Miracle-dz/UniFAR-FTUs huggingface dataset:
An In Situ Study of the Electrochemical Reaction of Li with Nanostructured Sn30Co30C40

The reaction of lithium with nanostructured Sn 30 CO 30 C 40 alloy prepared by mechanical attrition was studied using in situ X-ray diffraction (XRD) and in situ 119 Sn Mossbauer effect spectroscopy. During the first discharge (insertion of Li into the alloy) of Li/Sn 30 Co 30 C 40 cells, similarities between the structures of fully lithiated Sn 30 Co 30 C 40 and lithiated Sn were observed by XRD. Both XRD patterns showed evidence for the Sn tetrahedra, with neighboring Li atoms, as found in Li 22 Sn 5 . This implies that fully lithiated Sn 30 Co 30 C 40 is composed of nanoscale regions of Li 22 Sn 5 , nanoscale Co, and lithiated disordered carbon. In situ 119 Sn Mossbauer spectroscopy results were consistent with this interpretation and showed the presence of unlithiated Sn atoms at the bottom of the discharge. This observation agrees well with the fact that attrited Sn 30 Co 30 C 40 samples only reach about two-thirds of the theoretical capacity. During the first recharge, in situ results from both techniques show that Co is rebonded to the Sn atoms in the CoSn grains as Li is removed. Overall, the insertion of lithium in nanostructured Sn 30 Co 30 C 40 is believed to proceed via the following reaction where only the fully charged (left) and fully discharged (right) states are indicated: [132(1 - z) + 40y]Li + Sn 30 Co 30 C 40 ↔ (1 - z)(6Li 22 Sn 5 + 30Co) + zCoSn + 40Li y C, where y ≈ 0.5.
Near infrared spectroscopy: A diagnostic tool to evaluate effects of radiotherapy in the mandible?

Purpose. This study tests the hypothesis that near infrared spectroscopy can be used to detect changes in haemoglobin oxygenation status in the mandible, and therefore can be used to monitor the deleterious effects of radiotherapy and the possible reversion of these effects with therapeutic ultrasound. Methods. A probe was used to calculate the concentrations of deoxyhaemoglobin in the mandible bone of 30 volunteers with no known malignancies and 10 patients with malignancies in the head and neck region treated with radiotherapy. Results. Although the variability of the measurements was very high, when comparing the right side to the left side of the mandible, the measurements remained relatively similar. There was a great variability between the data for each patient, there was no correlation with age. Conclusions. The near infrared spectroscopy validation for the measurement of deoxyhaemoglobin concentrations in the mandible showed that the variability of the measurements was very high, therefore it is not appropriate to be used diagnostically for the evaluation of radiotherapy effects on the mandibular blood flow and metabolic status. Supply of oxygen is a vitally important function of the local tissue circulation. Along with the nu- trients necessary for life, it is taken up from the cells in the capillary blood and used in the oxidative metabolic process; at the same time, the metabolites are eliminated. Adequate blood flow is required to supply oxygen to the tissue. Physiological blood flow in the tissue requires an anatomically and phys- iologically intact vasculature. If this is impaired as a result of a disease, a state of oxygen deficiency occurs. Depending on its extent, this oxygen deficiency can lead to cell death and tissue necrosis. The timely clinical detection and rectification of a reduced oxygen supply are of crucial importance for the scale of therapy and successful treatment (47).
Self‐Assembled CuO Monocrystalline Nanoplatelets in Ionic Liquids

Two‐dimensional (2D) CuO nanoplatelets were synthesized in the presence of ionic liquids under the mild hydrothermal conditions at 140°C by self‐assembly. The thickness of CuO nanoplatelets was about 35 nm prepared in 1‐butyl‐3‐methylimidazolium chloride. XRD, FESEM, and TEM were used to characterize the structure features of the products. FTIR spectra and TG curves confirmed the adsorption of ionic liquids on the surface of CuO nanoplatelets. In addition, UV‐visible absorption spectra were employed to estimate the band gap energies of the CuO nanoplatelets as nanosized semiconductors. It was demonstrated that the ionic liquids could act as a cosolvent, soft template agent and modifiers.
Energy-efficient Two-Way Full-duplex UAV Relaying Networks With Imperfect Channel State Information*

An energy-efficient two-way (TW) full-duplex (FD) network with the assistance of an unmanned aerial vehicle (UAV) is proposed, where the UAV acts as a mobile relay to assist the information exchange between two terrestrial transceivers. In particular, the self-interference (SI) channel gains follow complex Gaussian distribution and the perfect channel state information (CSI) of SI channels is unavailable at the receiver. To maximize the energy efficiency (EE), UAV flight speeds are controlled and power adaptation at the UAV relay is performed. The genetic algorithm (GA) is applied to efficiently obtain the optimal solution. Numerical results show that our scheme performs better than the one-way (OW) FDR scheme, fixed power (FP) and fixed flight speed (FS) policy. In addition, the SI cancellation factor on the EE is also demonstrated.
Requirements on Delay of VRU Context Detection for Cooperative Collision Avoidance

Every year, approximately 350 000 vulnerable road users (VRUs) still lose their lives in road traffic accidents world-wide. To reduce this number, cooperative VRU collision avoidance systems aim at complementing current vehicle or infrastructure-based systems for VRU protection. The cooperative approach assumes user equipment (UE) like a smartphone or a smartwatch on the VRU side, which allows to obtain and exchange movement and contextual information with other road users for determining the current risk level for a collision. In recent publications, the usage of additional contextual information, such as the pedestrian’s current activity (e.g., standing, walking or crossing a curb), has been shown to improve the collision detection accuracy. However, those approaches focused on the detection accuracy, but did not investigate how time delays for both detection and communication of VRU contexts affect the ability to detect collisions. We fill this gap by investigating the influence of both activity recognition and communication delays on the collision detection performance. As a baseline, we use the standardized Euro NCAP test protocol. For the evaluation, we exemplarily consider the usage of a curb detection module for position correction, which improves the collision detection accuracy at the moment the pedestrian crosses the curb.
Envelope Correlation Coefficient for Multiple MIMO Antennas of Mobile Terminals

This paper introduces a new figure of merit called envelope correlation coefficient for multiple MIMO antennas (ECC-M) for mobile terminals working in a Rayleigh fading channel. The figure of merit is proved to be effective to reflect the impact of the radiation properties of an antenna array to the system channel capacity. The correlation of the ECC-M to the channel capacity of a MIMO system is demonstrated by simulating the channel capacity of four realistic MIMO antenna arrays using Monte-Carlo method. The proposed ECC-M can be used to evaluate the impact of a MIMO antenna array with more than two elements to the data throughput.
Compact and Reconfigurable Multiband 2-Element MIMO Slot Antenna for Advanced Communication Systems

In this work, a compact, multiband and reconfigurable MIMO antenna for wireless systems is designed and presented. The antenna element is composed of three open-end quarter-wave slots fed by proximity coupling via a 50 ohms microstrip feed line. The total area of the MIMO antenna is 24 $\mathbf{x\ 25\ mm}^{\mathbf{2}}$. For the decoupling mechanism, T-shaped and vertical slots are embedded between the two elements of the antenna. As a result, the MIMO antenna achieves a maximum isolation of 25 dB (15 dB of improvement), 12.5 dB (4.5 dB of improvement) and 35 dB (15 dB of improvement) at frequencies of 2.4 GHz, 3.5 GHz and 5.25 GHz, respectively. In addition, the envelope correlation coefficient (ECC) is less than 0.02 and the gain diversity (DG) is more than 9.9 for the three considered operating bands. The proposed reconfigurable antenna can be switched between simple, dual and triple band modes, which is a good candidate for use in cognitive radios and wireless systems.
Machine Learning Approach to Diagnose Schizophrenia Based on Effective Connectivity of Resting EEG Data

Schizophrenia is a severe mental disorder associated with neurobiological deficits. Despite the fact that the brain activity during tasks (i.e. P300 activities) are considered as biomarkers for diagnosing schizophrenia, brain activities at rest has the potential to reveal an intrinsic dysfunctionality in patients with schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we develop a machine learning (ML) algorithm based on eye closed resting-EEG data sets aiming to distinguish 63 schizophrenic patients (SCZs) from 70 healthy controls (HCs). The ML algorithm has three steps. In the first step an effective connectivity named symbolic transfer entropy (STE) is applied to the EEG waveforms. In the second step brain network properties are constructed from STE. In the third step the ML algorithm is applied to brain network properties to determine whether a set of features can be found that successfully discriminates SCZ from HC. The findings of this study revealed that the most discriminating features are shortest-path-length between different brain regions that could achieve an accuracy of 96.15%, with a sensitivity of 100% and specificity of 92.86% using 20% of data samples as test dataset that is not used for training. These findings imply that resting EEG could contribute to our ability to distinguish SCZs from HCs, and that the STE effective connectivity may prove to be a promising tool for the clinical diagnosis of schizophrenia.
Monogenic Wavelet Phase Encoded Descriptors for Brain Tumor Image Detection

Brain tumor has a low survival rate and also affect a patient’s social life. Early detection and further treatment of the abnormal growth of mass is a significant step during treatment to restrict the progression. MR image screening by the medical expert is a time-consuming and tedious task. This paper presents the development of computer-aided tool to detect brain tumor images. The proposed algorithm employs monogenic wavelet phase-encoded features for tumor detection. Phase component of the monogenic wavelet efficiently extracts the structural information from the input magnetic resonance images. The dimensionality of CLBP textural descriptors extracted from the phase component is further reduced using neighborhood component analysis feature selection. Finally, the support vector machine classifies the test magnetic resonance image as healthy or abnormal. The proposed approach is evaluated using two popular MR imaging databases and simulation results show enhanced performance compared to other existing algorithms.
Small Signal Model of VSC-HVDC Considering the Impact of Time Delay

The time delay of the control system is an important factor leading to the high-frequency resonance in VSC-HVDC. In this paper, a two-level VSC-HVDC electromagnetic transient (EMT) model is established for the research, and time delay links are added to the control system of the two-level VSC-HVDC EMT model to activate the high frequency resonance phenomenon. Then, the small signal model of the VSC-HVDC system considering the time delay is developed by adopting the Pade approximation to represent the time delay links. The dynamic response under power step change of the small signal model is compared with those of EMT, the correctness of the model is verified. Finally, the influence of time delay on the small signal stability of the VSC-HVDC system is analyzed by eigenvalue analysis.
A Novel and Efficient Link Discovery Mechanism in SDN

Software Defined Networking (SDN) is one of the hottest technology in the network field today. SDN divides the network into the data plane and control plane. The control plane needs to monitor the underlying network and provides an up-to-date global network view for the applications running on it by topology discovery. The most important one in topology discovery is link discovery. Based on the characteristics of SDN, most implementations use OpenFlow Discovery Protocol (OFDP), which is simple but wastes a lot of bandwidth resources on the control plane. In this paper, we divide link discovery into two stages and propose some modifications and extensions based on OFDP for the second stage. Our approach divides the initial network view into multiple paths, and each path contains multiple links. At the cost of sacrificing a small amount of flow table resources, our approach enables one probe packet to be responsible for the detection of a path, thereby significantly reducing the number of sent and received probe packets, and reducing the consumption of control plane’s bandwidth resources.
Identifying Settlements Using SVM and U-Net

Deep learning (DL) has shown promising results, especially in the area of computer vision, making it suitable for remote-sensing image analysis tasks such as land cover classification. However, the increased accuracy comes at a cost of increased training data requirements. This paper showcases our approach to address this shortcoming by using machine learning to bootstrap the process. To achieve this aim, supervised classification has been carried out on Landsat 8 imagery using Support Vector Machines (SVM). Out of the five classes segmented using traditional supervised classification (SVM), urban class is taken into consideration for training the DL model. The dataset is manually cleaned before feeding it into the DL model for training. The model achieves an accuracy of 0.92 at identifying settlements after 1,100 training iterations. This approach saved time required to prepare training data for the DL model, while producing highly accurate results. Such a process can reap the benefits of DL for geospatial mapping, while reducing the requirement to start with copious amounts of labelled training data.
Influence of Aleatoric Uncertainty on Semantic Classification of Airborne Lidar Point Clouds: A Case Study with Random Forest Classifier Using Multiscale Features

For semantic classification of LiDAR point clouds, the features derived from the local geometric descriptors are routinely used as features in (supervised) learning algorithms. In this study, our goal is to determine if the aleatoric uncertainty in the input to a supervised semantic classifier influences the outcomes. We consider two sources of such uncertainty – one from the computation of multiscale local geometric descriptors and the other, from class ambiguities at object boundaries. We perform ensembles of experiments to measure the significance of these uncertainties in the semantic classification of airborne LiDAR point clouds, when using random forest classifier. Our case study shows that the presence of aleatoric uncertainty improves the classification outcomes.
A Novel Multitask Adversarial Network via Redundant Lifting for Multicomponent Intelligent Fault Detection Under Sharp Speed Variation

Playing a vital role in the safe operation of equipment, intelligent fault detection has been extensively applied in mechanical systems. However, the complicated mapping of signal symptoms and multicomponent failure modes under sharp speed variation makes it hard. The classification error is large due to insufficient samples in the fault condition. Aiming at these problems, a novel multitask redundant lifting adversarial network (MRLAN) is proposed. First, inspired by redundant second-generation wavelet transform, a reconstruction network is established to generate high-quality time–frequency representation of vibration signal, thus eliminating speed fluctuations and expanding training samples. The reconstruction network consists of transposed convolution layer, resume update layer, resume predict layer, and merge layer. Then, multitask category discriminator, which forms dual discriminator together with distance discriminator, is constructed to separate the features of multiple components, thereby realizing multiple tasks (multicomponent fault location and single-component health assessment). Finally, the model is alternately optimized through adversarial process between the generation network and the double discriminators. The proposed method is validated by two cases and other methods are also established to show its superiority. The results show satisfactory performance of MRLAN in multicomponent fault diagnosis under sharp speed fluctuation and little training data.
Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

In this paper, the definition of three-dimensional generalized discrete fuzzy number (3-GDFN) is introduced based on the representation theorem of one-dimensional discrete fuzzy number and the similarity measure definition of two 3-GDFNs is given. Then the concept above mentioned is applied to color image representation and color mathematical morphology (CMM) in RGB space. The basic morphology operators, erosion and dilation, are extended to the CMM by defining the total preorder relation based on similarity measure between two 3-GDFNs instead of general vector sorting methods. The corresponding structuring elements in CMM are variable. Finally, the effectiveness and potential of the theoretical results are verified by comparative experiments. The proposed CMM operators are efficiently used in color image processing.
Robust Low Complexity Beamforming for Cell-Free Massive MIMO

In this paper, the robust transceiver design is investigated for "cell-free" massive MIMO system which is deemed as one of the promising technology for the future mobile communication network. When constructing the optimization problem, different access point (AP) sets are considered for different user equipment (UE) to simulate the true "UE-centric" system. Besides, the idea of interference leakage is used so that the overall optimization problem could be separated into several subproblems that can be carried out in parallel. To make the algorithm more practical, a low complexity robust design is proposed based on alternating direction method of multipliers (ADMM) algorithm. At the end of the paper, it is demonstrated by the simulation that the proposed robust algorithm has better performance than the non-robust design.
A Scheduling Scheme for Cooperative Merging at a Highway On-Ramp with Maximizing Average Speed of Automated Vehicles

Recently, connected and automated vehicles have potential to mitigate traffic congestion and improve traffic efficiency at a highway on-ramp. Especially, vehicle to infrastructure communication based scheduling schemes are paid attention because a central control server can assign merging timing to automated vehicles to improve traffic efficiency on whole roads. Although several schemes calculate merging timing based on their criteria, these schemes cause traffic congestion when inflow traffic becomes high. In order to solve this problem, it is necessary to schedule merging timing of vehicles such that density of vehicles decreases on whole roads. In this paper, we propose a scheduling scheme for cooperative merging at a highway on-ramp with maximizing average speed of automated vehicles. The main idea of the proposed scheme is to utilize the traffic model that density and speed of vehicles have a negative correlation. By utilizing the traffic model, the proposed scheme preferentially assigns merging timing to vehicles which are on a congested road by maximizing average speed of all vehicles on whole roads. Simulation result shows that the proposed scheme decreases density of vehicles on whole roads even if inflow traffic becomes high. Furthermore, the proposed scheme improves outflow traffic from a highway on-ramp by 400 (veh/h) at most in comparison with related schemes.
UAV-based FSO Systems using SC-QAM Signaling over Fading Channels with Misalignment

In this paper, we study the performance of unmanned aerial vehicle (UAV)-based free-space optical (FSO) systems over atmospheric turbulence channels considering the pointing misalignment and UAV hovering. The Gamma-Gamma distribution is used for modeling the turbulence-induced fading and the Gaussian beam is assumed for laser beam between two UAVs at the low and high altitudes. A closed-form expression for the symbol error probability is derived and validated by Monte-Carlo simulations. The selected results show the impact of some key factors such as UAV position and beam-waist on the system performance so that the practical system parameters can be determined.
Preparation, electrochemical properties, and adsorption kinetics of Ni3S2/graphene nanocomposites using alkyldithiocarbonatio complexes of nickel(II) as single-source precursors

The Ni3S2/graphene nanocomposites were designed and successfully prepared by a facile hydrothermal method, using nickel(II) alkyldithiocarbonatio complexes [Ni(S2COC6H11)2] and graphene oxide as the raw materials. Graphene sheets were covered with Ni3S2 nanoparticles having diameters in the range of 8–28 nm. Electrochemical properties showed that Ni3S2/graphene have a lower resistance and a better electrochemical activity. Moreover, the pseudo second-order kinetic model can well-describe the adsorption kinetics of methyl orange on Ni3S2/graphene.
Evaluation of α_s1 for Building Damage Mapping based on Touzi Decomposition

A prompt and accurate assessment of building damage is critical for disaster assistance and emergency monitoring. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective methods in damage assessment, owing to its all-weather/all-time observation ability and abundant polarization scattering information of target. In this study, we discussed the distribution law of Touzi decomposition polarization parameters before and after building collapse, based on the ALOS PALSAR data in the L band and the simulated SAR images in the Ku band. The experimental results show that the as1 component obtained by Touzi decomposition can be used for building damage assessment, and is universal in L-band and Ku-band SAR images.
Dynamic Spectrum Slicing and Optimization in SAG Integrated Vehicular Networks

In this paper, we propose an online control frame-work to dynamically slice the network resource for isolated service provisioning in Space-Air-Ground integrated Vehicular Network (SAGVN). In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which can be achieved via the Lyapunov optimization theory. By bounding the drift-plus-penalty, the problem then can be decoupled into four independent subproblems, which are readily solved. The merits of our control framework are three-fold: 1) the system can admit and process as many requests as possible; 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues can be stabilized over time. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning.