Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of MyPertamina Mobile Application with Unified Theory of Acceptance and Use of Technology (UTAUT) Method

The purpose of this research is to analyze the level of acceptance of information technology from users of the MyPertamina mobile application and what variables affect users in order to user can adopt the mobile application. In thi study, Unified Theory of Acceptance and Use of Technology (UTAUT) and one moderate variable, Voluntariness of Use (VoU), were used to evaluate the correlation between the variables. Purposive sampling has been used to test the relationship with total 405 respondents out of 432 respondents who meet the criteria. The data was examine using PLS-SEM method. This analysis convey that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC) towards Behavioral Intention (BI) have positive results while Voluntariness of Use (VoU) has negative results towards Behavioral Intention (BI). Final recommendation for this study are adding new payment methods and developing help center features.
Initial Velocity Models for Full Waveform Inversion

Full Waveform Inversion of seismic data requires the solution of a strongly non-linear optimization problem. Usually iterative gradient based methods are employed for the solution. These methods is based on the Born approximation and assumes that the derivative of the error function with respect to the velocity can be adequately represented by a linear approximation in each iteration step. This usually requires that the initial velocity model is close to the initial model to avoid cycle-skipping which cannot be described by the Born approximation. A common approach has been to use an initial velocity model based on ray tomography and depth migration which produces smooth models with kinematic properties similar to the true model. We suggest to replace ray tomography with Wave Equation Migration Velocity Analysis (WEMVA) based on a differential semblance error function. This method yields smooth velocity models comparable with models obtained from ray tomography, and in many cases requires only relatively simple one-dimensional models as a starting point. The advantage of this approach is that models obtained with WEMVA can then be used as initial models for Full Waveform Inversion to obtain a potentially automatic two-stage work flow for estimating accurate velocity models.
Dual-polarized broadband high-gain Fabry-Perot antenna with radiation pattern reconfigurable using liquid-based PRS

In this paper, a dual-polarized broadband high-gain radiation pattern reconfigurable Fabry-Perot antenna is designed, including a feed source section with ground contact and a partially reflecting surface (PRS). The antenna is fed by a 1×2 antenna array, and a 1 × 2 patch array coupled by two orthogonal slots is designed as the feed source of the FP antenna to achieve dual polarization. The partially reflecting surface consists of multiple F4B substrates with patches and PMMA containers, and the radiation pattern reconfiguration can be achieved by selectively adding liquid metal inside the containers while ensuring high gain. In the band range of 10.06 GHz - 12 GHz, both S11 and S22 are less than -10 dB, with a peak gain of 17.9 dBi when the antenna is excited by Port 1 and a peak gain of 17.5 dBi when the antenna is excited by Port 2.
Multiple Sensor Fusion-Based Positioning Robot for Steam Generator Inspection

Nuclear energy is presently the clean energy source most likely to massively displace fossil energy, and the secondary side of the steam generator is an important part of a nuclear power plant. Due to the small space of the secondary side of the steam generator and the confinement of the vessel, the precise positioning of the robot inside the steam generator cannot be achieved by a single sensor alone. This paper introduces a multi-sensor fusion steam generator secondary side inspection robot. Primary emphasis is on data fusion of multiple sensors, such as laser sensors, cameras, IMU, and wheel encoder. First of all, this paper integrates data from wheel encoder data and IMU data by using extended Kalman filtering (EKF), which decreases the drift error during the motion and enhances the robot positioning precision. Afterwards, we incorporate laser ranging sensors and camera picture data to enhance the overall spatial sensitivity of the robot through particle filtering algorithms. Experiments show that the robot works reliably and has high positioning accuracy.
SHRIMP dating and geological significance of Late Achaean high-Mg diorite (sanukite) and hornblende-granite at Guyang of Inner Mongolia

High-Mg diorite - hornblende-granite occurs at Guyang, Inner Mongolia. Geochemically, the high-Mg diorite is similar to late Archean sanukite that was formed in mantle wedge above subduction zones. Sanukite, which finds common in Archean terrains worldwide, is characterized by high MgO content and high Mg-# associated with enrichments of Cr and Ni. This paper reports SHRIMP U-Pb ages of a high-Mg diorite (2556 +/- 14 Ma) and a hornblende-granite (2520 +/- 9 Ma) in the Guyang area. Accordingly, the subduction of the North China plate possibly initiated during the late times of the late Achaean. The Archean Sanukites outside of China have been well documented in the range of 3.0 similar to 2.5 Ga with the youngest (2.51 similar to 2.55 Ga) occurring in the Dharwar Craton of India. It is suggested that the North China and synchronous India could be the latest areas developing plate tectonics on Earth.
A Metasurface Based High-Gain Dual-polarized Flat Luneburg Lens Antenna

A dual-polarized flat Luneburg lens antenna (FLLA) is designed using reflection cancellation method to improve its gain and bandwidth. First, the performance of conventional Luneburg lens (LL) and flat LL is compared to show that the increased permittivity in the flat LL degenerates the gain due to the severe reflection. During the implementation of flat LL, the reflection cancellation method is applied by adjusting the airgap between layers, so that a high-gain performance is achieved over a wide bandwidth. The designed FLLA is exemplified over 25-35 GHz, and fed by a dual-polarized patch antenna. The measurements show that the impedance bandwidth (|S11| ≤ -10 dB) is 27.6-32.7 GHz, and its peak gain reaches 18.8 dBi.
Microwave Staring Correlated Imaging of Coupled Block Targets Based on Autoencoder Networks

Microwave staring correlated imaging (MSCI) is a novel imaging technique for super-resolution imaging based on temporal-spatial stochastic radiation field. For the common clustered block sparse targets, traditional sparse Bayesian learning (SBL) based methods including pattern-coupled SBL algorithms (PCSBL) and adaptive clustered SBL algorithms (ACSBL), fail to get the satisfactory result since the coupled block targets can destroys the sparsity of the target. In this paper, an autoencoder network is proposed to achieve information extraction and reconstruction of coupled block targets. The encoder is concerned with extracting the target feature-containing echo information, and the decoder performs the target distribution mapping output. Finally, the proposed method is evaluated using simulated data. Simulation results have demonstrated that under low signal-to-noise ratio (SNR), the proposed method outperforms the traditional sparse Bayesian method significantly for coupled block targets.
A Survey of Personalized Adaptive Learning System

Personalized adaptive learning provides smart education with technologies and methodological techniques that enable the personalized aspirations of learners. This paper analyses the relevant literature and elaborates on the concept and impact of personalized adaptive learning. The research on personalized adaptive learning systems is systematically reviewed and interpreted from three aspects: learner models, learning resource models and algorithms for learning recommendations. The general framework of personalized adaptive learning systems is first summarized, followed by the ideas and methods for constructing learning resource models, then the implementation mechanism of learner models is explored, and then the recommendation algorithms for learning resources are summarized and their shortcomings are summarized. Finally, the future development direction of personalized adaptive learning systems is also foreseen, laying the foundation for further in-depth research on intelligent learning.
Design of LTCC Bandpass Filter with Broadband External Suppression for 5G Applications

This paper describes a design of Low Temperature Co-fired Ceramic (LTCC) bandpass filter for 5G (the fifth Generation) applications. This design consists of curved parallel coupling lines (CPCLs). The advantage of CPCL is that it can increase the coupling capacitance of the parallel coupled line structure to achieve good impedance matching. More coupling capacitors also make the parasitic passband of the filter farther away from the center frequency of the main passband, so as to have wider out-of-band suppression and better insertion loss. In this article, a curved line structure is proposed for the first time to design LTCC filter, and an equivalent circuit is proposed to describe its working principle. A LTCC filter using CPCLs is designed in the 702-798 MHz band. The test results show that the return loss in the passband of the filter is better than 32 dB and the insertion loss is better than 0.6 dB. Therefore, the proposed design can provide a promising solution for 5G wireless communication system.
A New Configuration for Waveguide Filters With Parallel Coupling Paths

A new configuration is proposed for filters with parallel coupling paths between the source and load in this paper. The proposed fourth-order waveguide bandpass filter (BPF) with two parallel coupled paths is formally designed through this structure. The filter is working at 6.5 GHz and with 300 MHz bandwidth. Due to the two parallel coupling paths, a pair of transmission zeros (TZs) are generated out of passband. Compared with previous waveguide filters, the coupling paths are connected to the source and load in different sides of input/output waveguide. In this way, the waveguide cavity of the resonator can be increased and the insertion loss of the filter can be greatly reduced. The effectiveness of the fourth-order waveguide bandpass filter is verified by simulation results.
Multi-Parameter Based Educational Recommendation System for Online Courses: A Content Based Filtering Approach

There are now an excessive number of courses available to students of all ages and skill levels as a result of the development of online education. Finding the courses that best meet their needs has become difficult for students as a result of this. In order to solve this problem, we describe an educational recommendation system (ERS) that uses cosine similarity (CS) and content-based filtering (CBF) to suggest online courses to users based on their search terms. This approach uses a dataset of online courses that includes information about several course parameters, including course title, enrolment figures, price, reviews, duration, and topic matter. In this paper, we begin with a comprehensive review of existing literature related to ERS. We then describe methodology and approaches to developing the system along with the process of dataset selection and pre-processing steps, feature extraction, and the evaluation metrics used to assess the system's performance. The system was able to identify and recommend courses that were highly relevant to the user's query, with a precision rate of over 90%.
Application and Performance Analysis of LoRa End Devices for Monitoring of Indoor Lighting Systems

This paper examines the implementation of an energy-efficient narrowband long-range (LoRa) communication network in the Internet of Things (IoT) concept for management and monitoring of indoor lighting systems. For this purpose, three variants of LoRa end devices configured at frequency 868 MHz and Spreading Factor (SF 9), used in particular for an indoor lighting system monitoring, were developed, tested and compared. The obtained results include determining the maximum network range (communication distance), packet delivery time and RSSI signal level.
A 22-32.7 GHz Linearized LNA in 65-nm CMOS Using Multigate Transistor Technique

This paper presents a linearized wideband LNA in 65-nm CMOS for 28-GHz 5G applications. The prototype LNA has three common-source stages coupled by transformers. The large-signal multigate transistor (MGTR) technique is used in the last stage, utilizing auxiliary transistors to suppress the third-order distortion with little additional power consumption. Layout simulation results shows the prototype LNA achieves -5.6 to 3.8 dBm IIP3, -16.8 to -13.9 dBm IP1dB, 20-23 dB gain, 3.4-4.4 dB NF over 22-32.7 GHz and consumes 43.6 mW from a 1 V supply.
Redline Stealer Malware Analysis with Surface, Runtime, and Static Code Methods

Data is becoming a new resource with high value, so many parties are interested in owning it. There are many ways to take it, one of which is planting and spreading malware known as stealer malware. Over time, malware has become more sophisticated, targeted, complex, commercialized, and scalable for a wider range of attacks. This makes malware analysis an important job requiring a lot of time, expertise, and extensive knowledge, both by individuals and teams of analysts. This study will analyze stealer malware using three analytical methods: surface, runtime, and static code. In malware analysis using the surface method, malware is tested by scanning by antivirus, hashing malware, and package/obfuscated detection, followed by Portable Executable analysis and malware sandbox analysis. In the runtime method, the malware is run for further observations of registry changes, DNS activity observations, and network data communication activities. In research using the static code analysis method, tests were carried out to find the relationship between the use of linked libraries and functions, string search as a guide for working steps of malware, and debugging malware to explore deeper into malware behaviour. The results obtained are information about the characteristics of the malware stealer and its impact on the test environment.
An Optimized Lightweight Crop Pest Detection Method

The practical application of crop pest detection methods has been limited by the large number of parameters and computations, and we built a lightweight crop pest detection method YOLOLite-CSG in our previous research, which basically removed this limitation. However, further analysis shows that YOLOLite-CSG still has problems that affect the performance in terms of the prior box generation method, downsampling method and bounding box regression loss function. In response to these problems, this paper proposes the prior box generation method based on label box oversampling and clustering (BS-Medians), the downsampling method based on parallel feature transformation (PSPD-Conv), and the bounding box regression loss function with size difference optimization capability (FIoU Loss). We optimize YOLOLite-CSG using the above methods to build YOLOLite-X, an optimized lightweight crop pest detection method. The experiment results show that YOLOLite-X exceeds YOLOLite-CSG in crop pest detection precision and is higher than other state-of-the-art methods. Meanwhile, YOLOLite- X has a smaller number of parameters and computations, which is more conducive to practical deployment and application.
Single-PRGW-Fed Dual-Band Dual-Circularly Polarized Antenna Array With Parasitic Pixel Layer

A dual-band dual-circularly polarized (CP) antenna array with printed ridge gap waveguide (PRGW) feeding is proposed in this work. The array element consists of dual substrate integrated waveguide (SIW) cavity-backed driven slots and a parasitic pixel layer. Dual resonances are supported by selecting physical parameters of the SIW cavity and two dumbbell-shaped driven slots appropriately. In addition, assisted by nondominated sorting genetic algorithm II (NSGA-II) and the internal multi-port method (IMPM), the connection status of the pixel layer is optimized to simultaneously achieve the right-hand CP (RHCP) radiation around 20 GHz and the left-hand CP (LHCP) one around 30 GHz. Finally, a four-way feeding network is designed integrated with the proposed array. Simulated results indicate that the four-element linear array has its realized peak gains of 13 dBi in both operation bands.
The Application of Optimization Learning Methods for Multilayered Neural Networks

The optimization methods Adam, AdamMax, and AMSGrad were tested in this research using the mapping function that describes the doubling process and the Fourier spectra of the error function for a multilayer neural network It was found that the application of the AMSGrad optimization method to the learning of a multilayer neural network (a three-layer network with 28 neurons in a layer) for the recognition of printed digits leads to blocking the doubling process of the local minima number by forming a block structure. We have shown that the hyperparameter $\beta_{1}$, which describes the contribution of the error function linear gradient and is the basis of the power function of the epochs number, is associated with the doubling of the number of local and global minima of the error function in the process of retraining the neural network. Moreover, the hyperparameter $\beta_{2}$, which describes the contribution of the error function gradient square, is associated with the formation of a modular structure that blocks the doubling process of the local minimum number and, thereby, leads to the gradients’ dilution. The obtained learning error is shown to be almost 3 times smaller than in the absence of the AMSGrad optimization method.
Design of Mixed-radix FFT Processor Based on The Modified CORDIC Algorithm

To address the problems of low computational flexibility and high hardware complexity of existing Fast Fourier Transform (FFT) processors, on one hand, this paper designs a Radix-2/4 Mixed FFT processor with a 5-stage pipeline structure, in which the first three stages are fixed Radix-4 butterfly units and the last two stages can be flexibly configured as Radix-2 or Radix4 butterfly units. On the other hand, this paper proposes a modified Coordinated FFT processor. On the other hand, a modified Coordinate Rotation Digital Computer (CORDIC) algorithm is proposed in this paper. Compared with the traditional CORDIC algorithm, the modified algorithm increases the convergence speed by $20 \%$ with the same computational accuracy by adding the non-rotation option and the step size definition of the lookup angle. The step size is reduced by half in turn. The hardware implementation reduces the hardware complexity by replacing the complex multiplication unit, which creates a lot of hardware complexity, with a modified CORDIC unit. The experimental verification and analysis conclude that this design can flexibly compute sequences of three lengths, 256, 512, and 1024, and the hardware area is reduced by $8.3 \%$ compared to the same type of design.
Multi-Index Modeling of Special-Shape UWB Antenna Using Convolutional Neural Network

A convolutional neural network (CNN) model with the transfer function is proposed for muti-index modeling of the special-shape microstrip antenna. The model consists of three parallel CNNs, and it can simultaneously predict the S-parameter, radiation pattern, and gain of the antenna according to its given images. Here a shape-changing technique is used to generate metallic strips with different shapes. A radial basis function network is employed for error correction. The effectiveness of the proposed model is confirmed with an example of the ultra-wideband antenna. The results demonstrate the potential application of this model in design of antennas with special shape.
Combined Interference Suppression Design of A 2~12 GHz Receiver Front-end

Anti-interference design is recognized as a very important consideration in the radar receiver design. Blocks, spur, and image interferers are discussed and studied based on the design of a 2~12 GHz receiver front-end. Receiver design and development are the art of trade-offs, the other performance, specifications and the cost should be designed at the same time. Through the selection of two-conversion scheme and the frequency planning of intermediate frequency, image frequency is adjusted to be far away from the RF operating frequency band and image interferer is no longer a problem. For blocks from mobile phone device and transmitter leakage, the filtering is an effective means for addressing. The spurs of LO leakage and interference need to be suppressed by targeted filtering and shielding design. And the specifications of spurious suppression and isolation for the first stage mixer are key to the half IF suppression. Finally, the proposed receiver front-end realizes image frequency rejection higher than 64dB within the whole frequency band. And communication and remote sensing interference can be suppressed more than 26dB. The transmission leakage is suppressed by 60dB through high-pass filter. Half-IF interference and half-RF interference suppression can reach more than 55dB and 40dB respectively.
Edge Caching Strategy Based on User's Long and Short Term Interests

In recent years, the exponential rise of mobile traffic has imposed a significant load on base stations and mobile core networks. Consumers' low-latency caching demands are difficult to satisfy using existing caching networks. Edge caching technology, which deploys caching servers in closer proximity to users, offers an effective solution and reduces content delivery time while avoiding network congestion. The capacity of cache servers at the network edge is restricted. Therefore, how to utilize the limited cache area for customers is a crucial concern of edge caching. To address this problem, a caching strategy that considers the long and short-term interests of users is proposed. First, users' interests are predicted based on their historical request information from the perspective of long and short-term interests, and a recommendation list is generated for each user. Then, the content is stored on one or more caching servers within the region, depending on the popularity of the content in the area. Decisions regarding cache replacement are made based on the revenue generated by the content. Results from simulations demonstrate the effectiveness of the suggested edge caching strategy in reducing the average delivery latency of the content as compared to current strategies.
Machine Learning-based Dynamic Granular Electric Outage Forecasting

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of utilities' efforts in hardening the grid, damage to the utility assets such as overhead cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Having a highly granular outage forecasting tool with a long lead time will be a great advantage for service restoration. In this study, we propose to develop and implement a multi-model framework as an operational tool based on a dynamic, granular, multi-day electric outage forecasting model using numerical weather forecasts and detailed component failure information. An innovative two-layered dynamic neural network and a sliding window are used to make better use of the available data. Case studies are performed to demonstrate the performance of the proposed framework.