esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implemented Controllers
### Interval Type-2 Fuzzy PID Controller  
Just like the T1-FPID controller, the IT2-FPID controller is composed of choosing \(E\) and \(\Delta E\) as inputs and the control action of the IT2-FLC \(U\) as output as displayed in Figure 3. The IT2-FLC has the same N = 9 rules and is presented in Table 1. The MFs of IT2-FPID are characterised by triangular MFs, and the control surface of the IT2-FPID is given in Figure 5. The linguistic fuzzy variables are N, Z, and P, respectively. Here, \(m_{n}\) is the only parameter that creates the footprint of the uncertainty of the IT2-FPID [40]. We employ the following parameters: \(m_{2}=\alpha\) and \(m_{1}=m_{3}=1-\alpha\) as suggested in [36; 40; 41; 42]. Therefore, the selection of the parameter (\(\alpha\)) is vital because it directly affects \(U\).  
Several methods have been used for the calculation of the controller output (\(U\)) [43; 40]. In this study, the Biglarbegian-Melek-Mendel method is used [40]. The equation relating to this method is given as follows:  
\[\begin{split}& g=\frac{\sum_{i=1}^{M}f_{h}^{i}y^{i}}{\sum_{i=1}^{M}f_{h }^{i}}\\ &\underline{y}=\frac{\sum_{i=1}^{M}f_{i}^{i}y^{i}}{\sum_{i=1}^{M} f_{i}^{i}}\\ &\mathrm{U}=\zeta\underline{y}+(1-\zeta)g.\end{split} \tag{11}\]  
where \(y^{i}\) shows consequent MFs, \(M\) is the total number of rules (Table 1 is used for IT2), \(\zeta\) is a weighting parameter for the type reduced set (\([\underline{y},g]\)), and \(U\) is the output of FLC. Lastly, \(f_{l}\) and \(f_{h}\) are defined as the lower and upper firing functions of IT2-FPID, respectively. In Figure 5, the control surface, which selected \(\zeta=0.25\) and \(\alpha=0.25\) for IT2-FPID, is illustrated.  
Parameters of T1-FPID and IT2-FPID are selected aggressively by using rule-based FLCs and MFs. The \(\zeta\) and \(\alpha\) parameters are selected by trial and error so that an aggressive control surface is created for IT2-FPID, then it is defined as \(\zeta=0.5\) and \(\alpha=0.25\) in the study. After that, a comparison of the control surfaces (\(\Delta U\)) of the T1-FPID (\(U_{T1}\)) and IT2-FPID  
Figure 4: Illustrated T1 fuzzy MFs and control surface.  
Figure 5: Interval type-2 fuzzy MFs and control surface.  
(\(U_{IT2}\)) is presented in Figure 6, taking into account their aggressiveness and smoothness. The differences between them are formulated as \(\Delta U=U_{IT1}-U_{IT2}\). An aggressive control action is characterised by a relatively higher control action output compared to the others. Specifically, if a line lies above another line in the positive region where \(E,\Delta E\geq 0\) (or below in the negative region where \(E,\Delta E<0\)), the resulting control action is deemed aggressive. Conversely, if this condition is not met, it is deemed a smooth control action.
# Exploring the Use of Landmarks to Aid Pedestrian Wayfinding
## 5 Discussion
### Implications of the Research
#### 5.1.2 Mobile Mapping Applications  
The findings advocate for the integration of landmark-based guidance in mobile mapping applications. Current mobile maps do not use images of landmarks in their interfaces. By incorporating landmarks, mobile maps could provide users with more intuitive and contextually relevant navigation experiences, potentially addressing issues like the \"keyhole problem\" and reducing reliance on automated systems that may fail.
## 3 Results
### Extraction Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Region** & \multicolumn{2}{c}{**Jinxiang**} & \multicolumn{2}{c}{**Laiwu**} & \multicolumn{2}{c}{**Lanling**} & \multicolumn{2}{c}{**Pizhou**} & \multicolumn{2}{c}{**Qixian**} & \multicolumn{2}{c}{**Zhongmuou**} \\ \hline
**Results** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** \\ \hline
1.me-sr & **91.77** & **97.20** & **87.58** & 94.37 & **94.61** & 91.91 & **95.58** & 86.32 & **92.28** & **91.10** & **94.50** & **96.41** \\
2.mg-sr & 91.45 & 96.57 & 84.91 & 93.75 & 92.82 & **92.38** & **94.41** & **93.86** & 90.25 & 92.58 & 96.39 \\
3.mn-sr & 91.45 & 96.57 & 85.81 & 93.01 & **93.66** & 91. As evidenced by the classification results of the feature combination n-m vs. n-, the addition of spectral information significantly enhances accuracy, with the Kappa improving by more than 1% and even more than 6% in most regions. Relative to N-SSWIR, N-SM contains three additional RGB bands, resulting in a relatively minor difference in accuracy across regions compared to that between N-R and N- (except for Jinxiang, where the former's accuracy difference is less than half of the latter's in all other regions). The shortwave infrared band contributes more significantly to improving classification accuracy compared to the RGB band. N-sm includes more SAR time series data (VH, VV, and VH/VV) compared to n-m, leading to better accuracy in most areas. Except for a 3% decrease in the Kappa in Laning County, Laiwu and Pizhou improved by approximately 1%, and all other areas exceeded 2%. The integration of active and passive time series remote sensing data has a more pronounced effect on enhancing classification accuracy in most areas. Overall, e-sm demonstrates good stability and achieves favorable results in most regions with strong generalizability. DITS exhibits advantages in enhancing accuracy in certain areas, but different combinations of vegetation indices require further testing. Moreover, the combination of active and passive remote sensing time series data can significantly enhance accuracy and is applicable across various regions.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 5 Conclusions  
The results of the suggested approach can be acquired in about two minutes, and it has been demonstrated that it outperforms Fuzzy clustering unsupervised segmentation and is on par with other neural networks methods such as U-Net and Cascaded Anisotropic CNN. It has slightly poorer performance than the Rescue Net approach; all deep learning methods require a lot of learning which takes several hours of processing time as well as a large dataset for learning. It also requires a lot of CPU power and memory. The power of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Performance of** & **Average Execution Time** \\ \hline Single path MLDDeepMedic & 79\% & \textasci{}6 h \\ U-Net & 80\% & \textasci{}6 h \\ Rescue Net & 95\% & \textasci{}6 h \\ Cascaded Anistropic CNN & 87\% & \textasci{}5 h \\ K\_Mean and FCM & 57\% & \textasci{}45 s \\ Proposed method “Multilevel HSO” & 87\% & \textasci{}2 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison between our proposed method and different approaches on the BraTS dataset [32].  
Figure 6: The figure represents the values of the metrics (Accuracy, Sensitivity, Precision, and Dice) index for 10 images according to the segmentation “Multi-level HSO threshold”.  
the proposed method is to provide real-time results comparable to CNN and deep learning, which makes the proposed method promising in terms of assisting clinicians in fast and accurate diagnosis, the location and size of MRI brain tumors in two minutes instead of waiting for hours. Furthermore, it is quasi-automatic and does not require training. The only parameter fixed is the number of levels required for the segmentation. The comparison of the results with other methods published recently (2021) in the literature makes the proposed method promising as an AI application for brain tumor detection.  
The future direction is to explore and add methods based on each pixel in the region of interest as a refinement of the segmentation in order to improve the accuracy and the Dice index of brain detection for more accurate diagnosis.  
Conceptualization, A.S.S.; Methodology, A.S.S.; Software, R.A. and M.A.; Validation, R.A.; Formal analysis, A.A.; Data curation, M.A.; Writing--original draft, R.A.; Writing--review & editing, A.A., K.A., O.A. and A.S.S.; Supervision, K.A.; Project administration, A.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Deanship of Scientific Research at King Saud University research group no (IFKSURG-698).  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this research through research group no (IFKSURG-698).  
The authors declare no conflict of interest.
## 5 Conclusions and Outlook
### Outlook  
Currently, our research is in the experimental phase and has not yet been applied on an industrial scale. Future work will focus on developing corresponding software and hardware systems to facilitate industrial applications. This includes:  
1. Developing software interfaces suitable for different industrial welding scenarios to integrate multi-sensor data.
2. Optimizing hardware systems to adapt to various operating conditions in industrial environments.
3. Collaborating with industry partners to conduct large-scale field tests to verify the system's performance and reliability in actual production.  
Through these efforts, we aim to translate the research outcomes into practical industrial applications, thereby improving welding quality and reducing production costs.  
Conceptualization, Q.L.; Data curation, X.H. and B.L.; Formal analysis, X.X., X.H. and Z.P.; Funding acquisition, Z.F.; Investigation, X.Z., X.X. and X.L.; Methodology, X.Z.; Project administration, Z.F. and B.L.; Resources, X.L.; Supervision, Z.F., Z.P. and Q.L.; Validation, X.X. and X.L.; Visualization, X.Z. and X.H.; Writing--original draft, X.Z.; Writing--review & editing, Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 52261044 and the Guangxi Science and Technology Major Project under Grant No. Guike AA23062037.  
Not applicable.  
Data Availability StatementData can be obtained by contacting the author [PERSON] (185239516886163.com). The data are not publicly available due to privacy concerns.  
The authors declare no conflicts of interest.
## 3 Calculation Results and Discussion
### Influence of Outer Over-Lamination  
The structural configurations of the OL model are shown in Table 4. The main difference between the PC and benchmark models was the lack of outer over-lamination in the OL model. The OL model removed the laminate element at the end of the bonding line to simulate the case with no outer over-lamination. Figure 21 shows the damage of the OL model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Configuration** & **Detail** \\ \hline Position of UD & 20 mm \\ Presence of outer over-lamination & No \\ Prefabricated component & Prefabricated filler \\ Chamfer & Straight and perpendicular to the bonding surface \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural configurations of the benchmark model.  
Figure 20: Comparison of the benchmark and PC models on each layer: (**a**) Comparison of the results of layer 1 in each model; (**b**) Comparison of the results of layer 2 in each model; (**c**) Comparison of the results of layer 3 in each model; (**d**) Comparison of the results of layer 4 in each model; (**e**) Comparison of the results of layer 5 in each model.  
A comparison of the results of each layer with those of the benchmark model is shown in Figure 22. The damage without outer over-lamination was much higher than that with outer over-lamination. Also, the influence range occupied nearly 20% of the total bonding width.  
Figure 21: Damage of OL models and the difference between the two models: (**a**) Outer over-lamination in the benchmark model; (**b**) No outer over-lamination in the benchmark model.  
Figure 22: _Cont._  
The analysis results of the OL model indicated that the outer over-lamination could significantly reduce the damage of the adhesive near the trailing edge. Therefore, improving the damage of the adhesive through the outer over-lamination in the structural design or blade maintenance was possible. However, the influence range of outer over-lamination is very limited.
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 5 Conclusions  
This paper uses the Stackelberg game model to analyze the resources allocation of double layer HetNet, and utilizes the average throughput capacity of users to build utility function with concavity. Unlike the allocation of traditional Stackelberg game models, players determine the optimal strategy for the sharing spectrum throughout the game, so as to maximize the overall system revenue. However,  
Figure 8: The average throughput of macro users (MUEs).  
Figure 7: The average throughput of all users.  
the strategy value scope for built system revenue function is continuous and in reality, users generally cannot know the strategy of other authorized users at the moment, the existing backward induction methods cannot be used to solve the Nash equilibrium strategy. Therefore, it is of more significance to obtain iterative solution methods without depending on other user strategies at present. The intelligent gradient algorithm proposed in the paper only utilizes player strategy at the previous moment and solves the Nash equilibrium strategy through iteration along the fastest changing direction of revenue function, hence solving Nash equilibrium in-game with a continuous value scope. Compared with the linear iteration algorithm, thanks to its dynamic and flexible choice of adjustment factors, it enjoys a shorter iteration cycle under the same conditions and avoids the failure to obtain a stable Nash equilibrium caused by the unreasonable choice of adjustment factors in the linear iteration algorithm.  
This paper is funded by the National Natural Science Foundation of China (Grant No. 51509049), the Natural Science Foundation of Heilongjiang Province, China (Grant No. F201345), the Fundamental Research Funds for the Central Universities of China (Grant No. GK2080260140), the National Key Foundation for Exploring Scientific Instrument of China (Grant No. 2016 YFF0102806).  
[PERSON] conceived the concept and performed the research, [PERSON] conducted experiments to evaluate the performance of the proposed security mechanism and wrote the manuscript, [PERSON] reviewed the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 4 Design
### A Closer Look into MARV  
As shown in the paper of [26], as the density of the network increases, the multi-rate-aware protocols will find more high-throughput paths. However, one problem of using Algorithm 1 is that the control overhead may also increase along with the network density. We use an additional constraint to solve this problem. By setting the constant value \(\alpha\) (as shown in line 2 in Algorithm 1) to 3, we can earn two benefits. First, each intermediate node can rebroadcast only once or twice, since no path MTM value is greater than 6 (as shown in Table 3). Second, this helps the MARV protocol to find only very-high-throughput paths, since it will rebroadcast an RREQ only if it is far better than the previous one.  
Network failures such as node or link failures can happen. The node or link failures can be considered either at the network layer or the link layer. First, at the network layer, our proposal is an enhanced version of the previously proposed routing protocols, such as DSR [10]. We can directly adopt the link failure recovery mechanisms used in these original routing protocols. At the link layer, the link failures can be dealt by MAC retransmissions.
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 3 Trajectory Planning  
All trajectories in this study are planned using the trapezoidal velocity profile method [28] without the third phase. The trapezoidal velocity profile method is used to plan trajectories based on the trapezoidal velocity-time graph shown in Figure 3. The symbols in the graph are defined as follows: \(a\) is the acceleration/deceleration during the accelerating/decelerating periods and \(t_{f}\) is the total time required to execute the planned trajectory. If the trajectory length is fixed, these parameters are not independent; that is, some parameters can be determined if others are specified. For safety considerations, the values \(a\) and \(v_{\text{max}}\) are specified. The trajectory length \(s\) can be determined by calculating the area of the trapezoid surrounded by the velocity profile curve and \(x\)-axis in Figure 3, given by \(s=v_{\text{max}}\big{(}t_{f}-t_{a}\big{)}\). From this equation, we can derive  
\[t_{f}=\frac{s}{v_{\text{max}}}+t_{a}. \tag{5}\]  
Additionally, \(t_{f}\) must be at least \(2t_{a}\). In other words,  
\[\frac{s}{v_{\text{max}}}+t_{a}\geq 2t_{a}. \tag{6}\]  
By substituting \(t_{a}=v_{\text{max}}/a\) into Equation (6), we obtain  
\[v_{\text{max}}\leq\sqrt{sa}. \tag{7}\]  
To ensure all trajectory plans meet this condition, if the specified \(v_{\text{max}}\) is larger than \(\sqrt{sa}\), it is automatically adjusted to \(\sqrt{sa}\).  
Based on this method, the trajectory plan for the TMS coil consists of four phases, which are proceeded sequentially. The first to third phases are related to locating the coil near the desired coil position while avoiding collisions with the subject's head. Each phase is illustrated in Figure 4. The meanings of the symbols are as follows: \(\mathbf{x}_{i}\) represents the initial position of the TMS coil when the trajectory planning begins. \(\mathbf{x}_{o}\) is the center of the virtual sphere for the path planning, as detailed in Section 3.1. \(\mathbf{x}_{si}\) and \(\mathbf{x}_{sf}\) are the points on the virtual sphere's surface closest to \(\mathbf{x}_{i}\) and \(\mathbf{x}_{f}\). Following the third phase, the fourth phase  
Figure 3: Velocity–time graph for the trapezoidal velocity profile method.  
begins, which involves the zero calibration of the force sensor attached to the manipulator's wrist. The detailed explanations are provided below.
# An Improved MbICP Algorithm for Mobile Robot Pose Estimation
## 2 Scan-Matching Algorithm
### Improved MbICP Algorithm  
#### 2.4.1 Correspondence Establishment  
The MbICP algorithm finds correspondence points by finding the nearest point through its defined metric-based distance, which is claimed to perform better than when using the Euclidean distance when large rotation exists. However, this algorithm is still based on a point-to-point metric, which is not as reliable because of mismatching from partial overlapping and the discreteness of laser scans. For example, given two successive laser scans that are captured at the beginning and end of robot motion, partial overlapping can be clearly observed in rectangular area 1 in Figure 6a,b. This area contains no actual correspondence points for points in the object scan, thus producing many incorrect correspondences. Additionally, different points in the object scan incorrectly correspond to the same point in the reference scan because of the discrete nature of the scan, as shown in rectangular area 2 in Figure 6a. These incorrect correspondences will distort the follow-up calculation of the optimal transformation and thus negatively affect the final matching result.  
Figure 5: (a) Resampling process in grid form; (b) resampled point clouds.  
To reduce problems that are introduced by the point-to-point metric, this paper improves the procedure of determining the correspondence by combining the point-to-point and point-to-line metrics. First, the nearest point \(m_{j}\) in the reference scan is determined for each point \(p_{i}\) in the object scan by using the metric-based distance, and \(\left(p_{i},m_{j}\right)\) is treated as a candidate correspondence pair. For those candidate correspondence pairs whose object points correspond to a reference point, only the pair with the smallest corresponding distance is preserved and the remaining pairs are modified by using an interpolation method. Taking \(\left(p_{i},m_{j}\right)\) as an example, we search for the second-closest point \(m_{j}^{\prime}\) for \(p_{i}\) in the reference scan, and a segment that begins at \(m_{j}\) and ends at \(m_{j}^{\prime}\) is found. We calculate the projection of \(p_{i}\) onto the segment along the normal and use this interpolation point as a new corresponding point (as shown in Figure 7a). This interpolation is less risky because it only utilizes the surface information of the reference point clouds.  
Figure 6: (**a**) Correspondences that were established based on the point-to-point metric (red dots-reference scan, blue dots-object scan, black lines-correspondences); (**b**) correspondences that were established based on the point-to-line metric (red dots-reference scan, blue dots-object scan, black lines-correspondences).  
This new procedure can improve the performance of correspondence establishment, especially when non-overlapping areas exist (as shown in Figure 8). However, a few incorrect correspondence pairs might be established because of the complexity of the environment. Thus, a rejection strategy will be explained in the next section.
## Materials and Methods
### Narrow Fraction Distillation  
The MLCT samples were dehydrated by an automatic dehydration device. The water-free MLCT samples were used for subsequent tests and analyses. All the distillation experiments were carried out on a true boiling point distillation instrument (German i-Fischer) equipped with a 20L distiller, a column with 15 theoretical plates and a reflux ratio of 10:1. The whole fraction of MLCT was separated into 11 narrow fractions containing IBP-170 \({}^{\circ}\)C, 170-190 \({}^{\circ}\)C, 190-210 \({}^{\circ}\)C, 210-230 \({}^{\circ}\)C, 230-250 \({}^{\circ}\)C, 250-270 \({}^{\circ}\)C, 270-300 \({}^{\circ}\)C, 300-320 \({}^{\circ}\)C, 320-340 \({}^{\circ}\)C, 340-360 \({}^{\circ}\)C, and \(>\)360 \({}^{\circ}\)C. The IBP-190 \({}^{\circ}\)C, 190-270 \({}^{\circ}\)C, and 270-360 \({}^{\circ}\)C distillate fractions were collected at atmospheric pressure, under a vacuum with a pressure of 100 mm Hg and 10 mm Hg, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **MLCT-Z** & **MLCT-S** \\ \hline Density (20 \({}^{\circ}\)C, g/ml) & 1.032 & 1.038 \\ Viscosity (50 \({}^{\circ}\)C, mm2/s) & 57.21 & 58.98 \\ molecular weight (wt.\%) & 203 & 187 \\ toluene insoluble (wt.\%) & 0.32 & 0.27 \\ quinoline insoluble (wt.\%) & 0.21 & 0.14 \\ carbon residue (wt.\%) & 5.76 & 5.93 \\ ash (wt.\%) & 0.038 & 0.042 \\ elemental component (wt.\%) & & \\ C & 85.57 & 84.94 \\ H & 9.55 & 9.30 \\ N & 0.65 & 0.66 \\ S & 0.12 & 0.14 \\ O & 4.11 & 4.96 \\ H/C ratio & 1.339 & 1.314 \\ metal content (\(\upmu\)g/g) & & \\ Fe & 59.41 & 55.02 \\ Ca & 16.80 & 13.94 \\ Mg & 13.62 & 2.34 \\ Al & 21.13 & 20.90 \\ Cr & 7.58 & 10.55 \\ Zn & 4.64 & 0.83 \\ Mn & 3.63 & 3.08 \\ distillation range (\({}^{\circ}\)C) & & \\ IBP & 197 & 198 \\
10\% & 246 & 238 \\
30\% & 307 & 297 \\
50\% & 350 & 348 \\
80\% & 395 & 397 \\ \hline \end{tabular}
\end{table}
Table 1: The typical properties of medium-low temperature coal tar (MLCT).