esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Concerns Related to User Identity and Privacy in 5G Core Networks
### Linkability
A survey conducted on 5G privacy scenarios underscores the importance of keeping the personal or hardware identifiers of a user within a 5G network private [35]. Constant connectivity to a 5G network can facilitate an environment for the unauthorized tracking of individuals using these identifiers. The authors state that it is important for 5G networks to adopt privacy-by-design principles to help mitigate user identity threats. The conclusions from this study state that a complete solution to achieve these privacy objectives goes beyond technology and ultimately also requires legal and regulatory actions. The work shown in [36] mentions that the large number of devices connected to a 5G network and the increasing number of IoT devices only further complicate the management of maintaining user privacy and ensuring non-linkability between device hardware identifiers and personal identity. The researchers in [11] mention the fact that, after a UE has been authenticated on the network, the CN is responsible for creating and periodically updating an associated new temporary identifier: the 5G-GUTI for Standalone networks or the 4G-GUTI for Non-Standalone (NSA) networks. The authors point out that there are no mechanisms put in place for the CN to avoid tracking its users, as all temporary identifiers are also linked back directly to the SUPI. This leaves a possible vulnerability for misuse by untrusted network operators, as end-to-end privacy and the anonymity of the user are not inherently built into the system and thus cannot be guaranteed. Takeaways from [37] cover implementations of the 5G CN stack in open-source repositories. The authors uncovered two novel attacks that potentially allow for attacks against UE privacy with vulnerabilities in the 5G-GUTI update mechanism.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of State Space  
To implement an efficient reinforcement learning algorithm, the key is to choose a reasonable state space. Only by selecting enough information for training can the ICCDRL algorithm choose the appropriate action. However, too much state information will greatly increase the amount of calculation and slow down the learning speed. In view of this, the parameters selected in the state space \(s_{t}\) of this paper include the size of the congestion window, the number of ACK packets fed back, round-trip time \(RTT\), throughput, and packet loss rate. It can be expressed as \(s_{t}=\{cwnd,ACK,RTT,throughput,P_{x}\}\).
## 1 Introduction  
The prevalence of transverse discrepancy in the permanent dentition is approximately 12-14% [1, 2]. The clinical implications of maxillary transverse deficiency include uni- or bilateral crossbite, narrow nasal cavity, and crowding of teeth [3, 4]. Rapid maxillary expansion (RME) occurs as a result of the application of transverse forces that are greater than those required for tooth movement and the application of tensile forces on the circummax-illary sutures, leading to their separation. Bone apposition and remodeling take place after the sutures are separated.  
RME using tooth-borne appliances may have undesirable effects, including alveolar bone bending and buccal tipping of the teeth along with maxillary expansion. This denotalveolar effect occurs especially in postpubertal patients, whose circummaximillary sutures are mature, possibly leading to side effects such as marginal alveolar bone loss, a decrease in buccal bone thickness, and root resorption [5, 6].  
Microimplant-assisted rapid palatal expansion (MARPE) has been introduced to exert its forces through the microimplants anchored in the maxilla, thus minimizing the dentoalveolar side effects. Use of MARPE has been widespread among clinicians, especially in young adults, and many cases of successful maxillary skeletal expansion have been reported in nongrowing patients with severe maxillary transverse deficiency [7,8]. [PERSON] et al. reported a success rate of 86.9% in adult patients who had undergone maxillary expansion using MARPE [9].  
Despite the high success rate of MARPE, a possibility of failure still exists. If the success of MARPE can be predicted, it would help clinicians in treatment planning. Several indicators of skeletal maturation using occlusal radiographs, hand wrist radiographs, lateral cephalosporars, and cone beam computed tomography (CBCT) have been proposed to predict the skeletal response to conventional RME [10, 11, 12, 13, 14]. [PERSON] et al. suggested the midpalatal-suture density (MPSD) on a CBCT image as a useful clinical predictor of the skeletal response to RME [15]. Regarding the factors associated with the success of MARPE, older patients were more likely to experience failure [16,17]. Additionally, patients with thin and long palatal bone showed a higher chance of MARPE failure [17,18].  
The purpose of this study was to investigate the possible predictors of the success of MARPE in skeletally mature patients. Among the factors affecting the success of MARPE, those associated with the amount of maxillary expansion in the circummaximillary sutures were also analyzed.
# Hybrid Model-Based Traffic Network Control Using Population Games
## 4 Population Game-Based Model for Urban Traffic Control  
In the urban traffic system, each node \(p\in\mathcal{V}:\mathcal{I}_{p}\
eq\phi\) is represented as a population of agents that share the effective green time \(m=\sum_{i\in\mathcal{I}_{p}}\hat{s}_{i}\). In this sense, agents form a mass \(m\) distributed between the set of pure strategies \(S=\{1,\ldots,n\}\) of the population. The set \(S\), corresponds to the set of stages \(F_{p}\) described in Section 3, i.e., the proportion of \(m\) that the input links of node p have for the right of way (ROW). Thus, each link \(j\in\mathcal{I}_{p}\) is assigned a proportion \(x_{j}\) of \(m\), which is a pure strategy of the population.  
The whole behavior of the agents defines the population state \(x\), which belongs to the simplex  
\[\Delta=\{x\in\mathbb{R}_{+}^{n}:\sum_{j\in S}x_{j}=m\}. \tag{12}\]  
The set of agents assuming a strategy \(j\in S\) allows us to obtain a payoff provided by the _fitness function_\(F_{j}(x):\Delta\to\mathbb{R}\), in a determined population state \(x\). The average fitness function is defined as \(\overline{F}(x)=\frac{1}{m}\sum\limits_{j\in S}x_{j}F_{j}(x)\), and the excess payoff of \(j\) is \(\hat{F}_{j}(x)=F_{j}(x)-\overline{F}(x)\).  
In this approach, the fitness function for a given link \(j\) is the relation between its queue length and its assigned proportion of \(m\). This lead us to Definition 1.  
Definition 1: The fitness function for the adaptive control strategies based in population games depends on the relation between the link queue capacity and its green time assignment. This is given by  
\[F_{j}(x)=\frac{l_{j}}{x_{j}}, \tag{13}\]  
which must be minimized.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Performance Measure  
We varied the length of the crossband components \(L_{\text{cb}}\) to examine how it affected the performance of the proposed method. We examined the performance in terms of three widely used measures for speech dereverberation [21; 33]: the frequency-weighted segmental SNR (FWSegSNR) [34; 35], the cepstral distance (CD) [36], and the perceptual evaluation of speech quality (PESQ) [37]. Given a clean ground-truth signal in the STFT domain \(x_{f,t}\) and the corresponding enhanced signal \(\hat{x}_{f,t}\), FWSegSNR was computed as follows:  
\[\text{FWSegSNR}=\frac{10}{T}\sum_{t=0}^{T-1}\frac{\sum_{f=0}^{F-1}w_{f,t}\log_{ 10}\frac{x_{f,t}^{2}}{\left(x_{f,t}-\hat{x}_{f,t}\right)^{2}}}{\sum_{f=0}^{F-1 }w_{f,t}}, \tag{14}\]  
where \(F\) and \(T\) are the numbers of frequency bands and time frames, respectively, and \(w_{f,t}\) is the weight assigned to the \(f\)-th frequency at the \(t\)-th frame. We set the weights \(w_{f,t}\) according to the standard AI weights [38]. The CD measure is defined as  
\[\text{CD}=\frac{1}{T}\sum_{t=0}^{T-1}\sqrt{\sum_{m=0}^{M-1}\left[C_{x}(m,t)-C_ {\tilde{x}}(m,t)\right]^{2}}, \tag{15}\]  
where \(C_{x}(m,t)\) is the cepstral coefficient of the \(m\)-th Mel band of \(x_{f,t}\)[36]. It is worth noting that a universally accepted suite of objective quality measures has yet to be fully established within the dereverberation landscape [33]. Given this ongoing evolution, our choice of performance measures aimed to shed light on the relative strengths and limitations of various approaches. For FWSegSNR and PESQ, larger values indicate better dereverberation performance. For CD, smaller values indicate better performance. To highlight the effectiveness of the method, we considered the \"gain\" concerning the observed signal, i.e., instead of presenting the absolute measures' values, we offer the following measures:  
\[\Delta\text{FWSegSNR}=\text{FWSegSNR}/\text{FWSegSNR}_{\text{observed}}, \tag{16}\]  
\[\Delta\text{CD}=\text{CD}_{\text{observed}}/\text{CD}, \tag{17}\]  
\[\Delta\text{PESQ}=\text{PESQ}/\text{PESQ}_{\text{observed}}, \tag{18}\]  
where \((\cdot)_{\text{observed}}\) is the performance measure when considering the observed signal instead of the enhanced signal. Based on this definition, larger values indicate better dereverberation performance across all measures. Values smaller than 1 indicate a degradation in performance. The performance gain was computed individually for each of the 10 speakers. The scores depict the mean improvement across these 10 speakers and the corresponding standard deviation.
# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 2 Materials and Methods
### Convolutional Neural Networks and Transfer Learning  
The most popular feedforward DL model for two-dimensional input data, like an image, is the CNN. We focused on drought stress prediction in sunflower plants using five pre-trained neural networks: VGG16, VGG19, InceptionV3, DenseNet, and MobileNet. All models were pre-trained on ImageNet, which provided robust frameworks due to their deep and complex architectures tailored for image recognition tasks [31].  
These CNN models were chosen due to several factors. Initially, we considered the design tenets and features of each architecture. Next, we selected architectures that have demonstrated strong performance on the ImageNet dataset. Computational efficiency was also factored into our selection, ensuring the model's amenability to fine tuning within our resource constraints. Additionally, our choice was informed by prior successful applications of these CNN models in agricultural contexts, suggesting their potential efficacy in our domain-specific task [32; 33]. Finally, all CNN architectures are accessible via TensorFlow Hub [34].  
We compared models' performance and identified the most effective CNN architecture, which was subsequently employed as the foundational model for developing a predictive framework for drought stress detection.
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.5 Analysis of Using a Different Template  
In the proposed method, the gesture \"Five\" serves as the template for recognizing all the ten gestures. To evaluate its effectiveness on the recognition accuracy, we tested the situation of recognizing a person's gestures using the template from another person. In other words, the \(i\)th gesture \"Five\" of the \(j\)th person was used as the template to recognize hand gestures of the other nine subjects, and this process was repeated for all \(i\) = 1-10 and \(j\) = 1-10. The result of the average recognition accuracy of each subject is shown in Table 3 Accuracy B. The accuracy of using a person's own template image is also displayed in Table 3 Accuracy A for comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Person Index** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **Average** \\ \hline Accuracy **A** (\%) & 100 & 98.1 & 100 & 99.9 & 96.8 & 99.5 & 100 & 100 & 100 & 99.5 & 99.38 \\ Accuracy **B** (\%) & 94.5 & 96.5 & 90.9 & 93.8 & 83.1 & 93.0 & 97.6 & 93.3 & 95.7 & 75.5 & 91.39 \\ \hline \end{tabular}
\end{table}
Table 3: Gesture recognition accuracy of each person when (**A**) using a person’s own template image and (**B**) using a different person’s template.  
Figure 9: Parameter sensitivity of (**a**) the contraction distance \(d_{in}\), the expansion distance \(d_{out}\), and (**b**) the exponent \(Exp_{L2}\) of the second term in loss function.  
The average recognition accuracy drops from 99.38% to 91.39% after using a different person's template. This is due to the differences between the template images, specifically the different finger angles and widths caused by different camera positions and hand configurations between subjects. It indicates that the proposed algorithm relies heavily on the right template. Accordingly, after the user wears this device, one template image needs to be taken by the camera. In this way, the template will fit and be applicable to the following recognition. It should be noted that this is a straightforward process, because the user only needs to spread out his/her five fingers to perform a \"Five\" gesture.
# L-Citrulline: A Non-Essential Amino Acid with Important Roles in Human Health
## 5 Ergogenic Effect of L-Citrulline Supplements
### Ergogenic Effect of NO from L-Citrulline Supplements  
Numerous bodily functions such as regulating vasodilatation, blood flow, mitochondrial respiration and platelet function are significantly influenced by NO. In mitochondrial respiration, NO inhibits cytochrome C oxidase, which decreases oxygen consumption and helps in the distribution of oxygen in the skeletal muscles. Hence, increasing the NO available could enhance oxygen and nutrient delivery to active muscles and thus reduce the ATP cost of muscle force production, leading to improved physiological responses relating to endurance performance and recovery [1]. The synthesis of NO by NOS enzyme action can be limited by the availability of substrate Arg and the activity of arginase [29]. NO can be increased through administering oral Arg supplements, albeit with the drawback of intense catabolism of Arg both in the gastrointestinal tract as well as in the liver where arginase catabolizes it and transforms it into ornithine and urea. However, an alternative to that is oral supplementation of CIT, with the advantage that arginase enzymes do not catabolize CIT and, moreover, arginase activity is inhibited [30]. Previous in vitro and in vivo studies have shown CIT to suppress arginase activity, acting as a strong allosteric inhibitor [58]. It has been shown that 2 g/day [59] or 3 g/day [43] of oral CIT supplementation for 7 days increased the plasma Arg concentration and elevated NO-dependent signaling. Studies in prolonged exercise have shown that CIT supplementation may increase plasma urea concentration and NO production [60], while suppressing exercise-induced hyperammone-maria [61]. As a consequence, oxygen and nutrients are better delivered to active muscles by CIT, so physical exercise is better tolerated by increasing NO [1], albeit with a modest effect in submaximal exercises in older men [62].
# Physical and Mechanical Properties of Reclaimed Asphalt
## 2 Review of Related Studies
Instead, the increase in residual cumulative strains during resilient modulus test was significant at RAP contents of 75% compared to the corresponding values for the fresh granular samples. In addition, [PERSON] et al. [25] found that the addition of RAP to VA generally increased permanent deformation and demonstrated other factors affecting the performance of the material. In this case, even with a constant proportion of RAP in the VA mix, the binder content of RAP showed that some of the mixes performed the same or better than VA alone. In another research, [PERSON] and [PERSON] [29] studied the behavior of RAP-VA mixtures when the moisture content was increased from the OMC to OMC + 2% and OMC + 4%, respectively. The results showed that the mixtures with RAP-VA performed better than VA due to their higher potential to excrete excess moisture. Regarding the gradation, [PERSON] et al. [25] studied the effects of different particle size distributions on the permanent deformations of RAP-VA blends, trying to establish suitable limits for these blends. They found that the addition of RAP to VA reduced the permanent strains to a level comparable to that of 100% VA samples. [PERSON] and [PERSON] [22] also mixed RAP and VA in two different gradations and evaluated their performance based on the CBR value at optimum moisture. Their results showed that the CBR value increased from 32 to 100% when RAP is mixed with VA at a ratio of 45-55%, and they concluded that these RAP-VA blends are suitable for subbase layer. In the same context, it was recently reported that the use of RAP in road construction varies from 10% to 50% for base and subbase layers [42]. Although the incorporation of RAP into the unbound pavement layers has been extensively researched, the results seem to be controversial in the worldwide literature. In particular, regarding the permanent deformations, there are researchers who claim that the developed permanent strains of RAP-VA blends are significant compared to those of pure VA [23; 24; 25], while there are studies that contradict the previous results [26; 27; 28]. This controversy leads to the fact that there is still room for further research. Therefore, the present study investigates RAP as an unbound material, using a laboratory procedure to determine the physical and mechanical properties of mixtures of RAP and VA, with the aim of determining the optimum proportion of the materials in the blend. The tests used are consistent with standard test procedures used for VA materials.
# Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods
## 2 Methods of MCDM  
### MARCOS Method  
Multi-criteria decision making using the MARCOS method is carried out according to the following steps [19]:  
Step 1: Forming an initial decision-making matrix:  
\[X=\left[\begin{array}{ccc}x_{11}&\cdots&x_{1n}\\ x_{21}&\cdots&x_{2n}\\ \vdots&\ldots&\vdots\\ x_{mn}&\cdots&x_{mn}\end{array}\right] \tag{1}\]In which \(m\) is the alternative number, \(n\) is the criteria number, and \(x_{mn}\) is the value of the criterion \(n\) in the alternative \(m\).  
Step 2: Making an extended initial matrix by adding an ideal (\(AI\)) and anti-ideal solution (\(AAI\)) into the initial decision-making matrix.  
\[X=\begin{array}{c}AAI\\ A_{1}\\ A_{2}\\ \vdots\\ A_{m}\\ AI\end{array}\left[\begin{array}{cccc}x_{aa1}&\cdots&x_{aan}\\ x_{11}&\cdots&x_{1n}\\ x_{21}&\cdots&x_{2n}\\ \vdots&\vdots&\vdots\\ x_{m1}&\cdots&x_{mn}\\ x_{ai1}&\cdots&x_{ain}\end{array}\right] \tag{2}\]  
In the above equation, \(AAI=min\big{(}x_{ij}\big{)}\) and \(AI=max\big{(}x_{ij}\big{)}\) if the criterion \(j\) is bigger is better; \(AAI=max\big{(}x_{ij}\big{)}\) and \(AI=min\big{(}x_{ij}\big{)}\) if the criterion \(j\) is smaller is better; \(i=1,2,\)\(\ldots,\)\(m;j=1,2,\ldots,\)\(n\).  
Step 3: Normalizing the extended initial matrix (\(X\)). The normalized matrix \(N=\big{[}u_{ij}\big{]}_{m\times n}\) can be determined by:  
\[u_{ij}=x_{AI}/\ x_{ij}\text{ if the criterion }j\text{ is smaller is better.} \tag{3}\]  
\[u_{ij}=x_{ij}/\ x_{AI}\text{ if criterion }j\text{ is bigger is better.} \tag{4}\]  
Step 4: Determining the weighted normalized matrix \(C=\big{[}c_{ij}\big{]}_{m\times n}\) by using the following equation:  
\[c_{ij}=u_{ij}\cdot w_{j} \tag{5}\]  
where \(w_{j}\) is the weight coefficient of criterion \(j\).  
Step 5: Determining the utility degree of alternatives \(K_{i}^{-}\) and \(K_{i}^{+}\) by:  
\[K_{i}^{-}=S_{i}/S_{AAI} \tag{6}\]  
\[K_{i}^{+}=S_{i}/S_{AI} \tag{7}\]  
In (6) and (7), \(S_{i}\) is determined by:  
\[S_{i}=\sum_{i=1}^{m}c_{ij} \tag{8}\]  
Step 6: Calculating the utility function of alternatives _f(Ki)_ by:  
\[f(K_{i})=\frac{K_{i}^{+}+K_{i}^{-}}{1+\frac{1-f\big{(}K_{i}^{+}\big{)}}{f(K_{ i}^{+})}+\frac{1-f\big{(}K_{i}^{-}\big{)}}{f(K_{i}^{-})}} \tag{9}\]  
where _f_(\(K_{i}^{-}\)) is the utility function related to the anti-ideal solution; _f_(\(K_{i}^{+}\)) is the utility function related to the ideal solution. These functions can be found by:  
\[f\big{(}K_{i}^{-}\big{)}=K_{i}^{+}/\left(K_{i}^{+}+K_{i}^{i}\right) \tag{10}\]  
\[\left(K_{i}^{+}\right)=K_{i}^{-}/\left(K_{i}^{+}+K_{i}^{i}\right) \tag{11}\]  
Step 7: Ranking the alternatives based on the final values of the utility functions to find an alternative with the highest possible value of the utility function.
# Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
## 4 Numerical Results
### First Experiment: Efficiency Analysis of CMT (\(\alpha\)) Family
With this, the theoretical results obtained in previous sections about the dynamical behavior of CMT(\(\alpha\)) family are verified. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Function** & **Method** & \(|\mathbf{x_{k+1}-x_{k}}|\) & \(|\mathbf{f(x_{k+1})}|\) & **iter** & **ACOC** & **tcpu** \\ \hline \(f_{1}\) & CMT(1) & \(6.8586\times 10^{-80}\) & 0 & 6 & 5.9981 & 0.2413 \\ \(x_{0}=-60\) & Newton & \(3.1826\times 10^{-73}\) & \(7.1179\times 10^{-146}\) & 13 & 2 & 0.2003 \\  & Ostrowski & \(1.2267\times 10^{-100}\) & 0 & 7 & 4 & 0.1793 \\  & ABA & \(1.3417\times 10^{-77}\) & 0 & 6 & 5.9978 & 0.273 \\  & CH & \(1.4971\times 10^{-82}\) & 0 & 6 & 5.9984 & 0.2776 \\  & AHR & \(8.7686\times 10^{-61}\) & \(3.8934\times 10^{-208}\) & 7 & 5.992 & 0.2246 \\ \hline \(f_{2}\) & CMT(1) & \(5.9893\times 10^{-27}\) & \(5.2167\times 10^{-158}\) & 6 & 6.0379 & 0.3503 \\ \(x_{0}=-60\) & Newton & \(1.6537\times 10^{-59}\) & \(5.201\times 10^{-118}\) & 15 & 2 & 0.3125 \\  & Ostrowski & \(8.0088\times 10^{-72}\) & 0 & 8 & 4 & 0.2956 \\  & ABA & \(2.9305\times 10^{-56}\) & 0 & 10 & 6.0024 & 0.5679 \\  & CH & \(8.4413\times 10^{-48}\) & 0 & 7 & 5.994 & 0.399 \\  & AHR & \(6.4484\times 10^{-60}\) & 0 & 7 & 5.9974 & 0.318 \\ \hline \(f_{3}\) & CMT(1) & \(3.7145\times 10^{-76}\) & 0 & 13 & 5.9983 & 0.6398 \\ \(x_{0}=-140\) & Newton & nc & nc & nc & nc & nc \\  & Ostrowski & \(6.9267\times 10^{-37}\) & \(3.3507\times 10^{-145}\) & 49 & 3.999 & 1.216 \\  & ABA & \(7.5885\times 10^{-54}\) & 0 & 11 & 5.9907 & 0.4246 \\  & CH & \(4.8283\times 10^{-28}\) & \(2.1045\times 10^{-164}\) & 21 & 5.8989 & 0.8005 \\  & AHR & \(3.4494\times 10^{-58}\) & \(6.2295\times 10^{-207}\) & 12 & 5.9928 & 0.3997 \\ \hline \(f_{4}\) & CMT(1) & \(9.2602\times 10^{-68}\) & \(2.3361\times 10^{-207}\) & 6 & 5.9954 & 1.0547 \\ \(x_{0}=-230\) & Newton & \(8.9492\times 10^{-96}\) & \(1.1348\times 10^{-190}\) & 14 & 2 & 0.8454 \\  & Ostrowski & \(7.8874\times 10^{-37}\) & \(5.0705\times 10^{-146}\) & 7 & 3.9985 & 0.8196 \\  & ABA & \(2.5587\times 10^{-21}\) & \(9.9754\times 10^{-126}\) & 6 & 6.2382 & 1.0537 \\  & CH & \(2.2055\times 10^{-60}\) & \(2.3361\times 10^{-207}\) & 6 & 6.0079 & 1.0555 \\  & AHR & nc & nc & nc & nc & nc \\ \hline \(f_{5}\) & CMT(1) & \(2.8545\times 10^{-38}\) & \(1.0707\times 10^{-207}\) & 6 & 6.2665 & 1.0249 \\ \(x_{0}=-90\) & Newton & \(9.6307\times 10^{-58}\) & \(6.4804\times 10^{-114}\) & 12 & 2 & 0.7181 \\  & Ostrowski & \(6.1241\times 10^{-52}\) & \(6.9183\times 10^{-202}\) & 8 & 3.9999 & 0.9291 \\  & ABA & \(1.2306\times 10^{-20}\) & \(2.1729\times 10^{-114}\) & 6 & 6.8491 & 1.0378 \\  & CH & \(3.4946\times 10^{-26}\) & \(1.6995\times 10^{-147}\) & 6 & 5.7567 & 1.0301 \\  & AHR & \(8.5778\times 10^{-51}\) & \(1.0901\times 10^{-182}\) & 25 & 5.902 & 3.0345 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical performance of iterative methods in nonlinear equations for \(x_{0}\)**very far from**\(\zeta\). Figure 6: Numerical results of the first experiment.