Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compact and Wideband Linear Array Antenna With Low Mutual Coupling

A low mutual coupling array antenna operating in the Ku-band is proposed. To reduce mutual coupling, side metallic walls with a small resistive film are incorporated into both sides of the dipole antenna. These walls can also help impedance matching across the whole Ku-band. By investigating the current density distribution on the side wall with frequency, the appropriate positions of the resistive films are determined. Finally, the design is tested by measurement, which is in good agreement with simulation results. The performance is as follows: the operating bandwidth (OBW) is 11.9–20.3 GHz (52.2%) for the impedance bandwidth (IBW) with −10 dB and the low mutual coupling band with less than −20 dB. The radiation efficiency is over 63.9%. The active element gain is 1.88–4.57 dBi. The measured array gain is 15–16.7 dBi as scanning the beam from 0° to 45° at 16 GHz. The OBW is almost constant up to scan angle of 45°. The overall dimensions of the array structure with 17 elements are $1.2 \times 9.2 \times 0.44 \lambda _{\mathrm {low}}$ at the lowest operating frequency of 12 GHz.
Modified SO-CFAR adaptive detector based on cell selection

To improve the radar adaptive detection performance in multiple interfering targets environments,a new CFAR detector is proposed and its detection performance is analyzed under the Swerling 2 assumption.The models for false alarm rate and detection probabilities in homogeneous clutter background and in the presence of strong interfering targets are theoretically deduced and are compared using MATLAB to other detection methods such as CA-CFAR,GO-CFAR and SO-CFAR,etc.The results indicate that the new method has favorable detection performance in both homogeneous clutter backgrounds and multi-target environments and especially has superiorities in multi-target environments.
MODELLING OF GENERALIZED NEWTONIAN LID-DRIVEN CAVITY FLOW USING AN SPH METHOD

Abstract In this paper a smoothed particle hydrodynamics (SPH) method is introduced for simulating two-dimensional incompressible non-Newtonian fluid flows, and the non-Newtonian effects in the flow of a fluid which can be modelled by generalized Newtonian constitutive equations are investigated. Two viscoplastic models including Bingham-plastic and power-law models are considered along with the Newtonian model. The governing equations include the conservation of mass and momentum equations in a pseudo-compressible form. The spatial discretization of these equations is achieved by using the SPH method. The temporal discretization algorithm is a fully explicit two-step predictor–corrector scheme. In the prediction step, an intermediate velocity field is obtained using a forward scheme in time without enforcing incompressibility. The correction step consists of solving a pressure Poisson equation to satisfy incompressibility by providing a trade-off between the pressure and density variables. The performance of the proposed scheme is evaluated by studying a benchmark problem including flow of viscoplastic fluids in a lid-driven cavity. Both Newtonian and non-Newtonian cases are investigated and the results are compared with available numerical data. It was shown that in all cases the method is stable and the results are in very good agreement with available data.
The Advantages of Using Behavior Trees in Mult-Robot Systems

Multi-robot teams offer possibilities of improved performance and fault tolerance, compared to single robot solutions. In this paper, we show how to realize those possibilities when starting from a single robot system controlled by a Behavior Tree (BT). By extending the single robot BT to a multi-robot BT, we are able to combine the fault tolerant properties of the BT, in terms of built-in fallbacks, with the fault tolerance inherent in multi-robot approaches, in terms of a faulty robot being replaced by another one. Furthermore, we improve performance by identifying and taking advantage of the opportunities of parallel task execution, that are present in the single robot BT. Analyzing the proposed approach, we present results regarding how mission performance is affected by minor faults (a robot losing one capability) as well as major faults (a robot losing all its capabilities).
Design of a New Radio-Frequency Switch Topology with Very Low Polarization Current

Embedded systems are generally powered by batteries, characterized by a limited duration of electrical energy storage. For that, low power consuming RF components are often used. This work focuses on the design of a new RF switch based on one varicap diode within a series/parallel circuit topology. The latter is low-power-consuming in both ON and OFF mode and inhibits eventual nonlinearities caused by series diodes such as common PIN diodes. Before performing simulations, the diode has been characterized experimentally in the whole UHF band of interest and the extracted S-parameters have been used in the simulations of the resonating structure. The optimized switch has been measured and has showed a good agreement with simulated results in terms of high isolation (50 dB) and low insertion loss (0.1 dB). These results showed better performances compared to other structures based on RF MEMS, PIN diode or FET transistor devices, available in the literature.
Development of a test device for the evaluation of journal bearings

Journal and thrust bearings are widely used in heavy industry. Today, there is a growing need for studying different kinds of new bearing material and coating solutions in operating conditions where full film lubrication cannot be achieved or sustained. A test device for the evaluation of journal bearings was developed. The device consists of a rotating shaft and four stationary test bearings. This scheme eliminates the need for support bearings, allowing an accurate measurement of friction. The initial tests were carried out with a variety of loads and sliding speeds in mixed and full film regimes. The friction results in the form of a Stribeck curve were obtained and found to be in line with general trends. The results also indicate that the bearing lift-off speed occurs when the value of the non-dimensional pro e p n / (cid:75) - parameter is in the range of 0.5·10 -8 - 1.0·10 -8 .
A Dynamic Priority Factor Loop for Fast Voltage Equalization Applied to High Power Density DC–DC Converter System

A fast output voltage equalization control strategy applied to 10-kV/100-kW high power density photovoltaic dc–dc converter system is proposed in this paper. The converter modules in the system have two dynamic priority factor loops for fast response with an inconsistent voltage between modules and a soft start-up circuit to precharge output capacitance for solving inrush current problem in boost circuit. The proposed strategy can realize fast voltage equalization control under inconsistent leakage inductance and parasitic resistance parameters condition between modules compared to the traditional unified duty cycle control strategy. The fast equalization performance is verified by 10-kV/100-kW photovoltaic dc–dc converter system. The converter system adopts an isolated boost full-bridge topology and input parallel output series structure, which meets high conversion ratio and high-power density requirements.
A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.
Compact All-Fiber Polarization Coherent Lidar Based on a Polarization Modulator

A compact all-fiber polarization coherent Lidar is demonstrated to simultaneously measure the velocity and depolarization ratio. In the Lidar system, a dual-polarization light beam is generated in a polarization modulator (PolM). The light beam consists of a linearly polarized optical carrier and an optical sideband with an orthogonal polarization state. If the light is depolarized, the polarization directions of the optical carrier and the sideband are rotated. Both of them can be detected along the polarization direction of the original optical carrier. In the receiver, the backscattered light along the polarization direction of the original optical carrier is selected and beats with a frequency-shifted optical local oscillator (LO) signal at a photodetector. A beat signal consisting of two single-frequency components will be generated. The velocity is extracted from the frequencies of the two single-frequency components, while the depolarization ratio can be extracted from the intensity ratio of the two single-frequency components. An experiment is carried out, in which a 159-MHz signal is applied to the PolM, and an 80-MHz signal is used to produce the frequency-shifted optical LO. Two single-frequency signals at 80 and 79 MHz are generated at the receiver and used to simultaneously measure the velocity and depolarization of a target in real time.
Processed human microRNA-overexpression data from GEO, and sequence information from TargetScan, and targetScore from TargetScore

We collected 84 Gene Expression Omnibus (GEO) series corresponding to 6 platforms, 77 human cells or tissues, and 112 distinct miRNAs. To our knowledge, this is by far the largest miRNAoverexpression data compendium. To automate the data download and processing, we developed a pipeline written in R, making use of the function getGEO from GEOquery R/Bioconductor package (Davis and Meltzer [2007]). For each dataset, the pipeline downloads the raw or processed data (if available) and calculates (when necessary) the log fold-change (logFC) in treatment (miRNA transfected) vs (mock) control, taking into account the unique properties of each data. Next, we combined all of the logFC data columns into a single N ×M matrix for all of the N = 19177 RefSeq mRNAs (NM_* obtained from UCSC) and M = 286 datasets. Missing data (logFC) for some genes across studies were imputed using impute.knn from impute R package (Troyanskaya et al. [2001]). For miRNA transfection data having multiple measurements (in different studies), we picked the one whose logFC correlate the most with the validated targets from mirTarBase Hsu et al. [2011] or average them if no validated target available.
Application of Modified Empirical Mode Decomposition Method to Fault Diagnosis of Offshore Wind Turbines

In recent years, many countries have actively studied wind power generation as a means of realizing low-carbon green growth through a new renewable energy source. The most efficient method of securing the stable operation of wind turbines and reduce maintenance costs is monitoring and analyzing their operational status in realtime through a remote monitoring system. Remote monitoring systems employ various sensor technologies and the Wireless Sensor Network to collect and transmit data on the status of individual parts in realtime, and they diagnose faults through a signal analysis system. Application of the fault analysis method can reduce fault resolution times and minimize losses. In this study, signals collected from wind turbines were analyzed, and their characteristics were extracted through empirical mode decomposition (EMD). In the experiment, EMD learning was carried out using the following fault signals as examples: The back-propagation (BP) neural network algorithm with generator vibration, an unbalanced rotor, and a bearing misalignment fault. This article proposes a method of diagnosing faults through signal analysis and recognition, and it demonstrates the validity of the method through a simulation.
Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board

The contribution describes the application of an Internet of Things device with an ESP32 microcontroller with the dual-core implementation of data processing and wireless communication. The first leading idea of using low-end hardware and software optimization to create software architecture in order to make the most of this hardware and reduce the cost of more expensive hardware solutions was tested. Our goal in architectural design was achieved. We created the basis of a general framework and defined its use scenarios, and then implemented these scenarios using components. This article describes the progressive development of our embedded vibration measurement testing system, on which we tested the individual software components for our framework. We created an application for our components using ESP32 processor cores, which divided the responsibility of these cores for the components of our framework.
EasyPDP: A Parallel Dynamic Programming Runtime System for Computational Biology and Scientific Computing Applications

Dynamic programming is a popular and efficient design technique in computational biology and scientific computing. Nevertheless, their performance is limited due to the burgeoning volume of scientific and genome data, parallelism is necessary and crucial to make at least certain application instances tractable in practice and to keep the computation time at acceptable levels. This paper built a runtime system named EasyPDP aiming at parallelizing dynamic programming algorithms on multi-core and multi- processor platforms. Under the concept of software reusing and complexity reduction of parallel programming, a DAG Data Driven Model was proposed, which supports for those applications with strong data interdependence relationship. Based on the model, EasyPDP runtime system was designed and implemented. It automatically handles thread creation, dynamic data task allocation and scheduling, data partitioning, and fault tolerance. Moreover, five frequently used DAG patterns from biological dynamic programming algorithms have been put into the DAG pattern library of EasyPDP, so that the user could choose to use it according to his/her specific application. We evaluate the performance potential and fault tolerance feature of EasyPDP in multi-core system. We also compare EasyPDP with other methods such as Block-Cycle Wavefront(BCW). The experimental results illustrate that EasyPDP system is fine and provides an efficient infrastructure for dynamic programming algorithms.
Analytical solution for the inductor current of BOOST converter

This study proposes an analytical solution for the inductor current of the BOOST converter using the relative inductance as an independent variable based on the relative variable method. First, the expressions of the actual and relative values of current and energy for BOOST circuits are derived, based on the principle of KCL and conservation of energy. Second, three different cases for calculating the minimum and maximum values of the critical inductance are discussed under a variable input voltage. Then, the analytical solutions for inductor current and duty cycle in CCM and DCM are derived by taking the relative inductance as an independent variable. Furthermore, based on the inductor current equations, the authors deduce the analytical solutions for RMS, derivatives of RMS, delta, the maximum and minimum of the inductor current, and the maximum energy of the inductor. Finally, simulation and experimental results are presented to verify those formulas.
Mixed Rain Attenuation Compensation Algorithm of Ka-Band Satellite Communication System

In order to reduce the influence of rain attenuation on Ka-band satellite communication system,the paper puts forward a mixed rain attenuation compensation algorithm. This algorithm combines the adaptive power control method based on principle linear forward prediction principle with rate of diversity,which can predict the rain attenuation value at moment n according to the rain attenuation values before moment n,then adjust the power. When the power control reaches to the limit,it can dynamically lower information rate according to SNR( Signal to Noise Ratio) and obtain lower BER( Bit Error Rate). The simulation results show that this method has better compensation effect and can effectively reduce BER lower than 10- 7.
Perancangan Alat Pencuci Galon Menggunakan Pendekatan Reverse Engineering & Redesign Methodology Di Cv. Barokah Abadi

Permasalahan yang ditemukan adalah proses penanganan dan alat pencuci galon yang terdapat di CV. Barokah Abadi. Pada proses penanganan membutuhkan banyak kegiatan seperti pembilasan, pencucian, penyikatan bagian dalam galon, pembilasan bagian dalam galon, membutuhkan tempat yang luas dan waktu yang banyak. Oleh karena itu dibutuhkan perancangan dan pengembangan terhadap alat pencuci galon guna membantu meminimasi kegiatan, tempat dan waktu dalam penanganan dan pencucian galon. Perancangan alat pencuci galon ini dilakukan dengan menggunakan pendekatan reverse engineering and redesign methodology. Alat pencuci galon ini dirancang dengan menyatukan beberapa proses seperti pembilasan bagian dalam galon beserta penyikatan, pembilasan bagian luar galon, pemberian sabun pembersih pada bagian luar galon dan penyikatan pada bagian luar galon. Sehingga pada alat pencuci galon usulan ini terdapat 3 proses yang menjadi 1 didalam alat. Kata Kunci : Alat pencuci galon, reverse engineering, redesign methodology, perancangan produk, galon. Problems found is the process of handling and washers gallons contained in the CV. Barokah Abadi. In handling process requires a lot of activities such as flushing, washing, brushing the inside of the gallon, rinsing the inside of the gallon, need a large place and a lot of time. Therefore it takes the design and development of the washer gallon to help minimize the activities, places and times in the handling and laundering gallon. The design of the washer gallon tool is done using reverse engineering and redesign approach methodology. Washers gallon is designed to unify multiple processes such as rinsing the inside of gallons along with brushing, rinsing the outside gallons, giving soap on the outside gallon and brush on the outside gallon. So that the washers gallon this proposal there are three processes into one within the tool. Keywords: washing machine gallon, reverse engineering, redesigin methodology, product design, gallon.
A low-cost IoT-based wireless sensor system for bridge displacement monitoring

The intelligent monitoring of bridges has become vital as the number of bridges increases. Displacement measurements are useful information for structural health monitoring, which is the key method to ensure structural safety. However, most cable-based displacement measurements are costly and labor-intensive, and wireless measurements are limited by transmission distance and have difficulties in equipment maintenance and data management. The Internet of things approach provides a new technical method for addressing this challenge. This paper designs a low-cost IoT-based sensor system for bridge displacement monitoring, which consists of a sensor transducer, an amplifier circuit, an microprogrammed control unit module, an IoT wireless communication module and a cloud server. The proposed system can collect the displacement data accurately and send the data to the server by utilizing low-power wireless communication technology-NB-IoT. The analysis and visualization can be remotely performed via a web interface. Experiments and field tests show that the system can be successfully implemented in bridge displacement monitoring applications.
Monitoring Forecasting Systems - Revisit Trigg's Tracking Signal

Time series forecast monitoring methods, such as the Brown and Trigg tracking signal, have been widely used to ensure that the underlying forecasting systems remain in control. After Brown’s initiation in 1959, Trigg (1964) improved the tracking signal. First, if the forecasting system starts to give exceptionally accurate forecasts, the tracking signal will not continue to degrade. Second, Trigg claimed the new tracking signal no longer needed resetting in order to avoid a false warning signal. However, in this study we found that Trigg’s tracking signal may still have these shortcomings in some business cases. We propose a new tracking signal – Complementary Tracking Signal (CTS) and show that this new measure overcomes the shortcomings of Trigg’s tracking signal. Secondarily, we prove that if the current absolute value of the relative error is less than the previous absolute value of the CTS, then the CTS is improving through time.
Improving Execution Speed of Models Implemented in NetLogo

NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulationâ€”facilitated by the time extension to NetLogoâ€”can dramatically increase speed. NetLogoâ€™s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.