esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Inertial Sensors  
Differences from the pre- and postoperative foot parameters are reported in Table 6.  
No significant differences were reported for most of the parameters between the pre- and postoperative period, and only the stride length decreased significantly after surgery, as well the step length regarding the operated feet.  
Regarding pelvic movement during gait (Table 7), no significant changes were recorded.  
Inertial sensors measurement of the ankle's ROM (Table 8) showed a significant decrease in the active and passive plantar flexion between the pre- and postoperative period.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Preop** \\ **Both Feet** \\ \end{tabular} & \begin{tabular}{c} **Postop** \\ **Both Feet** \\ \end{tabular} & \(p\)**-Value** & \
\begin{tabular}{c} **Preop** \\ **Operated** \\ **Foot** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Preop Non-** \\ **Operated** \\ **Foot** \\ \end{tabular} & \(p\)**-Value** \\ \hline Walking speed & & & & & & & \\ (m/min) & & & & & & & \\ \hline Cadence & & & & & & & \\ (steps/min) & & & & & & & \\ \hline Stride length (m) & 1.25 \(\pm\) 0.1 & 1.10 \(\pm\) 0.1 & _0.031_ * & & & & & \\ \hline Stride duration (s) & 1.33 \(\pm\) 0.1 & 1.32 \(\pm\) 0.3 & _0.911_ & & & & & \\ \hline Step length (m) & & & & 0.63 \(\pm\) 0.1 & 0.54 \(\pm\) 0.1 & _0.003_ * & 0.61 \(\pm\) 0.1 & 0.56 \(\pm\) 0.1 & _0.206_ \\ \hline Step duration (s) & & & & 0.65 \(\pm\) 0.1 & 0.61 \(\pm\) 0.2 & _0.581_ & 0.68 \(\pm\) 0.1 & 0.71 \(\pm\) 0.2 & _0.635_ \\ \hline Stance duration (\%) & & & & 60.8 \(\pm\) 1.9 & 62.0 \(\pm\) 5.4 & _0.564_ & 62.3 \(\pm\) 2.6 & 65.8 \(\pm\) 6.7 & _0.195_ \\ \hline Swing duration (\%) & & & & 37.0 \(\pm\) 2.1 & 36.5 \(\pm\) 4.9 & _0.812_ & 35.9 \(\pm\) 2.6 & 32.4 \(\pm\) 6.5 & _0.189_ \\ \hline Double support duration (\%) & & & & & & & \\ \hline Gait variability (\%) & & & & 4.9 \(\pm\) 0.9 & 6.4 \(\pm\) 4.7 & _0.423_ & 5.9 \(\pm\) 2.2 & 6.6 \(\pm\) 2.9 & _0.499_ \\ \hline Symmetry index & 0.9 \(\pm\) 0.1 & 1.1 \(\pm\) 0.3 & _0.265_ & & & & \\ \hline \hline \end{tabular}  
* **Bold \(p\)**-value indicates reaching statistical significance.  
\end{table}
Table 6: Inertial sensors outcomes of the feet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Preop** & **Postop** & _p_**-Value** \\ \hline
**Tilt (\({}^{\circ}\))** & 0.87 \(\pm\) 0.8 & 0.35 \(\pm\) 0.3 & _0.139_ \\
**Obliquity (\({}^{\circ}\))** & 0.17 \(\pm\) 0.1 & 0.61 \(\pm\) 0.9 & _0.198_ \\
**Rotation (\({}^{\circ}\))** & 0.28 \(\pm\) 0.2 & 0.48 \(\pm\) 0.4 & _0.283_ \\ \hline \hline \end{tabular}  
* Inertial sensors measurement of the ankle’s ROM (Table 8) showed a significant decrease in the active and passive plantar flexion between the pre- and postoperative period.  
\end{table}
Table 7: Inertial sensors outcomes of pelvic movement.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 4 Experiment and Evaluation
### Algorithm Performance Evaluation
#### 4.2.2 The Performance of Lightweight Degree  
In order to better understand the performance of user behavior recognition, this paper compares the accuracy, parameter count, and FLOPS of LIMUNet with other models, as shown in Table 3.  
* [PERSON] et al. [48] adopted a novel convolution operation, inspired by the idea of grouped convolution, which divides the filters within a specific convolution layer into two unequal groups. The two groups of filters complement each other, enhancing the feature extraction capability of the sensor signal.
* GTSNet [38] proposed the Grouped Temporal Shift (GTS) module, which reduces the number of network connections by grouping the channels of the signal and adds shifts in the temporal direction to enhance feature extraction capability, thereby achieving lightweight network implementation.  
Through the results of comparative experiments, it can be concluded that the basic model for user behavior recognition proposed in this paper demonstrates a high accuracy on the PAMAP2 and LIMU datasets. LIMUNet contains two LIMUNet modules, achieving the highest recognition accuracy among similar works. Moreover, its parameter count and computational complexity are relatively low. As for LIMUNet-Tiny, although it contains only a single LIMUNet module, it also possesses excellent behavior recognition capabilities. Compared to other lightweight behavior recognition algorithms, both the parameter count and computational complexity of LIMUNet-Tiny are significantly reduced. Although its recognition accuracy is slightly lower than that of LIMUNet, LIMUNet-Tiny remains the best-performing model among current lightweight activity recognition algorithms. Experimental results show that both the proposed base model and its lightweight version not only achieve high accuracy in activity recognition tasks but also attain high efficiency in terms of parameter count and computational complexity, which is of significant importance for practical applications in resource-constrained scenarios.
## 4 Results and Discussions
Table 4 presents the variation of the Nusselt number across various Hartmann numbers, the power law index \(n\), and the Darcy numbers. The outcomes indicate that the heat transfer rate increases when the Hartmann number increases for all values of \(n\) and the Darcy numbers. Similarly, when the Darcy number increases, the heat transfer rate increases for all power law exponent n and Hartmann number values. The results also indicate that the \(Nu_{ang}\) has a maximum value for lower values of \(n\) (power law exponent). Figure 5: Variation of streamlines with \(Ra\), \(Ha\), and \(n\) values at specified parameters of \(Da=0.001\), \(Pr=0.7\), and \(\gamma=0^{\circ}\). The line graph in Figure 7 illustrates the impact of the Hartmann number \(Ha\), Kinetic energy, and heat transfer rate for two different rheological behaviors of the fluid, shear-thickening and shear-thinning, at different angles of inclination (\(0^{\circ}\), \(30^{\circ}\), and \(60^{\circ}\)). For shear-thinning fluid \(n=0.7\), as the Hartmann number \(Ha\) increases from 0 to 40, the averaged Nusselt number decreases for all angles of inclination (\(0^{\circ}\), \(30^{\circ}\), and \(60^{\circ}\)), with the same behavior for kinetic energy. When \(Ha=0\), the averaged Nusselt number and kinetic energy have maximum value. With the increase in \(Ha\), the heat transfer rate and kinetic energy decrease. Moreover, the influence of the degree of inclination can be noticed with the \(0^{\circ}\) inclination having the greatest Nusselt number, followed by \(30^{\circ}\) and \(60^{\circ}\). For shear-thickening fluids \(n=1.2\), similar to shear-thinning fluids, the averaged Nusselt number decreases as the Hartmann number \(Ha\) increases from 0 to 40, irrespective of the inclination angle. The different angles of inclination, the \(0^{\circ}\) angle generally results in the highest  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(Da\)} & \multicolumn{2}{c}{\(Ha\)=0} & \multicolumn{2}{c}{\(Ha\)=20} & \multicolumn{2}{c}{\(Ha\)=40} & \multicolumn{2}{c}{\(10^{-3}\)} & \multicolumn{2}{c}{\(10^{-2}\)} \\  & \(10^{-4}\) & \(10^{-3}\) & \(10^{-2}\) & \(10^{-4}\) & \(10^{-3}\) & \(10^{-2}\) & \(10^{-4}\) & \(10^{-3}\) & \(10^{-2}\) \\ \hline  
Nusselt number. It can be concluded that shear-thinning and shear-thickening fluids are consistent regarding the effect of Hartmann number \(Ha\) on the averaged Nusselt number. As Ha increases, the Nusselt number decreases, indicating a reduction in convective heat transfer. The results show that the magnetic field-induced flow suppression has a substantial impact on the kinetic energy within the cavity, particularly for shear-thinning behavior.
# Improving Hardware in LUT-Based Mealy FSMs
## 6 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Benchmark** & **Auto [13]** & **One-Hot [13]** & **JEDI [14]** & **\(\mathbf{U_{2}}\) [61]** & **Our Approach** & **Group** \\ \hline bbtas & 204.16 & 204.16 & 206.12 & 200.38 & 199.48 & 0 \\ \hline dk17 & 199.28 & 167 & 199.39 & 199.87 & 198.03 & 0 \\ \hline dk27 & 206.02 & 201.9 & 204.18 & 196.65 & 194.18 & 0 \\ \hline dk512 & 196.27 & 196.27 & 199.75 & 194.17 & 192.34 & 0 \\ \hline ex3 & 194.86 & 194.86 & 195.76 & 191.22 & 188.14 & 0 \\ \hline ex5 & 180.25 & 180.25 & 181.16 & 178.06 & 176.74 & 0 \\ \hline lion & 202.43 & 204 & 202.35 & 200.18 & 199.12 & 0 \\ \hline lion9 & 205.3 & 185.22 & 206.38 & 199.12 & 197.07 & 0 \\ \hline mc & 196.66 & 195.47 & 196.87 & 193.17 & 191.52 & 0 \\ \hline modulo12 & 207 & 207 & 207.13 & 201.12 & 200.23 & 0 \\ \hline shiftreg & 262.67 & 263.57 & 276.26 & 256.69 & 253.24 & 0 \\ \hline bbara & 193.39 & 193.39 & 212.21 & 202.23 & 201.12 & 1 \\ \hline bbsse & 157.06 & 169.12 & 182.34 & 181.23 & 178.64 & 1 \\ \hline beecount & 166.61 & 166.61 & 187.32 & 185.14 & 183.72 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results (LUT counts for benchmarks of G2–G4). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Benchmark** & **Auto [13]** & **One-Hot [13]** & **JEDI [14]** & **\(\mathbf{U_{2}}\) [61]** & **Our Approach** \\ \hline ex1 & 70 & 74 & 53 & 40 & 34 \\ \hline kirkman & 42 & 58 & 39 & 33 & 29 \\ \hline planet & 131 & 131 & 88 & 78 & 71 \\ \hline planet1 & 131 & 131 & 88 & 78 & 71 \\ \hline pma & 94 & 94 & 86 & 72 & 68 \\ \hline s1 & 65 & 99 & 61 & 54 & 50 \\ \hline s1488 & 124 & 131 & 108 & 89 & 86 \\ \hline s1494 & 126 & 132 & 110 & 90 & 80 \\ \hline s1a & 49 & 81 & 43 & 38 & 34 \\ \hline s208 & 12 & 31 & 10 & 9 & 11 \\ \hline styr & 93 & 120 & 81 & 70 & 62 \\ \hline tma & 45 & 39 & 39 & 30 & 29 \\ \hline sand & 132 & 132 & 114 & 99 & 82 \\ \hline s420 & 10 & 31 & 9 & 8 & 9 \\ \hline s510 & 48 & 48 & 32 & 22 & 20 \\ \hline s820 & 88 & 82 & 68 & 52 & 48 \\ \hline s832 & 80 & 79 & 62 & 50 & 46 \\ \hline Total & 1340 & 1493 & 1091 & 912 & 830 \\ \hline Percentage,\% & 161.45 & 179.88 & 131.45 & 109.88 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Experimental results (the maximum operating frequency for all benchmarks).
## 2 Materials and Methods
### Formulas and Calculations
#### 2.5.3 Viscous Turbulence Model  
The commonly used temperature flow models for hydraulic numerical simulation include Prandtl mixing length, one-equation, two-equation k\(-\varepsilon\), RNG (Renormalization-Group), k\(-\omega\), and LES models. In this study, the RNG model, a commonly used model in hydraulic engineering and hydraulics research, is adopted. Its formula has more accurate theoretical values and can simulate low-intensity turbulence and complex vortex flow, so it is relatively widely used. Its governing equation are as follows [23]:  
Turbulent kinetic energy \(k\) equation:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_ {i}}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{ \sigma_{k}}\bigg{)}\frac{\partial_{k}}{\partial x_{j}}\bigg{]}+G_{k}+\rho\varepsilon \tag{7}\]  
Turbulence kinetic energy dissipation rate equation:  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\bigg{]}+\frac{C_{1\varepsilon}^{*}}{k}G_{k}-C_{2\varepsilon} \frac{\varepsilon^{2}}{k} \tag{8}\]  
\[\left\{\begin{array}{l}G_{k}=\mu_{t}\bigg{(}\frac{\partial u_{i}}{\partial x _{j}}+\frac{\partial u_{j}}{\partial x_{i}}\bigg{)}\frac{\partial u_{j}}{ \partial x_{j}}\\ \mathrm{C}_{1\varepsilon}^{*}=\mathrm{C}_{1\varepsilon}-\frac{\eta(1-\eta/ \eta_{0})}{1+\beta\eta^{3}}\\ \eta=(2E_{ij}\cdot E_{ij})^{1/2}\frac{\varepsilon}{\varepsilon}\\ E_{ij}=\frac{1}{2}\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\bigg{)}\end{array}\right. \tag{9}\]  
where \(\mu_{t}\) is the kinematic viscosity coefficient of turbulence, \(\mu_{t}=C_{\mu}k^{2}/\varepsilon\), \(C_{\mu}=0.085\), \(\sigma_{k}=\sigma_{\varepsilon}=0.7179\), and \(G_{k}\) is the turbulent kinetic energy generation term: \(C_{1\varepsilon}=1.42\), \(C_{2\varepsilon}=1.68\), \(\eta_{0}=4.377\), \(\beta=0.012\).
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 3 Simulation Algorithm of the 2D Profile  
### Calculation of the Nominal Profile  
Nominal profile refers to the profile of intersection between the nominal surface and plane \(P\). The nominal profile of a plane surface is a line segment of length \(L\) that passes the starting point \(P_{S}\) along the 2D profile measurement line \(V_{L}\). Thus, the point \(p_{i}\) on the nominal profile of a plane surface can be mathematically expressed in the form below:  
\[p_{i}=P_{S}+l^{\prime}\cdot V_{L}, \tag{8}\]  
where \(0\leq l^{\prime}\leq L\).  
However, the nominal profile of a free-form surface is different from that of a plane. Assume that, in the workpiece coordinate system, the parametric equation of the nominal surface can be expressed as follow:  
\[r=r(u,v), \tag{9}\]  
where \(u\) and \(v\) are parameters of nominal surface. Figure 6 shows that the nominal profile of the free-form surface is a plane curve that passes the start point \(P_{S}\). The start and end points of this curve are \(p_{S}\) and \(p_{E}\), respectively. Obviously, the point \(p_{S}(u_{S},v_{S})\) is on the nominal surface. Line \(L_{P_{E}}\) passes through point \(P_{E}\), and it is parallel to \(V_{M}\). It is worth noting that \(p_{E}\) is the intersection of line \(L_{P_{E}}\) with the nominal surface.  
As shown in Figure 7, the vector \(T_{S}\) is on plane \(P\), which is the tangent vector of the nominal profile at \(p_{S}\). \(T_{S}\) can be expressed in the form below:  
\[T_{S}=\frac{V_{n}\times n_{P}}{|V_{n}\times n_{P}|}. \tag{10}\]  
Figure 5: The nominal profile, points, and vectors on plane \(P\).  
Figure 6: The nominal profile of the free-form surface.  
Further, because \(\Delta v_{i}=k\cdot\Delta u_{i}\), we can obtain:  
\[\left\{\begin{array}{l}\Delta u_{i}=\frac{\Delta d_{i}}{\sqrt{{r_{n}}^{2}+2k \cdot r_{n}\cdot r_{n}+k^{2}\cdot r_{n}}^{2}}\\ \Delta v_{i}=k\cdot\Delta u_{i}\end{array}\right.. \tag{17}\]
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 2 Related Work
### Content Popularity Prediction  
Interests in mobile multimedia applications are increasing, and the necessity of reducing the consumption of resources has become a hot topic in recent times. Therefore, content popularity prediction is an effective way to regulate the content selected for storing or removing from content repository. The generated content can be managed efficiently by identifying its popularity. Rankings of the video content can be predicted in terms of popularity by using the IMDB (Internet Movie Database) system. Additionally, news websites can identify the most viewed news and predict the types of stories their customers are mostly interested in. Predicting the popularity of various kinds of content is a well-researched area. Several content popularity methods are surveyed in [15], where limitations of different existing approaches are presented. Typically, video content consumes most of the Internet bandwidth, and in [16], models to predict daily access patterns of YouTube content are proposed using the Autoregressive Moving Average (ARMA) and Hierarchical clustering methods. However, these approaches incur an additional computational cost that may be a disadvantage. Temporal evolution prediction is used in [17] to classify various content and predict the content popularity. A Digg dataset was used to predict the popularity of news in [18]. The life duration of popular Tweets was predicted in [19] based on the static characteristics and patterns of dynamic retweeting. Placing the frequently requested content by predicting the content popularity in the ICN can improve network performance by reducing response time and traffic load of the servers. A distributed content placement strategy based on popularity for ICN was proposed in [20] that considered several aspects, including the distance between the node on the content return path and the requesting node, the content popularity trend, prediction of the future popularity of the content with the Markov Chain, and based on these, proposed to push the content with the local popular trend to the network in advance.
## 2 Methodology
### Study Area and Climatological Analysis
#### 2.1.1 Radiation and Wind Analysis  
The study showed that radiation and energy captured per square meter in the study region was considerable, with a maximum radiation value of 729.24 W/m\({}^{2}\) and a minimum value of 407.81 W/m\({}^{2}\) per day. The low general radiation mean was 150.04 W/m\({}^{2}\) and the high mean was 287.24 W/m\({}^{2}\) (Figure 3).  
Likewise, peak sun hours (PSH) for each month of the year were evaluated as shown below:  
August shows 7.29 peak hours and January the lowest number of hours, at 4.08. There is a great potential of solar radiation energy that can be exploited as a possible source of energy for AWGs. With regard to wind energy, Figure 4 shows the variation of wind speed in the region throughout an average day for each month from 2010 to 2017.  
Figure 4: Average solar peak hours (PSH) for each month of the year (2010–2017).  
Figure 3: Statistical data of solar radiation in Matehuala SLP, Mexico (2010–2017).  
The results show top wind speeds of 6 m/s for a Beaufort scale classification of \"Strong Breeze\". A minimum wind speed of 0 m/s, classified as \"Calm\", is also observed (Figure 5). In the early hours of the morning, winds range from 2.8 to 6.1 m/s, a \"Gentle and Moderate Breeze\" that fluctuates during the early hours of the morning. The wind speed tends to decrease, reaching soft winds, light winds and calm winds in an average of approximately 3 h, after which time, the wind increases and its speed in the last remaining hours reaches a high of 6 m/s.  
Results showed that the highest wind speeds occur at night in spring and the end of summer with average speed ranging from 2.15 to 6.2 m/s. In addition, average speeds prevailing throughout the year were 2.15 to 4.2 m/s. These results suggest that the study area may be suitable for the generation of electrical or mechanical energy on a small scale, with several small turbines connected together to generate enough electric power.  
Weather conditions for the city of Matehuala, SLP, are favourable for AWG use because its RH makes it a good source of water extraction. At the same time its high levels of solar radiation and wind can be used as an energy source for AWGs.
# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
## 1 Introduction
In particular, in formulating our objective function, we trade off flows delivery latency and overall network survivability, as we focused on typical IoT constraints, mainly in terms of residual battery available at each device. As a consequence, the SDN Controller, upon continuously monitoring the underlying network status (mainly in terms of both available communications links and remaining battery level) and QoS requirements, is able to evaluate the best path by executing the Dynamic Routing Module (DRM) and to update the forwarding tables of the involved devices with a limited overhead, when compared with traditional distributed approaches, regardless the nodes deployment. The rest of the paper is organised as follows. In Section 2, the most relevant approaches already proposed in the literature to address SDN design for IoT domain are briefly presented. The proposed architecture and forwarding strategies are characterised in Section 3, whereas its performance is validated in Section 4 by means of numerical results. Finally, in Section 5, conclusions are drawn.
## 3 Results & Discussion
### Parameter Analysis  
In order to study the influence of parameter changes on the torsional stress of the improved composite box girder with corrugated steel webs, based on the model beam, the width of each cell, \(b\), is 0.425 m, a beam height, \(h\), is taken as 0.425 and 0.85 m respectively, and the height/width ratio, \(h/2b\), is taken as 0.5 and 1 respectively. The width ratio of the cantilever plate, \(c/b\), was increases by 0.05 from 0 to 1. This means that the width of the cantilever plate, \(c\), was increased from 0 to 0.425 m. The thickness of the bottom steel flange, \(t_{\mathrm{u}}\), was increased from 3 mm to 8 mm using 0.5 mm increments. Under the external torque of \(\widetilde{M}=8.5\) kN\(\cdot\) m, the changes of torsional normal stress at measuring points S\({}_{1}\), S\({}_{2}\) and X\({}_{1}\) on Section A are shown in Figure 12. The changes of the total shear stress at measuring points S\({}_{4}\), W\({}_{2}\) and X\({}_{3}\) on Section A are shown in Figure 13.  
It can be seen from Figure 12 that with the increase of the width ratio of the cantilever plate and the thickness of the bottom steel flange, the torsional normal stress at each measuring point shows different trends, and there is tensile and compressive stress conversion at some measuring points. Under the same height/width ratio, the torsional normal stress at measuring point X\({}_{1}\) is significantly larger than that at measuring points S\({}_{1}\) and S\({}_{2}\). With the increase of the height/width ratio, the difference between the torsional normal stress at measuring point X\({}_{1}\) and that at measuring points S\({}_{1}\) and S\({}_{2}\) becomes larger. The change of the width ratio of the cantilever plate has a significant impact on the torsional normal stress at measuring points S\({}_{1}\), S\({}_{2}\), and X\({}_{1}\). The change of the thickness of the bottom steel flange also has a significant impact on the torsional normal stress at measuring point X\({}_{1}\), but the impact is small on the torsional normal stress at measuring points S\({}_{1}\) and S\({}_{2}\).  
It can be seen from Figure 13 that with the increase of the width ratio of the cantilever plate and the thickness of the bottom steel flange, the total shear stress of the box girder section changes along the positive direction (counterclockwise). For a box girder with a small height/width ratio, the total shear stress is significantly greater than that of a box girder with a large height/width ratio. With the increase of the width ratio of the cantilever plate, the total shear stress at measuring point W\({}_{2}\) decreases gradually, and the total shear stress at measuring point X\({}_{3}\) increases gradually. With the increase of the thickness of the bottom steel flange, the total shear stress at measuring point W\({}_{2}\) changes relatively gently, and the total shear stress at measuring point X\({}_{3}\) decreases gradually. Since the total shear stress at measuring point S\({}_{4}\) is smaller than that at measuring points W\({}_{2}\) and X\({}_{3}\), the influence of the parameter change on it is not obvious.