Miracle-dz/UniFAR-FTUs huggingface dataset:
High-efficiency topological waveguides in substrate-integrated valley photonic crystals

Recently, the study of waveguides in substrate-integrated planar platforms has attracted much attention due to its significance for microwave and millimeter-wave technologies. Here, we propose substrate-integrated topological photonic crystals (PhCs) that support robust valley edge states. However, such systems need to be interfaced with classical waveguide or transmission line techniques to bring this advanced field of physics to practical applications. A substrate-integrated topological waveguide with high-efficiency transmission is designed and presented in this paper, in which high-efficiency conversion from conventional transmission line waves to topologically nontrivial edge waves mimicking the quantum valley Hall effect is achieved by transition structure. In addition, we numerically verify the robustness of valley edge waves through random defects and Z-shaped interfaces at microwave frequencies. The proposed topological PhC waveguides have the characteristics of planar integration, easy fabrication, and efficient transmission, which pave the way for the manipulation of topological valley edge waves in an integrated platform.
Inverse Design of Multilayer Single-polarized Filtering Antenna Based on Invertible Neural Nets

In this paper, an inverse design method based on invertible neural network (INN) is adopted to complete the design of filtering antenna. INN gives the posterior distribution of the design parameters based on the target response and gives multiple candidate solutions by sampling the conditional posterior distribution. A multilayer single-polarized filtering antenna is designed based on INN, which verifies the accuracy and efficiency of this method.
Design of Structured Query Language Online Judge Systems

The research described in this paper aims to implement a novel SQL query evaluation system, known as the SQL Online Judge (SQLOJ), and conduct mining and analysis on the relevant data obtained from SQLOJ. This system is intended to provide guidance and inspiration for student learning, teacher instruction, and course design. The paper focuses on introducing the technologies and system framework utilized by the system, and proposes a system availability tradeoff strategy to address the issue of excessive concurrent online users. The research results presented in this paper establish SQLOJ as a comprehensive platform and data resource for practical teaching in database courses, offering an innovative foundation that effectively complements traditional approaches to professional and course descriptions.
High Efficiency Leaky-Wave Antenna with Equiangular Spiral Slots

In this paper, a novel leaky-wave antenna (LWA) with equiangular spiral slots is presented. Compared with the traditional transverse slot, the equiangular spiral slot has longer and wider radiation aperture which generates larger energy leakage, thus improving the radiation efficiency. The antenna is designed with 11 equiangular spiral slots, and the simulation results show that the radiation efficiency is more than 90% in the operating frequency band of 29.5-35 GHz.
IOT-Based Pest Detection in Maize Plants Using Machine Learning

Smart Farming is an agricultural technique that utilizes internet of things (IoT) technology and artificial intelligence to monitor plant development. Recently, smart farming has been widely used to monitor corn to improve the quality and quantity of production. However, pests are still a major problem in corn smart farming. This is because the existing corn smart farming still has low pest detection accuracy. This research provides a solution to the problem by developing an IOT-based intelligent pest detection system for corn smart farming. The system developed uses a machine learning algorithm for detecting a pest that is tracked through a camera connected to an IOT device. The camera works based on the trigger given by a Passive Infrared sensor (PIR). The camera will capture image if there is an input from PIR sensor. Images that are captured by the camera will be processed by a controller for pest detection. The pest detector controller uses a machine learning algorithm to track the presence of pests. Then, a buzzer (connected to the controller using the Message Queuing Telemetry Transport (MQTT) protocol) will sound if the controller successfully detects pests. Rigorous experiments on this pest detection system have been carried out, and it has been proven that the detection accuracy is up to 81%.
A Design of Current Mismatch Compensation for Sub-Sampling Charge Pump Circuits

A compensation technique for sub-sampling charge pump with high current mismatch is proposed, which implemented in 65-nm standard CMOS process with 2.5 V power supply. Channel-length-modulation causes gain distortion of phase-frequency detector, introduces input voltage offset of sub-sampling charge pump, and decreases the locking range of the voltage-controlled oscillators control voltage. By incorporating high-gain rail-to-rail operational amplifiers, the proposed sub-sampling charge pump can diminish channel-length-modulation effect and achieve good matching of charging and discharging currents. Compared with the conventional sub-sampling charge pump, simulation results show that the maximum charge/discharge current mismatch decreases from 46.4 % to 2.74% within the output voltage dynamic range of 0.17 V to 2.25 V. The mismatch compensation design helps to broaden the locking range of voltage-controlled oscillators control voltage.
Design of Broadband Antenna Based on Loaded Metasurface

In this paper, a broadband antenna based on a loaded metasurface is proposed. First, a metasurface is loaded on the upper radiating substrate using slot-coupled feeding to excite the TM02 mode. A ground plane is loaded in the middle of the upper and lower radiation substrates, and a rectangular slot is loaded on the ground plane to couple the upper and lower dielectric substrates. The rectangular slot mode on the ground plane can enhance the radiation of the TM02 mode, which finally covers the resonant bandwidth range of 4.35 - 5.65 GHz (26%). In-band gain is stable up to 8 dBi. The height of the antenna body is only 0. 018λ, which can be used in various miniaturization occasions.
Beam Steering Lens Antenna Using Novel Dual-layer Phase-shifting Surfaces

In this paper, a novel phase-shifting surface (PSS) is designed to develop a passive beam steering lens antenna. The lens antenna is composed of two cascaded PSSs, and continuous beam steering is achieved by mechanically adjusting the rotation angle of the two PSSs. Each PSS is a transmissive metasurface composed of multiple phase-shifting elements. The element has a simple dual-layer metal structure printed on a single-layer dielectric substrate. It has achieved a high transmission efficiency and full 360° phase coverage. Based on the element, a lens antenna is designed and simulated. The lens antenna can reach a peak gain of 28.32 dBi, and the aperture efficiency of 30.6%. The simulated beam steering range in the zenith is ±60°, with a complete 360° steering radius in the azimuth. Furthermore, the simulated 1 dB gain bandwidth reaches 6% (19.6–20.8 GHz) across the entire steering range, which is of great significance in achieving efficient beam steering antennas.
Application of the Cauchy Method in Near-field Measurements

This paper addresses the issue of missing data gaps in near-field measurements, which often result from physical limitations such as restricted space in the anechoic chamber. This work demonstrates that the Cauchy method is an effective solution for reconstructing missing near-field data. In addition, for the problem of long measurement times in near-field measurements, we use compressed sensing to reduce the measurement effort and shorten the measurement time. This work focuses on reconstructing the near-field data which is obtained by spherical near-field measurements using the Cauchy method, and the far field is calculated by spherical near-field to far-field transformation. The numerical results show that the Cauchy method has good applicability in reconstructing antenna missing data.
An Open-Source 1.44-MS/s 703-μW 12-bit Non-Binary SAR-ADC Using 448-aF Capacitors in 130-nm CMOS

This paper presents the design of a self-clocked 12-bit non-binary fully differential SAR-ADC using the SKY130 open-source PDK. The entire mixed-signal circuit design and layout were created with free and open-source software. The ADC reaches a sample rate of up to 1. 44MS/s at 1. 8V supply while consuming 703 $\mu$W of power on a small 0.175m$\mathrm{m}^{2}$ area. A configurable decimation filter can increase the ADC resolution up to 16 bits while using an oversampling factor of 256. A 9-bit thermometer-coded and 3-bit binary-coded DAC matrix using a 448 aF waffle-capacitor results in a total capacitance of 1.83pF per input. Realizations of configurable analog functions using the form factor of SKY130 high-density standard cells allow the parametrization of an analog circuit in a hardware description language and hardening of the macro in an intentionally digital workflow.
The e-Learning Models Adopts Metacognitive Strategies to Support and Influence Independent Learning: Literature Review

Metacognitive processes play an important role in independent learning, which supports and influences independent learning. e-Learning is one of the ICT systems that can be adapted to several rows of learning strategies, which are used to achieve learning success. The purpose of this study is to review previous studies that have discussed metacognitive strategies adopted in e-Learning, which will influence or support self-regulated learning. Research The research method used is a literature review sourced from several digital libraries, obtained from 39 of the latest studies from 2018 to 2022. The aspects that will be the main focus are the e-Learning model, various metacognitive strategies, data, and methods used for validation, and the effect of metacognitive strategies on self-regulated learning. The results show that the most widely used metacognitive strategies are planning, monitoring, and evaluation, the most widely used method is the t-test followed by descriptive statistics and Anova with the data sources most used in research coming from students. The problem that is still felt by researchers is the generalization of research results where time and research limitations are used. In addition, there are many models of metacognitive strategies that are widely used and adopted in e-Learning, but what is often used is the use of artificial intelligence technology in e-Learning to increase user interaction and attractiveness. From the results of the study, it can be concluded that there are several metacognitive strategies adopted into e-Learning that can support self-regulated learning.
S-Band Four-Way High Power Combiner with Improved Performance Used for Air Traffic Control Radar

A S-band four-way high-power combiner used for air traffic control radar is investigated in this paper. Three Gysel power divider were implemented in the form of shielded plate, and used as the units for the proposed secondary combining network. A coaxial-to-waveguide transition was introduced to convert the sum port to standard waveguide (WR-284). The proposed power combiner has excellent performance in the operating band 2.65–2.95 GHz: the port isolations are higher than 25 dB, the phase imbalance is within 0.2°, and the amplitude imbalance is within 0.1 dB. In addition, this design is capable of tens of kW-level power combining.
A Vivaldi antenna with stepped bending structure applied to 5G

-In this paper, a Vivaldi antenna with stepped bending structure applied to 5G is proposed. The stepped bending structure can effectively expand the bandwidth, especially reduce the lower limit of bandwidth. The finally designed SBWS-VA has good impedance matching in the three application frequency bands of FR2. At the same time, the gain is also improved, with good and stable gain in the three application bands, and the average gain reaches 9.74dBi. At the same time, the directivity is stable. Good bandwidth and stable gain make the antenna especially suitable for 5G
Sensor Fault Detection for the Attitude Measurement System of Rotary Steerable Tools

The attitude measurement of drilling tools is essential to the control of rotary steering drilling tools. However, sensor faults seriously affect its measurement accuracy. Therefore, this article focuses on the problem of sensor fault detection under drilling disturbances in the attitude measurement system, ensuring the reliable operation of the measurement system. Firstly, the attitude measurement system including accelerometers and gyroscope is modeled as a linear system with unknown inputs, measurement noise and parameter uncertainties. These bounded disturbances are described by zonotopes. Secondly, a set membership unknown input observer generating residual in finite frequency domain is proposed. In order to make the residual sensitive to faults and robust to disturbances, design conditions for observer gain are given based on the P-radius criterion and generalized KYP lemma. Then, based on whether the boundary of the residual cross zero, it is determined whether sensor faults have occurred in the system. Finally, the effectiveness of the proposed method was verified through experiments, and the results show that the method successfully detected accelerometer and gyroscope faults.
Design of A Balanced-to-Balanced Filtering Power Divider With High Selectivity

Based on the theoretically investigation of a square patch resonator, this article puts forward a new structure of second-order bandpass balanced-to-balanced filtering power divider. Two square patch resonators are arranged on two-layered substrate in a back-to-back manner. Due to coupling apertures, electric coupling, magnetic coupling and the direct coupling between the source and load are all obtained. Therefore, two transmission zeros are generated outside the bandpass for improving frequency selectivity. Meanwhile, the well performances of common-mode suppression and port-to-port isolation are also obtained through three pairs of ports.
Pharmacologically and Electrically-induced Network-wide Activation of Olfactory Bulb with Large-scale Biosensor

The olfactory bulb is a multi-layered structure that dynamically tunes high-dimensional olfactory information from the olfactory epithelium and conveys spatiotemporal codes to higher-order areas of the brain. Available techniques lack adequate spatiotemporal resolution, challenging understanding of the dynamic mechanisms underlying information coding in the olfactory bulb (OB) circuitry. Large-scale ex-vivo recordings from OB interconnected layers were recently obtained using high-density CMOS-based biosensors, allowing network-wide investigations of OB electrophysiological properties to uncover layer-specific firing patterns and their dynamical features. Here, we examine changes in simultaneously recorded multi-unit spiking activity and local field potential oscillations focusing on three OB layers in different drug-induced activations. Furthermore, electrically evoked responses of OB circuitry were also analyzed, as delivery of electrical stimuli to the glomerulus layer elicits a response that mimics the natural activation following a sensory input. Our results delineate that this approach effectively describes the spatiotemporal dynamics of OB circuitry. It can be further exploited to investigate the computational implications of large-scale activity patterns in olfactory processing.
Supervised and Unsupervised ML Methodologies for Intrusive Detection in Nuclear Systems

This study investigates the use of machine learning (ML) techniques to improve nuclear intrusion detection. In the context of nuclear warfare, which is constantly evolving, conventional technologies may be unable to detect sophisticated, adaptive assaults. For this purpose, ML techniques including Isolation Forest for unsupervised learning and Convolutional Neural Networks (CNN) for supervised learning are investigated. To evaluate these ML methods, case studies utilizing data from operational nuclear systems are conducted. Both Isolation Forest and CNN are effective intrusion detectors, with CNN outperforming the baseline and Isolation Forest. This investigates the challenges of employing ML for intrusion detection in nuclear systems and demonstrate how CNN may assist by extracting subtle patterns and features from large datasets to improve the accuracy of detection. This research contributes to the improvement of nuclear system security and casts light on how to apply machine learning techniques to intrusion detection.
A K-band low-pass filtering power divider with two transmission zeros base on four-line interdigitated coupling structure

In this paper, a K-band low-pass filtering power divider (FPD) based on four-line interdigital coupling structure is proposed. There are four LC series resonators in parallel at the input and output ports, which can generate two transmission zeros at Ka band. The RLC is connected in series between two output ports to form an isolation network for better in-band isolation. The length of the transmission line can be reduced effectively by compensating the imaginary part of the input admittance. The two transmission lines have positive mutual inductance, making them less than 1/4 λ0 which helps to reduce the size of the devices. The EM simulation of the low-pass FPD designed in this paper shows that the 10-dB return-loss bandwidth is range from 13.42 to 28.14 GHz, the 10-dB isolation bandwidth is from 18.13 to 30.74 GHz. |S22| is smaller than -10 dB for 12.67-29.78 GHz, |S33| is smaller than -10 dB for 13.79-28.63 GHz. Each of the four identical coupling lines has a length of 0.058 λ0. The low-pass FPD occupies an area of $8.7 \times {10^{ - 3}}{\lambda _0}^2$.
Design of 3-bit Programmable Metasurface Element Based on Phase Shifting Structure

In this paper, a 3-bit metasurface element operating at 3.55 GHz is proposed. The element is composed of a reconfigurable patch and a reflective phase shifter. By utilizing four PIN diodes, the patch and the phase shifter achieve 1-bit and 2-bit phase resolution, respectively, thereby enabling the element to achieve a 3-bit reconfigurable phase response. Simulation results show that the element achieves 3-bit phase resolution at 3.55 GHz with y-polarized incidence, which can greatly reduce the phase quantization error of reconfigurable metasurface.
Transmission line inspection models improved by synthetic data from diffusion models

The training of deep learning models is inseparable from sufficient data as the driving force. However, in the power transmission model training task, it is very difficult and expensive to obtain sufficient training samples, so data augmentation has become a common means of increasing training samples. In recent years, various deep generative models have demonstrated strong sample generation capabilities in different modalities, especially generative adversarial networks (GANs) have made important progress in synthesizing compelling image and audio samples. However, the traditional GAN model faces difficulties in the large amount of data and computing resources required for training in the field of transmission lines, and there may be problems such as lack of diversity and detail in generated images, and unstable training. In order to solve these problems, this paper proposes to use a diffusion model-based method to generate transmission line images, expand the data set and improve model performance. The final transmission image generated by the model is complete and clear, and the overall model performance is improved by 1.5%.
Deep Learning for Music: A Systematic Literature Review

Recently, Artificial Intelligence development and implementation are becoming faster and more popular. Artificial Intelligence has appeared to help humans in their daily activities. Several examples that are currently hype are Chat-GPT, and AI Art. With the emergence of applications like Chat-GPT many people have started using applications that have Artificial Intelligence in it. However, there are still rare applications or research that discuss the implementation of Artificial Intelligence or deep learning in music. Therefore, this research will conduct a systematic literature review (SLR) on Deep Learning in music. In this systematic literature review we will research and answer three research questions. Those research question are, What kind of deep learning architecture that most widely used for developing, classifying, and making music; What implementations of deep learning can be done in music, Whether the existence of Artificial Intelligence / Deep learning can help musicians or composers in making music. Predetermined research questions will be answered using the Kitchenham & Cochrane method. From the results of the analysis that has been carried out we concluded that the deep learning methods that are widely used for training deep learning in music are CNN and RCNN, While the implementation of deep learning in music is used for classification and recommendation systems. For conclusion in this paper, we conclude that deep learning can be used to help musicians and composers in creating music.
Exploratory Design Research on Interaction System for L3 Autonomous Vehicle

This paper starts from the shortcomings of the current domestic L3 autonomous driving interactive system design combined with the safety, responsibility, and task challenges it faces, and tries to clarify the main differences between L3 and L2(and below) automatic driving systems, and proposes an interaction model for L3 automatic driving vehicles. Based on the model and referring to the requirements of the autonomous driving regulations on the interaction level, the interactive system design is carried out. The research focusing on safety, responsibility and tasks proposes a TSE design method (driving training, driving safety and performance evaluation) to conduct closed-loop design of L3 autonomous vehicle interaction. The research combines norms and standards such as ethical guidelines, regulations, and exposure draft to summarize the requirements for human-computer interaction. It provides a certain guiding significance for the research and design practice of the current human-computer interaction direction of autonomous driving.
Carbon footprints of food of animal origin

Animal production contributes substantially to global greenhouse gas emissions (about 14.5%). So-called carbon footprints (CFs) consider the greenhouse gas potential of climate-relevant gases (e.g. CO2 1; CH4 23; N2O 296), which is given in carbon dioxide (CO2)-equivalent g –1 or kg–1 of product or unit of edible protein. CFs may help to assess the greenhouse gas emissions associated with the production of food of animal origin such as milk, meat, eggs or fi sh, and they may contribute to sensitizing producers and consumers to a more resource-effi cient and environmentally friendly production and consumption of food of animal origin and to avoiding food wastage. Th e highest CFs per unit edible protein are calculated for products of growing ruminants (beef and lamb), followed by milk, pork, eggs and poultry meat, with the lowest values. Discrepancies in the results of various studies are explained mainly by diff erent system boundaries, allocation methods and computation of emissions, especially with regard to land-use changes, enteric methane (CH4) and nitrous oxide (N2O) emissions. A more standardized approach for CF calculations would be a very useful tool to compare CFs between production systems, regions and countries, and as an indicator for food labelling. Th e production of food of animal origin is a very complex process, and a selective consideration, i.e. focusing on single factors, does not provide an assessment that refl ects the complexity of the subject.