Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Agile and Resilient UAV Networks Based on SDN and Blockchain

In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly and has been widely used in military operations, medical rescue, environmental monitoring, and so on. UAV networks are an important foundation for large-scale UAV collaborative work. Traditional mobile self-or-ganizing network technology is too complicated to be quickly constructed and dynamically adjusted according to application requirements. In this article, we propose a new type of UAV network based on SDN architecture and blockchain technology, which is named SUV. By using blockchain technology, SUV implements a logically centralized but physically distributed control plane, which is responsible for the routing calculation and configuration management of the UAV network. SUV can quickly build UAV networks according to the application requirements and communication environment. It has features such as flexibility, survivability, security, and programmability, and is suitable for 5G-oriented UAV networking.
Ethylene Homopolymerizaton and Copolymerizaton by Vanadium(III) Complexes Containing Tridentate or Tetradentate Iminopyrrolyl Ligands

Iminopyrrolyl vanadium(III) complexes 2a-b bearing tridentate ligands [C(4)H(3)NCH=NC(6)H(4)L]VCl(2)(THF)[L = 2-P(C(6)H(5))(2) (2a), 2-SMe (2b)] and complexes 2c-d with tetradentate ligands [(C(4)H(3)NCH = N)(2)R]VCl(THF) [R = 1,2-C(6)H(4) (2c), 1,2-C(2)H(4) (2d)] have been synthesized in high yields. With diethylaluminium chloride as a cocatalyst, complexes 2a-d were investigated as efficient catalysts for ethylene polymerization under various reaction conditions, and exhibited high catalytic activity and remarkable thermal stability. With these complexes, high molecular weight polymers with unimodal molecular weight distributions were obtained, indicating that the polymerization reaction took place in a single-site nature. Ethylene/1-hexene copolymerizations were also investigated in the presence of Et(2)AlCl. Both increasing ligand denticity and introducing softer atom into the sidearm of the ligands significantly influenced catalytic activity, comonomer incorporation, and the molecular weights of the resultant polymers, suggesting that both the steric and the electronic effects of the ligands played an important role in adjusting chain propagation and transfer rate. The chain transfer mechanisms involved in the copolymerization process were investigated by carefully analyzing the microstructure of the copolymers. The signals of vinyl, disubstituted and tri-substituted vinylene double bond end groups were detected in the copolymer obtained by 2a/Et(2)AlCl system but not in those by 2b-c/Et(2)AlCl systems, indicating that bulky electron- donating group, AP(C(6)H(5))(2), may lead to those unusual transfer reactions. (C) 2011 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 49: 2700-2708, 2011
Ultrafiltration of kraft black liquor from two Swedish pulp mills

Extraction of lignin by ultrafiltration of black liquor from two Swedish pulp mills has been studied. One of the mills employs batch digestion and the other continuous digestion. At both mills softwood was used as raw material. The black liquor was withdrawn before the evaporator unit. A ceramic membrane with a cut-off of 15 000 g mol(-1) was used in the experiments. The average flux during concentration to 90% volume reduction was 160 and 110L m(-2) h(-1) for the liquors from the batch and the continuous digestion processes, respectively, when the temperature was 90 degrees C and the transmembrane pressure 200 kPa. The retention of lignin was about the same for both liquors, 35%. A cost estimate of extraction of lignin fuel from black liquor was made, based on experimental data from ultrafiltration at various operating conditions. The cost was found to be about 20 E per MWh of calorific value of the lignin fuel. (Less)
EEG Band Separation Using Multilayer Perceptron for Efficient Feature Extraction and Perfect BCI Paradigm

For treatment of mental and brain diseases and diagnosis of abnormalities electroencephalogram (EEG) is an important measurement of brain activity. Feature extraction is vital in brain-computer interface (BCI) in the zone of biomedical and bioinformatics research alongside developing and adopting advanced signal processing techniques. Nonstationary and the nonlinear behavior of the EEG signal is the main challenge in feature extraction process. For the betterment of healthcare services, effective and affordable interpretation methods are the emerging keys. In this paper, the main focus is to separate different frequency band from EEG signal to extract features more efficiently using Multilayer Perceptron (MLP). B-Alert X10 is used for EEG acquisition and for analyzing the signal data, a virtual platform MATLAB has been used. For the classification of EEG bands Multilayer Perceptron Neural Network has been implemented which has been proved to be a more effective method with 95.47% accuracy for the classification.
Research on Seismic Signal Classification and Recognition Based on EEMD and CNN

The accurate classification of seismic signals is of great significance for updating clear earthquake catalog, early-warning the arrival of strong earthquake, and further researches in the field of seismology. This paper first preprocesses the seismic signal data of two types of events: earthquakes and explosions, and then uses the ensemble empirical mode decomposition (EEMD) method to decompose these preprocessed seismic data. A set of intrinsic mode functions being distributed from high-frequency to low-frequency are obtained from each seismic waveform, and then corresponding Hilbert spectrum is obtained by applying Hilbert transform to each of these intrinsic mode functions (IMFs). The image of Hilbert spectrum is scaled to a suitable and comparable size, by repeated trial-and-error experiments, to the size of 32*32 pixels, and then the 32*32 pixels gray-scale image is fed to a convolutional neural network(CNN) to classify the types of seismic signals. In this paper, 1674 waveforms of 54 earthquake events and 1509 waveforms of 63 explosion events are processed as above procedures. By five-fold cross-validation, 80% of the data is used as the training set, remaining 20% data as testing set. The highest classification accuracy rate of testing set is 94.98% with the average accuracy rate 94.41%，this results are significant higher than those of multi-layer perceptron(MLP) and Support Vector Machine(SVM), which implying stronger classification capability of the scheme unifying EEMD and CNN proposed in this paper.
Magnetic properties of MnEr2S4, MgEr2S4, and MnxMg1-xY2S4

Low-temperature magnetization and susceptibility data are reported of the isostructural compounds MnEr2S4, MgEr2S4, MnY2S4 and the solid solutions MnxMg1−xY2S4. A comparison of the data of MnEr2S4, MgEr2S4, and MnY2S4 reveals the presence of strongly negative Mn-Mn against very small Mn-Er and Er-Er exchange interactions. The properties of the dilute systems MnxMg1−xY2S4 are explained by the presence of isolated Mn ions and finite clusters of exchange-coupled Mn ions. A critical concentration for long-range ordering xc = 0.5 is found for MnxMg1−xY2S4; this is consistent with exchange interactions of each Mn ion with five neighbouring sites. Magnetization data of very dilute MnxMg1−xY2S4 at very low temperatures and at high magnetic fields exhibit the presence of pairs of exchange-coupled Mn ions; from these data an isotropic Heisenberg exchange J = −15.6 K is calculated in good agreement with the value found from the high-temperature susceptibilities. Die Magnetisierung und Suszeptibilitat der isotypen Verbindungen MnEr2S4. MgEr2S4, MnY2S4, sowie der festen Losungen MnxMg1−xY2S4 bei tiefen Temperaturen wurden gemessen. Ein Vergleich der Daten von MnEr2S4, MgEr2S4 und MnY2S4 last auf eine starke negative Mn-Mn-Wechsel-wirkung schliesen, gegenuber sehr schwachen Mn-Er- und Er-Er-Wechselwirkungen. Die Eigenschaften von verdunnten Losungen MnxMg1−xY2S4 konnen erklart werden durch die Existenz von isolierten Mn-Ionen neben endlichen Clustern von Austausch-gekoppelten Mn-Ionen. Eine kritische Konzentration fur Fernordnung, xc equals; 0,5, wird bei MnxMg1−xY2S4 gefunden; dies stimmt uberein mit einer Austausch-Wechselwirkung jedes Mn-Ions mit funf benachbarten Metall-Ionen. Die Magnetisierung sehr verdunnter Losungen MnxMg1−xY2S4 bei tiefen Temperaturen und hohen magnetischen Feldern zeigt die Existenz von Paaren von Austausch-gekoppelten Mn-Ionen. Aus den Werten wird ein isotroper Heisenberg-Austausch J = −15,6 K berechnet, in guter Ubereinstimmung mit dem aus der Hochtemperatursuszeptibilitat abgeleiteten Wert.
Deep Learning-based Selection Strategy of Polling Switch for Low-cost and High-accuracy Flow Statistics Collection in SDN

The key challenge of network monitoring and measurement in software-defined networking (SDN) is how to design a high-accuracy flow statistics collection (FSC) scheme with minimum polling cost. At present, there are two kinds of FSC mode, such as push-based FSC and pull-based FSC. As for the most studied pull-based mode, in response to the challenge, there are some FSC strategies, such as sampling, task decomposition and minimum polling switches. However, most of them collect flow statistics with partial collection, linear fitting and known flow characteristics. With the advantages of deep learning in data coverage, nonlinear fitting and knowledge discovery, we proposed a deep learning-based selection strategy of polling switch for low-cost and high-accuracy flow statistics collection in SDN. In our strategy, we select the switches that are intensively traversed by the top-k shortest link paths with bandwidth load balancing as polling switches. Moreover, to minimize the communication cost of the FSC on unknown flow, the deep learning technique is used to predict the flow forwarding path, and the switch in the predicted path whose polling communication cost is smaller than that of any existing polling switch is selected as the new polling switch. The experimental results show that, compared with PFP (per-flow polling) and Greedy, the FSC average delay of our strategy is reduced by 30.2026% and 14.2042%, the communication cost is reduced by 60.0494% and 35.7943%, and the non-redundant flow ratio is increased by 13.9883% and 8.7765%, respectively.
Multi-Dimension CNN for Hyperspectral Image Classificaton

Hyperspectral remote sensing plays role in the field of earth observation research because of rich spatial, radiation and spectral information. With the rapid development of deep learning, deep neural networks are widely used in hyperspectral remote sensing image classification tasks, but at the same time, a series of difficulties have arisen, such as high demand for training samples, time-consuming model training. Convolutional neural network (CNN) is well known for its capability of feature learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, the one-dimension CNN (1D-CNN) modules was added after the two-dimension CNN (2D-CNN), so the complete network structure contains two different dimension CNN, called multi-dimension CNN (MD-CNN). The proposed 1D-CNN blocks can continue to learn contextual features and is expected to have more discriminative power. Experimental results with hyperspectral image benchmark datasets demonstrate that the proposed method can outperform the state-of-the-art CNN-based classification methods.
A General Formula for the Half-Power Beamwidth of 1-D Unidirectional Leaky-Wave Antennas

A general and accurate formula for evaluating the single-sided beamwidth in one-dimensional unidirectional leaky-wave antennas has been recently proposed. The half-power beamwidth is then estimated by simply doubling the value of the single-sided beamwidth. However, this approximation may lead to inaccurate results as the beam approaches endfire; at that point, the left- and the right-sided beamwidth may considerably differ one another. This conference paper proposes and numerically validates a more general formula to solve this issue.
Energy-Aware Dynamic Computation Offloading in High-Speed Railway Networks with D-TDD

This paper investigates the energy-aware dynamic resource allocation and computation offloading in high-speed railway networks with dynamic time division duplex (D-TDD), where all tasks of passengers in the high-mobility train are aggregated to be a "big task", and the vehicle server (VS) deployed on the train completes the "big task" by two processing modes, i.e., local computing and vehicular edge offloading. For such a system, an optimization problem is formulated to minimize the energy consumption by jointly optimizing the size of task block, the offloading ratio, the transmit power and the central processing unit (CPU) speed of VS with multiple system constraints. As the problem is non-convex with unknown solution, a bi-level optimization method is proposed to solve it. Via one-dimensional searching and Lagrangian multiplier method, the global optimal solution is obtained with high computational complexity. Then, a suboptimal approach is proposed with low computational complexity. Numerical results show that the performance of our proposed scheme significantly outperforms some other benchmark schemes, and a great performance gain is obtained via employing D-TDD. Besides, the effect of Doppler shift on the system performance is also discussed.
Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications

The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm’s accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.
A Dual-Band Antenna Backed by AMC Surface Using Genetic Algorithm for 2.4/5.8 GHz Underground Mining Communications

In this paper, a novel design of dual-band antenna using a genetic algorithm (GA) for wireless body area network (WBAN) device is proposed. It covers the Industrial, Scientific and Medical (ISM) bands of 2.45 GHz and 5.8 GHz. An AMC reflector is utilized to isolate the human body from undesired electromagnetic radiation and improve the antenna radiation characteristics. Moreover, the proposed antenna is placed on a human equivalent flat phantom in order to evaluate the performance of the antenna on a human body. The simulated results indicate that the proposed antenna is a good candidate for WBAN applications.
Class-Wise Adversarial Transfer Network for Remote Sensing Scene Classification

In this paper, we proposed a class-wise adversarial transfer network (CATN) for remote sensing scene classification. A class-wise discriminator is used to achieve conditional alignment on a per class basis. The CATN employs deep convolutional neural network to learn domain invariant classification layer features in a class-conditional manner. The classification probability output of the target data is utilized to determine the weights of the class adversarial losses. The proposed method can promote positive adaptation, and does not need labeled instances in the target domain. The experiments results using two remote scene image data sets with different resolution indicates its good performance.
Hyperspectral Unmixing via Recurrent Neural Network With Chain Classifier

Recently, the development of deep learning brings new opportunities for hyperspectral unmixing. In this paper, we propose a new architecture based on Recurrent Neural Networks and Bidirectional-LSTM (BiLSTM) that consists of two blocks: the feature extraction stage based on BiLSTM and the abundance estimation stage via Chain Classifier. BiLSTM can capture the long-distance relation among spectral bands better and Chain Classifier is more suitable to deal with multi-label task that exists in abundance estimation process. We evaluate the proposed method on two real HSI data sets including Jasper Ridge and Urban compared with three state-of-the-art approaches, and the results show significant improvements in accuracy of unmixing.
Interweave Shared-Use Model for Dynamic Spectrum Access in Millimeter-Wave Mobile Systems for 6G

In this paper, we propose an interweave strategy based shared-use (ISSU) model to share the licensed 28 GHz millimeter-wave (mmWave) spectrum of one mobile network operator (MNO) with another in a country. The proposed ISSU model considers sharing the whole mmWave spectrum of one MNO, termed as primary MNO (p-MNO), with small cells deployed in any building of another MNO, termed as secondary MNO (s-MNO), only if no user equipment (UE) of p-MNO is present inside the corresponding building of s-MNO to avoid co-channel interference between UEs of p-MNO and s-MNO. We derive average capacity, spectral efficiency (SE), and energy efficiency (EE) of an s-MNO in a country. Extensive numerical and simulation results and analyses for an s-MNO of a country consisting of four MNOs show that the maximum amount of shared mmWave spectrum obtained using ISSU is 1.5 times the licensed spectrum of s-MNO. This results in improving both average capacity and SE by about 150%, whereas EE by about 60%, of s-MNO. Finally, we show that the proposed ISSU model can satisfy the expected SE and EE requirements for sixth-generation (6G) mobile systems by reusing the mmWave spectrum of s-MNO to its small cells of roughly 56.67% less number of buildings.
The Use of Artificial Neural Networks for Predicting Response of Frequency Selective Surfaces

In the real world problems, mostly there is difference between dimensions of simulated and corresponding fabricated structures. In case of Frequency Selective Surface (FSS), this variation can be even larger as the frequency gets into terahertz range. One of the approach to study such variations is metamodeling. In this work, we address the problem of modeling such structures with statistical variations in their geometries by using artificial neural network (ANN). We train and test this metamodel and finally show its performance statistics.
Joint Group Sparse Collaborative Representation for Hyperspectral Image Classification

Collaborative representation (CR) has attracted great interest in hyperspectal imagery (HSI) classification because of its efficiency. However, existing CR-based classifiers ignore the group structure characteristics among the training pixels. In this paper, a group sparse CR with Tikhonov regularization (GSCRT) classifier is proposed to consider the group prior information. In order to fully utilize both spatial and spectral information, we further propose joint GSCRT (JGSCRT) based on the idea that pixels belonging to the same class in the neighboring region should have similar group sparse constraint. Considering the limitations of traditional class decision based on the reconstruction error of a single pixel, the introduction of local decision rule can improve the overall classification accuracy by reducing the misjudgment of pixels within the class. The experimental results on University of Pavia dataset show that the proposed methods outperform other CR-based classifiers.
An Experimental Evaluation of Data Classification Models for Credibility Based Fake News Detection

The existence of fake news is a problem challenging today's social media enabled world. Fake news can be classified using varying methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research attempts to investigate nine such machine learning algorithms to understand their performance with Credibility Based Fake News Detection. This study uses a standard dataset with features relating to the credibility of news publishers. These features are analysed using each of these algorithms. The results of these experiments are analysed using four evaluation methodologies. The analysis reveals varying performance with the use of each of the nine methods. Based upon our selected dataset, one of these methods has proven to be most appropriate for the purpose of Credibility Based Fake News Detection.
Electrical properties and microstructure of vanadium-based contacts on ICP plasma etched n-type AlGaN:Si and GaN:Si surfaces

Light-emitting diodes emitting in the UV-B spectral range usually contain an n-type AlxGa1–xN contact layer with x of about 0.4 and require a low specific contact resistivity in the range of 10−6 Ω cm2 to operate at their optimal capacities. We compare the Ti/Al/Mo/Au metal system commonly used on GaN with the V/Al//V/Au metal system on n-Al0.4Ga0. 6N:Si and GaN:Si surfaces. On Al0.4Ga0. 6N, the lowest specific contact resistivity, (2.3 ± 0.2) ×10−6 Ω cm2, was reached with V/Al/V/Au whereas for Ti/Al/Mo/Au the lowest resistivity was (1.7 ± 0.7) ×10−3 Ω cm2. Independently of the metal system, lower contact resistivities are obtained on GaN where Ti/Al/Mo/Au is still performing at least one order of magnitude better than V/Al/V/Au. The contact resistivity of V/Al/V/Au on Al0.4Ga0. 6N was found to be sensitive to surface treatments (wet chemical etching or sputtering) applied prior to metal deposition, to the thickness of the first vanadium layer and to that of the aluminum layer above. Structural and compositional investigations showed that a low contact resistivity is accompanied by the formation of an interfacial layer between AlGaN and V/Al/V/Au on the nm-scale, which contains aluminum, in the form of aluminum nitride, and separately vanadium as metal or metal nitride.
Non-Foster Self-Oscillating Huygens Radiator

A recently proposed concept of a self-oscillating non-Foster antenna is extended to a Huygens radiator. The system is comprised of two orthogonal pairs of electric and magnetic near-field parasitic radiators, each of which is driven by a small electric dipole. The input ports of the driving dipoles are connected to a floating negative impedance converter, thus forming a broadly-tunable and nearly perfectly matched self-oscillating system. The developed concept is verified with a series of full-wave/circuit theory (CST/ADS) co-simulations. Obtained results reveal self-oscillations that can be tuned within a 1: 1.5 frequency range, with familiar cardioid radiation patterns.