Miracle-dz/UniFAR-FTUs huggingface dataset:
Traveling-wave microstrip array antenna using substrate integrated wavequide

Reconfigurable antennas have an increasing demand in communication systems due to their compatibility with variable system demands, environment conditions. With respect to their application need reconfigurable can change their, propagation pattern, frequency and polarization characteristics autonomously. Moreover, the ability of pattern steering has an utmost importance in, RADAR applications. By using leaky-wave antenna model in RADAR arrays, a small increase in frequency band can be provided by which an effective continues scanning can be achieved. By increasing the frequency of the array element it is possible to steer the pattern of array for providing a scanning in time, range and direction axis. Similarly by changing the frequency values it is possible to optimize the range and propagation pattern of the array. Herein, design of a frequency depended pattern steering Leaky-wave antenna in operation band of 6.8–8.2 GHz had been presented. The gain of the proposed antenna is around 7–10 dB and the main beam direction is steerable with the range of 0∼33 degree.
Concept of the phase reference line for the European Spallation Source

The 600 m long accelerator and beam transport of the European Spallation Source requires precise phase synchronization of LLRF and Beam Diagnostics systems. Accelerator subsystems are operating at frequencies of 352 MHz and 704 MHz. The required phase accuracy at both frequencies is 0.1° for short term (during 3.5 ms pulse), 0.1° for long term between adjacent outputs and 2.0° for long term (hours to days) between any two points. The main part of the phase reference distribution system is a Phase Reference Line (PRL), which is a fully passive system based on a single 1–5/8” coaxial rigid line placed along the tunnel. The PRL was designed to distribute both reference frequencies from a Master Oscillator to 56 tap points in the tunnel. Each tap point has several (3 or 6) signal outputs. The total length of the PRL is around 580 meters and the total number of signal outputs equals 294. To achieve the best drift performance and fulfill ESS synchronization requirements, the PRL will be equipped with temperature stabilization system (+/-0.1 oC) and with an internal gas pressure stabilization system. This paper covers the concept of the PRL, technical assumptions, components selection considerations, general modeling results and initial tests of the ESS PRL prototype.
Intestinal Microbiota Disruption Reduces Regulatory T Cells and Increases Respiratory Viral Infection Mortality Through Increased IFNγ Production

Alterations in gastrointestinal microbiota indirectly modulate the risk of atopic disease, but effects on respiratory viral infections are less clear. Using the murine paramyxoviral virus type 1, Sendai virus (SeV), we examined the effect of altering gastrointestinal microbiota on the pulmonary antiviral immune response. C57BL6 mice were treated with streptomycin before or during infection with SeV and resulting immune response studied. Ingestion of the non-absorbable antibiotic streptomycin led to a marked reduction in intestinal microbial diversity without a significant effect on lung microbiota. Reduction in diversity in the gastrointestinal tract was followed by greatly increased mortality to respiratory viral infection (p < 0.0001). This increase in mortality was associated with a dysregulated immune response characterized by decreased lung (p = 0.01) and intestinal (p = 0.03) regulatory T cells (Tregs), and increased lung IFNγ (p = 0.049), IL-6 (p = 0.015), and CCL2 (p = 0.037). Adoptive transfer of Treg cells or neutralization of IFNγ prevented increased mortality. Furthermore, Lin−CD4+ cells appeared to be a potential source of the increased IFNγ. Together, these results demonstrate gastrointestinal microbiota modulate immune responses at distant mucosal sites and have the ability to significantly impact mortality in response to a respiratory viral infection.
Prediction of stock returns in Istanbul stock exchange using machine learning methods

The stock market parameter forecasting is an important research subject both for financial professionals and the machine learning experts due to the challenges and opportunities it possess. Despite the difficulties in financial data, interest in this research area is growing rapidly. In this study we examined the data of the 25 leading companies of Borsa Istanbul and applied several estimation algorithms. As a result, we observed that the Random Forest algorithm gave the best result with an accuracy of 57.37%.
Online parameters updating method for least squares support vector machine using Unscented Kalman filter

It is well known that the performance of Least Squares Support Vector Machine (LSSVM) is guaranteed by k-fold cross-validation (k-CS) or other optimized approaches to choose an appropriate setting of a number of parameters including such as the regularization parameter (7) and the kernel parameter (σ) and so on. However, it is mentioned in a large number of research on k-CS that it need more computational time and large computational burden in the process of optimizing parameters for LSSVM. Moreover, the obtained parameters by CS method are fixed and it lead easily to a poor generalization capabilities in various application. In order to avoid k-CS and to implement parameters update online, this paper proposes a novel method which applied Unscented Kalman Filter (UKF) to dynamically implement parameter updating problem for LSSVM. To estimate LSSVM's parameters online, the state and measurement equations of UKF are first constructed by considering LSSVM's parameter choice as state variable and treating LSSVM model as the measurement equation, respectively. Then, the UKF approach is used to update the LSSVM parameters timely according to the last obtained instance. Applying the proposed method, LSSVM parameters are not any more fixed as tuned parameters on the training dataset, but are adjusted dynamically as new measurements arrived. Finally, the viability and superiority of the proposed method are verified by simulation.
A method for quadruplet sample selection in deep feature learning

Recently, the deep learning based feature learning methodologies have been developed to recognize the objects in fine-grained detail. In order to increase the discriminativeness and robustness of the utilized features, this paper proposes a sample selection methodology for the quadruplet based feature learning. The feature space is manipulated by using the hierarchical structure of the training set. In the training process, the quadruplets are selected by considering the distances between the samples in the feature space in order to improve the effectiveness of the training. We have shown by the experiments that the proposed method improves the fine-grained recognition accuracy.
DFBICA: A new distributed approach for sentiment analysis of bibliographic citations

Sentiment analysis of citations in scientific papers is a new and interesting research area. In this paper, we focus on the problem of automatic identification of positive and negative sentiment polarity of citations in scientific papers. In this work, we conducted empirical research to investigate the classification of positive and negative citations. It is based on word vectors as a feature space, to which the examined citation context was mapped to. In order to handle with the huge amount of data, we have implemented our proposed approach in a distributed manner according to MapReduce paradigm through the Hadoop framework.
Bond graph modeling and fault injection of CRH5 traction system

CRH5 electric multiple unit (EMU) traction system is a multi-domain mechatronical system with complex structure. Mechanisms on various faults or failures are not clear yet. To develop accurate system model for fault prognosis, diagnosis and propagation analysis, bond graph (BG) theory is adopted due to its unique ability in multi-domain modeling and clear cause-effect representation. With the aid of a commercial software (20-SIM), a BG topological structure model which contains inverter, motor and gearbox, and direct torque control (DTC) unit have been developed, where DTC control the stator flux and electromagnetic torque. Common faults in EMU traction system such as motor and sensor faults are injected into the BG model. Broken rotor bar fault in driving motors is simulated by increasing gradually the number of broken bars. Three kinds of speed sensor faults are also injected to analyze their influences to the whole traction system. The simulation results are well in accordance with real CRH5 traction systems. The developed model can provide a useful platform for the study and verification of fault prognosis, diagnosis and propagation methods.
Analysis of electrical impedance tomography sensitivity field based on electroencephalograph electrodes model

In recent years, brain function imaging based on EIT has become a hot research topic. In this paper, the Electroencephalograph(EEG) electrode cap was used as the measuring electrode array of EIT, to analyse the imaging sensitivity field of EIT. Firstly, we built a 3D head model with a 16-electrode collar, and then compared the differences of sensitivity field while placed the 16-electrode collar on the upper, middle and lower parts of the 3D head model. The results had shown that when the 16-electrode collar was placed between lower parts and middle parts, the quality of sensitivity field was good. When the 16-electrod collar was placed on the upper parts, the quality of sensitivity field was poor.
Message-passing cooperative localization in sensor networks with AOA measurements

This paper proposes an approximate message passing cooperative localizer (AMP-CL) for wireless sensor networks (WSNs) with angle-of-arrival (AOA) ranging measurements and unknown orientation angles. At first, an incremental localizer is developed to provide initial position estimations. Subsequently, orientation angles of WSN nodes are separately estimated based on current node position estimations and AOA observations by maximum-likelihood (ML) method. Based on the current orientation estimations, cooperative localization problem is converted as a generalized linear mixing problem, and resolved by AMP algorithm. Finally, AMP-CL will iterate between AMP-based positioning recovery and ML orientation angle estimation until convergence conditions are satisfied. It is featured on low-computational complexity, and distributed implementation manner. Simulation results also validate the state-of-the-art positioning performance and cooperative gains of AMP-CL after comparing itself with some main-stream localizers under different communication ranges and noise contaminations.
Neural network for finding mathematical formulas in videos from data science conferences

This paper presents a novel approach to the problem of finding mathematical formulas on video frames. Distinguishing equations from other text and objects on given image involves a deeper understanding of individual symbols, patterns, and their relative positions. Standard segmentation methods used for this kind of task will fail immediately because they mostly rely on noticeable differences between color and shape of different objects. By using a fully convolutional neural network and image to image transformation, we were able to achieve state of the art results in finding mathematical formulas in movie frames extracted from data science conferences videos. Current status of the work, results and further development plans are presented.
User-centered ambient assisted living: Brain environment interface

This paper presents a P300-based Brain Computer Interface for the environmental control in ambient assisted living (AAL) context. The system exploits a machine-learning algorithm based on spatio-temporal characterization of the P300, which realizes the binary discrimination from a multiclass classification. The BCI architecture is made up by (i) the acquisition unit, (ii) the processing unit and (iii) the home management. The acquisition unit is realized by a wireless EEG headset collecting data from 6 electrodes. The processing unit is a hybrid PC-Raspberry Pi architecture that: (i) provides stimulation, operates a Machine Learning (ML) and (iii) realizes a real-time multidimensional classification. The ML stage is based on a custom algorithm (t-RIDE) which emphasizes the subjective P300 characteristics and trains the classifiers. The extracted features are functionally reduced and, thus, used to define proper prediction boundaries. The Raspberry-based core classifies and actuates the received commands, interfacing the environment through a dedicated module that manages the home via TCP/IP. The experimental results on a dataset of 7 subjects enable a classification with an accuracy of 84.28% ± 0.87 % in less than 10ms.
Design and development of a UAV Experimental Platform

This paper deals with the design, construction, and instrumentation of a UAV experimental platform. It is formed by a mechanism and a UAV hexacopter. The mechanism allows the movement around of non-inertial frame of the vehicle while restricting the movement around of inertial frame. The UAV hexacopter is fixed to the mechanism and contains an IMU sensor to measure the angular position of the vehicle. The data acquisition, processing and compute of the control signals is developed on board the vehicle through a NI myRIO®. By considering the dynamical behavior of the UAV hexacopter, it can be divided in rotational and translational motion. The stabilization of the rotational dynamics is addressed in this work, where two control loops around the Euler angles roll and pitch are implemented and tested in the experimental platform. The development of the instrumentation and the construction of the experimental platform are presented to explain how the system measures the angular position around the non-inertial frame of the UAV. The main contribution of this work is the design of the experimental platform. The effectiveness and practicality to stabilize a UAV hexacopter are shown through experimental results.
Inspection of Delamination Defect in First Wall Panel of Tokamak Device by Using Laser Infrared Thermography Technique

First wall panels (FWPs), which adjoin the inner wall of the blanket modules in the vacuum vessel (VV) of a Tokamak device, are in structures of multilayer bounded together with a solid welding technique in order to perform its heat exchange, VV protection, and neutron breeding functions. The quality of the welding joint between layers is the key factor for FWP integrity. In order to conduct online inspection of the delamination defect in the FWPs, a nondestructive testing (NDT) method capable to detect delamination defect without accessing into the VV is required. In this paper, the feasibility of the laser infrared thermography (LIRT) testing NDT method was investigated experimentally for this purpose. To clarify its detectability under practical VV environment, inspections of several inspection modes were conducted based on the practical structure of FWP and VV of the EAST Tokamak device, i.e., modes of different distances and angles of FWPs toward the LIRT transducers. In practice, an LIRT testing system was established and several double-layered plate specimens with different artificial delamination defects were inspected under the selected testing conditions. Through thermography signal reconstruction, an image processing algorithm was proposed and adopted to enhance the defect detectability. From the results of different inspection modes, it was found that the angle factor may worsen the inspection precision and reduce the detectability for delamination defects in case of big defect depth-to-width ratio, even though the LIRT method is still applicable for inspection of relative large defects in FWP. Finally, the detectability in different inspection modes was clarified, which proved the feasibility of LIRT for FWP online inspection.
Enhancing ferroelectric photovoltaic effect by polar order engineering

Destabilizing the polar order unexpectedly boosts the ferroelectric photovoltaic performance in bismuth ferrite. Ferroelectric materials for photovoltaics have sparked great interest because of their switchable photoelectric responses and above-bandgap photovoltages that violate conventional photovoltaic theory. However, their relatively low photocurrent and power conversion efficiency limit their potential application in solar cells. To improve performance, conventional strategies focus mainly on narrowing the bandgap to better match the solar spectrum, leaving the fundamental connection between polar order and photovoltaic effect largely overlooked. We report large photovoltaic enhancement by A-site substitutions in a model ferroelectric photovoltaic material, BiFeO3. As revealed by optical measurements and supported by theoretical calculations, the enhancement is accompanied by the chemically driven rotational instability of the polarization, which, in turn, affects the charge transfer at the band edges and drives a direct-to-indirect bandgap transition, highlighting the strong coupling between polarization, lattice, and orbital order parameters in ferroelectrics. Polar order engineering thus provides an additional degree of freedom to further boost photovoltaic efficiency in ferroelectrics and related materials.
Clinical impact of nasal budesonide treatment on COPD patients with coexistent rhinitis

Background A high percentage of patients with COPD report chronic nasal symptoms. The study aims to evaluate the clinical impact of a 2-month treatment with inhaled nasal budesonide (100 µg per nostril twice daily) in patients affected by COPD with chronic rhinitis comorbidity. Patients and methods Fifty-three stable COPD patients in therapy according to the Global initiative for chronic Obstructive Lung Disease recommendations were enrolled; 49 completed the study. At enrollment (visit 0), patients underwent skin prick test and rhinoscopy. At visit 0 and after 1 month (visit 1) and 2 months (visit 2) of therapy with nasal budesonide, patients underwent spirometry, and COPD assessment test (CAT), Sinonasal Outcome Test (SNOT 22), and modified Medical Research Council dyspnea scale were administered. Differences in continuous variables, after 2 months of treatment with nasal budesonide, were evaluated using a paired t-test or Wilcoxon matched-pairs signed-ranks test. Results Two months of treatment with nasal budesonide showed a significant statistical improvement in the total scores of CAT, SNOT 22, and modified Medical Research Council (p<0.001). A significant relationship between CAT and SNOT 22 total scores at baseline and after treatment was observed. Conclusion The results of the present study indicate the importance of careful evaluation of the presence of chronic nasal symptoms in all COPD patients and suggest beneficial clinical effect from treatment with nasal budesonide in terms of COPD symptoms and quality of life.
Rethinking the data wheel: Automating open-access, public data on cyber conflict

To date, researchers studying cyber conflict through publicly available information sources have either selected on the actor or selected on the intrusion method when coding events. Both approaches lead to distinct challenges when it comes to result validation and the avoidance of selection bias. This article describes prospects for open-source, public data collection for cyber security events. We present an initial data collection and analysis effort of interstate cyber conflict incidents involving the United States as a pilot study. Using a tailored collection of more than 155,000 documents from print-only media sources, we describe a method to process data, parse document elements, and populate an event dataset. Human coders are then tasked with validation of incident information, after which the search code is updated to ensure greater accuracy in subsequent runs. In the study, the data produced are compared with previously available data on cyber conflict involving the United States. We demonstrate that the method can effectively capture and describe cyber conflict incidents for researchers to study in a broad range of research efforts. Moreover, this method captures greater granularity within cyber conflict episodes, which are inherently multi-faceted. This approach to cyber conflict analysis carries with it several distinct advantages over alternative research designs, in that it promises to produce significantly larger amounts of pertinent metadata than might otherwise be possible.
Centroid-based KNN query in mobile service of LBS

With the increasing use of GPS-embedded mobile phone in location-based services (LBSs), people's location privacy is facing considerable challenge. Therefore, we propose a centroid- based method of K-nearest neighbor (KNN) query for LBSs. Unlike previous dummy-based approaches, our method sends a request to the LBS server that does not contain the genuine user location, and only requires the server to return the results of the client needs thus to reduce the downstream communication cost. We also propose an effective KNN(k-nearest neighbor) search algorithm to reduce the processing time of the server side . This algorithm can reduce bandwidth usages and efficiently support KNN queries without revealing the private information of the query issuer. Security analysis and empirical evaluation results verify the effectiveness and efficiency of our scheme.
Adaptive velocity-free feedback relative pose control of cooperative rendezvous and docking

An adaptive output feedback control design methodology is presented for cooperative spacecraft rendezvous and docking system without velocity and angular velocity measurements. A Lagrangian derivation of the rendezvous and docking model is considered to produce position and attitude dynamics for pursue spacecraft relative target spacecraft, assuming that the target spacecraft follows controlled attitude trajectory and transmissions its attitude information to the pursuer real time. Then, a control law is designed to provide a filtered relative velocity and angular velocity measurement and a desired adaptive compensation with uniformly ultimately bounded relative position and attitude tracking under the pursuer inertia uncertainties and bounded external disturbances. The simulation results demonstrate the effectiveness of control algorithm for the cooperative spacecraft rendezvous and docking systems.
XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference

Binary neural networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR neural engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM/standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65- and 22-nm technology for the XNE IP and post-layout results in 22 nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6 fJ per operation at 0.4 V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2 mJ per frame at 8.9 frames/s.