Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of SCADA Test Beds and Design of a New Software-Based Test Bed

Critical infrastructure sectors such as energy, public services, finance, transportation, water management, and production are crucial for national security. They rely on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. A failure or cyber-attack on these systems can have severe consequences, including service disruptions, data breaches, and threats to human life, societal order, economy, and security. Hence, ensuring robust security measures for these systems is essential. This study conducts research on test bed centers designed to enhance the security of SCADA systems considered as critical infrastructure. Comparisons and analyses are made among physical simulation, virtualization, and software-based emulation environments in these test bed centers. It is evaluated that test bed centers based on physical simulation are both costly and limited to local environments. In the proposed study, a test bed is designed with a software-based approach, specifically focusing on the S7comm industrial communication infrastructure predominantly used by Siemens devices, which has been less utilized in the literature. The aim is to enhance the security of the S7comm communication protocol through experiments conducted on the test bed. The evaluation conducted at the end of the study suggests that working with various cybersecurity disciplines on the designed test bed would significantly contribute to the security of critical infrastructure.
RAANet: Residual Aggregation Attention Network for Classification of Small Intestinal Endoscopic Images

Due to blurred boundaries between lesions and normal tissues, different intra-class scales, and high inter-class similarities, it is very challenging to accurately classify small intestinal lesions. In this paper, a residual aggregation attention network (RAANet) is proposed for multi-classification of small intestinal lesions with wireless capsule endoscopy (WCE) images. A relative position sensing encoding (RPSE) module is developed to attack the boundary blurring problem between lesion areas and normal tissue. By learning important information between different lesion categories, the characterization ability of obscure lesion areas is greatly enhanced. Then, a Non-local attention (NLA) module is introduced to collect multi-scale information to reduce the impact of high inter-class similarity and large intra-class differences. In addition, a dataset with five common small intestinal lesions is generated. By evaluating the proposed RAANet in the five-class dataset, it is verified that better classification results can be achieved compared with some existing state-of-art classification algorithms.
Common Information Model Extension for Multi-Energy Microgrids to Enable Digital Twin

The net-zero transition is a global trend, during which -microgrid_(MG) plays an important role in integrating renewable generation and low-carbon demand. Facilitated by the advance in Information and Communication Technology (ICT), a Microgrid Digital Twin (MGDT) allows further increasing the utilization of renewable energy sources and identifying customer flexibility by capitalizing on the value of real-time monitoring and machine learning-facilitated decision-making. However, there is a lack of consistency and standards in implementing MGDTs. In this paper, an information model for MGDT based on the popular Common Information Model (CIM) and CIM extension studies is proposed to define the key information structure of an MGDT. This work provides a reusable metadata model to facilitate future MGDT implementations.
LDV/INS Integrated Navigation Aided by Map Matching

A laser Doppler velocimeter (LDV) is usually integrated with an inertial navigation system (INS) to autonomously acquire a vehicle’s position, velocity and attitude. A LDV/INS integrated navigation system can work as an alternative to the global navigation satellite system (GNSS) in non-GNSS environments, which can not only maintain the position accuracy, but also keep a continuous navigation output. Although a LDV can exhibit a higher velocity measurement accuracy, the position error of LDV/INS integrated navigation still accumulates with time due to the limitation of the dead-reckoning principle. In this manuscript, the map matching technique is utilized to help reducing the position error drift of a LDV/INS integrated navigation. An improved road conversion and translation vector correction algorithm is proposed to enhance the integrated navigation performance. The road conversion method considers the relationship between the original road segment and all the road segment candidates, which shows an enhancement in determining a right road segment. The translation vector correction algorithm can compensate the longitudinal position drift effectively. The simulation and experiment verify the correctness of the proposed method and the positioning accuracy can be greatly improved.
Dopamine Fluorescent Sensor Based on Green Synthesized Copper Oxide Nanoparticles and Tyrosinase

Dopamine (DA) is a catecholamine that is an essential neurotransmitter in the human body. Slight variation in its concentration and metabolism leads to severe neurological damage or diseases, such as Parkinson’s, Alzheimer’s, schizophrenia, and many more. It could be addressed at some scale by early detection. In this study, copper oxide nanoparticles (NPs) were synthesized by green route (P-Cu2O NPs) with Artemisia absinthium leaf extract, which acts as a reducing agent, accompanied by the enzyme tyrosinase (Tyr) used as a sensing material. Molecular docking is performed to predict the interaction between DA, Cu2O NPs, and Tyr. The structural characterization of the NPs and interaction with DA via fluorescence spectroscopy was carried out to determine the sensitivity. The central composite design (CCD) was used to optimize the experimental conditions, such as the volume of NPs, enzyme, and temperature. The combination P-Cu2O + Tyr (PT) showed a selective response to DA and did not show any response to interfering analytes, such as ascorbic acid (AA), cysteine, and tyrosine. The limit of detection (LOD) of DA is observed to be 5 $\mu \text{M}$ . This study indicates that PT can be a fluorescent probe in detecting DA.
A Scene Understanding and Positioning System from RGB Images for Tele-meeting Application in Augmented Reality

As augmented reality (AR) technology advances, there is a growing demand to apply it to various applications. With the outbreak of the COVID-19 epidemic in 2020, online meetings have become increasingly common to prevent human contact, creating an opportunity to implement AR technology and its related applications. In this paper, we propose a new AR solution for tele-meeting applications that combines neural networks and simultaneous localization and mapping (SLAM) to achieve scene understanding and user localization using only RGB images. Our system offers the flexibility to develop the target application solely based on our custom devices/software, rather than relying on existing AR software development kits (SDKs) with their limitations. Existing SDKs, such as ARCore, can only be used on officially certified devices by Google, and developing custom AR kits to resolve compatibility issues among multiple technologies and devices can be challenging and time-consuming. This work presents a new system to address the challenges of scene understanding and user positioning for realizing tele-meeting applications, which can also be used as an AR development kit on any device with a camera. We conducted several experiments on the system modules to verify its performance, and the results show that our system provides an efficient and stable user experience, making it a promising technology and application.
GSC-YOLOv5: An Algorithm based on Improved Attention Mechanism for Road Creak Detection

Pavement damage detection is of great significance for traffic safety and pavement maintenance. Traditional detection methods require a lot of time and cost, and there are many problems in image detection. Therefore, an improved YOLOv5 pavement damage detection algorithm, GSC-YOLOv5, is proposed. First of all, to solve the problem that small cracks are similar to pavement and difficult to detect, we introduced the self-designed Spatial Pyramid Pooling module into the backbone network of the original YOLOv5 model: GM-SPPF to enhance the feature extraction of the network and improve the detection ability of fine cracks. Secondly, to solve the problem that it is difficult to locate defects under special lighting conditions, ShuffleAttention, an improved lightweight attention module, is introduced to help the model accurately locate features and improve the accuracy of object detection. Finally, the upsampling mode of the original model is changed to the lightweight CARAFE upsampling, so as to reduce the number of parameters in the model. The experiment was carried out on RDD2022 data set. The results showed that the improved YOLOv5 model mAP0.5 reached 54.6%, which was 3.3% higher than the Baseline, and the F1 score reached 57.5%. Our algorithm can detect and locate the defects on the outlet surface, saving a lot of manpower and material resources, which is of great significance for the evaluation of highway damage and its maintenance.
A New Image Encryption Scheme Based on 6D Hyperchaotic System and Random Signal Insertion

With the development of communication techniques and the Internet, how to protect private image transmission on the Internet has been a research highlight. Scholars have proposed different kinds of image encryption algorithms. Some of them are not secure enough. A novel cryptosystem is presented which is based on a 6D hyperchaotic system and random signal insertion. To enhance the dynamic performance of the hyperchaotic system, insert some random signals into the system variables during iteration. The sum value of all plaintext pixels is used to produce the initial values of the system. Thus, the proposed cryptosystem is closely related to the plain image. Split a pixel into two equal parts and form a larger matrix. Scrambling, cycle shift, and diffusion are operated on the new matrix. Finally, we obtain the encrypted image. The simulation results reveal that the proposed method has a very large key space, high key sensitivity and robustness, and is able to resist many different attacks. It is more secure than many other image encryption schemes.
Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine

The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people’s demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice.
Research of Willingness to Use Digital Currency in International Shipping — based on Operations Research Theory

International shipping is an important part of the digital economy. Digital RMB is essential to promote national economic development and paperless development. In this study, SOR theory and perceived value model were integrated to construct an influencing factor model for using digital RMB. The innovation of this paper lies in the use of structural equation model to verify the influence path of assumptions and variables, and analyze the condition configuration that affects willingness to use digital currency from the perspective of factor combination. The empirical results show that the stimulus variables positively affect the willingness to use digital RMB through perceived usefulness. The desire to use digital RMB is negatively affected by perceived risk.
Parameter Prioritization for Efficient Transmission of Neural Networks in Small Satellite Applications

Low-earth-orbit (LEO) satellites can be used to launch cost-effective Earth observation missions. Onboard processing, in particular using machine learning (ML) approaches, is often discussed as a way to reduce the amount of data transmitted back to Earth. However, the combination of LEO satellites and ML brings unique communication challenges, as requirements - and therefore ML models - often change throughout the lifetime of a satellite mission. In this paper, we propose a novel communication protocol that deals with model updates efficiently by providing incremental updates with low communication overhead and quick improvements in onboard classification accuracy. Our initial evaluation shows that the proposed, priority-based approach significantly outperforms the baseline of transmitting updated models without considering prioritization.
Comparison of Two 3D Printing Technologies for Microfluidic Applications

In this work, a 3D model was designed that has the negative of several widely used microfluidic mixers and channel patterns, and which could be used to cast polymers, e.g. PDMS (polydimethylsiloxane). This model was printed by using two of the most common 3D printing technologies, namely fused deposition modeling (FDM) and stereolithography (SLA), and the precision of the fabricated models was measured and compared in order to find out which printing technology could be applied more effectively for microfluidic applications.
Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin

Secure multiparty protocols are useful tools for parties wishing to jointly compute a function while keeping their input data secret. The millionaires’ problem is the first secure two-party computation problem, where the goal is to securely compare two private numbers without a trusted third-party. There have been several solutions to the problem, including Yao’s protocol [Yao, 1982] and Mix and Match [Jakobsson and Juels, 2000]. However, Yao’s Protocol is not secure in the malicious model and Mix and Match unnecessarily releases theoretically breakable encryptions of information about the data that is not needed for the comparison. In addition, neither protocol has any verification of the validity of the inputs before they are used. In this thesis, we introduce Variance, a privacy-preserving two-party protocol for solving the Yao’s millionaires’ problem in a Bitcoin setting, in which each party controls several Bitcoin accounts (public Bitcoin addresses) and they want to find out who owns more bitcoins without revealing (1) how many accounts they own and the balance of each account, (2) the addresses associated with their accounts, and (3) their total wealth of bitcoins while assuring the other party that they are not claiming more bitcoin than they possess. We utilize commitments, encryptions, zero knowledge proofs, and homomorphisms as the major computational tools to provide a solution to the problem, and subsequently prove that the solution is secure against active adversaries in the malicious model.
Music emotion prediction based on multidimensional feature fusion

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets.
Anti-Sway Control for Bulk Terminal Gantry Cranes Based on MFAC

To address the problem of lifting weight sway generated by underactuated gantry cranes during operation, considering the load diversity of the portal machine and other disturbance effects, this paper proposes a control method that does not rely on an exact model. Firstly, a simple kinematic model of the gantry cranes is established and dynamically analyzed, then a compact form dynamic linearization is performed to obtain the data model, a pseudo-partial derivative estimation law is computed online, a model-free adaptive control based gantry cranes anti-sway controller is designed, and finally a simulation analysis is performed. The simulation results show that the load swing angle amplitude is effectively suppressed and quickly dissipated, while it has strong robustness to external unknown disturbances. Compared with the PID control simulation analysis, this control algorithm has more effective anti-sway control effect and can reach the stable state faster.
A Semi-Supervised Deep Learning Fault Diagnosis Method Based on Uncertainty Estimation and Weighted Labels

The fault diagnosis method based on deep learning has been widely used due to its powerful feature extraction ability. However, its excellent performance relies on sufficient labeled data participating in model training. Semi-supervised learning is a very effective method to address the issue of low diagnosis accuracy caused by insufficient labeled data. FixMatch, which combines consistent regularization and pseudo-labels, has recently performed well on semi-supervised learning. However, the pseudo-labels of unlabelled samples obtained in FixMatch by high and fixed thresholds can lead to slow model convergence, and such pseudo-labels obtained using pre-defined high thresholds may still be incorrect, and guided by these incorrect pseudolabels can lead to significant degradation in model performance. Also it is obviously unreasonable to use the same weight for the consistency loss of all unlabelled samples in FixMatch without considering the differences between samples. To address the above issues, a semi-supervised deep learning fault diagnosis method based on uncertainty estimation and weighted labels is designed and experimentally validated using the Case Western Reserve University bearing dataset. Experimental results show that when there are only 2 labels per class, the accuracy of the test set reaches 90.59% and the fault diagnosis accuracy of our proposed method is improved by 9.08% compared to FixMatch.
Multidimensional Taylor network optimal control of SISO nonlinear systems for tracking by output feedback

It is difficult or even impossible for the existing nonlinear system control methods to realize the real‐time tracking control of nonlinear systems in the presence of modeling errors by output feedback. For that sake, multidimensional Taylor network (MTN) optimal control method is proposed for the real‐time optimal tracking control of the SISO nonlinear constant system with modeling errors as such. On the basis of the original MTN, the control input item is added to the nonlinear dynamic model, which is then termed as the MTN optimal controller (MTNOC). Its initial parameters are trained by the conjugate gradient method and the minimum principle, according to the calculated optimal input and output of the controlled object in open‐loop status. The propagation learning algorithm is adopted to improve the MTN product term weights and eliminate the modeling errors during the real system adjustment process. Simulation results show that the MTNOC promises a high response rate. Despite the errors in the modeling process, MTNOC manages to stabilize the system for tracking the desired output. The experiment of overlaying an additional signal on the input signal proves MTNOC to be of excellent tracking characteristics, capable of inhibiting the disturbance signal to some extent. In short, by realizing the optimal closed‐loop tracking control of the SISO nonlinear constant system by output feedback, MTNOC guarantees its real‐time control accuracy, dynamic performance, robustness, and anti‐disturbance capability.
DeepDDM: A Compact Deep-Learning Assisted Platform for Micro-Rheological Assessment of Micro-Volume Fluids

An emerging differential dynamic microscopy technique has been successfully used for quantitative dynamic investigation of micro-particle suspension, leading to a rheological assessment of the solution. This technique exploits an optical microscope equipped with a digital camera for the assessment. However, the accessible measurement ranges at high frequencies are limited by the video frame rate of the camera, resulting in a limitation in investigating distinguish responses at the high-frequency region. With advanced deep learning technology, image-synthesizing deep learning-based algorithms can significantly increase the video frame rate, producing additional in-between frames. As a result, the rheological responses at the high-frequency region can be obtained. To address this problem, a video frame interpolation integrated differential dynamic microscopy-based device (DeepDDM platform) was developed. Our DeepDDM platform interpolates video frames to extend the maximum measuring angular frequency up to quadruple from 30.1 rad/s to 123.0 rad/s, resulting in a more comprehensive rheological assessment without hardware modification. Unlike reducing the camera exposure time approach, our approach requires only a single camera and works without brightness reduction. Furthermore, the device is compact and portable. It comprises a few main components, and requires only $8 \mu \text{L}$ sample volumes for the rheological assessment. Thus, it is easy to relocate to measure biological samples which are often do not retain their natural properties in a storage allowing for in situ studies of the fluids. In comparison, the obtained responses agreed with the reference mechanical rheometer, although the employed partially coherent source and out-of-focus image acquisition bring difficulties to our system.
Development of a Technique for Design and Optimization of the Bearing Structure of a Strapdown Inertial Navigation System

The paper describes a technique for designing and optimizing the bearing structure of a strapdown inertial navigation system aiming to reduce the instrumental errors of sensors caused by the deformation of the bearing structure under the complex action of an external and internal loads considering the mass minimization of an inertial measurement unit at the same time. In order to solve current tasks an algorithm that combines multi-objective parametric optimization and structural topological optimization was developed. As an illustration of the described technique application, the process of optimizing the bearing structure of a particular device of a strapdown inertial navigation system was considered. Two-stage multi-objective parametric optimization was performed in order to maximize the stiffness of the structure, and then, using topological optimization, the mass of parts was minimized. It was shown that the proposed combination of parametric and topological design optimization techniques allows us to switch from multi-objective optimization to single-objective optimization with the assumption of insignificant change of stiffness.