esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Results and Discussion
#### 3.4.5 Comprehensive System Reliability Index of Tunnel Structure  
The system reliability is usually adopted to estimate the failure probability range of a structural system. Assuming that the failure of each part of a soft rock tunnel structure is completely related, the failure probability of a tunnel section may be denoted as Formula (20).  
\[p_{t\_{\text{lower}}}=\max(p_{t1},p_{t2},\dots p_{tn}) \tag{20}\]  
From Formula (20), we can obtain the minimum failure probability of the tunnel as \(p_{t\_{\text{lower}}}=0.333\%\).  
Assuming that the failure of the tunnel for each element is independent, its corresponding displacement failure probability may be expressed as Formula (21). From Formula (21), we can obtain the maximum failure probability of the tunnel \(p_{t\_{\text{upper}}}=0.96\%\).  
\[p_{t\_{\text{upper}}}=1-\prod_{i=1}^{n}(1-p_{ti}) \tag{21}\]  
Thus, the final failure probability of the tunnel initial support structure system in this study is between \(p_{t\_{\text{lower}}}\) and \(p_{t\_{\text{upper}}}\), and may be expressed as Formula (22).  
\[0.333\%\leq P_{t}\leq 0.96\% \tag{22}\]  
As the range of \(p_{t\_{\text{lower}}}\) and \(p_{t\_{\text{upper}}}\) is very small, only 0.6465%, their average value, can be taken as the point estimate value, and its corresponding comprehensive reliability is 2.486.
## 6 Experimental Results
### Phase Shift and Group Delay  
The received signal phase shift relative to the transmitted signal is also analyzed. First, the overall distance of the propagation between receiving and transmission is calculated using Equation (7) for clear air (high visibility). In this case, the distance can be estimated from the slope of the graph appearing in Figure 13 for 1000 m visibility:  
\[d=c\cdot\tau_{0}=-\frac{c}{2\pi}\frac{d\phi_{0}(f)}{df} \tag{16}\]  
It is found that the overall length which the signal passes is d = 18.775 m. This value is close to the value measured manually.  
Figure 12: Simulation and experiment results for the attenuation mmW at 28 GHz. The environmental conditions are: T = 16 \({}^{\circ}\)C, P = 101 KPa, RH = 100%.  
Figure 13: Graph of experimental results of received signal phase at 28 GHz for different fog visibilities.  
Now, the real part of the refractivity \(N_{0}+N^{\prime}(f)\) is calculated by the absolute phase obtained in the measurement using Equation (6):  
\[N_{0}+N^{\prime}(f)=\frac{\Delta\phi(f)}{\phi_{0}(f)}\cdot 10^{6}=\frac{\phi(f)- \phi_{0}(f)}{\phi_{0}(f)}\cdot 10^{6} \tag{17}\]  
Once these coefficients have been found, they can be used for calculating of the time delay using (10). The incremental group delay is found by:  
\[\Delta\tau_{d}(f)=\frac{d}{c}\cdot\left[\frac{\phi(f)-\phi_{0}(f)}{\phi_{0}(f) }\right] \tag{18}\]  
where \(\tau_{0}=\frac{d}{c}=62.61n\)s is the approximated delay expected in clear air. The results are summarized in Table 3.  
Figure 14 demonstrates the increase in the refractivity as the water droplet concentration W is increased.  
Figure 14: Simulation and experimental results for the real part of refractivity at 28 GHz. Environmental conditions: T = 16 \({}^{\circ}\)C, P = 101 kPa, RH = 100% (For reference, RH = 60% for clear air).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Visibility[m]** & **W[g/m\({}^{3}\)]** & **\(\phi\)[deg]** & **\(\Delta\phi\)[deg]** & **\(\Delta\phi\)[deg/m]** & **\(N_{0}+N^{\prime}\)[ppm]** & **\(\Delta\tau_{d}\)[ps]** \\
**Measured** & **Calculated** & **Measured** & **Measured** & **Calculated** & **Calculated** \\ \hline
1000 & 0.02 & 162.7 & 0 & 0 & 0 & 0 \\ \hline
8.5 & 1.85 & 115.4 & 47.3 & 1.3 & 39.0698 & 2.44 \\ \hline
6.8 & 2.35 & 111.2 & 51.5 & 1.4 & 42.539 & 2.66 \\ \hline
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 1 Introduction
[22; 23; 24] extended the three-terminal model to a four-terminal model with the assistance of helper nodes. These studies explored the issue of generating two keys with different security levels over a four-terminal system. In large-scale dynamic wireless networks, it is crucial to consider both the efficiency and security in group key generation to ensure the reliable implementation of secure group communication. However, most existing schemes are based on pairwise key generation [11; 12; 13; 14; 22], which involves multiple key agreements and results in a low efficiency in key generation. To address this issue, ref. [16] proposed a secure network coding (SNC)-based group key generation scheme that requires only one key agreement phase. However, this scheme did not utilize external relay nodes to enhance the key rates, and only one type of keys was generated. Motivated by this, this paper explores physical layer group key generation in wireless relay networks with a star topology. In this setup, the relay node can act as either a trusted or untrusted central node, while one legitimate node (Alice) serves as the reference node. The channel between Alice and the relay node is considered as the reference channel. The main contributions are summarized as follows:  
* We propose a cooperative jamming (CJ)-based group key generation scheme. This scheme allows the relay to obtain superimposed channel observations from both the reference channel and other relay channels. Then, a public discussion is utilized to enable all nodes to obtain estimates of the reference channel. Subsequently, either an SK or a PK is generated among the group of legitimate nodes, where the PK is secret from both the relay and Eve. This scheme only requires two rounds of key agreement regardless of the increase in the number of users within the group. * We derive the lower and upper bounds on the SK/PK capacity within the framework of the corresponding discrete memoryless source (DMS) models. The lower bounds are determined by exploiting the zero forcing (ZF) method. Additionally, enhanced DMS models are constructed to obtain the upper bounds. * We demonstrate that the derived lower bounds are close to the corresponding upper bounds. Specifically, at a high signal-to-noise ratio (SNR), for both the SK and PK, there is merely a constant difference of \(\log_{2}\left(\frac{3}{2}\right)\) bits per channel measurement (BPCM)separating the upper and lower bounds. This difference is negligible as the transmit SNR approaches infinity.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model
### Data Preprocessing
#### 3.2.3 Data Normalization  
Since the magnitudes of all 21 types of aero-engine sensor data vary, directly using these parameters for subsequent data processing and RUL prediction will cause bias in the prediction outcomes. Thus, in this study, the min-max function was used to normalize the sensor data. The normalization formula is as follows:  
\[x_{i,j}^{*}=\frac{x_{i,j}-x_{j}^{min}}{x_{j}^{max}-x_{j}^{min}} \tag{6}\]  
where \(x_{j}^{max}\) is the maximum value within the \(j\)th sensor, \(x_{j}^{min}\) is the minimum value within the \(j\)th sensor, \(x_{i,j}\) are the original data of the \(j\)th sensor at the \(i\)th moment, and \(x_{i,j}^{*}\) are the data after normalization.
## 4 Experimental Results and Discussion
### Case Study of Defect Detection
However, the contours of light and dark scratches could still be effectively detected. In conclusion, our method proved effective for detecting defects that could \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method Name**} & \multicolumn{3}{c}{**Severe Defect**} & \multicolumn{3}{c}{**Tiny Defect**} & \multicolumn{3}{c}{**Severe Scatch**} & \multicolumn{3}{c}{**Minor Scatch**} \\ \cline{2-11}  & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline Nd & 0.5417 & 0.4877 & 0.5133 & 0.3463 & 0.8496 & 0.4920 & 0.5631 & 0.5802 & 0.5715 & - & - & - \\ Cd & 0.5629 & 0.5078 & 0.5339 & 0.3688 & 0.8547 & 0.5153 & 0.5698 & 0.5902 & 0.5841 & - & - & - \\ Mf & **0.9634** & 0.9512 & 0.9573 & **0.9220** & 0.9416 & 0.9368 & 0.8857 & 0.8404 & 0.8625 & 0.3505 & 0.7297 & 0.4735 \\ Ours & 0.9528 & **0.9607** & **0.9612** & 0.9233 & **0.9588** & **0.9407** & **0.9413** & **0.9505** & **0.9454** & **0.7375** & **0.7649** & **0.7510** \\ \hline \hline \end{tabular}  
**Note:** Bold text represents the optimal values of different methods under various metrics.  
\end{table} 
Table 3: Evaluation results of different methods for various defects. severely affect the quality of magnetic tiles, though it showed slightly lower robustness for objective cosmetic interference.
## 5 Optimization Design of Degree Distributions for Proposed Enhanced Distributed Fountain Codes
### Optimization of the Relays  
We assume that a robust soliton distribution \(\text{RSD}(k_{i},c_{i},\
u_{i})\) can be used as an initial degree distribution of source \(S_{i}\), where \(k_{i}\) represents the data block length, \(c_{i}\) is a constant, and \(\
u_{i}\) is the allowable failure probability.  
For relay \(R_{1}\) and \(R_{2}\), the objective of the optimization is to achieve the desired decoding failure probability \(\delta\) while minimizing the overall overhead \(1+\varepsilon\). By substituting (13) into (1), we can obtain the code overhead as follows  
\[1+\varepsilon=\frac{2\alpha_{i}k_{i}}{\Phi^{\prime}_{i}(1)\Big{(}\frac{1}{n} \Gamma^{\prime}_{1}(1)+\frac{1}{l}\Gamma^{\prime}_{2}(1)\Big{)}\sum\limits_{j=1 }^{n}k_{j}} \tag{15}\]  
which can be regarded as the objective function. By substituting (5) into the decoding convergence condition \(y_{i,l}\leq y_{i,l-1}\), we can obtain  
\[\alpha_{i}\phi_{i}(1-y_{i,l-1})\frac{\left(\frac{1}{n}\Gamma^{\prime}_{1} \Bigg{(}\frac{1}{n}\sum\limits_{j=1}^{n}\Phi_{j}\Big{(}1-y_{j,l-1}\Big{)} \Bigg{)}+\frac{1}{l}\Gamma^{\prime}_{2}\Bigg{(}\frac{1}{l}\sum\limits_{j=n-l+ 1}^{n}\Phi_{j}\Big{(}1-y_{j,l-1}\Big{)}\Bigg{)}\right)}{\left(\frac{1}{n} \Gamma^{\prime}_{1}(1)+\frac{1}{l}\Gamma^{\prime}_{2}(1)\right)}\geq-\ln(y_{ i,l-1}) \tag{16}\]  
Let \(\delta\) be the maximum value of the allowable decoding failure probability and \(z_{i}=1-y_{i,l-1}\), then we have \(z_{i}\in[0,1-\delta]\). To calculate the inequality condition in (16), we can divide the interval \([0,1-\delta]\) into \(m-1\) equally spaced subintervals. \(\{z_{i,1},z_{i,2},\ldots,z_{i,m}\}\) are \(m\) equidistant points on \([0,1-\delta]\), and satisfy the condition \(0=z_{i,1}<z_{i,2}<\cdots<z_{i,m}=1-\delta\). The inequality in (16) must be satisfied on each equidistant point, that is  
\[\phi_{i}(z_{i,p})\frac{\left(\frac{1}{n}\Gamma^{\prime}_{1}\Bigg{(}\frac{1}{ n}\sum\limits_{j=1}^{n}\Phi_{j}(z_{j,p})\Bigg{)}+\frac{1}{l}\Gamma^{\prime}_{2} \Bigg{(}\frac{1}{l}\sum\limits_{j=n-l+1}^{n}\Phi_{j}(z_{j,p})\Bigg{)}\right)} {\left(\frac{1}{n}\Gamma^{\prime}_{1}(1)+\frac{1}{l}\Gamma^{\prime}_{2}(1) \Big{)}}\geq\frac{-\ln(1-z_{i,p})}{\alpha_{i}},\ \ p=1,2,\ldots,m \tag{17}\]  
which can be regarded as an inequality constraint. Therefore, the optimization equation of relay can be expressed as follows  
\[s.t.\Bigg{\{}-\frac{\frac{1}{n}\Gamma^{\prime}_{1}\Big{(}\frac{1}{n}\sum \limits_{j=1}^{n}\Phi_{j}\Big{(}z_{j,p}\Big{)}\Big{)}+\frac{1}{l}\Gamma^{ \prime}_{2}\Bigg{(}\frac{1}{l}\sum\limits_{j=n+l+1}^{n}\Phi_{j}\Big{(}z_{j,p} \Big{)}\Big{)}}{\frac{1}{n}\Gamma^{\prime}_{1}(1)+\frac{1}{l}\Gamma^{\prime}_{ 2}(1)}-\frac{\ln(1-z_{i,p})}{\alpha_{i}\phi_{i}(z_{i,p})}\leq 0,p=1,2, \ldots,m \tag{18}\]
## 5 Simulations Results and Discussions
### Case Study 1: Home Is Not Equipped with DGs
#### 5.1.3 Carbon Emission  
Figure 7 depicts the carbon emissions for the scheduled and unscheduled appliance operation when the supply is only from the electricity EUC. Where the GA algorithm-based  
Figure 5: Case study 1: Electricity cost reduction.  
Figure 6: Case study 1: PADR reduction.  
ECS emits a peak emission of 152 pounds in the 18 th hour, PSO gave 158 pounds of carbon emissions in the 18 th hour, BFA-based scheduling results in peak emissions of 147 pounds in the 18 th hour, WDO results in 149 pounds in the 18 th hour, and ACO results in 145 pounds, which is also in the 18 th hour. Moreover, the GPSO, GACO, GBFA, and GWDO gave the highest peaks of 148 pounds, 147 pounds, 153 pounds, and 142 pounds, respectively. The total carbon emissions for the 24 h are 3029, 2946, 2991, 2653, 2598, 2933, 2693, 2666, 2761, and 2567 for an unscheduled load, GA, PSO, BFA, ACO, WDO, GPSO, GBFA, GACO, and GWDO, respectively.  
The results show that our proposed GWDO algorithm-based ECS decreases carbon emissions by 15.25% when compared with the case of the unscheduled load operation, whereas 2.74%, 1.25%, 12.4%, 14.2%, 3.1%, 11%, 11.9%, and 8.8% for cases of using the GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO, respectively.
## 2 Existing Expected Threat (xT) Models Theory
### Lack of Realism in Expected Threat (xT) Models
#### 2.3.2 Static xG Estimates  
One major limitation of traditional xT models is their reliance on static xG estimations, which fail to account for dynamic aspects of goal probability evaluation. In these models, goal probabilities are often estimated based on the historical frequency of goals scored from each grid cell on the pitch. However, this frequency-based approach fails to capture the true nature of scoring opportunities, leading to an oversimplified and often misleading representation of goal likelihoods. A player does not have a 0% chance of scoring from midfield merely because no goals have been historically recorded from that location in the datasets used to estimate goal probabilities. Exceptional circumstances, such as a goalkeeper being out of position during a counterattack, can lead to successful long-range attempts, which static xT models fail to account for.  
More advanced xG models are required to build realistic xT models, particularly those integrating dynamic player positioning and game context. Several xG models have been proposed [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], yet most fail to fully incorporate off-the-ball player positioning. Some models [30; 31] attempt to consider defensive positioning by quantifying the number of defenders ahead of the ball, but this approach remains insufficient. A scenario where a shooter faces four defenders directly blocking the shooting angle differs significantly from one where those defenders are present but do not obstruct the shooting angle.  
A promising advancement in xG modeling is the work of [PERSON] and [PERSON] [14], who introduced the KOS angle to refine shooting angle evaluations. This metric subtracts the angles formed by the extremities of players positioned ahead of the ball, effectively incorporating defensive width, orientation, and positioning into the xG calculation. Subsequently, [PERSON] et al. [32] developed an xG model that builds upon the KOS angle formulation. Despite these advancements, xT models still lack the integration of such dynamic xG methodologies, which limits their ability to provide a realistic assessment of football actions.
## 1 Introduction
In this, [PERSON] et al. [26] issued a bibliometric analysis using VOSviewer software to develop research related to TES categories and identify research gaps. For TES topics, the approach performed using the co-occurrence of the keywords found is illustrated in Figure 3. Through this diagram, it appears that terms relevance can be identified from the circles size. Simply stated, a larger circle identifies the subject with more studies. Furthermore, the research gaps are identified by smaller circles. As shown and indicated in Ref. [27], subjects TES (_red circle_) and LHS with PCMs (_purple circle_) indicate high relevance, meaning that they are the focus of intense investigation both experimentally and/ or numerically. The benefit of this overview is to serve as a guide for any reader interested in the decision-making process in evolving TES solutions for different industries. The main aim and scope of this article is to provide an in-depth review of the state of the art of TES technologies with particular attention to SHS and LHS systems and their applications in various sectors. It also addresses the subject of phase change heat transfer and the sensible energy storage processes in porous media. Furthermore, in recent years, the research has moved toward numerical methods to explore the potential of these technologies at a theoretical level. Thereby, numerical approaches such as mesoscopic REV- and pore-scale approaches will be taken up herein to point out their ability to deal with some mechanisms that may occur in these systems. It is worth mentioning here that this review article can serve as a guide for researchers seeking to identify trends in research on latent and sensible heat storage technologies using porous structures as a new strategy to evaluate performance of these systems. Among these trends, we can cite LBM approaches where an overview is given here with a comparison to other traditional numerical methods to draw the attention of readers to the emerging trends. The remaining parts of this review are arranged as follows. First, in Section 2, a comprehensive analysis of sensible energy storage technique is provided while highlighting the different sensible heat storage materials and the porous medium effect. Then, in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TES Technique** & **Capacity (kWh/ton)** & **Power (MW)** & **Efficiency (\%)** & **Storage Time** & **Cost (\&/\(kWh\))** \\ \hline Sensible heat & 10–50 & 0.001–10 & 50–90 & d/m & 0.1–10 \\ Latent heat & 50–150 & 0.001–1 & 75–90 & h/m & 10–50 \\ Thermochemical heat & 120–250 & 0.01–1 & 75–100 & h/d & 8–100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of TES solutions [21]. Section 3, the technical design, economic properties and many other characteristics of latent energy storage technologies are reviewed. In Section 4, a comparison between the SHS and LHS options is presented. The LBM at REV and pore scale methods, including new solutions of simulating heat transfer mechanisms in porous supports and recent developments, are covered in Section 5. Finally, in Section 6, conclusions of the present review are drawn.
# Research on Performance Prediction Model of Impeller-Type Breather
## 4 Data Analysis
### Significance of Factors and Optimal Method
#### 4.2.2 Characteristics of Resistance  
Ventilation resistance is defined as the pressure difference between the inlet and outlet of the breather; it indicates flow loss, which is another important index to evaluate the performance of the breather. The range analysis of the ventilation resistance in the calculation results was carried out to determine the significance and optimal level of the various influential factors. The results are shown in Table 3.  
Among all operating condition factors, the ventilation rate has the largest influence on ventilation resistance, while other factors have a small influence. Among all structural factors, the diameter of the vents has the greatest influence, while the other factors have a small influence.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Average \(\eta\)/(\%)**} & \multicolumn{6}{c}{**Operating Condition Factors**} & \multicolumn{6}{c}{**Structural Factors**} \\ \cline{2-13}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** & **K** \\ \hline \(T_{1}\) & 95.30 & 98.25 & 92.25 & 97.56 & 95.73 & 92.57 & 95.28 & 97.61 & 95.49 & 97.83 & 96.22 \\ \(T_{2}\) & 94.09 & 97.21 & 96.24 & 96.47 & 97.47 & 96.02 & 95.50 & 96.76 & 96.00 & 96.46 & 96.83 \\ \(T_{3}\) & 94.65 & 97.73 & 97.83 & 94.29 & 96.27 & 97.43 & 96.94 & 94.29 & 96.08 & 95.67 & 96.75 \\ \(T_{4}\) & 97.21 & 95.76 & 97.11 & 96.05 & 97.07 & 97.07 & 97.76 & 95.58 & 97.02 & 94.61 & 96.62 \\ \(T_{5}\) & 99.43 & 92.72 & 98.25 & 97.30 & 95.13 & 97.58 & 96.20 & 97.43 & 96.68 & 97.10 & 95.67 \\ Range \(R\) & 5.34 & 5.53 & 6.01 & 3.27 & 2.34 & 5.01 & 2.48 & 3.31 & 1.53 & 3.22 & 1.16 \\ \hline Significance & & & & & & & & & & & & \\ \hline Optimal Level & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range analysis of each influential factor on separation efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Average**} & \multicolumn{6}{c}{**Operating Condition Factors**} & \multicolumn{6}{c}{**Structural Factors**} \\ \cline{2-13}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** & **K** \\ \hline \(T_{1}\) & 49.81 & 14.12 & 49.59 & 50.86 & 50.73 & 55.93 & 54.02 & 51.04 & 48.84 & 57.89 & 88.33 \\ \(T_{2}\) & 60.34 & 29.67 & 54.66 & 50.09 & 59.22 & 49.58 & 54.90 & 50.95 & 54.92 & 56.80 & 58.90 \\ \(T_{3}\) & 53.51 & 48.34 & 60.46 & 55.94 & 48.80 & 56.53 & 51.73 & 57.44 & 59.45 & 54.41 & 45.96 \\ \(T_{4}\) & 50.86 & 74.70 & 52.43 & 61.62 & 59.17 & 60.63 & 53.23 & 57.54 & 50.08 & 51.21 & 41.37 \\ \(T_{5}\) & 59.33 & 105.83 & 55.51 & 54.14 & 54.74 & 49.99 & 56.78 & 55.69 & 59.37 & 52.36 & 38.10 \\ Range \(R\) & 10.53 & 91.71 & 10.87 & 11.53 & 10.43 & 11.05 & 5.05 & 6.59 & 10.62 & 6.68 & 50.23 \\ \hline Significance & & & & & & & & & & & \\ \hline Optimal Level & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Range analysis of each influential factor on ventilation resistance.
## 3 Experimental Study
### Running Times Comparison and Analysis  
The running times of the five times are also studied here. Note that FLICM and KGC are programmed in C and the other three are in MATLAB 7.9. And the experimentations are implemented at Lenovo workstation Think-Centre M8400t (3.40 GHZ, 4 GB RAM; Intel (R) Core(TM) i7-3770 CPU). In Figure 7, it can be seen that KWFLICM needs more CPU running times (about 500 seconds), which is same to its original literature. FGFCM takes the least computational times (about 10 seconds). The times of MKCIS are about 50 s (15 s for the Step 1 and Step 2, 35 s for the Step 3 and Step 4 of MKCIS in Table 1). Thought, MKCIS is not the fastest algorithms among the five ones, it can supply the best segmentation performance among them. Thus we expect that it can provide an effective option for SAR imagery segmentation.  
Finally, Figure 8 illustrates the computational cost on the images with different size for MKCIS. The proposed algorithm is computational consuming when the number of image pixels increases, since the covariance matrixs are required to decompose in NLM in each iteration step. The computational burdens of Step 3 and Step 4 of MKCIS lies in the merged local images patches, not the original number of image pixels; therefore, though the size of synthetic SAR images increase, the computer cost of Step 3 and Step 4 of MKCIS grow linearly with increase of image size.  
Figure 6: Mean values of partitioning accuracy of five algorithms in segmenting the synthetic SAR image by adding simulated speckle from 1-look to 10-look.  
Figure 7: Error-bar graphs of running times of KWFLICM, FLICM, FGFCM, KGC and MKCIS in segmenting the two SAR images. **(a)** The right figure is CPU times for the synthetic SAR image; **(b)** the left one is that of real SAR image.