esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Studies
#### 3.5.1 Equilibrium Study
In addition, the standard deviation values (\(SD\)) are obtained as follows:  
\[SD=\sqrt{\frac{\sum\left[\left(q_{t,exp}-q_{t,cal}\right)/q_{t,exp}\right]^{2 }}{\left(N\,-\,1\right)}} \tag{4}\]  
where \(N\) is the number of the equilibrium data; \(q_{t,exp}\) and \(q_{t,cal}\) represent the experimental and calculated equilibrium points. It is possible to determine a non-temperature dependence for the adsorption, since the fitting curves with Freundlich isotherms follow similar behavior with an optimal at 25 \({}^{\circ}\)C, as displayed in Figure 7. The 1/\(n\) values are comparable lower values than unity for all solids, which indicates that the Freundlich model agreed reasonably well at low adsorbate concentrations. However, both of aforesaid models do not adequately explain the adsorption of the dye Acid Red 66 on the adsorbents at high adsorbate concentrations due to the surface coverage, as found elsewhere [50]. In addition, the results in Table 4 illustrates that the Langmuir adsorption isotherm fits better to all experimental data, as shown by the values of correlation coefficient (0.919 \(<\) R\({}^{2}\)\(<\) 0.999). This means that the \(SD\) parameters of the Langmuir adsorption model keep low values having \(SD\) values between 0.009 and 0.065 under the same experimental conditions. The theoretical and experimental data agree well when comparing with \(SD\) value in between 0.011 and 0.091.
## 4 Experimental Analysis  
### Test Environment  
(1) Test environment configuration  
To evaluate the performance of the RDMA-based FastDFS presented in this paper, seven virtual machines were built under the VMware virtual machine platform. The physical hardware and software configuration of the virtual machine is shown in Table 1, indicating the processor, memory, hard disk size and other information of the server, and the operating system uses Windows 7 64-bit.  
There are seven nodes in the cluster, two nodes as Tracking Servers, four nodes as Storage Servers (of which Storage 1 and Storage 2 form Group 1, Storage 3 and Storage 4 form Group 2), and one node as the Client. Deploy the CentOS 7 environment for each of the seven nodes. The detailed configuration of the IP, role, and virtual machine for each server in the system is shown in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Configuration** & **Role** \\ \hline CPU & Intel® CoreTM i7-8300 HQ CPU @2.30 GHZ \\ RAM & 32.0 GB \\ Disk & 500 GB \\ Translator & GCC 4.47 \\ OS & Windows 10 64-bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware environment configuration.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ID** & **IP Address** & **Configuration** & **Role** \\ \hline A & 192.168.156.61 & & Tracker 1 \\ B & 192.168.156.62 & & Tracker 1 \\ C & 192.168.156.63 & & Group 1 Storage 1 \\ D & 192.168.156.64 & CentOS7/2 GB RAM/20 G & Group 1 Storage 2 \\ E & 192.168.156.65 & Disk/CPU/100 M & Group 2 Storage 3 \\ F & 192.168.156.66 & & Group 2 Storage 4 \\ G & 192.168.156.67 & Client \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test environment configuration.  
(2) Optimized FastDFS environment deployment  
After installing the operating system CentOS 7 for each virtual machine, one can start to install FastDFS. Download the installation package from the official website address for decompression and installation. The FastDFS version used in this experiment is v5.05.
## Methodology
### Generation of Meta-Models Using DOE
#### 3.4.1 Factor Identification  
The goal at this stage was to detect features (or factors) \(F_{V}=\{f_{1},f_{2},\cdots,f_{n}\}\) that (1) were related to the von Mises stress and (2) could be controlled. Additionally, the features had to be based on the displacements over the lattice, so that they could be retrieved from the FEA simulation over the homogeneous domain \(\Omega^{Q}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relative Density (\(\rho\))** & **Equivalent Young’s Modulus (\(E^{Q}\))** & **Equivalent Poisson’s Ratio (\(\
u^{Q}\))** \\ \hline
0.25 & 7.5 GPa & 0.05 \\
0.3 & 14.0 GPa & 0.09 \\
0.4 & 24.0 GPa & 0.13 \\
0.5 & 35.0 GPa & 0.17 \\
0.6 & 48.0 GPa & 0.21 \\
0.7 & 61.0 GPa & 0.23 \\
0.8 & 81.0 GPa & 0.27 \\
0.9 & 97.0 GPa & 0.29 \\
1.0 & 114 GPa & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of numerical homogenization of Schwarz primitive cells: Young’s modulus and Poisson’s ratio.  
Our set of factors initially contained the strains at the flat faces (extreme faces) of the boundary of each unit cell of the Schwarz primitive lattice domain. For convenience, the flat faces of the boundary were denoted as \(\{X,-X,Y,-Y,Z,-Z\}\). \(\{-X,-Y,-Z\}\) were the flat faces at \(x=0,y=0,z=0\). \(\{X,Y,Z\}\) were the flat faces at \(x=L,y=L,z=L\). We defined the strains at the flat faces as:  
\[\begin{split}\varepsilon_{ij}&=\text{sgn}(i) \cdot\frac{U_{ij}-U_{-jj}}{L},\\ i&=\pm X,\pm Y,\pm Z,\quad j=x,y,z,\end{split} \tag{6}\]  
where \(U_{ij}\) represented the average displacement in \(j\) direction of the face \(i\). For instance, \(U_{-Xx}\) was the displacement in \(x\) direction of the flat face at \(x=0\). The normal strains at the flat faces corresponded to \(\{\varepsilon_{-Xx},\varepsilon_{Xx},\varepsilon_{-Yy},\varepsilon_{Yy}, \varepsilon_{-Zz},\varepsilon_{Zz}\}\). However, from Equation (6), \(\varepsilon_{-Xx}=\varepsilon_{-Yy}=\varepsilon_{-Zz}=0\), which prevented the introduction of false strains due to pure translation of the Schwarz primitive cell.  
In Equation (5) we can see that the von Mises stress depends on the shear stress, and therefore, it is influenced by the shear strain. We conducted preliminary tests considering shear strains at the flat faces of the cell but our results overestimated the von Mises stress by a large factor. We found that the shear strain interaction is not fully understood at the level of DOE. Consequently, our set of factors was reduced to the normal strains at the flat faces of the cell \(\{\varepsilon_{Xx},\varepsilon_{Yy},\varepsilon_{Zz}\}\).
## 2 Methodology
### Hilbert-Huang Transform
#### 2.4.1 Empirical Mode Decomposition (EMD)  
EMD decomposes a complex signal into a finite set of intrinsic mode functions (IMFs). Each IMF represents a simple oscillatory mode inherent to the original signal. For a function to qualify as an IMF, it must satisfy the following:  
* The number of extrema and the number of zero crossings must either be equal or differ at most by one.
* The mean of the upper and lower envelopes--defined by local maxima and minima--must be zero at all points. The step-by-step EMD process for a signal \(x(t)\) is as follows:  
1. Identify local maxima and minima. Construct the upper envelope \(e_{\text{max}}(t)\) and lower envelope \(e_{\text{min}}(t)\) using interpolation.
2. Compute the mean: \[m(t)=\frac{e_{\text{max}}(t)+e_{\text{min}}(t)}{2}\] (1)
3. Subtract the mean from the signal (sifting process): \[h_{k+1}(t)=h_{k}(t)-m_{k}(t)\] (2)  
Figure 1: Schematic representation of the artificial neural network (ANN) architecture utilized in this study. The network comprises an input layer with 6 features derived from Hilbert–Huang transform (HHT), followed by two hidden layers with 20 and 15 neurons, and an output layer with 4 neurons corresponding to the classification labels normal, sag, swell, and interruption.  
When IMF conditions are met, set:  
\[c_{k}(t)=h_{k}(t) \tag{3}\]  
Subtract \(c_{k}(t)\) from \(x(t)\) to compute the residue:  
\[r_{k}(t)=x(t)-\sum_{j=1}^{k}c_{j}(t) \tag{4}\]  
Continue the decomposition until the final residue is obtained:  
\[x(t)=\sum_{k=1}^{n}c_{k}(t)+r_{n}(t) \tag{5}\]  
This adaptive decomposition preserves both time and frequency localization [44; 45; 46].
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Evaluation and Comparison for the Case of Extrapolation
A better performance may be obtained when longer radar image sequences are used, and the effectiveness of interpolation models is probably still superior to that of extrapolation models for the case of multiple frames being missing. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Missing Patterns** & **Metrics** & \begin{tabular}{c} **Nearest** \\ **Frame** \\ \end{tabular} & **Linear** & \begin{tabular}{c} **Optical** \\ **Flow** \\ \end{tabular} & \
\begin{tabular}{c} **CNN-** \\ **ConvLSTM** \\ \end{tabular} & **3 DCNN** &
\begin{tabular}{c} **CNN-** \\ **BiConvLSTM** \\ \end{tabular} \\ \hline \multirow{4}{*}{111111100} & MAE & 2.5520 & 3.5274 & 1.9849 & 1.7530 & 1.8870 & **1.7283** \\  & CSI & 0.3518 & 0.2467 & 0.4232 & 0.4304 & 0.3613 & **0.4558** \\  & HSS & 0.6147 & 0.5067 & 0.6883 & 0.7075 & 0.6893 & **0.7112** \\ \hline \multirow{4}{*}{111111100} & MAE & 3.0322 & 4.5577 & 2.3037 & 1.9694 & 2.1186 & **1.9465** \\  & CSI & 0.2903 & 0.1954 & 0.3718 & 0.3723 & 0.3238 & **0.4043** \\  & HSS & 0.5585 & 0.4367 & 0.6443 & 0.6746 & 0.6544 & **0.6784** \\ \hline \multirow{4}{*}{111111000} & MAE & 3.4213 & 5.4307 & 2.6010 & 2.1713 & 2.3390 & **2.1469** \\  & CSI & 0.2475 & 0.1651 & 0.3285 & 0.3210 & 0.2594 & **0.3543** \\  & HSS & 0.5157 & 0.3874 & 0.6062 & 0.6448 & 0.6221 & **0.6495** \\ \hline \multirow{4}{*}{111110000} & MAE & 3.7422 & 6.1667 & 2.8694 & 2.3512 & 2.5471 & **2.3260** \\  & CSI & 0.2160 & 0.1440 & 0.2938 & 0.2778 & 0.2216 & **0.3111** \\ \cline{1-1}  & HSS & 0.4813 & 0.3505 & 0.5736 & 0.6181 & 0.5919 & **0.6236** \\ \hline \multirow{4}{*}{000111111} & MAE & 3.3575 & 5.2202 & 2.5439 & 7.7030 & 2.3127 & **2.1017** \\  & CSI & 0.2411 & 0.1643 & 0.3264 & 0. & 0.3203 & **0.3490** \\ \cline{1-1}  & HSS & 0.5093 & 0.3935 & 0.6020 & 0.0367 & 0.6140 & **0.6431** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results of MAE, CSI-40 scores, and HSS scores for the CNN-BiConvLSTM model and baseline models, and for 5 missing patterns with respect to extrapolation. Figure 9: Comparison of different models in reconstructing missing radar frames for the missing pattern ‘1111111000’. The first line shows the 7 th and 10 th input radar images (ground truths); the 2 nd line to the 7 th line show reconstructed radar images from different models.
## 2 Materials and Methods  
### Materials  
In our investigations, the so-called Cr-Ni-MoV steel RPV material was used. The 15 Kh2 NMFA forging steel is manufactured by the Russian IZHORA company for a 1000-MW WWER reactor. The original heat number of it is 181,358, produced according to the Russian specification TU 108.765-78. The chemical composition is given in Table 1. The as-received specimens have a mixed tempered ferrite-bainite microstructure.  
Charpy specimens (ISO-V) were cut out from the same vessel block at \(\frac{3}{4}\) depth. Orientation of the specimens is selected as T-L according to ASTM E23-16b (Standard Test Methods for Notched Bar Impact Testing of Metallic Materials).  
Altogether 13 pieces (Nos. 1 to 13) of the manufactured Charpy specimens were used for the present investigations. They were measured by the nondestructive MAT method before and after neutron irradiation. Specimens were irradiated by neutrons in the BR2 reactor at SCK CEN, at different irradiation fluences in the primary water pool. Temperature of irradiation was between 100 and 120 C. Neutron irradiation was done in a rig called NOMAD_3 [16]. This process ensured that the created damage is large enough to detect it by nondestructive methods. The fluence was between 1.55 and 7.90 \(\times\) 10\({}^{19}\) n/cm  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**C** & **Mn** & **Si** & **S** & **P** & **Cr** & **Ni** & **Mo** & **V** & **Cu** \\ \hline
0.16 & 0.42 & 0.29 & 0.008 & 0.0012 & 1.97 & 1.29 & 0.52 & 0.12 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition (wt.%) of the 15 Kh2 NMFA material.  
Figure 1: Estimated DBTT values as functions of the measured DBTT values for the investigated three base metals, 22 NiMoCr37, A508-B, and HSST-03, and for one weld metal, 18 MND5-W [11].  
(E \(>\) 1 MeV). Charpy impact testing standard (ASTM-23-16b, [17]) was used to get the 41 J ductile-to-brittle transition temperature (DBTT). A 300 J Toni MFL pendelum was used for testing with an instrumented ISO up. All the tests were made according to the ASTM E23 and E2215 prevailing standards. The values of DBTT were determined for groups of specimens irradiated by the same neutron fluence. The error of DBTT usually does not exceed \(+/-15\)\({}^{\circ}\)C, except in inhomogeneous materials.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 4 Numerical Simulation  
First, the vehicle is modeled crossing a 20 m approach distance followed by a 15 m simply supported bridge, as shown in Figure 2. The vehicle consists of two identical quarter-cars of equal sprung mass, \(m\), and unsprung mass, \(m_{1}\). As the vehicle consists of independent quarter-cars, the pitching effect [39] is not explicitly considered in the simulation. The properties of the vehicle and bridge are listed in Table 1, which are intended to be realistic and are selected based on the references of [28; 40; 41]. Road surface roughness is considered in the form of a randomly generated profile of Class A, in accordance with ISO 8608 [42]. The scanning frequency is 500 Hz.  
Figure 3 presents the calculated strain at mid-span when the vehicle passes over the bridge at a constant speed of 8 m/s. In practice, the FAD sensors are installed underneath the bridge slab, modeled here as a beam. It is not clear from the figure when either axle passes mid-span (times 0.94 and 1.13 s). A wavelet transform is used to further process this strain signal. At first, the signal is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Properties** & \multicolumn{3}{c}{**Bridge Properties**} \\ \hline D\({}_{1}\) & 1.5 m & Span & 15 m \\ m & 5000 kg & Density & 4800 kg/m\({}^{3}\) \\ k\({}_{1}\) & 3500 kN/m & Second moment of area of cross-section & 0.365 m\({}^{4}\) \\ c\({}_{1}\) & 10 kN s/m & Modulus of elasticity & \(3.5\times 10^{10}\) N/m\({}^{2}\) \\ m\({}_{1}\) & 750 kg & Damping ratio & 0.01 \\ k\({}_{t1}\) & 350 kN/m & Breadth & 6 m \\ c\({}_{t1}\) & 0 & Depth & 0.9 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of vehicle and bridge models.  
Figure 2: Bridge and vehicle model.  
subject to a wavelet transform with a 'db2' Base wavelet and scales from 1 to 64, in unit increments. To find the optimal scale using the proposed Shannon entropy concept, the energy of the wavelet coefficients at each scale from 1 to 64 are calculated using Equation (4). Then, the relative wavelet energy at each scale is obtained by applying Equation (6). Finally, the wavelet entropy at each scale is computed with Equation (5) and the results presented in Figure 4. As shown, the minimum entropy occurs at a scale, \(a=5\), corresponding to a pseudo-frequency is 66.7 Hz.  
The wavelet function at this optimum scale is illustrated in Figure 5. Two peak values are clearly visible, indicating the times when the axles pass mid-span. This suggests that Shannon entropy can successfully identify a suitable scale for axle identification. Since the effect of road roughness is considered in the simulation, no clear peaks are evident in the mid-span strain signals as the axles arrive/leave the bridge.  
Figure 4: Shannon entropy values for each scale.  
Figure 3: Calculated strain signal at bridge mid-span.
## 6 Simulation Verification and Analysis
### The Analysis of Transient Damping Characteristics of TDP-VSG Control  
To analyse the effect of first-order hysteresis-based transient damping characteristics \(D_{\text{s}}\) on the transient stability performance of the system, simulation experiments are carried out for the TDP-VSG control with various transient damping coefficients. The simulation results are detailed in Figure 13.  
When \(D_{\text{s}}=5\), the TDP-VSG control experiences a given power disturbance at 2 s, leading to significant transient oscillations with the power overshoot of 5 kW and the steady-state recovery time of 1.6 s. At 4 s, during the grid frequency disturbance, the TDP-VSG control effectively eliminates the steady-state deviation induced by the damping characteristics, with a peak overshoot power of 3.7 kW. A comparison of active power outputs under various transient damping characteristics indicates that the TDP-VSG's capability to suppress transient oscillations progressively enhances as \(D_{\text{s}}\) increases. However, a significant overshoot issue still exists, especially in the case of grid frequency disturbance, as the overshoot of the system output power increases with the increase in \(D_{\text{s}}\). The simulation results are consistent with the output characteristics and transient performance theoretical analysis of the TDP-VSG control as shown in Equation (10), demonstrating that while the introduction of transient damping effectively addresses transient oscillation suppression and steady-state deviation elimination, it leads to considerable power overshoot issues. This is particularly evident under grid frequency disturbances, where the system's active power overshoot positively correlates with \(D_{\text{s}}\).
## 2 Materials and Methods  
### Model Framework  
We developed the Farquhar GPP model (FGM) model [12] to estimate terrestrial ecosystem GPP on a daily time scale using eddy flux tower data from FLUXNET 2015 ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 19 February 2023) [27] and remotely sensed data [12; 28]. Based on the nonlinear response of the leaf photosynthetic rate to solar radiation, leaves in canopies can be separated into two categories: sunlit leaves and shaded leaves. The GPP in the FGM was estimated as the sum of the photosynthesis rate for all sunlit leaves and shaded leaves using the two-leaf model:  
\[\text{GPP}=\text{L}_{\text{sunlit}}+\text{A}_{\text{n}\text{shade}}\text{L}_{ \text{shade}} \tag{1}\]  
where A\({}_{\text{nsunlit}}\), A\({}_{\text{nshade}}\), L\({}_{\text{sunlit}}\), and L\({}_{\text{shade}}\) are the net photosynthesis rate and LAI for sunlit and shaded leaves, respectively.  
The FGM model can estimate large-scale GPP with acceptable accuracy. The FGM model was developed based on a site-level model, the [PERSON] et al. [29] photosynthetic model, which can precisely simulate the GPP and evapotranspiration (ET) simultaneously by solving the leaf photosynthetic rate using three complex equations: the [PERSON]'s law, the photosynthesis model of [PERSON] [22], and the model for stomatal conductance. To calculate GPP more efficiently at a high spatiotemporal resolution on large scales, the FGM incorporates the optimal stomatal theory [30; 31] to reduce the computational demand for solving the ratio of the intercellular CO\({}_{2}\) concentration (\(C_{i}\)) and leaf photosynthetic rate. Compared with other models, FGM also considers changes in leaf photosynthetic capacity (V\({}_{\text{cmax}}\)) with time and space. The theoretical framework [12], model parameters [12], model calibration and validation of the GPP estimated by the FGM for China [12] and Europe [28] can all be found in our previous publications. For more details about how we improved the FGM for estimation of GPP in Europe by considering changes in V\({}_{\text{cmax}}\), please refer to our most recent work [28].
## 4 Analysis of Existing Solutions
### Overview of Existing Educational Applications in Education
#### 4.1.2 Examples of XR Educational Applications  
XR technologies, which encompass AR and VR, offer new opportunities in education by allowing students to interact with digital content in entirely new ways. The following examples illustrate how XR technologies are being utilized in education:  
1. Google Expeditions--an application that allows students and teachers to explore virtual tours together through various parts of the world, including historical sites and natural landmarks. The application uses VR to provide students with an immersive experience that cannot be achieved in the classroom [40].
2. Merge Cube--AR allows students to hold digital objects in their hands using a physical cube and AR application. This technology is used in educational content to enable students to explore human anatomy, geological layers, and other scientific phenomena [41].
3. Tilt Brush--a VR application used in the artistic education sector, allowing users to paint and create three-dimensional artworks within Virtual Reality. This application fosters creativity and enables learning through experimentation in a new medium [42].
4. CoSpaces Edu--an application that enables students to create their own VR and AR worlds using a simple programming tool. This allows students to combine knowledge of programming, design, and storytelling to create educational projects [43].
5. XR technologies expand the possibilities of traditional education, enabling students to immerse themselves in content in ways that would otherwise be impossible. However, their wide implementation comes with challenges related to costs, equipment availability, and the need for teacher training.
## 5 Experimental Findings  
### Area-Based Metrics Segmentation Results  
Area-based metrics obtained by each segmentation algorithm are shown in Tables 3 and 4--expressed as mean \(\pm\) standard deviation. As easily appreciable, the results showed that the fuzzy framework offered by FCM and sFCM reflects the intrinsic uncertainty that characterizes medical images, allowing us to achieve better segmentation results compared to the hard clustering performed by k-means. Furthermore, it is worth to note that spatial constraints taken into account by sFCM contribute to reduce the standard deviation of the final result, thus ensuring higher reliability with respect to SMRG and k-means. On the other hand, the large value in standard deviation indicates that SMRG has a high variability of the results that affects its reliability. The boxplots in Figures 8 and 9 summarize the obtained results.  
Regarding sensitivity, the results showed that the FCM-based approach offered better performance in terms of both mean value and standard deviation; on the contrary, the hard partition offered by k-means did not provide satisfying results. The specificity values showed that the clustering-based approaches obtained better performances compared to SMRG. In particular, sFCM and k-means achieved slightly better results than FCM.  
Figure 10 shows some segmentation examples focusing on scenarios where the results are not particularly satisfactory in terms of FPs and FNs. In general, it is possible to note that SMRG and k-means have a greater tendency to leave out parts of the lesion (FN)--especially, when these are not uniform--and to include areas outside the lesion (FP), while the techniques based on clustering, and in particular the sFCM guarantees a better lesion detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **False Positive Ratio** & **False Negative Ratio** \\ \hline SMRG & \(0.55\pm 1.05\) & \(0.33\pm 0.11\) \\ k-means & \(0.25\pm 0.20\) & \(0.25\pm 0.09\) \\ FCM & \(0.16\pm 0.13\) & \(0.23\pm 0.12\) \\ sFCM & \(0.14\pm 0.12\) & \(0.22\pm 0.09\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: False Positive and False Negative Ratios achieved by the considered unsupervised segmentation approaches: the results are expressed as average value \(\pm\) standard deviation.  
Figure 9: False Positive Ratio and False Negative Ratio boxplots. The lower and upper bounds of each box represent the first and the third quartiles of the metric distribution, respectively. The median is represented by a red line, while outliers are displayed as red crosses.  
Figure 10: Examples of interesting segmentation results showing FPs and FNs. In particular, the examples compare results yielded by the four investigated unsupervised method—namely, SMRG (cyan), k-means (yellow), FCM (red), sFCM (blue)—against the ground-truth (green).