esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue on HVDC for Grid Services in Electric Power Systems
## 2 Improved HVDC Operation and Control Strategy for System Stability
### Novel Transient Power Control Schemes for BTB VSCs to Improve Angle Stability [3]  
This paper proposes two novel power control strategies to improve the angle stability of generators using a Back-to-Back (BTB) system-based voltage source converter. The power control strategy can emulate the behavior of the ac transmission to improve the angle stability while supporting the ac voltage at the primary level of the control structure.
# Physical and Mechanical Properties of Reclaimed Asphalt
## 3 Materials and Methods
### Methodology
#### 3.2.5 Mechanical Properties: Resilient Modulus and Permanent Deformation  
In this study, the evaluation of mechanical behaviors of the unbound materials under investigation was completed in accordance with AASHTO T307-99 [49]. According to [49], all base and subbase materials were classified as Material Type 1 and Material Type 2. In this study, the investigated blends met the requirements of Material Type 1 because the proportion of material passing through the 2-mm sieve was less than 70% and the proportion of material passing through the 0.075-mm sieve was less than 20%. As for sample preparation, a moisture content equal to OMC was considered for all blends, since this was the target moisture content in the field for unbound pavement layers, according to the practice for conventional materials [22]. After the assembly at the bottom of the mold, the material with OMC was compacted in 5 layers, each 6 cm thick, using a vibratory compaction device. Finally, the specimen was assembled at the top, installed in the triaxial chamber and the test started. As for the loading conditions, the specimens were imposed to an initial load of 103.4 kPa (confining pressure and maximum axial stress), while the cycle stress was set to be 93.1 kPa, in order to eliminate the effects of the interval between compaction and loading and between initial loading and reloading. Afterwards, the value of confining pressure was set to be 20.7 kPa, which was increased in every three sequences.  
The applied stress and the developed strains describe the mechanical behavior of the material [50], as it is shown in Figure 3.  
For the estimation of the resilient modulus \(M_{R}\), the experimental data of the last five loading cycles were taken into account. The \(M_{R}\) value for each loading cycle was estimated through Equation (4):  
\[M_{R}=\frac{S_{cyclic}}{e_{r}} \tag{4}\]  
where \(S_{cyclic}\) is applied cyclic stress; \(e_{r}\) is the elastic deformation; and \(S_{cyclic}\) is estimated through Equation (5):  
\[S_{cyclic}=\frac{P_{cyclic}}{A} \tag{5}\]  
where \(P_{cyclic}\) = \(P_{max}\) - \(P_{contact}\) (\(P_{max}\) is the maximum applied axial force and \(P_{contact}\) is the contact load, which maintains a positive contact between the specimen cap and the specimen) and \(A\) is the initial cross-sectional area of the specimen.  
The permanent deformations were determined directly from the experimental results by accumulation. The concept of accumulation of permanent deformations with increasing number of loading cycles is shown in Figure 3.
## 3 Results and Discussion  
In this section, some examples of the proposed method are presented using the data described in Section 2. In particular, three different test bed sites (study cases) in terms of roughness and slope (infrastructure, urban and natural) were used to validate the positional accuracy of the ALS. The examples are developed for 1D and 3D cases, but the methods for handling of any other cases are similar (e.g., 2D). For simplicity, all the examples are developed using two metric tolerances: \(T_{1}\) and \(T_{2}\). \(T_{1}\) is linked to a 50% proportion of error cases, and \(T_{2}\) is linked to a 90% proportion of error cases.  
To demonstrate the validity of the proposed method, two analyses are performed: the first one is focused on the type I error (risked by the producer/the significance of the test) and the second on the type II error (risked by the user/ the power of the test. For additional information see [38]). For the type I error analysis, three different quality controls are presented to show that the acceptance of the null hypothesis performs well. For the type II error analysis, small modifications are introduced to the tolerances to observe the null hypothesis rejection behavior of the method.  
For the analysis of each quality control case, several sample sizes are used, and a simulation procedure is applied to demonstrate the proposed method. Five sampling sizes (20, 50, 100, 200, 500) were considered. These sample sizes are in the range of values that have been used in other simulation studies [39; 40] on the behavior of positional accuracy control methods. For each sample size, 10,000 iterations were performed. In general, the larger the number of iterations, the more the overall results of the simulation are stable. According to [41], 10,000 iterations is a suitable number for simulations of this type. For each iteration, the proposed method was applied, and the test's behavior was analyzed. For each quality control case and sample size, the proportion of times in which the corresponding null hypothesis has been rejected is presented as the main result. When the hypothesis is true, the percentage of rejections must be close to the value of the significance that is considered (type I error). When the hypothesis is false, the percentage of rejections is the power of the test (type II error).
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 4 Experimental Evaluation  
In general, as the installation of patches and security solutions is limited to ensure the availability of the IIoT smart factory, it can detect cyberattacks that exploit new and variant malicious codes. This is carried out using a malicious code detection system built by deploying a deep learning-based malicious code detection model to Edge G/W of the edge layer, which is an intermediate node. Therefore, this section describes the information and results analysis of experiments conducted to classify multiple malware classes using CNN for the implementation of the edge computing and deep learning-based malware detection system.  
Since CNN is an image detection algorithm, the implementation of the predictive indicators such as accuracy, precision, recall, and F1-score involves multiple steps such as data collection, data processing, feature extraction, training, testing, etc. [101]. However, the data collection and data processing steps are excluded from the experimental scope of this study. As shown in Figure 5, assuming that the malicious code is converted into an image in the Edge G/W of the edge layer and sent to the Edge Server, we limited our experiment to the classification of malware, using the CNN algorithm in the Edge Server.  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt}} \hline \hline
**Step** & **Description** \\ \hline \multirow{3}{*}{1} & The raw malware binary files are converted into images. A one-dimensional array of malware raw binary files is converted to grayscale image pixels with values ranging from 0 (black) to 255 (white) and reconstructed into a two-dimensional array. \\ \hline \multirow{3}{*}{2} & The image is normalized to the size of 112 \(\times\) 112 pixels. During the dimensionality reduction stage, certain important features may be omitted, but most of the malware images in the Malimg dataset maintain their texture properties in the normalization stage. \\ \hline \multirow{3}{*}{3} & The dataset is split into a training set (70\%) and a test set (30\%). \\ \hline \multirow{3}{*}{4} & The data converted into images are applied to the CNN. \\ \hline \multirow{3}{*}{5} & CNN is converted to flatten. As the last layer of the CNN is in the form of an n-dimensional matrix, it is flattened. Flattening refers to the conversion of an n-dimensional matrix into a one-dimensional column matrix. \\ \hline \multirow{3}{*}{6} & The malware family is classified by the CNN model and Softmax is used for multiclass classification. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Step-by-step malware classification process.  
Figure 7: Malware image generation and malware classification process using the proposed CNN-based architecture.
## 3 Numerical Simulations
### Results with Different Noises  
#### 3.2.1 Vehicle Load with Turbulence Noise  
Considering the dynamic effects of the noise derived from the car movements, or the so-called vehicle-bridge interaction, the moving load may not remain constant during its passage. In this part, the noise of the moving load is assumed to follow harmonic oscillations. The analysis includes different noise magnitudes and two-point loading configurations (front and rear axes with a 3.0 m distance). The simulation test cases are shown in Table 2 below. It should be noticed that the vehicle weight was set to 10 kN and the speed of the vehicle set to 3.0 m/s, except for case 3, which was set to 5.0 m/s. The  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Modal Order** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Simulation & 0.53 & 0.53 & 0.58 & 0.63 & 0.79 & 0.88 & 1.10 & 1.29 \\ Measured value & 0.52 & 0.52 & 0.53 & 0.68 & 0.84 & 0.89 & 1.14 & 1.36 \\ Errors & 0.01 & 0.01 & 0.05 & \(-\)0.05 & \(-\)0.05 & \(-\)0.01 & \(-\)0.04 & \(-\)0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The frequency of the simulation and the test (Hz).  
Figure 4: The influence line for the eight measurement points of the Chengxi bridge.  
Figure 7 shows a comparison of the predicted values using different calculation methods under a 5% noise intensity. It can be observed that, under a 5% noise intensity in the moving load itself, all three methods are capable of effectively identifying the moving load with errors less than 5%. It is important to note that the influence line method is somewhat sensitive to the data from the monitoring points, while the absolute value method can, to some extent, mitigate the influence of the monitoring points. And the modified absolute influence line has the best accuracy of identification with a 0.5% RMSE error.  
It can be observed that the results from all three methods are very good, achieving accuracy rates of over 98%. The case study results show that, consistent with the findings in [PERSON] et al. [33], the influence line method (IL) demonstrates high identification accuracy for cases involving only moving loads, and it exhibits strong noise resistance.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation  
For the purpose of evaluating the proposed algorithm and analyzing the performance gains compared to other algorithms, we conduct experiments with a well-known simulator, ns-3 [40]. We also used Python to implement the proposed RL algorithm. The main simulation and analytical parameters are explained in the following subsections. We consider two main criteria: (1) learning performance and (2) the algorithms' relative performance compared to other previous proposals, including \(Q\)-based algorithms, RSRP-based selection algorithms, and random selection algorithms.
## 2 Materials and Methods
### Description of Analysis
#### 2.2.1 Varying Air Gap Width  
Air gap width ('w2') was varied independently with all other parameters held nominally to assess its effect on force production. By running a parametric sweep varying 'w2' from 20 \(\upmu\)m to 200 \(\upmu\)m using a step size 10 \(\upmu\)m, we aimed to quantify how the introduction of and variation in air gaps influenced the electrostatic forces generated within the muscle fibers. The force output was measured and compared to the non-gap design, providing insights into the efficacy of this new design. These values were compared to the non-gap model, where 'w2' was zero.
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 5 Conclusions and Prospects  
In recent years, scholars have conducted elaborate studies on the swelling pressure of bentonite in the deep geological repositories of HLW, focusing on experimental methods, influencing factors, change patterns, and intrinsic influencing mechanisms. However, considering the coupled thermochemistry and hydrodynamics phenomena involved in the actual nuclear waste containment process, the existing research findings still have certain limitations. Additional research is desired in the future in the following areas:  
(1) Various test methods will result in different values of measured swelling pressure. A comparative analysis of different test methods should be conducted, specifically considering the coupled environmental effects in the nuclear waste containment process to identify the optimal test method.  
(2) Diffuse nuclear radiation heat is a major influencing factor in nuclear waste containment. Considering the effect of temperature, studies on the hydrodynamic properties of bentonite remain to be conducted.  
(3) Analysis of the mechanism of swelling pressure changes: from the microscopic perspective, the process and mechanism of swelling pressure changes still lack profound recognition. In addition, from the macro-mechanics perspective, there are only sporadic studies which generally lack experimental verifications. In the future, the analysis of the swelling pressure variation law and the internal mechanism of swelling pressure evolution needs to be further refined.  
Conceptualization, J.L. and C.Y.; validation, J.L., W.S. and Y.Z.; investigation, J.L., W.S. and Y.Z.; resources, J.L., W.S. and Y.Z.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and C.Y.; supervision, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 52109133), Shanghai Sailing Program (Grant No. 21 YF1432700).  
Figure 11: Comparison of measured and predicted swelling pressures for eight soils [93]: (**a**) low swelling pressure; (**b**) high swelling pressure [92].  
**Institutional Review Board Statement:** The study did not involve humans or animals.  
**Informed Consent Statement:** The study did not involve humans or animals.  
**Data Availability Statement:** All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** This research was supported by the National Natural Science Foundation of China (Grant No. 52109133), Shanghai Sailing Program (Grant No. 21 YF1432700).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Results
### Comparison with Existing Methods
* Autism-specific feature representation: Based on our comparative experiments, we selected Mel spectrograms for speech representation rather than the commonly used MFCCs. This choice was specifically informed by the unique speech characteristics of children with autism, who typically exhibit distinctive prosodic patterns including abnormal intonation, rhythm, and vocal quality [13]. Mel spectrograms preserve the temporal dynamics and frequency distributions that contain critical emotional information, particularly the distinctive patterns like \"babbling\" or \"growing\", mentioned in our introduction, that often convey negative emotions in children with autism. Our experimental comparison in Table 7 validates this theoretical advantage, with the Mel spectrogram approach achieving 83.47% accuracy compared to 25.72% for MFCCs. Our visualization analysis (presented later in Section 7.5) further substantiates this choice by illustrating how our model identifies distinctive frequency patterns: high energy values in high-frequency regions for happy emotions versus energy concentration in low-frequency domains for calm states. This pattern discrimination ability explains why Mel spectrogram representation achieves remarkably consistent performance across all emotion categories (between 82.15% and 84.12%), demonstrating its robustness in capturing the full spectrum of emotional speech characteristics in children with autism. These methodological advancements collectively contribute to the significant performance improvement (90.73%) compared to previous methods (\(\leq\)76.50%). By specifically addressing the unique challenges in analyzing the emotional expressions of children with autism--including reduced facial expression complexity, asynchronous emotional cues, and atypical speech patterns--our approach achieves substantial accuracy gains that demonstrate the importance of a tailored architectural design for this specialized application domain.
## 2 Materials and Methods
### Cost-Effective Data Acquisition System
#### 2.3.3 Signal Processing Algorithms
Store frequency centers in array **f**. Frequency is expressed in cycles per unit of sample spacing (zero at the beginning). Example: if sample spacing is in seconds, frequency unit is cycles/second. Calculate DFT  
Calculate the one-dimensional discrete Fourier transform (Fyy). Use the fast Fourier transform (FFT) algorithm to compute DFT of n points. Find Maximum Frequency  
Use numpy.argmax() to find the index with the maximum value in the Fyy array. Extract the corresponding frequency value from the array **f**. Calculate Amplitude  
Determine the standard deviation of all signal records. Multiply by the square root of 2 to find the amplitude. Determine Phase  
Use scipy.optimize.curve_fit to determine the phase. Calculate Offset  
Calculate the mean value of the data to find the offset. Define an internal function to represent the sinusoidal function based on extracted parameters. Follow the equation: \(y(t)=A\cdot\text{numpy.sin}(wt+p)+cy(t)=A\cdot\text{numpy.sin}(wt+p)+c\)  
EndConsequently, the Arduino code is loaded, and the Python code is executed in the terminal for the measurement of output acoustic power. As discussed at the beginning of Section 2.3, simultaneous data acquisition is crucial for validating the proposed system through a comparison against the high-cost B&K data acquisition system (DAQ) using the same pressure sensors. To ensure that measurements from both systems start at the same moment for the sensors being evaluated (P1.1 linked to the B&K DAQ and P1.2 linked to the presented low-cost system), a trigger mechanism is implemented that generates a voltage on a digital pin of the Arduino board. This pin is connected to one of the channels of the B&K, which is configured to start measurements when a drop in potential is detected at its input. As a result, when the Arduino code is loaded and the Python code is executed in the terminal, both acquisition systems automatically begin measuring at the same time for different sensors (that monitor points in close proximity). Additionally, a LED diode was installed to indicate when the code is running, and both systems start logging data into a discrete time series in a '_.dat_' file.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 2 Basic Principles
### Convolutional Neural Network  
Convolutional neural network is one of the core algorithms of deep learning and has achieved great success in the field of computer vision, including tasks such as image classification, target detection and image segmentation. In recent years, CNNs have also been applied in areas such as natural language processing and fault diagnosis. CNNs are deep feed-forward neural networks consisting of convolutional layers, pooling layers and fully connected layers.
# Vision Systems for a UR5 Cobot on a Quality Control Robotic Station
## 3 Vision-Based Measurement Algorithm
### Image Acquisition  
Two cameras were used to acquire images: a _Mako_ camera permanently mounted and a Savio camera placed in the cobot's flange (Figure 2).  
The Savio USB camera enabled estimation of the location of a semi-finished product in the cobot coordinate system, and the Mako camera collected images for geometric measurements employing the vision system algorithms. Measurement parameters of the cameras are presented below.  
The a priori estimation of spatial resolution and the measurement resolution of Mako and Savio cameras are as follows:  
Make:  
Image resolution, Ri = 1292 \(\times\) 964 pixel;  
Field of view (FOV), 214.4 mm \(\times\) 159.9 mm;  
Spatial resolution, Rs = 0.165 mm/pix (Rs = FOV/Ri);  
Measurement resolution, Rm = 0.008 mm, with subpixel measurement capability for CCD camera, Mp = 0.05 pixel (the system's measurement resolution capability in a fraction of a pixel), Rm = Rs \(\times\) Mp;  
Frame rate: 30 [frame/s].  
Savio:  
Image resolution, Ri = 640 \(\times\) 480 pixel;  
Field of view (FOV), 463.4 mm \(\times\) 347.5 mm;  
Spatial resolution, Rs = 0.724 mm/pix;  
Measurement resolution, Rm = 0.072 mm, with subpixel measurement capability for CMOS camera, Mp = 0.1 pixel, Rm = Rs \(\times\) Mp;  
Frame rate: 30 [frame/s].  
For the robotic line presented in Section 1, groups of semi-finished products were developed, designed, and printed using 3D-printing technology (Figure 10). A symmetrical dimensional deviation of \(\pm\)0.5 mm was assumed for the overall dimensions and \(\pm\)0.3 mm for the radii of the internal holes. Tolerances were adopted based on the guidelines of design assumptions.  
Measurements employing the deployed vision system were performed with a measurement resolution of at least an order of magnitude greater than the acceptable tolerances.  
The semi-finished product, in the shape of a circle (shown in Figure 11c), was utilized to determine the level of measurement noise, accuracy, and repeatability of the vision system (with the proposed and implemented methods). Outer diameter calculation results and internal diameters are discussed in Section 4 in the tables (Tables 1 and 2).  
Figure 9: Conceptual diagram of the developed vision system software tool. Dotted line—separates the hardware or software part, arrows—data flow.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 2 Methodology
### Preprocessing of Dermoscopic Images  
From the above, dermoscopic images are affected mainly by noise such as hairs and bubbles, edge, and contrast problems. For this purpose, morphological methods [20], filters as well as sharpening were used to solve these problems and improved the quality of the image. Considering the possible damage to the original image during the removal of hairs etc., a masking technique was used to perform some restoration process. Figure 2 shows the comparison of dermoscopic images before and after processing, and the above-mentioned problems such as noise were improved.  
The k-means clustering algorithm is an unsupervised clustering technique based on partitioning, which is known for its fast convergence and easy implementation [21]. The k-means clustering algorithm is known to provide locally optimal solutions [22]. It is suitable for the segmentation of color images, including the classification of skin lesion areas and background regions in an image. In dermoscopic images, there is a clear color contrast between lesion areas and normal skin areas. Typically, diseased skin appears brown or black, while normal skin appears white or yellow. Given this difference, the RGB color space is effective in identifying skin lesion regions. Therefore, the k-means clustering algorithm was used for RGB color-based segmentation of skin fluoroscopy images [23].  
The k-means clustering [24] algorithm was used to segment dermoscopic images with two initial cluster centers representing skin lesions and normal skin regions, respectively. The image masking technique was then used to extract the skin lesion region and ignore the background. To further minimize the background effect, the skin lesion region of the segmented dermoscopic image was cropped. Figure 3 illustrates the segmentation and cropping process.  
Figure 2: (**a1–a3**) The original images; (**b1–b3**) noise removed, sharpened and filtered images.