esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Analysis
### Datasets, Metrics, and Tests Configurations  
The datasets used in our experiments are a subset of MSAR-1.0, consisting of 28,449 SAR images categorized into four classes: ships, bridges, oil tanks, and planes. From this dataset, we selected 1777 images as the training set and 239 images as the validation set. The distribution of each category is depicted in Figure 8a. Additionally, as shown in Figure 8c, the dataset exhibits a multi-scale characteristic, with the majority of targets being small. This is evident from the darker color concentrated in the lower-left corner of the plot.  
The model training environment and its parameters are listed in Table 1.  
To ensure convergence during training, we employed several hyperparameters, as detailed in Table 2. The selected optimizer was SGD, and we used mosaic augmentation to enhance the images, which was disabled for the final 20 epochs.  
To evaluate model performance, we utilized a universal set of metrics, including precision (P), recall (R), mean average precision (mAP), and F1 score. Table 3 provides detailed explanations for each metric.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Environment** & **Configuration** \\ \hline CPU & Intel(R) Xeon(R) Gold 5318Y @2.10 GHz \\ GPU & NVIDIA Tesla A800 \\ GPU memory & 80 GB \\  & Pytorch 2.1.2 \\ Deep-learning architecture & Python 3.11.7 \\  & Cuda 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configurations of model training.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Epochs & 200 \\ Momentum & 0.937 \\ lr0 & 0.01 \\ lrf & 0.01 \\ Weight decay & 0.0005 \\ Batch size & 32 \\ Image size & 256 \(\times\) 256 \\ Optimizer & SGD \\ Mosaic & 1.0 \\ Close mosaic & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyper-parameters for model training.  
Figure 8: The distribution of targets in the training set. (**a**) shows the instances of each category; (**b**) is the space distribution of targets; (**c**) is the sizes of the bounding boxes.
# Adjustable Structure for Feedback Active Headest System Using the Virtual Microphone Method
## 3 ANC Algorithms for Active Headrest
### Adaptive Feedback Algorithm  
A block diagram of the typical multichannel feedback FxNLMS algorithm is shown in Figure 2. \(\mathbf{s}_{m}(n)\) and \(\mathbf{s}_{v}(n)\) represent the secondary paths from the secondary loudspeakers to the physical and target microphones, respectively.  
Figure 1: The 3D design model of the proposed manually adjustable headrest structure for users.  
In this system, the output signal vector is defined as  
\[\mathbf{y}(n)=\mathbf{w}^{\mathsf{T}}(n)\mathbf{x}(n), \tag{1}\]  
where \(\mathbf{y}(n)=\big{[}y_{1}(n),y_{2}(n),\ldots,y_{j}(n),\ldots,y_{j}(n)\big{]}^{ \mathsf{T}}\cdot y_{j}(n)\) indicates the output signal of the \(j\)th secondary loudspeaker at the time sample \(n\) and \((\cdot)^{\mathsf{T}}\) denotes the matrix transpose operator. The control filter matrix is expressed as  
\[\mathbf{w}(n)=\big{[}\ \ \mathbf{w}_{11}(n),\mathbf{w}_{12}(n),...,\mathbf{w}_{ 1j}(n),\mathbf{w}_{21}(n),...,\mathbf{w}_{Vj}(n)\ \ \big{]}, \tag{2}\]  
where \(\mathbf{w}_{vj}(n)\) is the \((L_{uv}\times 1)\) coefficient vector of the control filter from the \(v\)th input signal to the \(j\)th output signal.  
\[\mathbf{x}(n)=\big{[}\ \ \mathbf{x}_{1}^{\mathsf{T}}(n),\mathbf{x}_{2}^{ \mathsf{T}}(n),...,\mathbf{x}_{v}^{\mathsf{T}}(n),...,\mathbf{x}_{v}^{ \mathsf{T}}(n)\ \ \big{]}^{\mathsf{T}}, \tag{3}\]  
is the matrix with the last \(L_{uv}\times V\) reference signals and  
\[\mathbf{x}_{v}(n)=[x_{v}(n),x_{v}(n-1),\ldots,x_{v}(n-L_{uv}+1)]^{\mathsf{T}}. \tag{4}\]  
\(x_{v}(n)\) is synthesized based on the IMC and expressed as  
\[x_{v}(n)=e_{v}(n)-\hat{y}_{s}(n), \tag{5}\]  
where \(e_{v}(n)\) denotes the signal measured by the \(v\)th error microphone installed at the desired position and is given by \(e_{v}(n)=d_{v}(n)+y_{s}(n)\). \(d_{v}(n)\) represents the disturbance signal received by the \(v\)th error microphone. \(y_{s}(n)\) and \(\hat{y}_{s}(n)\) denote the output signal vector \(\mathbf{y}(n)\) filtered by the actual and estimated secondary paths, respectively.  
Utilizing the FxNLMS algorithm, the updated equation for the control filter coefficient vector \(\mathbf{w}_{vj}(n)\) can be expressed as  
\[\mathbf{w}_{vj}(n+1)=\mathbf{w}_{vj}(n)-\mu\sum_{v=1}^{V}\frac{\mathbf{r}_{v }(n)e_{v}(n)}{\|\ \mathbf{r}_{v}(n)\|^{2}+\beta}, \tag{6}\]  
where \(\mathbf{r}_{v}(n)=[r_{v}(n),r_{v}(n-1),\cdots,r_{v}(n-L_{uv}+1)]^{\mathsf{T}}\) is the filtered reference signal vector that is derived from the convolution of the reference signal with the estimated secondary path. \(\|\cdot\|\) denotes the \(l_{2}\)-norm of the vector. \(\mu\) and \(\beta\) are the step size and regularization factor, respectively.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 5 Results and Discussion
### Image Processing and Quantitative Evaluation
#### 5.2.2 Transform-Based Techniques: Step Phase and Amplitude Thermography
A significant change occurred between the sound and defective areas in the amplitudes, leading to a sharp boundary around the defects. The normalized phase contrast distribution of thermograms obtained after heating for 75 s is depicted in Figure 12b. Bigger defects closer to the surface generated higher phase contrasts and were subsequently more visible in the phase image. By analyzing these plots, all defects, except D5, on the defect plate were detected. Figure 10: (**a**) Amplitude image and (**b**) phase image of the thermograms captured during cooling after 75 s of heating. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** \\ \hline
**A** & 25.5 (56\%) & 26.8 (45\%) & 24.9 (35\%) & 23.2 (69\%) & 20.7 (511\%) \\
**B** & 23.1 (127\%) & 26.5 (67\%) & 26.9 (58\%) & 23.4 (96\%) & 19.9 (591\%) \\
**C** & 19.6 (677\%) & 20.8 (397\%) & 19.8 (52\%) & 14.2 (190\%) & 12.9 (401\%) \\
**D** & 13.9 & 16.4 & 12.8 & 7 (234\%) & NA \\ \hline \end{tabular}
\end{table} 
Table 8: SNR (dB) for Pulsed Phase Thermography (PPT) data. from thermal data captured during cooling had higher SNR values and revealed more details of subsurface defects. The SNRs of both the phase and amplitude images show that larger defects closer to the surface provided higher values. By comparing the results presented in Tables 8-11, it can be concluded from the results presented in Tables 9-11 that the application of the FFT transform to step heating thermography was more effective than its application to flash thermography. This was especially true for the amplitude data, where higher SNRs were achieved. The amplitude images extracted from step heating thermography were an effective means of revealing subsurface damage, as they detected even small and deep defects. By comparing the results presented in Tables 1-9 related to the SNRs of raw and processed thermal data, it is concluded that the image processing algorithms significantly increased the visibility of subsurface defects especially those of smaller sizes, which are embedded in deeper areas.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 6 Comparison  
In this section, we present a comparison of results of our approach with the approaches described in the Related Work Section. Table 24 shows a comparison of our approach with the approaches discussed in the related work.  
As shown in the above table (Table 24), no other work has displayed their result using accuracy, precision, recall, and F1-score, except [PERSON] et al. [22]. Some methods have displayed their result using only an accuracy performance metric, which is not enough to check the performance of a method. Some methods did not mention whether their dataset contained P2P bots or not. Some methods use IRC datasets, where P2P bot communication is not available. But our method is validated on a publicly available P2P benchmark dataset  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **TP** & **TN** & **FP** & **FN** \\ \hline
99.99 & 99.49 & 98.98 & 99.23 & 2132 & 322,752 & 11 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 23: Result of the proposed approach for peer-to-peer botnet detection system.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**SI. No.** & **Author** & **Botnet Type Detected** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
1 & [13] & IRC & 100\% & Not mentioned & Not mentioned & Not mentioned \\
2 & [14] & IRC & 95.09\% & Not mentioned & Not mentioned & Not mentioned \\
3 & [15] & P2P & 99.64\% & Not mentioned & 99.82\% & Not mentioned \\
4 & [16] & P2P and IRC & Not mentioned & Not mentioned & 99.46\% & Not mentioned \\
5 & [8] & Not mentioned & Not mentioned & 99.17\% & 96.85\% & Not mentioned \\
6 & [17] & Not mentioned & 95\% & 95\% & 95\% & Not mentioned \\
7 & [18] & Not mentioned & 99.68\% & Not mentioned & Not mentioned & Not mentioned \\
8 & [19] & P2P & 99\% & Not mentioned & Not mentioned & Not mentioned \\
9 & [20] & P2P & Not mentioned & Not mentioned & Not mentioned & Not mentioned \\
10 & [21] & Not mentioned & 97.8\% & Not mentioned & 97\% & Not mentioned \\
11 & [22] & P2P & 99.04\% & 99.35\% & 99.35\% & 99.21\% \\
12 & Our approach & P2P & 99.99\% & 99.49\% & 98.98\% & 99.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 24: Comparison of our approach with the approaches presented in related work.  
Figure 6: F1-score vs. number features for different models for peer-to-peer botnet detection system.  
and it outperforms all other methods by achieving 99.99% acc, 99.49% pre, 98.98% rec (slightly less than other methods), and 99.23% F1-score.
## 2 Methodology
### Data and Data Quality Requirements (Sources and Geography)
#### 2.2.4 End-Of-Life Stages
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Description** & **Qty** & **Unit** & **Data Sources/References** \\ \hline \multicolumn{5}{c}{_Background data_} \\ \hline
**Raw materials—Sago starch** & & & & \\ \hline \multicolumn{5}{c}{_Inputs from Nature_} \\ \multicolumn{5}{c}{Standing sago tree filling for sago starch production (9.449 kg for 1} \\ \multicolumn{5}{c}{Standing sago tree} & kg sago starch), 70\% of 1 kg SPF-reinforced Sago composite & 6.6143 & kg & [23,26,30] and USICI database \\ \multicolumn{5}{c}{_Inputs from technosphere—MaterialsFuchs_} \\ \multicolumn{5}{c}{Tap Water} & Used in sago starch production (3.7 kg used for 1 kg of} \\ \multicolumn{5}{c}{Tap Water} & \multicolumn{5}{c}{sago, 70\% of 1 kg SPF-reinforced Sago composite)} \\ \multicolumn{5}{c}{Plasticizer for sago starch, 30\% of 1 kg sago starch,} \\ \multicolumn{5}{c}{Glycerol} & (70\%Sago starch content for 1 kg SPF-reinforced Sago} \\ \hline \multicolumn{5}{c}{_Transportation_} \\ Mill to storage & Mill to storage & Munkah-Kuching 463 km, Lorry 16-32 metric ton) per 0.7 kg sago starch, & 0.32411 & tkm & [33] and Ecoinvent 3 database \\ \multicolumn{5}{c}{Bomeo to Peninsular (Ports)} \\ \multicolumn{5}{c}{freight, large tanker) 0.7 kg sago starch,} \\ \multicolumn{5}{c}{Port to Manufacturing, Klang-Shah Alam 35 km, Lorry 16–32 metric ton, 0.7 kg sago starch} & 0.0245 & tkm & [33] and Ecoinvent 3 database \\ \multicolumn{5}{c}{**Raw materials—Sugar palm fiber**} \\ \multicolumn{5}{c}{_Inputs from technosphere—MaterialsFuchs_} \\ \multicolumn{5}{c}{Tap Water} & Sugar palm fiber preparation (Assumed 10 kg water used to wash 1 kg sugar palm fiber), 30\% SPF content of 1 kg & 3 & kg & Agri-footprint database \\ \multicolumn{5}{c}{SPF-reinforced Sago composite} \\ \multicolumn{5}{c}{_Inputs from technosphere—Electricity/Hat_} \\ Electricity—Sugar palm fiber production & Drying of sugar palm fiber (30\% SPF content of 1 kg & 0.67 & kWh &
\begin{tabular}{c} Assume similar equipment used as in [25], Ecoinvent 3 database \\ Assume similar equipment used as in [25], Ecoinvent 3 database \\ Assume similar equipment used as in [25], Ecoinvent 3 database \\ \multicolumn{5}{c}{SFP-reinforced Sago composite)} \\ \multicolumn{5}{c}{_Transportation_} \\ \multicolumn{5}{c}{Mill to composite manufacture plant (lempol-Shah Alam,} \\ \multicolumn{5}{c}{Lory 16–32 metric ton) (30\% SPF content of 1 kg} \\ \multicolumn{5}{c}{SPF-reinforced Sago composite)} \\ \hline \end{tabular}
\end{table} 
Table 1: Inventory data used to analyze the cradle-to-grave of the 1 kg SPF-reinforced sago starch biocomposite takeout food container.
## 2 Data and Methods
### Methods
#### 2.3.4 Accuracy Assessment  
The Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to quantitatively compare the water reflectance or turbidity values derived from different satellite data:  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{n}{(x_{i}-y_{i})^{2}}}{n}}, \tag{4}\]  
\[\text{MAPE}=\frac{\sum\limits_{i=1}^{n}{\left|{(x_{i}-y_{i})}/{y_{i}}\right|}}{n }\times 100\%, \tag{5}\]  
where \(x_{i}\) and \(y_{i}\) are respectively the estimated reflectance or turbidity and reference reflectance or turbidity (in situ measurements or already-validated satellite products), \(n\) is the total number of samples.  
The difference (Diff) between satellite-derived and field-measured turbidity values was computed to assess the validity of the satellite product as  
\[\text{Diff}.=\frac{(x_{i}-y_{i})}{y_{i}}\times 100\%, \tag{6}\]  
where \(x_{i}\) is the turbidity retrieved from satellite data and \(y_{i}\) is the turbidity measured in situ (Pauillac autonomous station).
## 3 Formulation of Research Questions and Objectives  
The primary aim of this research is to develop a reliable methodology for estimating the energy consumption of electric vehicles (EVs) to have a clearer understanding of the dynamic electric consumption characteristics of passenger cars, focusing specifically on the key electrical subsystems. In order to achieve this overarching goal, the following research questions have been formulated:  
* How can a more accurate and comprehensive simulation framework be developed to estimate the energy consumption of electric vehicles, compared to existing regression models? This question addresses the current gap in the literature, where traditional regression models are often insufficiently accurate in predicting energy consumption, particularly for new electric vehicle platforms.
* What are the key subsystems that significantly impact the energy consumption of electric vehicles, and how can they be incorporated into a simulation model? This question focuses on identifying and modeling the most critical subsystems of an electric vehicle, such as the drive system, steering system, lighting system and HVAC system, for a more holistic estimation.
* How does the performance of the developed simulation model compare with that of existing regression models in terms of estimation accuracy? This question aims to validate the simulation model by comparing its results with real data and assessing its accuracy against two commonly used regression models in the field.  
To address these questions, the main objective of this research is to create a multi-element, model-based simulation framework that reflects real-world vehicle behavior by integrating empirical measurements and accurate vehicle parameters. The specific research objectives derived from these questions include:  
* Objective 1: Develop a generalized, detailed simulation framework for energy consumption estimation in electric vehicles, incorporating critical electrical subsystems.
* Objective 2: Validate the simulation model using real data and compare the results with traditional regression models to assess the model's performance.
* Objective 3: Ensure that the error margin of the simulation model remains below 4%, demonstrating its reliability and accuracy compared to alternative methods.  
These objectives are aligned with the goal of advancing the accuracy of energy consumption predictions for electric vehicles, contributing to:  
* clearer understanding of the dynamic electric consumption characteristics of passenger cars
* more efficient energy management, and
* optimization of power distribution in the context of electric vehicle use.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 3 Leveraging Software Engineering for Sustainable Smart Cities with Electric Vehicles
### Software Engineering Solutions for Intelligent Transportation Systems (ITS)  
Software engineering plays a critical role in the development and implementation of intelligent transportation systems (ITS) solutions for electric vehicles (EVs) in smart cities. The software used in these systems is responsible for managing the charging infrastructure, optimizing the use of energy resources, and ensuring the safety and security of the EVs and the smart city infrastructure. As such, the software must be designed, developed, and implemented with the highest standards of quality, reliability, and security.  
Software engineering practices such as requirements analysis, design, testing, and maintenance are essential to the development and implementation of ITS solutions for EVs. Requirements analysis involves identifying the functional and non-functional requirements of the software, such as its ability to support different types of EVs and integrate them with other smart city systems. Design involves creating a software architecture that can accommodate these requirements and ensure the scalability and maintainability of the system. Testing involves verifying that the software meets the requirements and performs as expected, while maintenance involves ensuring the continued operation and optimization of the system.  
One of the main challenges in software engineering for ITS solutions for EVs is the need for interoperability. Different systems and devices used in the charging infrastructure and other smart city components may use different protocols and standards, making it difficult to exchange data and communicate effectively. To address this challenge, softwareengineers must develop solutions that can support multiple protocols and standards and ensure that the software can communicate effectively with other systems and devices.  
Finally, cybersecurity is a critical concern in the development and implementation of ITS solutions for EVs. Software engineers must design software that can protect against cyber attacks and ensure the safety and security of EVs and the smart city infrastructure. This involves implementing robust security features such as encryption, authentication, and intrusion detection systems, as well as regularly updating the software to address new threats and vulnerabilities.  
Software engineering plays a critical role in the development and implementation of ITS solutions for EVs in smart cities. The software used in these systems must be designed, developed, and implemented with the highest standards of quality, reliability, and security to ensure the safe, efficient, and sustainable deployment of EVs in smart cities. Achieving this requires a collaborative effort between software engineers and other stakeholders involved in the deployment of the ITS solutions and the development of innovative solutions that can address the challenges and opportunities of the smart city infrastructure.
## 5 Discussion
### Future Scope  
The architecture has the potential to enable a new type of hotspot network deployment in the future. Ordinary mobile and physical network objects should be included in the current hotspot network structure. The security features of hotspots should be considered. The architecture is expected to provide a large area for the distributed coverage of the hotspots. Therefore, it is required that all hotspots follow similar rules and software updates. Edge computing devices need to be evaluated for their suitability as alternative hotspot machines. Third-party cloud service providers can be considered to add value to this architecture.
## 4 Case Studies
### Parameter Setting  
The typical parameters of Typhoon Maria are given in Table 1.  
The thermal units participating in emergency dispatch are installed as nodes 1, 2, and 3. Table 2 provides the typical parameters of thermal units.  
The ESS participating in emergency dispatch is labeled as node 4. The typical parameters are given in Table 3  
The typical parameters of identifying abnormal states of wind power and transmission lines are listed in Table 4.  
\begin{table}
\begin{tabular}{l l l l l} \hline Parameter & 1 h & 2 h & 3 h & 4 h \\ \hline \(v_{max,i}\) & 48 m/s & 50 m/s & 50 m/s & 48 m/s \\ \(R_{max,i}\) & 25.1 m & 24.5 m & 30.2 m & 27.6 m \\ \((x_{o,i},y_{o,i})\) & (123.9 E, 25.4 N) & (123.6 E, 25.5 N) & (123.4 E, 25.6 N) & (123.1 E, 25.8 N) \\ \hline Parameter & 5 h & 6 h & 7 h & 8 h \\ \hline \(v_{max,i}\) & 48 m/s & 48 m/s & 48 m/s & 48 m/s \\ \(K_{max,i}\) & 28.4 m & 28.4 m & 26.2 m & 23.2 m \\ \((x_{o,i},y_{o,i})\) & (122.7 E, 25.9 N) & (122.3 E, 26.1 N) & (121.9 E, 26.3 N) & (121.4 E, 26.3 N) \\ \hline Parameter & 9 h & 10 h & 11 h & 12 h \\ \hline \(v_{max,i}\) & 45 m/s & 45 m/s & 45 m/s & 43 m/s \\ \(R_{max,i}\) & 25.8 m & 22.4 m & 27.3 m & 32.8 m \\ \((x_{o,i},y_{o,i})\) & (121.0 E, 26.3 N) & (120.8 E, 26.3 N) & (120.6 E, 26.4 N) & (120.3 E, 26.4 N) \\ \hline Parameter & 13 h & 14 h & 15 h & 16 h \\ \hline \(v_{max,i}\) & 43 m/s & 43 m/s & 43 m/s & 40 m/s \\ \(R_{max,i}\) & 32.5 m & 32.8 m & 39.4 m & 39.9 m \\ \((x_{o,i},y_{o,i})\) & (119.9 E, 26.4 N) & (119.5 E, 26.3 N) & (119.1 E, 26.2 N) & (118.9 E, 26.3 N) \\ \hline Parameter & 17 h & 18 h & 19 h & 20 h \\ \hline \(v_{max,i}\) & 40 m/s & 40 m/s & 40 m/s & 40 m/s \\ \(R_{max,i}\) & 35.9 m & 39.9 m & 39.4 m & 37.5 m \\ \((x_{o,i},y_{o,i})\) & (118.7 E, 26.4 N) & (118.4 E, 26.5 N) & (118.1 E, 26.7 N) & (117.8 E, 26.9 N) \\ \hline Parameter & 21 h & 22 h & 23 h & 24 h \\ \hline \(v_{max,i}\) & 40 m/s & 40 m/s & 40 m/s & 38 m/s \\ \(R_{max,i}\) & 41.1 m & 43.7 m & 45.7 m & 42.8 m \\ \((x_{o,i},y_{o,i})\) & (117.6 E, 27.0 N) & (117.4 E, 27.1 N) & (117.2 E, 27.1 N) & (117.0 E, 27.1 N) \\ \hline \end{tabular}
\end{table}
Table 1: Typical parameters of Typhoon Maria over 24 h periods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{p}_{j}^{dis,max(cha,max)}\) & \(E_{j,min}\) & \(E_{j,max}\) & \(\eta_{j}^{dis(cha)}\) & \(\lambda_{j}^{cha}\) & \(\lambda_{j}^{dis}\) \\ \hline
0.2 p.u. & 0.54 p.u. & 0.06 p.u. & 0.9 & 205/p.u. & 225/p.u. \\ \hline \end{tabular}
\end{table}
Table 3: Typical parameters of ESS participating in emergency dispatch.  
In the planning and management of power systems or other critical infrastructures, since even disaster events with very low probabilities should be taken seriously, large values should not be set for the threshold \(Pr_{0}\), to contain more \"low-probability\" emergency scenarios for emergency dispatch. In this paper, the range of \(Pr_{0}\) is set \((0.1,0.5)\).