN and Data Augmentation

The online dissolved gas analysis (DGA) sensors can diagnose the incipient faults of the transformer in real-time. However, these sensors suffer from low accuracy, and may provide unreliable even misleading maintenance suggestions. To tackle this problem, this paper proposes a hybrid genetic algorithm neural network (GANN) to adaptively build the calibration model for online DGA devices. As offline data is too few, a perturbation data augmentation method is also proposed to generate synthetic offline DGA data. The online data can be calibrated by the model thus providing real-time power transformer condition monitoring. The comparison with popular calibration methods confirms the superior of the proposed method.
P4-NSAF: defending IPv6 networks against ICMPv6 DoS and DDoS attacks with P4

Internet Protocol Version 6 (IPv6) is expected for widespread deployment worldwide. Such rapid development of IPv6 may lead to safety problems. The main threats in IPv6 networks are denial of service (DoS) attacks and distributed DoS (DDoS) attacks. In addition to the similar threats in Internet Protocol Version 4 (IPv4), IPv6 has introduced new potential vulnerabilities, which are DoS and DDoS attacks based on Internet Control Message Protocol version 6 (ICMPv6). We divide such new attacks into two categories: pure flooding attacks and source address spoofing attacks. We propose P4-NSAF, a scheme to defend against the above two IPv6 DoS and DDoS attacks in the programmable data plane. P4-NSAF uses Count-Min Sketch to defend against flooding attacks and records information about IPv6 agents into match tables to prevent source address spoofing attacks. We implement a prototype of P4-NSAF with P4 and evaluate it in the programmable data plane. The result suggests that P4-NSAF can effectively protect IPv6 networks from DoS and DDoS attacks based on ICMPv6.
Reconfiguration of the electric vehicle integrated distribution system considering carbon-reduction benefit

The extensive use of electric vehicles (EVs) can reduce carbon emission. However, when electric vehicles are getting plugged into the grid on a large scale, it will bring power fluctuations to the grid and affect its stable operation. In order to ensure the smooth, economic and low-carbon operation of the distribution network, this paper proposes a reconfiguration method of the electric vehicle integrated distribution system with consideration of carbon-reduction benefits. The proposed model takes the factors that affect the comprehensive cost of the distribution network into accounts, such as network losses cost and system carbon emission cost, and realizes the optimal dispatch of power resources. To verify the effectiveness of the model, this paper uses the IEEE 33-node distribution system connected to electric vehicle charging stations as an example, and uses MATLAB to conduct simulations. The simulation result shows that the established optimization model can effectively cut down the comprehensive cost of distribution system.
Transmitter and Receiver in 0.13 μm SiGe for Gas Spectroscopy at 222–270/444–540 GHz

This paper presents a transmitter (TX) and a receiver (RX) with a cross-polarized bowtie-antenna on a silicon lens for gas spectroscopy at 222–270 GHz and the doubled frequency at 444–540 GHz. TX and RX are fabricated in 0.13 μm SiGe BiCMOS technology. Both use two integrated local oscillators for frequency subbands 222–256 GHz and 250–270 GHz, which allow operating in one branch of the TX and RX at 222–270 GHz and in a second branch of the TX and RX at 444–540 GHz by frequency doubling. The directivity of the cross-polarized bowtie-antenna of the TX and RX is optimized for these two frequency bands with an estimated value of 24.4 dBi at 260 GHz, and 29.5 dBi at 520 GHz. Absorption spectroscopy of gaseous methanol is used as a measure for the performance of the TX and RX in the lower and upper frequency bands.
SEIG-Based Renewable Generation for MVDC Ship Power System with Improved Power Quality

Abstract The global energy consumption is increasing day by day. This increased energy demand can only be fulfilled through large-scale renewable energy integration. This article presents the mathematical modeling, control, and integration of a bio-fuel prime mover fed self-excited induction generator (SEIG) for medium voltage direct current (MVDC) ship power system. A three-phase current controlled voltage source inverter (VSI) working as a static synchronous compensator (STATCOM) injects compensating currents at the point of common coupling (PCC) for harmonic elimination and regulation of the generated voltage under varying load. The VSI is connected at the PCC through a three-phase zig-zag/star transformer which reduces the voltage stress at the DC link. An indirect current technique based controller is used to generate gating pulses for the STATCOM. The MVDC load zone is modeled as an integral sliding mode controlled DC-DC converter feeding ship service loads in shipboard system. Hardware realization of the proposed system has been implemented with 3.7kW induction generator.
Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.
STABILITY OF THE HADAMARD PRODUCT OF k-UNIFORMLY CONVEX AND k-STARLIKE FUNCTIONS IN CERTAIN NEIGHBOURHOOD

For a constant fc € [0, oo) a normalized function / , analytic in the unit disk, is said to be fc-uniformly convex if Re(l + z f " ( z ) / f ' ( z ) ) > k \ z f " ( z ) / f ' ( z ) \ at any point in the unit disk. The class of fc-uniformly convex functions is denoted k U C V (cf. [3]). The function g is said to be fc-starlike if g ( z ) = z f ' ( z ) and / € k U C V . For analytic functions / , g, where f(z) = z + a^z 2 H and g(z) = z + b^z 2 H , the convolution is defined as follows: oo ( f * g ) ( z ) = z + Y ^ a n b n z n .
Reliable and Secure Distributed Smart Road Pricing System for Smart Cities

Vehicular networks have emerged as a promising technology for the development of traffic management systems in smart cities. They are expected to revolutionize a variety of applications such as traffic monitoring and pay-as-you-drive services. Recently, the notion of road pricing has become crucial in most big cities as it contributes in road congestion avoidance, fuel consumption saving, and pollution reduction. However, as the road pricing systems need trip data to invoice citizens, it is vital to ensure geolocation privacy while keeping drivers honest. In this paper, we propose a security approach for smart road pricing systems, which prevents toll evasion violations. The proposed approach operates under a fully distributed threshold-based control system to detect fraudulent drivers trying to cheat on their tolls. The accused drivers are reported to the toll server in order to take the appropriate countermeasures. Through the security analysis, we show the robustness of the proposed approach against a range of potential attacks. We also evaluate the proposed approach through simulations considering important metrics, namely, the storage and communication overheads. The proposed approach shows better performance results in comparison to the existing approaches. Furthermore, we evaluate the proposed approach efficiency in terms of detection precision, where it demonstrates promising results.
Uptake of Calcium by Apples from Postharvest Dips in Calcium Chloride Solutions1

Uptake of Ca by apples (Malus domestica Borkh.) from a postharvest dip in CaCl2 solutions increased as CaCl2 concentration in the dip solution increased up to 3-4%; a 4% solution increased flesh Ca level by 60 to 100 ppm after 3 months of storage. Uptake occurred primarily during storage, apparently as Ca from residues left by the dipping treatment diffused through openings in the fruit surface. Absorbed Ca remained largely near the fruit surface, although some deeper penetration occurred. Uptake from a 2% CaCl2 solution was not influenced significantly by thickener, surfactant, fungicide, or scald inhibitor in the dip solution, and was not consistently influenced by storage at relative humidities of from 90% to greater than 95%. When relative humidity had an effect, uptake was reduced at concentrations greater than 95%. A 4% CaCl2 dip treatment reduced softening and development of senescent breakdown in ‘McIntosh’, but did not significantly modify quality of ‘Cortland’ or ‘Baldwin’ apples.
Near-Saturation Single-Photon Avalanche Diode Afterpulse and Sensitivity Correction Scheme for the LHC Longitudinal Density Monitor

Single-Photon Avalanche Diodes (SPADs) monitor the longitudinal density of the LHC beams by measuring the temporal distribution of synchrotron radiation. The relative population of nominally empty RF-buckets (satellites or ghosts) with respect to filled bunches is a key figure for the luminosity calibration of the LHC experiments. Since afterpulsing from a main bunch avalanche can be as high as, or higher than, the signal from satellites or ghosts, an accurate correction algorithm is needed. Furthermore, to reduce the integration time, the amount of light sent to the SPAD is enough so that pile-up effects and afterpulsing cannot be neglected. The SPAD sensitivity has also been found to vary at the end of the active quenching phase. We present a method to characterize and correct for SPAD deadtime, afterpulsing and sensitivity variation near saturation, together with laboratory benchmarking.
Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations

Frontal boundaries have been shown to cause large changes in CO2 mole‐fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole‐fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport‐America (ACT‐America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting‐—Chemistry (WRF‐Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF‐Chem for summer‐2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically‐active weather systems.
Intelligent Spectrum Sensing: An Unsupervised Learning Approach Based on Dimensionality Reduction

In Cognitive radio (CR), users take advantage of vacant licensed bands to transmit their data as they become available. Cognitive users employ an autonomous perception-action decision cycle that starts with sensing the activity of licensed users. By using machine learning techniques, CR users can attain their full cognitive potential and smartly detect empty frequency bands. Learning-based CR systems that utilize supervised learning for spectrum sensing require labeled data for model training. Having readily accessible labeled data is a challenging task for CR networks, since it necessitates cooperation between licensed and unlicensed users. In interweave CR networks, such cooperation is not feasible and imposes a significant communication overhead. Motivated by the above, we address the practical limitation of labeled data scarcity in learning-based CR networks by designing a novel unsupervised learning framework for cooperative spectrum sensing based on a Gaussian mixture model (GMM) and principal component analysis (PCA) that uses a small amount of unlabeled data for training and requires no prior knowledge of the radio environment. The system is mathematically simulated, and its performance is evaluated based on various detection performance metrics. According to our findings, our proposed approach outperforms the GMM algorithm and is on par with supervised learning algorithms such as SVM, RF, and DT. Furthermore, the proposed approach is shown to be robust to low SNRs.
Information Bottleneck Receivers for ISI Channels

This paper leverages the information bottleneck method to design receivers for ISI channels working with coarsely quantized messages. The proposed equalizer is based on the forward-backward algorithm. In contrast to conventional approaches, state reliability information is exchanged with a finite alphabet message instead of a real-valued probability vector. Moreover, each node update performs only a single lookup instead of many arithmetic operations. The lookup table construction aims at maximizing the preserved relevant mutual information. We propose a symmetric KL-means IB algorithm applied in an iterative discrete density evolution procedure. Based on the resulting distributions, a new static design technique creates three reusable symmetric lookup tables to perform forward, backward and final node updates. This way, coarse quantization is an integral part of the system design in the first place rather than a separate follow-up process. Two different ISI channel setups show that the proposed equalizers can achieve comparable performance to the high-resolution alternatives. Remarkably, the state metrics require an order of magnitude fewer bits, which potentially improves area and energy efficiency. Also, a lookup table sharing approach is presented to spread the implementation cost across sub-block equalizers operating in parallel.
Harmonic Analysis of V2G System Based on Integrated Filter Inductor

Integrated On-Board Charge (IOBC) is a very promising technique for hybrid/electric vehicle charging and discharging because it reused the existing winding inductance of the traction motor for filtering the harmonics. However, when harmonic components in the power grid is an important factor in evaluating the quality of power supply, the traction motor cannot be treated as an ideal inductance anymore. It is necessary to consider the extra harmonic components bring in by slot effect and saturation effect of traction motors. In this paper, the high fidelity reduced order model of a typical permanent magnet machine is built and adopted in a vehicle to grid charging/discharging system as the filter inductor. The harmonics of the phase currents at different locations are analyzed and compared with that of an ideal filter inductor. It is found that the harmonic generated by the inverter PWM voltage has a very different spectrum when compared with the integrated inductor. To ensure the total harmonic distortion of the system meets the requirement of the power grid, performance and design specifications for the traction motors are suggested.
Wideband Fractal Microstrip Antenna for Wireless Applications

The necessity for wireless communication and its estoric nature is enlarge during the two decades. In future, it is deduced to be more challenging and elaborate with evolution of different types of fractal antennas. Nowadays, smaller size of electronic equipment demand same size antenna element in order to place properly in the wireless devices without changing the radiation properties of antenna. Antenna plays an important role in WLAN communication system because it performance depend upon the quality of wireless communication. There are different types of fractal antennas like Sierpinski Gasket, Sierpinski Carpet and Minkowski fractal antenna for wireless communication. In Sierpinski Gasket, an equilateral triangle patch antenna is designed by the iterative procedure. Two or more copies of this triangle are generated attached to the original triangle. Sierpinski Carpet fractal antenna consists of square patch antenna which is also obtained by iterative procedure. Maximum iteration is applied to designed antenna is3.The behavior of designed antennas are enquired into such as return loss, gain and ultrawidebandwidth. The various types of slot on the patch helps in increasing the ultrawideband and give well planned result. In this thesis report, different antennas design are given and then study the various effects of different parameter like patch length, patch width, substrate height and dielectric constant for WLAN and WIMAX applications. These antennas are designed using CST 2014 microwave studio. The antennas parameter like return loss, resonating frequency, gain and bandwidth are allocated for each antenna design in order to get the desired antenna.