esa-sceva/satcom-chunk-collection huggingface dataset:
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 4 Results and Discussion
### Fabrication of Fiber Bragg Gratings  
Using the optimal parameters (\(f\) = 19 mm, BE = \(\times\)2.25, \(z\) = 1000 \(\upmu\)m), we fabricated 15-mm-long FBGs inside the optical fiber core by means of a femtosecond laser beam with a laser power of 400 mW and an exposure time of 20 s. An image of the FBGs fabricated inside  
Figure 4: Formation of Bragg gratings inside fused silica glass for optimization of irradiation conditions of the femtosecond laser beam. (**a–d**) Using a cylindrical lens with \(f\) = 12.7 mm: (**a**) P: 817 mW, \(z\): 0 \(\upmu\)m, BE: \(\times\)1.88; (**b**) P: 817 mW, \(z\): 1000 \(\upmu\)m, BE: \(\times\)1.88; (**c**) P: 610 mW, \(z\): 500 \(\upmu\)m, BE: \(\times\)2.25; (**d**) P: 610 mW, \(z\): 1500 \(\upmu\)m, BE: \(\times\)2.25; (**d**) P: 610 mW, \(z\): 1500 \(\upmu\)m, BE: \(\times\)2.25; (**e–h**) using a cylindrical lens with \(f\) = 15 mm: (**e**) P: 787 mW, \(z\): 500 \(\upmu\)m, BE: \(\times\)1.88; (**f**) P: 787 mW, \(z\): 1500 \(\upmu\)m, BE: \(\times\)1.88; (**g**) P: 805 mW, \(z\): 0 \(\upmu\)m, BE: \(\times\)2.25; (**h**) P: 805 mW, \(z\): 1000 \(\upmu\)m, BE: \(\times\)2.25; (**i–l**) using a cylindrical lens with \(f\) = 19 mm: (**i**) P: 787 mW, \(z\): 1500 \(\upmu\)m, BE: \(\times\)1.88; (**j**) P: 787 mW, \(z\): 1000 \(\upmu\)m, BE: \(\times\)1.88; (**k**) P: 600 mW, \(z\): 1500 \(\upmu\)m, BE: \(\times\)2.25; (**l**) P: 600 mW, \(z\): 1000 \(\upmu\)m, BE: \(\times\)2.25.  
the fiber core is shown at the top of Figure 5a. A magnified image of the FBGs is illustrated at the bottom of Figure 5a. From the interrogator we found that the Bragg wavelength of the fabricated FBGs was 1542 nm. Hence, we obtained a large peak of the reflected signal (15 dB) at the 1542 nm wavelength, as depicted in Figure 5b. We also investigated the transmission spectrum of the incident light that passed through the FBG-engraved optical fiber using an OSA, as represented in Figure 5c. The transmission spectrum also confirmed the absence of the optical signal at the 1542 nm wavelength.
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 3 Data Analysis and Results
### Acoustic Parameters Analysis  
The graphs below show the variations of the room acoustic parameters at different frequencies. The graphs report the reverberation time (T\({}_{30}\)), early decay time (EDT), strength (G), definition (D\({}_{50}\)), clarity (C\({}_{50}\) and C\({}_{80}\)), lateral fraction (LF), lateral efficiency (LE), and interaural cross correlation coefficient (IACC).  
The statistical findings of the data may be divided into four different curve types according to the significant auditory characteristics reported:  
* The frequency bands show similar parametric trends, although with more or less distinctly different amplitudes (EDT and G, as shown in Figure 6a,b);
* The various frequency bands show different and distinct trends, sometimes inverted. However, the overall trend is still clearly discernible similar to the temperature and humidity parameter curves due to changes in air velocity (C\({}_{50}\), C\({}_{80}\), and D\({}_{50}\), the overall variation is still discernible despite the insignificant change in the lower frequency range, as shown in Figure 7a-c);  
Figure 5: Temperature (**a**), relative humidity (**b**), and air velocity values (**c**).  
Figure 8: Interaural cross correlation coefficient (IACC) trends, not sensitive to variations in temperature and humidity.  
Figure 10: Reverberation time (T\({}_{30}\)) trends, indistinguishable effects.  
Figure 9: Lateral fraction (LF) trends (a), lateral efficiency (LE) trends (b), high frequencies are more sensitive.
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
## 3 Results and Discussion
### Synthesis of SiC  
In a preliminary test, the Si sludge and C were mixed in such a ratio that, after the synthesis, no excess silicon would remain in the product, as Si removal is a complicated process. The heat treatment was also performed at various temperatures to get a rough idea on the formation of SiC from Si sludge and C. Table 1 shows the preliminary experimental molar ratios and temperatures. The XRD patterns of the synthesized products with different molar ratios at different temperatures are shown in Figure 5. All the mixtures were suitable to form SiC with an excess amount of C at the provided temperatures. However, it was clearly seen that a higher amount of SiC was formed at 1550 \({}^{\circ}\)C from a mixture with a Si sludge-to-C molar ratio of 1:1.4, than from mixtures with 1:1.2 and 1:4 molar ratios at 1400 \({}^{\circ}\)C and 1600 \({}^{\circ}\)C, respectively.  
On the basis of this preliminary experimental result, the mixing ratio was fixed at 1:1.4, and then the temperature of the heat treatment was changed to find the optimum temperature for SiC formation. The comparison among the XRD patterns of SiC synthesized with a Si sludge-to-C molar ratio of 1:1.4 at different temperatures and with the raw mixture is shown in Figure 6. At 1450 \({}^{\circ}\)C, the reaction between the Si sludge and C was not complete, a significant amount of both Si and C remain unreacted, and, as a result, very little SiC formed. A small increase in temperature, i.e., to 1500 \({}^{\circ}\)C, showed that SiC formation was possible, and the amount of SiC formed at 1550 \({}^{\circ}\)C was even higher than that at 1500 \({}^{\circ}\)C.  
Figure 4: Particle analysis of the Si sludge used in the present study: (**a**) number of particles, (**b**) volume of particles.  
Since the temperature of 1550 \({}^{\circ}\)C was suitable for the formation of SiC, in order to find the optimum mixing ratio, different ratios of Si sludge to C were tested at 1550 \({}^{\circ}\)C. Figure 7 shows their XRD patterns and reveals that a Si sludge-to-C molar ratio of 1:1.4 was the best, as it contained a large amount of SiC.  
Three types of experiment were performed to find the optimum conditions to synthesize SiC from Si sludge in the presence of C. From these experimental results, i.e., on the basis of the formation of more SiC with minimum impurity, the 1:1.4 molar ratio of Si sludge to C led to SiC formation at 1550 \({}^{\circ}\)C in an Ar atmosphere. However, as some C remained in the synthesized SiC as an impurity, the removal of C is necessary to obtain pure SiC.  
Figure 5: XRD patterns of the products after SiC synthesis treatments.  
Figure 6: XRD patterns of the products after SiC synthesis at different temperatures (Si sludge-to-C molar ratio = 1:1.4).
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion
### About Our Model and Future Improvement  
Judging from the experimental results, our model undoubtedly has high detection accuracy, and with the use of the SAHI framework, our model can directly detect large -sized remote sensing images. However, compared to the original model, our improved YOLOX model has more parameters and calculations, so the detection speed has decreased. The results of the ablation experiments in Table 5 show that the increased parameters and calculations mainly come from the ASFF structure we used.  
For the problem of detection speed drop, our future research is mainly aimed at improving the detection speed, and we want to improve our detection speed while maintaining the high accuracy of the model. We are considering redesigning the structure of ASFF, reducing its number of parameters, or trying other variants of the FPN structure. We will also consider deploying our model using TensorRT to reduce the inference time of the model.
## 3 Experimental Results
### Visual Distinctiveness  
The example in Figure 5 presents the results of testing the 'Pepper' image, observing the changes in the image after different stages of processing. Through visual observation, it can be noted that there is no visual similarity between the original image (Figure 5a), the encrypted image (Figure 5b), and the stego image (Figure 5c). It means that from a visual quality perspective, our proposed scheme is secure and will not attack malicious attention and no information related to the original image or hidden secret data will be leaked.  
Figure 4: Test images: (**a**) peppers; (**b**) deer; (**c**) dance; (**d**) city.  
Figure 5: Test results: (**a**) original peppers image; (**b**) encrypted image; (**c**) stego image; (**d**) restored image.
# Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control
## 5 Conclusions  
This study presented the design of an adaptive control scheme based on synergetic theory to reduce the effect of a building structure's vibration due to earthquake. A Lyapunov-based stability analysis was conducted to develop the control algorithm so as to guarantee the stability-controlled building system. In addition, a comparison in performance was made between ASC and SC. The numerical simulations showed that better vibration suppression was obtained using ASC as compared to its counterpart. Moreover, the design of the adaptive synergetic controller resulted in a control law and adaptive laws that led to bounded estimates of the building viscosity damping and stiffness coefficients.  
This study can be extended to apply the proposed controller to buildings of multiple stories, instead of one-story buildings, or reconsidered for RC heritage buildings. In addition, techniques based on frequency analysis can be conducted either for identification  
Figure 10: Behavior of estimates of the building viscosity coefficient.  
Figure 9: Estimated value of stiffness coefficient by adaptive law.  
or for tuning purposes [41; 42; 43]. Moreover, the proposed controller can be implemented in a real environment in an actual building or a prototype one. Further, filtered-based synergetic control design of a building system could be conducted to improve the noise rejection capability of the proposed controller [44; 45; 46].  
Methodology, A.Q.A.-D.; software, A.J.H.; validation, M.E.S.; formal analysis, A.Q.A.-D.; investigation, A.J.H.; resources, Z.T.A.; writing--original draft preparation, M.E.S.; writing--review and editing, Z.T.A.; visualization, A.J.H.; supervision, Z.T.A.; funding acquisition, A.Q.A.-D.; data curation, M.E.S.; conceptualization, A.J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Solution of the Problem
### Nusselt Number and Skin Friction  
In order to determine the two entities of physical interest, namely the Nusselt number \(Nu\) and the skin friction coefficient \(C_{f}\), we use the relations:  
\[\begin{array}{c}Nu=\frac{L_{qr}}{k_{f}(T_{w}-T_{\infty})}=-\frac{Lk_{uf}}{k_ {f}(T_{w}-T_{\infty})}\frac{\partial T(y_{1},t_{1})}{\partial y_{1}}\Big{|}_{ y_{1}=0}=\\ -\frac{k_{uf}}{k_{f}}\frac{\partial\theta(y,t)}{\partial y}\Big{|}_{y=0}=- \frac{k_{uf}}{k_{f}}\lim\limits_{y\to 0^{+}}L^{-1}\Big{\{}\frac{\partial \theta(y,q)}{\partial y}\Big{\}}.\end{array} \tag{35}\]  
\[\begin{array}{c}C_{f}=\frac{\tau_{w}}{\rho_{f}\big{(}\frac{\
u_{f}}{L}\big{)} ^{2}}=\frac{\mu_{uf}}{\rho_{f}\big{(}\frac{\
u_{f}}{L}\big{)}^{2}}\frac{\partial u _{1}(y_{1},t_{1})}{\partial y_{1}}\Big{|}_{y_{1}=0}=\\ \frac{\mu_{uf}}{\mu_{f}}\frac{\partial u(y,t)}{\partial y}\bigg{|}_{y=0}=\frac{ 1}{(1-\varphi)^{25}}\lim\limits_{y\to 0^{+}}L^{-1}\Big{\{}\frac{ \partial\pi(y,q)}{\partial y}\Big{\}},\end{array} \tag{36}\]  
where \(q_{w}\) is the constant heat flux from the surface of the plate and \(\tau_{w}\) is the skin friction or shear stress on the boundary.  
Introducing Equations (24) and (32) into (35) and (36), respectively, we find that:  
\[Nu=\frac{k_{nf}}{k_{f}}\sqrt{a_{3}}\frac{t^{-\frac{\alpha}{2}}}{\Gamma\big{(} 1-\frac{\alpha}{2}\big{)}},\ \ \ C_{f}=\frac{a_{1}a_{2}}{\sqrt{a_{1}}+\sqrt{a_{2}}}\frac{t^{\frac{\alpha}{2}}}{ \Gamma\big{(}1+\frac{\alpha}{2}\big{)}}\frac{1}{(1-\varphi)^{2.5}}. \tag{37}\]  
Using the identity (A3) from the Appendix A, we also provide equivalent forms for \(Nu\) and \(C_{f}\), namely:  
\[Nu=2\frac{k_{nf}}{k_{f}}\sqrt{\frac{a_{3}}{\pi}}cD_{t}^{\frac{1+\alpha}{2}} \left(t^{1/2}\right),\ \ C_{f}=\frac{1}{\left(1-\varphi\right)^{2.5}}\frac{a_{1}a_{2}}{\sqrt{a_{1}}+ \sqrt{a_{2}}}cD_{t}^{1-\frac{\alpha}{2}}(t), \tag{38}\]  
in terms of the Caputo derivatives of \(t^{1/2}\) and \(t\).
## 4 Stability Analysis
 \tag{53}\]  
where \(\lambda_{f_{i}}\), \(\lambda_{f_{2}}\), and \(\lambda_{G_{1}}\) are the number of nodes in the hidden layers of NNs for approximating \(f_{1}\), \(f_{2}\), and \(G_{1}\), respectively; control gains, disturbance observer parameters, and NN parameters are chosen such that \[\lambda+k-\frac{1}{\kappa_{1}}>0;\eta_{2}-\frac{5\lambda}{2}-\frac{5k}{2} +\frac{5}{2\kappa_{1}}-4>0 \tag{54}\] \[\frac{\Gamma_{f_{1}}}{2\gamma_{f_{1}}}-\frac{2\lambda_{f_{1}}}{ \kappa_{1}}>0;\frac{\Gamma_{f_{2}}}{2\gamma_{f_{2}}}-\frac{2\lambda_{f_{2}}}{ \kappa_{2}}>0;\frac{\Gamma_{G_{1}}}{2\gamma_{G_{1}}}-\frac{2\lambda_{G_{1}}}{ \kappa_{2}}>0\] \[\frac{7}{8\kappa_{1}}-\frac{\lambda}{8}-\frac{k}{8}-\frac{1}{2}>0; \frac{1}{2\kappa_{2}}-\frac{5}{8}>0\]  
Equation (53) can be rewritten in a simple form as  
\[\dot{V}\leq-\Pi V+\psi \tag{55}\]  
where  
\[\Pi=\min\left\{ \begin{aligned} & 2\eta_{2}-5\lambda-5k+\frac{5}{ \kappa_{1}}-8;\frac{\Gamma_{f_{1}}}{\gamma_{f_{1}}}-\frac{4\lambda_{f_{1}}}{ \kappa_{1}};\frac{\Gamma_{df_{1}}}{\gamma_{df_{1}}},\frac{\Gamma_{f_{2}}}{ \gamma_{f_{2}}}-\frac{4\lambda_{f_{2}}}{\kappa_{2}};\frac{\Gamma_{g2}}{ \gamma_{g2}};\\ &\frac{\Gamma_{G_{1}}}{\gamma_{G_{1}}}-\frac{4\lambda_{G_{1}}}{ \kappa_{2}};\frac{\Gamma_{G_{2}}}{\gamma_{G_{2}}};\frac{7}{4\kappa_{1}}-\frac {\lambda}{4}-\frac{k}{4}-1;\frac{1}{\kappa_{2}}-\frac{5}{4}\end{aligned} \right\} \tag{56}\]  
and  
\[\psi =\frac{\Gamma_{f_{1}}}{2\gamma_{f_{1}}}W_{f_{1}M}^{2}+\frac{ \Gamma_{df_{1}}}{2\gamma_{df_{1}}}W_{df_{1}M}^{2}+\frac{\Gamma_{f_{2}}}{2 \gamma_{f_{2}}}W_{f_{2}M}^{2} \tag{57}\] \[+\frac{\Gamma_{g2}}{2\gamma_{g2}}W_{g_{2}M}^{2}+\frac{\Gamma_{G_{1 }}}{2\gamma_{G_{1}}}W_{G_{1}M}^{2}+\frac{\Gamma_{G_{2}}}{2\gamma_{G_{2}}}W_{G_ {2}M}^{2}\] \[+\left(\frac{\lambda}{2}+\frac{k}{2}+\frac{7}{2\kappa_{1}}\right) \varepsilon_{f_{1}M}^{2}+\frac{\varepsilon_{df_{1}M}^{2}}{2}+\left(\frac{1}{ 2}+\frac{2}{\kappa_{2}}\right)\varepsilon_{f_{2}M}^{2}\] \[+\left(\frac{1}{4}+\frac{2}{\kappa_{2}}\right)\varepsilon_{G_{1}M }^{2}+\frac{1}{4}\varepsilon_{G_{2}M}^{2}+\delta_{1}^{2}+\frac{\delta_{2}^{2} }{2}\]  
From (55), one obtains  
\[V\leq V(0)e^{-\Pi t}+\frac{\psi}{\Pi}\Big{(}1-e^{-\Pi t}\Big{)} \tag{58}\]  
Based on this, one can conclude that \(\sigma\) is bounded, which implies that the tracking errors of the system are also bounded. Therefore, the ultimately uniformly bounded tracking performance of the system is guaranteed by using the proposed control strategy. Hence, Theorem 2 is completely proven. **Remark 3**. : _Firstly, the parameters \(\kappa_{1}\) and \(\kappa_{2}\) of disturbance observers (37) are chosen to be as small as possible based on the sampling time to achieve high estimation accuracy; however, the selection of the excessively small disturbance observer gains may make the disturbance observers unstable. Consequently, the remaining parameters, including controller gains in (14) and (15), parameters of adaptation mechanisms (36), (40) and (47) of RBF-NNs, are designed based on the closed-loop system stability condition (54)._
## 7 Results and Analysis
### Summary of Key Results  
Table 21 provides an overview of key performance metrics achieved by the proposed framework, including energy savings, latency, security, and privacy-preserving capabilities.  
Figure 21: Integrated DP + HE privacy workflow combining DP’s statistical protection with HE’s computational security. The workflow applies DP noise injection first to avoid encryption-amplified privacy loss, followed by homomorphic encryption to protect the already DP-noised gradients before secure aggregation.  
Figure 22: Privacy–energy tradeoffs of different approaches.  
Novel Contributions  
Our work introduces three key innovations that collectively address the _Energy\(/\)Security\(/\) Privacy_ triad in O-RAN environments:  
* _Predictive LSTM-based DU Sleep Scheduling_: First application of workload-aware LSTM control in O-RAN, yielding substantial energy and latency improvements.
* _Unified Zero-Trust Fabric_: Continuous behavioral authentication, micro-segmentation, and anomaly detection integrated into the RAN Intelligent Controller to drastically reduce attack surfaces.
* _Adaptive Differential Privacy_: Dynamic, layer-wise noise scaling guided by a Renyi accountant, achieving superior utility and attack resistance under stringent O-RAN timing constraints.  
These advances position our framework as a practical, scalable, and regulation-compliant solution for next-generation federated edge intelligence, bridging the gap between theoretical constructs and real-world O-RAN deployments.
## 2 Experimental Tests
### Material Properties  
The laboratory test was carried out in the Xi'an Key Laboratory of Geotechnical and Underground Engineering with a geotechnical model test box. The bottom surface of the model box is hard soil, simulating the bearing stratum of the pile. The test prototype selected in this study is the CFG piles. The elastic modulus of the CFG pile is small and can be simulated by using beech wood for the model test [33; 34]. In this study, the loess from the Xi'an section of the Xikang high-speed railroad project is fine-grained loess after screening. The test was carried out in accordance with the Highway Geotechnical Test Procedure (JTG 3430-2020) [35], and the results of the performance tests, such as the quadruple straight shear test, are shown in Table 2. When the pile diameter (\(B\)) to the grain size of soil (\(d50\)) is greater than 30 (\(B/d50\geq 30\)) [36], the effect of errors due to non-reduced soil particles can be neglected. The pile diameter B = 40 mm was selected for this modeling test, which means that the grain size of the soil used is less than 1.33 mm to meet the design requirements, and the grain size of loess usually does not exceed 1 mm. Therefore, the effect of the grain size of the soil can be neglected in this model test. The soil sample was saturated and consolidated for a 1-week period before conducting the tests. The gravel cushion and geogrid were made of the same material as in the actual project. The embankment was prepared by mixing gravel and sand with a particle size of 10-15 mm (mass ratio 4:1) and compacting in layers to achieve the required compaction coefficient for the test. Information on material parameters is provided in Table 2. The gravel cushion was 40 mm thick, with a bi-directional geogrid layer built into the middle of the cushion, and the specific parameters of the bi-directional tensile plastic geogrid are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Unit Weight** & **Internal Friction** & **Cohesive** & **Moisture** & **Poisson** & **Elasticity** \\  & **(kN/m\({}^{3}\))** & **Angle (\({}^{\circ}\))** & **Forces (kPa)** & **Content (\%)** & **Ratio** & **Modulus (MPa)** \\ \hline Loess & 19.4 & 28.24 & 37.8 & 17.6 & 0.32 & 7.99 \\ Cushion & 25.0 & 40 & 38.5 & 16.3 & 0.14 & 65 \\ Embankment & 22.0 & 40 & 36.2 & 15.5 & 0.17 & 38 \\ Pile & 22 & - & - & - & 0.33 & \(1\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on soil properties.  
Figure 2: Model test setup.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 3 Proposed Method
### Pixel Expansion Elimination
This method ensures both security and efficiency in image sharing. ```
Input: Secret image \(I\) with the size \(W\times H\), Weighting factors \(w_{t}\), \(w_{g}\) Output: Encrypted image \(I_{e}\)
1 FunctionPreprocess(\(I\), \(w_{t}\), \(w_{g}\)): whilenot convergeddo
2 Apply error diffusion to generate halftone image \(I_{h}\);
3 Calculate \(G(I,I_{h})\) and MSSIM(\(I,I_{h}\));
4 Evaluate Objective(\(I,I_{h}\)) using Equation (4);
5 Update \(I_{h}\) to minimize Objective(\(I,I_{h}\));
6
7 end while
8 returnHalftone image \(I_{h}\)
9 return
10\(I_{s}\leftarrow\) Apply bilinear interpolation in Equation (9) to downsample \(I\) to size \(W/2\times H/2\)
11\(I_{h}\leftarrow\)Preprocess(\(I_{s}\),\(w_{t}\), \(w_{g}\))
12C\({}_{0,1}\) Generate encryption matrices for white and black pixels by Equations (1)-(3)
13\(I_{e}\leftarrow\) Encrypt the secret image \(I_{h}\) with encryption matrices \(C_{0,1}\) pixel-by-pixel
```  
**Algorithm 1**The integrated visual cryptography for efficient and secure image sharing.
# Local Convergence Analysis of an Eighth Order Scheme Using Hypothesis Only on the First Derivative
## 3 Numerical Example and Applications
### Results and Discussion  
[PERSON] and [PERSON]'s study was only valid for simple roots of the scalar equation. The order of convergence was shown using Taylor series expansions, and hypotheses up to the fourth order derivative (or even higher) of the function involved, which restricts the applicability of the proposed scheme. However, only the first order derivatives appear in the proposed scheme. In order to overcome this problem, we propose the hypotheses only up to the first order derivative. the applicability of the proposed scheme can be seen in the Examples 1, 3 and motivational example, where earlier studies were not successful. We also provide the radius of convergence for the considered test problem which gives the guarantee for the convergence.  
In addition, we have also calculated residual error in the each corresponding test function and the difference between the exact zero and approximated zero. It is straightforward to say from the Tables 5-8 that the mentioned methods have smaller residual error in each corresponding test function and a smaller difference error between the exact and approximated zero. So, we can say that these methods converge faster towards the exact root. Moreover, the mentioned methods also have simple asymptotic error constant in most of the test functions which can be seen in the Tables 5-8. The dynamic study of iterative methods via basins of attraction also confirm the faster convergence. However, one can find different behaviour of our methods when he/she considers some different nonlinear equations. The behaviour of the iterative methods mainly depends on the body structure of the iterative method, considered test function, initial guess and programming software, etc.