esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification  
To verify the correctness of the regenerated computational model, the numerical modeling step is recalculated. As shown in Figure 8, under gravity, when boundary conditions and mechanical parameters (Table 1) are unchanged, the calculation converges, and the plastic zone distribution range is small, indicating that the landslide simulation is realized. After the landslide occurs, the unstable zone is eliminated, and the slope is in a stable state.  
To verify the effectiveness of the proposed LREM, the results using the proposed LREM for modeling the progressive failure of the simplified slope induced by sublevelaving mining are compared with those obtained using the Fstability. A frequently used strategy for determining the FOS of a slope is the SRM, which can calculate the safety factor accurately; however, it cannot simulate the deformation and failure of a slope. As listed in Table 2, when the first seven layers are excavated, the FOS \(>1\), which is calculated using the FDM and SRM. At this time, the maximum shear strain increment belt is not connected, the calculation converges, and the slope is in a stable state. When the eighth layer is excavated and FOS \(=1\), penetration of the maximum shear strain increment belt occurs, the calculation does not converge, and the slope is in an unstable state. After using the adaptive local remeshing technique, the FOS \(>1\).  
Table 2 and Figure 8 show that the proposed LREM can accurately evaluate and predict the slope stability and eliminate the unstable factors when the slope is unstable.  
Figure 9 shows the contours of the displacement before and after removing the landslide body. As shown in Figure 9, the displacement before the transfer of the physical field variables is compared with the displacement after the transfer of the physical field variables. After removing the landslide body, the residual mesh and the updated mesh accurately inherit the physical field variables of the old mesh, which indicates that the proposed adaptive local remeshing technique reduces the scale of remeshing and retains the original nodes as much as possible. In the process of transferring physical field variables, the errors due to interpolation are small.  
Figure 8: The plastic zone after removing the landslide body.
# Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
## 2 Unveiling the Inner Workings: Exploring the Principles and Model of an EDS System
### Three Superconducting Coils  
The approach can be extended to include three SC coils, as illustrated in Figure 18. This spatial arrangement was selected due to its resemblance to the magnetic field generated by Halbach array permanent magnets [12].  
The polarities between two adjacent SC coils were reversed, similar to the JR-Maglev configuration. This reversal helps to reduce the magnetic field inside the train by creating a magnetic path between adjacent coils. Consequently, the magnetic field levels within the car remain within safe limits for passengers, and less robust magnetic shielding is required [13].  
The magnetic coupling tables for the two new coils were shifted by a polar pitch of 1.35 m, coil \(p1\) leading for coil \(m1\) lagging. The same procedure was applied in calculating the drag, guidance, and lift forces presented in the table. Ultimately, the table imported for the computing of each of the three forces was the superposition of the three tables, one data table for each of the three SC coils. The results, at a speed of 600 km/h, can be observed in Figure 18, which supports the analysis of more complex null-flux EDS systems.  
Figure 17: Guiding force as a function of train travel speed. A comparison between the model using the circuit simulator and experimental measurements obtained from the Yamanashi test line. The setup includes four SC coils onboard and twenty 8-shaped coils on the guideway.  
Figure 18 was plotted to demonstrate the potential for increased system complexity, either on both sides of the guideway or with additional 8-shaped coils. The mutual inductances between each newly added coil and the previous ones had to be calculated, always utilizing the symmetry and periodicity properties of the system by shifting the calculated values by one polar pitch. It was even possible to cross-connect both sides of the guideway, making the electrical circuit equivalent to a null-flux levitation and guidance system.
# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
###### Abstract  
Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss and affect 1 in 3000 individuals worldwide. Their rarity and genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative models offer a promising solution by creating synthetic data to improve training datasets. This study carried out a systematic literature review to investigate the use of generative models to augment data in IRDs and assess their impact on the performance of classifiers for these diseases. Following PRISMA 2020 guidelines, searches in four databases identified 32 relevant studies, 2 focused on IRD and the rest on other retinal diseases. The results indicate that generative models effectively augment small datasets. Among the techniques identified, Deep Convolutional Adversarial Generative Networks (DCGAN) and the Style-Based Generator Architecture of Generative Adversarial Networks 2 (StyleGAN2) were the most widely used. These architectures generated highly realistic and diverse synthetic data, often indistinguishable from real data, even for experts. The results highlight the need for more research into data generation in IRD to develop robust diagnostic tools and improve genetic studies by creating more comprehensive genetic repositories.  
Keywords:generative models; data augmentation; synthetic data generation; retinal diseases; inherited retinal diseases +
Footnote †: journal: Applied sciences
# A Web Interface for Analyzing Hate Speech
### Related Work: Hate Speech in Social Media and Proposed Algorithmic Solutions
More recent methods rely on deep learning and word embeddings [42; 43]. The robustness of a supervised machine learning algorithm and its ability to generalize for the detection of hate in unseen data relies on the retrieval of vast amounts of textual data. Big data analytics of social media contents is an emerging field for the management of the huge volumes that are created and expanded daily [44]. Most social media services offer dedicated application programming interfaces (APIs) for the collection of posts and comments, to facilitate the work of academics and stakeholders. Using a dedicated API, or a custom-made internet scraper makes it easy to retrieve thousands of records automatically. Twitter is the most common choice, due to the ease-of-use of its API, and its data structure that makes it easy to retrieve content relevant to a specific topic [36; 37; 41]. While textual analysis is the core of hate speech detection, metadata containing information about the record (e.g., time, location, author, etc.) may also contribute to model performance. Hate speech detection cannot be language-agnostic, which means that a separate corpus or lexicon and methodology needs to be formed for every different language [36; 37; 45]. Moreover, a manual annotation process is necessary, which, inevitably introduces a lot of human effort, as well as subjectivity [36]. Several annotation schemes can be found in literature, differing in language, sources (e.g., Twitter, Facebook, etc. ), available classes (e.g., hate speech, abusive language, etc. ), and ranking of the degree of hate (e.g., valence, intensity, numerical ranking, etc.) [37]. The selected source itself may influence the robustness of the algorithmic process. For instance, Twitter provides a maximum message length, which can affect the model fitting in a supervised training process [36]. Multi-source approaches indicate the combination of different sources for analytics [46]. In [47] for example, Twitter data from Italy are analyzed using computational linguistics and the results are visualized through a Web platform to make them accessible to the public.
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 3 Results and Discussion
### Adsorption Studies
#### 3.4.3 Adsorption Isotherms
In contrast, the higher energy value for biochar ARHB (E = 3.74 kJ/mol) suggests a stronger interaction, potentially involving a degree of chemical adsorption. These findings highlight the coexistence of physisorption and chemisorption in the adsorption of silver ions by biochars, underscoring the complex nature of the process. While physisorption, is characterized by weaker interactions between silver ions and the biochar surface, is dominant for most materials, the variation in adsorption energy reflects differences in surface properties and mechanisms across the biochars. This interplay of chemical and physical processes collectively determines the overall adsorption capacity. The data also reveal that the ARHB material exhibits the highest adsorption capacity, which can be attributed to its superior textural properties, such as specific surface area and pore volume. The high pore volume enhances accessibility and provides increased sorption sites for Ag species. Furthermore, the presence of additional functional groups and structural defects in the carbon matrix, which act as active sites, contributes to the material's enhanced affinity for Ag ions. This combination of a well-developed porous structure and surface functionalization makes ARHB a highly efficient adsorbent for Ag\({}^{+}\). ARHB demonstrates an impressive and competitive adsorption capacity for silver ions, positioning it as a highly promising biosorbent (see Table 4). Its performance suggests that ARHB could serve as an effective and sustainable alternative for silver ion removal in various applications. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Langmuir Parameters**} & \multicolumn{3}{c}{**Freundlich Parameters**} & \multicolumn{3}{c}{**Dubinin–Radushkevich**} \\  & **Q\({}_{0}\)** & **K\({}_{1}\)** & **r2** & **k\({}_{\text{F}}\)** & **n** & **r2** & **Q\({}_{\text{m}}\)** & **E** & **(mg g-1)** & **(k] mol-1)** & **r2** \\  & **(mg g-1)** & **(L mg-1)** & **r2** & **(mg1-n Ln g-1)** & **(L mg-1)** & **r2** & **(mg g-1)** & **(k] mol-1)** & **r2** \\ \hline CB & 19.49 & 0.125 & 0.9125 & 2.38 & 1.16 & 0.9992 & 10.60 & 0.86 & 0.6783 \\ \hline RHB & 10.84 & 0.077 & 0.9949 & 1.08 & 1.90 & 0.9418 & 5.74 & 0.35 & 0.8348 \\ \hline ARHB & 23.11 & 0.173 & 0.9202 & 3.91 & 2.09 & 0.9894 & 8.73 & 3.74 & 0.7907 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Isotherm constants and correlation coefficients for silver adsorption.
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 2 Materials and Methods
### The CBAM Module
#### 2.3.1 Channel Attention Module  
Channel attention focuses on the information useful for the desired task in the image; the specific structure is shown in Figure 4, which consists of 3D maximum pooling, 3D average pooling, and a multi-layer perceptron. It mainly focuses on the relationship between different channels in the feature map and emphasizes the channel information that is more important to the current task by learning the channel weights. Specifically, 3D global average pooling and 3D maximum pooling are first performed on the input feature graph in the channel dimension to downscale the input feature graph \(F\) from the data dimension direction to one dimension, and then the feature is fed into a shared multilayer perceptron (MLP) network in order to extract channel-related features and compute their importance scores. This process generates a channel attention graph, \(M_{c}\in R^{C\times 1\times 1}\), which is used to adjust the importance of each channel. In order to reduce the number of parameters, a reduced dimensionality coefficient \(r\) is used in the MLP, \(M_{c}\in R^{\frac{C}{r}\times 1\times 1}\). Finally, it is normalized by a sigmoid function so that the weight of each channel is between 0 and 1, and the sum is multiplied with each channel in the original feature map to generate a new feature map as defined by Equation (11). Based on Equation (9), the output feature map \(M_{c}(F)\) can be obtained with Equation (12).  
\[M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))) \tag{11}\]  
\[M_{c}(F)=\sigma\Big{(}W_{1}\Big{(}W_{0}\Big{(}F^{c}_{avg}\Big{)}\Big{)}+W_{1} (W_{0}(F^{c}_{max}))\Big{)} \tag{12}\]  
where \(F\) denotes the input feature map; \(AvgPool\) denotes the three-dimensional average pooling operation; \(MaxPool\) denotes the three-dimensional maximum pooling operation; \(F^{c}_{avg}\) and \(F^{c}_{max}\) denote the features from the average pooling and maximum pooling oper  
Figure 3: The general structure of a CBAM module, where the input and output feature maps are shown in blue color, the channel attention module and the spatial attention module are shown in green and purple colors respectively.  
ations, respectively. MLP denotes the multilayer perceptron, \(\sigma\) is the sigmoid activation function. Finally, the output can be obtained with \(F^{\prime}=M_{c}(F)\otimes F\).
## 2 3D LiDAR Fundamentals
### Multispectral LiDAR  
Multispectral LiDAR allows more spatial information and reflectance values to be recorded from the image area in the same measuring operation. The reflection intensities of laser beams at different wavelengths will also be different for the surfaces. In this way,  
Figure 2: Discrete and full-waveform LiDAR data.  
more information can be obtained about the entire surface. If three different wavelengths are used, three point-cloud measurements are performed simultaneously. Multispectral LiDAR is useful for water depth and topography mapping, vegetation, and forestry applications. Their measurement data especially provides greater accuracy in the classification and segmentation of image area details [27]. In particular, different plant species can be detected from multispectral LiDAR data [21]. Optech Titan commercial multispectral LiDAR incorporates three independent laser wavelengths into a single sensor with beams at 532, 1064, and 1550 nm.
# Parameter Optimization for Ship Antiroll Gyros
## 2 Dynamical Model of Ship's Antiroll Gyro
### Ship Rolling Mathematical Model under Random Wave Excitation  
The stress of the ship is shown in Figure 1. \(\alpha(t)\) is the wave slope angle, \(\left(I_{\phi\phi}+J_{\phi\phi}\right)\ddot{\phi}\) is the mass inertia moment, \(R\left(\dot{\phi}\right)\) is the roll-damping moment, \(K(\phi)\) is the roll-restoring moment, and \(M(\chi,\omega,t)\) is the wave excitation moment.  
Based on the Mathieu equation [21], considering the nonlinear damp and ship nonlinear restore moment, the ship rolling motion equation was established as follows [22]:  
\[\left(I_{\phi\phi}+J_{\phi\phi}\right)\ddot{\phi}+R\left(\dot{\phi}\right)+K( \phi)=M(\chi,\omega,t) \tag{4}\]  
where \(I_{\phi\phi}\) is the ship's moment of inertia, \(J_{\phi\phi}\) is the moment of inertia of additional mass, \(\phi\) is the roll angle, \(\ddot{\phi}\) is the roll angular acceleration, and \(\chi\) is the course angle.  
The roll-damping moment \(R\left(\dot{\phi}\right)\) was calculated as the linear damping plus cubic damping [22]:  
\[R\left(\dot{\phi}\right)=c_{1}^{\prime}\dot{\phi}+c_{3}^{\prime}\dot{\phi}^{3} \tag{5}\]  
where \(\dot{\phi}\) is the roll angular velocity, and \(c_{1}^{\prime}\) and \(c_{3}^{\prime}\) are the damping moment coefficients.  
To simplify the calculation, the roll-restoring moment was approximated to a fifth-degree polynomial [22]:  
\[K(\phi)=K_{1}\phi+K_{3}\phi^{3}+K_{5}\phi^{5} \tag{6}\]  
where \(K_{1}\), \(K_{3}\), and \(K_{5}\) are the restoring moment coefficients.  
The wave excitation moment was expressed as a function of wave slope angle [22]:  
\[M(\chi,\omega_{e},t)=Dha(t) \tag{7}\]  
where \(D\) is the ship's displacement, \(h\) is the transverse metacentric height, and \(\alpha(t)\) is the wave slope angle.  
The ship rolling mathematical model could be transformed into:  
\[\ddot{\phi}=-c_{1}\dot{\phi}-c_{3}\dot{\phi}^{3}-k_{1}\phi-k_{3}\phi^{3}-k_{5} \phi^{5}+Dha(t)/\left(I_{\phi\phi}+J_{\phi\phi}\right) \tag{8}\]  
where \(c_{i}=c_{i}^{\prime}/\left(I_{\phi\phi}+J_{\phi\phi}\right),i=1,3\) and \(k_{j}=K_{j}/\left(I_{\phi\phi}+J_{\phi\phi}\right)\), \(j=1,3,5\).
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 2 Theory
### Response Time of VCDM  
The optical communication system requires DM with fast response speed because the temporal error will deteriorate the compensation effects. The frequency of atmospheric turbulence aberrations is much faster than that in the eye [18]. The bandwidth of the VCDM should meet the atmospheric turbulence wavefront correction. The Greenwood frequency \(f_{c}\) related to the required control bandwidth of an AO system is [19]  
\[f_{c}=\left[0.0196(k/\sigma_{r})^{2}\int_{0}^{L}C_{n}^{2}(z)v^{5/3}(z)\mathrm{ d}z\right]^{3/5}, \tag{5}\]  
where \(k=2\pi/\lambda\), \(\lambda\) is the wavelength, \(\sigma_{r}\) is the uncorrected power, \(L\) is the path length, \(C_{n}^{2}(z)\) is the refractive-index structure constant, and \(v(z)\) is the wind speed. For a single turbulence layer with constant wind speed \(v\), the Greenwood frequency can be approximated as  
\[f_{c}=0.43\frac{v}{r_{0}}. \tag{6}\]  
For strong atmospheric turbulence with wind speed \(v\) of 10 m/s and \(r_{0}\) of 5 cm, the Greenwood frequency is approximately 86 Hz. The operating bandwidth of the DM is limited by several factors including the response time of the VCA and the first mechanical resonance frequency of the mirror. With 100 Hz operating bandwidth as a goal, the response time of the actuator should be less than 5 ms and the first resonance frequency of the mirror is aimed at 2000 Hz.
# On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization
## 2 Logistic Models Review  
### Verhulst Model  
The Verhulst model is given by  
\[\frac{\mathrm{d}y}{\mathrm{d}t}=ry\Big{(}1-\frac{y}{K}\Big{)}, \tag{1}\]  
where \(r\) is the growth rate parameter, and \(K\) is the carrying capacity of the environment.  
The right-hand of (1) can be written as  
\[ry-\frac{r}{K}y^{2}=(r_{1}-r_{2})y-r_{3}y^{2}.\]  
In the study of the population of organisms, \(r_{1}\) means the birth rate, \(r_{2}\) means the death rate, and \(r_{3}\) means the resultant between competition and cooperation rate among them [34].  
By separating variables, the differential Equation (1) has solution  
\[y(t)=\frac{K}{1+\big{(}\frac{K-y(0)}{y(0)}\big{)}\exp(-rt)}. \tag{2}\]  
The value of \(y\) that causes the right side of (1) to become maximum is called the inflection point,and the time when that happens is called inflection time. In the study of an epidemic, the inflection time can be viewed as the date when the daily cases reach maximum numbers. The inflection point of the logistic model can be found by solving the following equation for \(y\),  
\[\frac{\mathrm{d}^{2}y}{\mathrm{d}t^{2}}=0. \tag{3}\]  
Let \(y=y_{\mathrm{inf}}\) be the solution of (3), i.e., the inflection point. Then, by solving (2) for \(t\) so that  
\[y(t)=y_{\mathrm{inf}}, \tag{4}\]  
we will have the inflection time. Let us denote the inflection time with \(t_{\mathrm{inf}}\).  
The Verhulst model has inflection point \(y_{\mathrm{inf}}=\frac{K}{2}\) and inflection time \(t_{\mathrm{inf}}=\frac{1}{r}\ln\big{(}\frac{K-y(0)}{y(0)}\big{)}\).
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 5 Discussion  
In this study, GIS-based optimization algorithms have been applied for WSN deployment in health and environmental applications. GIS has several advantages in WSN deployment. GIS with modeling sensor nodes as point geometries makes WSN deployment simple due to simply managing the positions and topology between sensors. Another advantage of GIS is spatial analyses such as proximity analyses that make it possible to calculate the total unique coverage of the WSN (by removing overlapping coverage of the sensors) precisely. Applying the MST routing protocol not only can implement in the GIS easily due to modeling positions of the sensors but also help to find optimal topology of WSN with the minimum length for reducing energy consumption and then prolonging the lifetime. In addition, the evolutionary optimization algorithms, BA and PSO algorithms, help to  
Figure 8: Constancy of two algorithms for IoT-based sensor network deployment algorithm in sequential executions: (**a**) BA; (**b**) PSO.  
find optimum positions of the sensors in WSN with trading-off between the coverage and lifetime. Therefore, the GIS-based optimization algorithm is a suitable solution to find the optimal spatial distribution of the sensors with a limited number of sensors in a region.  
PSO algorithms help decision makers of health and environment fields to manage the WSN deployment in their scenarios by finding the optimal spatial distribution of the sensors as PSO algorithm has less complexity and better convergence rate than BA as well as has good performance in repeatability. The better performance of PSO is due to the continuity of the problem of WSN deployment. Based on the research [91; 92], the PSO algorithm is suitable for continuous optimization problems. WSN deployment is a continuous problem as the sensor nodes can be located at any position in the region. In addition, PSO can find better positions for sensors and the network topology with more trade-off between the network coverage and lifetime.  
Finally, the GIS-based optimization algorithm for WSN deployment has advantages in health and environmental application, although it seems simply WS deployment. This research highlights two important conflict issues of WSN deployment, coverage and lifetime, in the health and environmental applications. These issues are modeled using the GIS-based optimization algorithm in this research. In addition, this algorithm helps to enable IoT-based system and smart cities, because it is possible to provide ubiquitous health and environment services at any time and at any place with maximizing coverage and lifetime.