Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Self-Learning Flaw Detection in Ultrasonic C-Scan Non-Destructive Testing

The ultrasonic C-scan technique is widely utilized for corrosion monitoring of metal plates and pipelines due to its ability to generate comprehensible images of flaws. However, the application of this technique has been challenging because of the large-scale and unbalanced signals reflected from the tested objects. In this paper, we present a method for identifying ultrasonic response signals of defects using an ultrasonic detection with contrastive learning (UD-CL) framework. This self-learning framework is used to unsupervisedly learn latent representations of echo signals obtained from ultrasonic C-scans. The algorithm addresses both the imbalance in the distribution of raw echo data and the lack of sufficient annotated ultrasonic signals. The UD-CL method consists of two components: a feature extractor based on a convolutional network and a learnable nonlinear transformation layer, which are used to obtain an efficient latent representation and perform contrastive optimization, respectively. We demonstrate the effectiveness of our approach by applying it to an aluminum plate with various types of flaws. The results show that the UD-CL outperforms previous deep learning techniques in terms of accuracy in identifying the location and category of flaws when trained using only 10% of the labels.
The Influence of HF Wireless Power Transmitter on nearby Thin-Wire Objects: A Simplified Analytical Model

Wireless power transfer system performance is determined by the antenna’s radiation effectiveness and operational resonant frequency. In high-frequency wireless power transfer systems, surrounding objects influence antenna characteristics and, as a result, wireless power transfer system performance. In this paper, the interaction of thin, lossy wire objects with wireless power transmitters is investigated. A small electric dipole transmitter antenna and a lossy wire at a near distance are modeled using analytical and numerical methodology. The study is performed in terms of induced current along the wire at 13.56 MHz for a lossy thin wire placed in free space and subjected to high-frequency radiation. The analytical technique entails deriving the approximate sinusoidal current distribution and analyzing field integrals. Analytical results are tested using numerical simulations in the commercial program 4Nec2. It is demonstrated that that the difference between the findings by the methodologies investigated in the paper are only moderate. Thus, the proposed approximate analytical method can be very useful in practice for fast estimations of the electromagnetic compatibility.
Calculation-based Beam Training Scheme for Massive MIMO Systems

In future communication systems, the exploration of high-frequency communication, such as mmWave and THz communication, has garnered significant attention due to its potential to enhance channel capacity. However, the utilization of high-frequency signals brings about the challenge of severe path loss. Fortunately, the introduction of massive MIMO technology offers a promising solution by enabling the generation of directional beams with a high array gain, effectively compensating for the substantial path loss. For accurate beamforming in massive MIMO systems, the acquisition of reliable Channel State Information (CSI) plays a crucial role. Beam training, a conventional method of obtaining CSI, involves an exhaustive search of the entire spatial domain using predesigned codewords from a codebook. The codeword with the highest received power is subsequently chosen as the direction for the intended users. However, this conventional beam training approach is limited by the angular resolution of the available codewords, which restricts the precision of beam alignment. Furthermore, as the number of antennas increases, the beam training overhead escalates proportionally. To address these limitations, this paper proposes a novel beam training scheme that achieves efficient beam training in only four time slots, regardless of the angular resolution of the codewords. The proposed scheme offers improved accuracy, circumventing the constraints imposed by the codeword resolution. Simulation results illustrate that the proposed method attains near-optimal beam training performance while requiring significantly reduced beam training overhead. By introducing this innovative beam training scheme, we aim to contribute to the advancement of massive MIMO systems, ensuring enhanced channel capacity and more precise beamforming capabilities, even in scenarios with high-frequency communications and severe path loss.
A new ship detection model based multi-distributions on SAR imagery

In this paper, we propose a new ship detection model on SAR imagery. The model uses the Pearson distribution system to simulate the backscattering distribution from ocean surface on SAR imagery. In the Pearson distribution system, four distributions including the pearson distributions of type I, III, IV and VI are employed in the model. Using these four distributions, we form a CFAR equation. A distribution selected machine basedβplane is used to select which distribution is adopted for specify SAR imagery. We can get four equations and get the threshold of gray level. Then, using the threshold, the model can find ships from SAR images.
Temperature Dependence of Li-ion Battery Hysteresis for Battery Modeling Purposes

Battery modeling is becoming more and more important in advanced battery management systems (BMS). Equivalent circuit models are widely used for their low computational requirements but they struggle with lower accuracy, which remains a challenge for the future. In this paper, a special focus is dedicated to the battery hysteresis effect, which significantly complicates the whole battery modeling and estimation processes. Measurement of the open circuit voltage (OCV) and parameter identification of the equivalent circuit model for the most used Li-ion battery technologies nickel-manganese-cobalt (NMC) and lithium-iron-phosphate (LFP) were performed. Experimental results showed that the most common approaches of battery modeling are not accurate enough and can cause problems, especially in the state of charge (SoC) estimation. Besides that, the lower the temperature, the bigger impact of the hysteresis effect, especially during discharging. That indicates this effect is closely linked to diffusion processes inside the electrode material, which are slower at lower temperatures. These findings have a great potential to help in the modeling of the hysteresis effect in the future.
Forest birds in changing rural landscapes: effects of area, isolation and regional abundance.

Because of modernization of agriculture and the associ ated conversion and removal of connecting landscape elements, forest fragments have become smaller and more isolated in the Dutch landscape. Effects of wood lot size and isolation on the distribution of forest-inte rior birds were analysed as a basis for landscape plan ning. Regional variation in abundance of species was taken into account and habitat variation minimized. The following predictions were tested:
An Multiscale Global Contrast Measure for Small Infrared Target Detection

The current state of infrared small target detection methods suffers from limitations in background suppression and multi-target detection capabilities, resulting in a high false alarm rate and high omission factor within infrared search and track systems. To address these issues, a new method known as multiscale global contrast measure (MSGCM) was proposed. Through extensive experimentation, it was found that this method resulted in a significant improvement in performance, with an average increase of 2.95 times in background suppression factor and 6.17 times in signal-to-clutter ratio gain for single-target detection, and 10.49 times for multi-target detection, when compared to similar detection methods.
Construction of Symbol Transformations for Non-Binary Turbo Codes with Lowered Error Floor

Non-binary variants of Turbo and LDPC codes are known to provide significantly improved error correcting performance over their binary counterparts in particular for short frame sizes. One of the reasons is the possibility to directly map the code symbols to constellation symbols of higher order modulations. Moreover, being defined over higher-order Galois fields GF(q), Non-binary Turbo Codes offer high degrees of freedom in the code and interleaver design. In this work, first we analyse the interplay between component codes through the interleaver. Then, we propose a suitable design methodology for a GF(q) symbol transformation applied to the encoded frames by one of the component codes. By modifying the values of encoded symbols by one component code with respect to the other, the aim of such a transformation is to avoid reproducing error-prone sequences while taking into account the effect of the interleaver. Without added complexity to the encoding or to the decoding process, the transformations constructed through the proposed methodology significantly lower the error floor of non-binary turbo codes. Indeed, in a case study for two GF(16) codes, we show an improvement of up to 3 decades in the error floor.
Single-probe Near-field Phase Retrieval using On-The-Fly Scan and Hilbert Transform

A novel near-field measurement method is proposed for fast phase retrieval using a single probe. The method is based on a new understanding of the on-the-fly (OTF) scan process as a spatio-temporal sampling operation of near-field responses. As a result, the near-field measurement is interpreted as a signal modulation of a source signal and a variable spatio-temporal function defined by the field responses and the motion of the probe. To retrieve the complex field responses, the Hilbert Transform (HT) is utilized for envelope and phase demodulation of signals recorded by an oscilloscope. To demonstrate the proof of concept, both numerical and experimental examples are provided with a detailed description of the method implementation. Measurement-to-measurement correlations at 125 kHz are conducted using a six-axis industry robot with multiple probes and instruments, showing its efficiency, effectiveness, and flexibility. Compared to conventional near-field measurement methods, the proposed method allows 90% reduction of measurement time and achieves a relative accuracy of phase retrieval above 96% with a simplified measurement setup, opening up new opportunities for near-field measurement applications.
JDart-Hea: Hybrid Evolutionary Algorithm in Dynamic Symbolic Execution

Symbolic execution is a widely-used program analysis technique. It collects and solves path conditions to guide the program traversing. However, due to the limitation of the constraint solvers, it is difficult to apply symbolic execution on programs with complex path conditions, like nonlinear constraints. In this paper, we propose a dynamic symbolic execution method based on the assistance of hybrid evolutionary algorithm, JDartHea, to solve this problem. We firstly generate an initial population according to the collected path constraints, and then perform crossover and mutation operations to generate new offspring. Secondly, we improve the quality of offspring using simulated annealing algorithm. Population iterations are continuously performed to produce solution that satisfy the path constraints. The experimental results show that JDart-Hea can achieve better coverage on complex programs.
Confidential Transmission Based on Statistical CSI Using DFT Beamforming

Statistical channel state information (CSI) can be obtained by pre-measuring the user’s channel, unlike instantaneous CSI which is only available after the user has accessed the network. Hence, the statistical CSI can be used to achieve confidential transmission and protect the access process of legitimate users. In this paper, we propose a beam selection scheme based on statistical CSI for discrete Fourier transform beamforming. Assuming that the statistical CSIs of the legitimate user and the eavesdropper are available at the base station, we adopt artificial noise (AN) in transmission and minimize the signal-to-noise-plus-interference ratio of the eavesdropper. The beam selection scheme can be converted into a semidefinite program problem. The performance of the system has been tested in both line-of-sight and non-line-of-sight scenarios using a 5G clustered delay line channel model. The simulation results show that, by transmitting AN, the performance of eavesdropper rapidly degrades when the eavesdropper moves away from the legitimate user.
A Cs Rydberg Atomic Microwave Oscillator for Electric Field Sensing

In this paper, we report an atomic oscillator in Ku band and its application for electric field (E-field) sensing with Cs Rydberg atoms in a room-temperature vapor cell. The oscillator is associated with the Cs Rydberg transition of 362D5/2 – 372P3/2 at 16.316 GHz which is generated with a two-laser double-resonance excitation. We characterized the energetics and the dipole moment of the Rydberg transition and used Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) effects for detecting continuous and amplitude/frequency modulated microwave E-fields. To enhance the oscillator’s response to weak signals, a superheterodyne technique was incorporated into the Rydberg oscillator system, which resulted in an improvement in the oscillator’s sensitivity by 100 times. Furthermore, our modeling shows that the Cs Rydberg atomic oscillator can be tuned to broad frequency ranges from MHz to THz for various RF applications.
Investigation of Guard Trace Utilization for EM Coupling Reduction Between Closely-Spaced Microstrip Patch Antennas

This paper presents an electromagnetic (EM) decoupling technique for a closely-spaced microstrip patch antenna array. To reduce the EM coupling, a guard trace, a structure consisting of a microstrip line with grounded vias, is inserted between the antenna patches. Parametric studies are carried out to determine the parameters of guard-trace, including the number of vias and the via diameter. To investigate the effectiveness of guard trace utilization for EM coupling reduction, a two-elements microstrip patch antenna array with a guard trace is designed to work at the frequency of 2.4 GHz. While to verify the design, the prototype of microstrip patch antenna array is realized and characterized. Measurement results indicate a coupling reduction of about 5 dB can be achieved when a 4-vias guard trace with the via diameter of 1 mm is inserted between the antenna patches separated by an edge-to-edge distance of 4 mm. Since the strongest coupling reduction occurs at very close distances between antennas, implementation of the proposed concept to massive elements antenna arrays is potential to reduce their footprint.
Analysis of Point and Collective Anomalies in Energy Consumption Data

The article describes various methods for detecting point and collective anomalies in energy consumption data. The authors suggest a construction method for the typical daily power consumption profile, any deviations from which are considered anomalies. In this paper, to detect point anomalies, well-known algorithms for determining outliers in data and the DBSCAN clustering method were used. These algorithms produce similar results and can be used independently or combined to analyze point anomalies. The article also describes a method that allows detecting collective anomalies in energy consumption data. To identify collective anomalies, some data generated according to specified templates were added to the data sets. After that, the initial data set was recoded as a collection of monochrome images, marked up, and recognized using a two-dimension convolutional neural network. While solving a multi-class classification problem, the suggested method helps to detect anomalies and their types and, therefore, can identify their potential causes.
Ligand-Based Drug Discovery on SARSCOV2 Target Protein

Severe Acute Respiratory Syndrome Coronavirus2 (SARSCOV2) is the virus that causes a respiratory disease called coronavirus (COVID-19). The symptoms of SARSCOV2 are fatigue, shortness of breath and cough that lasts more than four weeks. This paper aims for discovering active compounds for a particular target thatisSARSCOV2. Anattempttodiscover a novel compound based on the quantitative structure-activity relationship (QSAR) and drug-likeness evaluation was taken in the experiment. However, conventional laboratory processes are costlier and more time-consuming. Thus, it is extremely appropriate to develop an alternative method for predicting to inhibition of SARSCOV2. A non-redundant data set of 98 large molecules of SARSCOV2 inhibitory activity is used. Random forest (RF), sup- port vector regressor (SVR), and linear regression (LR) are used to predict the -log (IC50) value. The predictive performances for RF, SVR and LR in terms of R- squared, mean absolute error (MAE),and root mean square error(RMSE)are(0.77,0.89,0.80), (0.50, 0.26, 0.40) and (0.34, 0.17, 0.23), respectively. It can be concluded that the data preprocessing and the proposed QSAR model seems to be high-throughput tool for detecting novel inhibitors against SARSCOV2.
Portable Optical Atomic Clock Based on a Dichroic Two-Photon Transition in Rubidium

We report progress on development of a next generation timing reference based on an optical transition in rubidium coupled with a compact optical frequency comb to deliver a high stability RF reference signal. Engineering models that indicate the entire system including all optical components and control electronics could occupy a volume of 25 L, have a weight of 17 kg and consume 100 W of power. Such a device would be useful in GPS denied or contested environments.
Data Driven Decision Making Algorithm for Self Organizing Networks by Q-Learning Methods for Heterogeneous Networks

Q-learning is an algorithm for data-driven decision making for self-organizing networks (SONs). This approach utilizes reinforcement learning principles to automatically optimize various network parameters, enabling SONs to adapt and self-organize in order to maximize network performance metrics such as mean throughput, system capacity, and handover rate. Q-learning has been used in several SONs, such as those for LTEs, WiMaxs, and Mobile WiFis. Q-learning works by considering various network parameters as states or actions, defining a reward function based on desired network performance metrics such as mean throughput, and leveraging algorithms to learn which actions produce higher rewards. With this approach, Q-learning can be used to self-configure, self-optimize, and self-protect SONs, allowing networks to evolve autonomously and remain reliable under diverse conditions.
Designing a Prototype of an Adaptive Expert System Using Fuzzy Logic and a Genetic Algorithm

The paper presents the design processes of an adaptive fuzzy expert system. The adaptability of the system was achieved by changing the parameters (optimization) of the fuzzy rule base in accordance with the change of output parameters (defuzzification) using a genetic algorithm. The jFuzzyLogic software library was used to implement the prototype. This software library has the ability to use the fuzzy rules database optimization functions necessary to achieve it. In the course of the study, a UML-activity diagram was formed and the stages that can be used by researchers and developers when creating their own intelligent systems that increase the level of decision-making in difficult situations were determined.
Research on DS/FH Signal Acquisition Algorithm Based on Slow Scanning Strategy

The FH/DS hybrid spread spectrum technology integrates the advantages of direct sequence spread spectrum and frequency hopping spread spectrum technologies in terms of resistance to multipath interference, anti-tracking interference, and interception. At the same time, it overcomes significant near-far effect and poor concealment performance. It is widely used in military communication systems worldwide. However, the unique structure of FH/DS signals directly increases the difficulty of synchronization scheme design and implementation. In this paper, taking the context of inter-ballistic, inter-satellite, and ground-to-ballistic networking as a background, a new frequency hopping signal capture algorithm is proposed using the sync header method and combined with the slow scanning strategy. The algorithm adopts a new strategy of extending the slow scanning time during the frequency hopping demodulation process to expand the number of effectively accumulated frequency points, thus achieving energy accumulation under low signal-to-noise ratio conditions. In the phase search part, a half-hop is used as the search step for signal detection, maximizing the utilizttion of synchronization hop resources. Overall, this algorithm saves more than 50% of the synchronization resources and reduces the capture time to half of that of the original sync header method, thereby improving the survival probability of the communication system.
Mobile Application for Real-Time monitoring of an Air Quality Station

The quality of the air around us is a major concern nowadays, a lot of health issues are associated with air pollution. This article presents an application of mobile and cloud technologies for real-time air quality monitoring. An Android mobile application was developed for monitoring the values of pollutants in the air from an air quality station. The station was designed to continuously acquire values from different sensors and to send them to databases. The mobile application provides real-time updates, notifications and historical values for the air parameters monitored.
On the Critical Value of Time-Delay in Multiagent Systems

The paper studies the problem of agreement of characteristics in a multiagent system with information influences. In particular, a model of a multiagent system is studied, in which information is transmitted between agents with a constant delay for all agents. For systems with delay, a formula for the critical value of delay with a nonsymmetric matrix is obtained using the Nyquist criterion applied by Tsypkin for a time-delay system. A condition is found under which the stability of the system does not depend on the delay The presented results generalize some of the previously obtained expressions for the time-delay and can be applied to the analysis of agreement of characteristics in a multiagent system with a complex protocol.
Transformerless Wind Energy System Based on a Series Double NPC Multilevel Rectifier and a Six-Phase Asymmetrical PMSG

This paper introduces a transformerless configuration of generator and power converter that is suitable for offshore wind energy systems. The proposed configuration is based on a series connection of two neutral point clamped (NPC) multilevel rectifiers, powered by a six-phase asymmetrical permanent magnet synchronous generator (PMSG). Additionally, this work proposes a control strategy that opens the door to a fault-tolerance mode, by the use of independent controllers for each NPC converter, directly operating in a large-signal nonlinear space. This closed-loop system permits to prevent disruptions due to faults at either the generator winding or one of the NPC converter, by allowing a modular modification of set-points over the whole system operating region. Simulation results show the feasibility of the proposed configuration, rendering a promising solution for future wind energy generation systems.