esa-sceva/satcom-chunk-collection huggingface dataset:
# An Anchor-Free Location Algorithm Based on Transition Coordinates
## 1 Introduction
The anchor-free location protocol designed in [17] adopts the management mechanism of network clustering and combines each local coordinate map to form a set of overall coordinate system of the network. Although the protocol has good scalability, it is computationally heavy when combining each cluster plot. The above-mentioned methods of solving position coordinates by constructing the minimum cost function or matrix inversion are very complex formulas. Therefore, when applying these algorithms for positioning, a large number of calculations need to be performed each time, and there is an error between the calculated node coordinates and the real coordinates, resulting in unstable graphics, large jumping ratios, and significant errors compared to the real position coordinates. In military exercises, this affects, for example, the positioning problem of the movement trajectory of combat personnel, frequent changes in the position coordinates of multiple personnel, and other scenarios. However, in reality, the current position of a node at any given moment should have a high correlation with its previous position, meaning that the new position should have an offset based on the original position. Contributions to this article include the following. Firstly, an anchor-free location algorithm \(\mathbf{p}_{i}^{k+1}=f(d_{ij}^{k+1})\) based on transition coordinates is proposed. The position coordinates \(\mathbf{p}_{i}^{k+1}\) at time \(k+1\) are related to both \(\mathbf{p}_{i}^{k}\) and \(d_{ij}^{k+1}\). In the positioning process, the transition coordinates of each node are correlated with the previous transition coordinates, which can dynamically reflect the relative motion of the transition coordinates of each node. Therefore, the algorithm proposed in this paper is very suitable for the occasions where the node coordinates change frequently or the indoor GPS signal is weak. Secondly, the proposed algorithm requires less computation in the positioning process, the positioning error can reach less than 1% and there is no error accumulation problem. In addition, the algorithm can also be extended to the field of moving coordinates or three-dimensional spatial positioning, which has broad application prospects. The rest of this article includes the following content. In Section 2, we described the system model of the proposed an anchor-free localization algorithm. In Section 3, we described the specific principles and steps of the proposed algorithm. In Section 4, we compared the complexity of the proposed algorithm with existing traditional algorithms. In Section 5, we conducted extensive experiments in MATLAB R2016a to verify the accuracy and convergence of the proposed algorithm. Finally, in Section 6, we summarized the conclusions of this article and discussed future work.
## 5 Discussion
### Economic Cost
Nevertheless, greater OMC costs penalize MNO UANs in the long term. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sensor Nodes**} & \multicolumn{4}{c}{**Backhaul Nodes**} & \multicolumn{4}{c}{**Gateways**} \\ \hline  & **Aeq.** & **Subsc. Fee** & **Battery** & **Num-Ber** & **Aeq.** & **Instal.** & **Num-Ber** & **Aeq.** & **Instal.** & **Internet** & **Elec. Fee** \\  & **Cost** & **Subsc. Fee** & **Aeq. ct.** & **Cost** & **Cost** & & **Cost** & **Cost** & **Cost** & **Fee** & **Elec. Fee** \\ \hline
**LR-WPAN** & 200 & - & 7 & 200 & 200 & 330 & 20 & 1000 & 1130 & 20 & 2.7/month \\
**WLAN** & 200 & - & 14 & (a) & (a) & (a) & - & - & - & - & - \\
**MNO (GPRS)** & 300 & 1/month & 20 & - & - & - & (b) & (b) & (b) & (b) & - \\
**SO** & 200 & 2/year (c) [36] & 14 & - & - & - & - & - & - & - & - \\
**DTN** & 250 & - & 7 & - & - & - & 1 & 800 & 500 & 10/month & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pricing data in Euro used in estimating the economic cost of UANs. A yearly maintenance cost equal to 15% of the acquisition cost, and a battery replacement cost equal to the installation cost are assumed. A sensor node installation cost of 100 Euro is assumed. (a) WLAN backhaul has been deployed a priori; (b) In MNO UANs, the sensor node and the gateway are implemented in the same device; (c) The indicated SO subscription fee includes data web hosting services. Remarkably, the economic cost of storing the data collected by UANs is significantly lower than the UAN economic cost. Cloud storage and computing has been identified as a vital technology for data storage and processing in the Smart City [6, 46, 47]. Cloud servers commonly make the biggest contribution to cloud hosting infrastructure costs. Cloud providers offer cloud server resources such as RAM, storage capacity, CPU power, and the supporting operating system. Current cloud provider pricing schemes depend on the amount of resources to be used by the customer, and may be in the order of 506-1000e per year for 10-200 GB of storage [48]. As a reference, 1000 sensor nodes transmitting individually a 100-byte payload every hour produce a total amount of data (which has to be enriched with metadata for useful information analysis) greater than 1 GB/year. Note that other services such as camera surveillance produce notably greater data volumes. Future Smart City cloud data storage and computing systems may be complemented by the emerging Fog computing paradigm [49].
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
###### Abstract  
Monitoring and forecasting air quality is essential for public health and environmental management. It details the air's cleanliness, pollution levels, and any related health risks that the general public may find concerning. This research investigates how effective machine learning techniques and particle swarm optimization are in predicting air quality. An array of machine learning algorithms, including XGBoost, support vector regression, linear regression, and random forest, was selected to ensure effective modeling outcomes. The models were trained on an open access dataset and performed performance evaluation. The answers from an on-site gas multisensor device in an Italian city were included in the dataset. The findings from the dataset were shown to accurately model and predict environmental factors affecting air quality (e.g., air temperature, humidity, indium oxide, tin oxide, NO\({}_{x}\), NO\({}_{2}\), etc.) using real-world air quality data. The experiments were repeated by optimizing the relevant machine learning methods with PSO. PSO is a metaheuristic optimization method widely used in feature selection and feature extraction processes. The metrics MAE, MSE, RMSE, and R\({}^{2}\), commonly used to evaluate regression algorithms, were utilized to assess the models' performances. Particle swarm optimization-based support vector regression performed best, with MAE, MSE, RMSE, and R\({}^{2}\) values of 0.071, 0.015, 0.122, and 0.999, respectively. In addition, Shapley additive explanations (SHAP) analysis was performed to show which feature of the PSO-based SVR model was practical and to what extent. The results show that the proposed model successfully predicts air quality.  
a 2025152546
## 2 Materials and Methods
### Experimental Design  
The experiment was conducted in the solar greenhouse of the Beijing Academy of Agriculture and Forestry Sciences (39\({}^{\circ}\)94\({}^{\prime}\)35.28\({}^{\prime\prime}\) N; 116\({}^{\circ}\)28\({}^{\prime}\)66.02\({}^{\prime\prime}\) E; altitude 50 m) from June to September 2020. The study included five treatments: fully inorganic nutrient solution irrigation (control) and OIINS irrigation at the flowering-fruit setting, fruit expansion, color turning, and harvest stages of the first truss, denoted as NNNN, ONNN, NONN, NNNO, and NNNO, respectively. The irrigation scheduling and concentrations of the organic and inorganic nutrient solutions were consistent. The irrigation scheduling adopted a cumulative light radiation method [26]. The irrigation concentration was adjusted based on the growth stages of tomato: 1.62 mS/cm at vegetative growth, 2.15 mS/cm at flowering-fruit setting, and 3.58 mS/cm at the fruit expansion to harvest stages. The irrigation concentrations of the organic and inorganic nutrient solutions are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Serial Number** & **Ingredient** & **Mass (kg)** & **Serial Number** & **Ingredient** & **Mass (kg)** \\ \hline
1 & Wheat bran & 1.50 & 6 & Potash mineral powder & 0.45 \\
2 & Rape cake & 1.50 & 7 & Cow dung & 1.00 \\
3 & Corn stalk & 1.50 & 8 & Wood ash & 2.00 \\
4 & Bone meal & 0.55 & 9 & Brown sugar & 1.00 \\
5 & Phosphate rock powder & 0.45 & 10 & Zymogeneous bacteria & 0.02 \\ \hline \end{tabular} Note: The corn stalk was 2–3 cm in size, and the cow dung was fully decomposed.  
\end{table}
Table 1: Organic nutrient solution formula per 100 kg of water.  
Figure 1: Schematic diagram of automatic fertigation applicator and irrigation system for organic and inorganic nutrient solutions. **(I)** The automatic fertigation applicator for organic and inorganic nutrient solutions; **(II)** The irrigation system and the cultivation system. 1. Controller; 2. Water source; 3. Fermentation tank; 4. Inorganic nutrient solution bucket; 5. Organic liquid fertilizer bucket; 6. Clean water pipe; 7. Self-priming pump; 8. Solenoid valve; 9. Filter pump; 10. Conductivity sensor; 11. Organic liquid fertilizer irrigation pipes; 12. Inorganic nutrient solution irrigation pipes; 13. Cultivation tank; 14. Substrate (coconut bran brick).  
Tomatoes with five leaves were planted at a density of 4.76 plants/m\({}^{2}\). Each treatment was repeated thrice, arranged randomly, and irrigated independently. The cultivation management was consistent for all treatments, with three fruit trusses topped and 3-4 fruits left on each truss.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Uncertainty Estimate Method  
The fitting process is only a statistical correlation, not a deterministic functional relationship, and only the regression value \(f\) of the growth curve can be obtained. While the actual value \(y\) will be affected by other random factors and must fluctuate around the regression value, and the uncertainty of such fluctuations can be measured by the residual standard deviation S, the formula was as follows:  
\[S=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(y_{i}-f_{i}\right)^{2}}{n-2}} \tag{5}\]  
where \(f\) was the fitting regression value of the growth curve, and \(y\) was the accumulative coverage area of the actually extracted green tide.  
Usually, the fluctuation of the actual value from the regression value \(f\) follows a normal distribution. So, for a fixed \(x\) value, the \(y\) value is distributed symmetrically around the regression value \(f\). The probability of a \(y\) value occurring is related to the \(S\). The smaller the \(S\), the more centralized the data, the higher the accuracy of the estimate of \(y\). Therefore, in the process of the approximate nonlinear method, the \(S\) was taken as the precision mark of fitting, and two parallel lines are drawn near the regression line \(y^{\prime}=cx+bb\), as shown in Equation (6).  
\[\left\{\begin{array}{l}y^{\prime}_{1}=cx+bb-2s\\ y^{\prime}_{2}=cx+bb+2s\end{array}\right. \tag{6}\]  
Then, the corresponding curve formula can be obtained according to the approximate nonlinear method. In all possible \(y\) values, about 95% of data points will fall in these two curves.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 2 PV Floating Plants
The operating temperature of the module is reduced  
Figure 1: Schematic representation of a typical large-scale FPV system with its key components [21]. due to direct heat transfer into the water below, exhibiting higher yields than standard air-cooled systems. Buoyancy is obtained by an HDPE ring that encloses the membrane. In all described floating solutions, rigid PV modules are used. An alternative approach using flexible thin-film photovoltaic technologies has been demonstrated, employing foams for flotation [26; 27]. The PV modules are positioned so close to the water that waves clean the panels of dust and provide additional cooling. Using foam for flotation significantly reduces the cost per watt of the racking system. The floating structures need to be moored for several reasons [28]:  
1. To keep floating solar arrays within reasonable proximity of a target location (station keeping). 2. To minimize the movement of solar arrays caused by environmental forces such as winds, waves, and currents. 3. To maintain a minimum distance between solar arrays. 4. To cope with varying water levels. 5. Finally, the design of mooring systems must address the following challenges:  
* A large number of mooring lines (especially in the case of pure float designs). * Site constraints. * Varying water levels. * Unequal load distribution. * Array shape and size. Rigid supports in the form of anchorages are provided using plinths at the bottom of the reservoir to manage dead loads and lateral forces [29]. In 2021, DNV recommended practices for designing floating PV mooring systems in freshwater [30]. While these recommendations provide a good technical reference, significantly larger environmental loads in marine environments need to be fully considered [19].
## 6 Fast Terminal Sliding Mode Control with ESO
### Design and Analysis of Fast Terminal Sliding Mode Controller  
The outputs of the tracking differentiator and ESO are input to the controller to replace the corresponding parameters required by the controller design:  
\[\left\{\begin{array}{ll}e=r-y=r-x_{1}=v_{1}-z_{1}\\ \dot{e}=\dot{r}-\ddot{x}_{1}=\dot{r}-x_{2}=v_{2}-z_{2}\\ \ddot{e}=\ddot{r}-\dot{x}_{2}=\ddot{r}-f-bu=v_{3}-z_{3}-bu\end{array}\right. \tag{15}\]  
In order to make the system stable in finite time have rapid convergence, and improve the robustness and stability, a fast terminal sliding surface is constructed by combining the characteristics of the traditional linear sliding mode control, which converges quickly near the equilibrium point, and the terminal sliding mode control, which converges in finite time. The constructed sliding surface is defined as [35]  
\[s=c_{1}\dot{e}+c_{2}e+c_{3}|e|^{\sigma}\text{sgn}(e) \tag{16}\]  
where \(c_{1},c_{2},c_{3}\) are greater than 0, \(0<\sigma=\frac{m}{n}<1\), and \(m,n\) are positive odd integers.  
We differentiate the sliding surface as  
\[\dot{s}=c_{1}\ddot{e}+c_{2}\dot{e}+c_{3}\sigma|e|^{\sigma-1}\dot{e}\text{sgn} (e) \tag{17}\]  
The exponential reaching law is adopted. In order to reduce chattering, a nonlinear function is used to replace the sign function:  
\[\dot{s}=-k_{1}s-k_{2}\frac{s}{|s|+\Delta} \tag{18}\]  
where \(k_{1},k_{2}\) are greater than 0, and \(\Delta\) is a tiny quantity.  
Based on (15) to (18), the control quantity is calculated as  
\[u=\frac{1}{c_{1}b}\bigg{[}c_{2}\dot{e}+c_{3}\sigma|e|^{\sigma-1}\dot{e}\text{ sgn}(e)+c_{1}v_{3}-c_{1}z_{3}+k_{1}s+k_{2}\frac{s}{|s|+\Delta}\bigg{]} \tag{19}\]  
The Lyapunov function is constructed to verify the stability of the system, defined as follows:  
\[V=\frac{1}{2}s^{2} \tag{20}\]Differentiating the Lyapunov function, we obtain  
\[\dot{V}=\dot{s\dot{s}}=s\bigg{(}-k_{1}s-k_{2}\frac{s}{|s|+\Delta}\bigg{)}=-k_{1}s^ {2}-k_{2}\frac{s^{2}}{|s|+\Delta}<0 \tag{21}\]  
As \(\dot{V}<0\), the proposed fast terminal sliding mode controller meets the Lyapunov stability condition, and the system under the action of the controller has asymptotic stability.  
The convergence time of the fast terminal sliding mode control is calculated, where \(s=0\), we have  
\[\frac{de}{dt}=\frac{-c_{2}e-c_{3}|e|^{\sigma}\text{sgn}(e)}{c_{1}} \tag{22}\]  
The definite integral is calculated for Formula (22):  
\[\frac{-1}{c_{2}(1-\sigma)}\ln\Bigl{\{}c_{2}e^{1-\sigma}(0)+c_{3}\text{sgn}[e(0 )]\Bigr{\}}=-\frac{1}{c_{1}}t_{s} \tag{23}\]  
where \(e(0)\) is the initial error value.  
The convergence time \(t_{s}\) is  
\[t_{s}=\frac{c_{1}}{c_{2}(1-\sigma)}\ln\Bigl{\{}c_{2}e^{1-\sigma}(0)+c_{3}\text {sgn}[e(0)]\Bigr{\}} \tag{24}\]  
Therefore, the output tracking error will converge to zero along the sliding surface in finite time, and the electro-hydraulic servo loading system has the characteristics of convergence in finite time under the proposed control strategy.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology  
An exhaustive set of experiments has been undertaken using the aforementioned experimentation environments and tools. This section presents the general experimentation methodology, which includes (i) tool installation, (ii) topology generation, (iii) selection and configuration of the routing protocols daemons, and (iv) setting up a framework to collect and analyze results, considering the stop criteria, i.e., algorithms to determine the convergence of routing protocols. Particularities for each environment are presented as well.  
In all the environments, we deployed the data center routing protocols presented in Section 2, namely, BGP, ISIS, Openfabric, and RIFT. We did not run experiments with LSVR due to the lack of available open-source implementations.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sham**} & \multicolumn{4}{c}{**OVX**} \\ \hline  & **ND** & **ND + CSM** & **HFHS** & **HFHS +** & **ND** & **ND + CSM** & **HFHS** & **HFHS +** & **PS Values** \\ \hline IL-1B (pg/mL) & \(8.68\pm 3.63\) & \(7.03\pm 3.91\) & \(5.93\pm 1.77\) & \(4.48\pm 1.44\) & \(4.23\pm 1.61\) & \(7.50\pm 2.00\) & \(6.57\pm 1.68\) & \(3.73\pm 0.88\) & 0.800 \\ \hline IL-6 in (pg/mL) & \(32.96\pm 4.53\) & \(26.79\pm 5.04\) & \(28.91\pm 5.35\) & \(27.69\pm 4.60\) & \(36.96\pm 9.36\) & \(32.44\pm 3.87\) & \(27.88\pm 4.27\) & \(28.75\pm 4.05\) & 0.900 \\ \hline IL-10 & \(0.28\pm 0.29\) & \(1.27\pm 1.04\) & \(0.34\pm 0.24\) & \(0.07\pm 0.05\) & \(0.44\pm 0.32\) & \(0.46\pm 0.22\) & \(0.87\pm 0.58\) & \(0.17\pm 0.17\) & 0.470 \\ \hline TNF-atpg/mL & \(1.08\pm 0.18\) & \(1.88\pm 0.28\) & \(1.78\pm 0.28\) & \(2.09\pm 0.18\) & \(1.53\pm 0.31\) & \(2.04\pm 0.84\) & \(1.89\pm 0.17\) & \(1.88\pm 0.56\) & 0.750 \\ \hline MCP-1 pg/mL & \(340.65\pm 9.07\) & \(387.69\pm 53.60\) & \(320.93\pm 55.72\) & \(376.20\pm 71.69\) & \(4832.6\pm 81.78\) & \(589.44\pm 69.88\) & \(574.41\pm 723.6\pm 8\) & **0.020** \\ \hline PCE2 (pg/mL) & \(1254.75\pm 9.13\) & \(1314.51\pm 126.31\) & \(1155.18\pm 146.57\pm 1017.02\pm 1323.9\pm 16837.1\pm 18637.1\pm 1865.6\pm 234.97\pm 212.19\pm 237.85\pm 221.96\pm 123.24\pm 313.98\pm 321.77\) & 0.660 \\ \hline IFN-\(\gamma\) & \(241.55\pm 35.38\) & \(279.23\pm 40.09\) & \(222.36\pm 27.98\) & \(241.99\pm 35.16\) & \(263.20\pm 30.81\) & \(226.20\pm 33.19\) & \(217.63\pm 33.88\) & 2409.71\(\pm 26.38\) & 0.695 \\ \hline CXCL1 & \(23.65\pm 6.42\) & \(19.74\pm 5.36\) & \(16.83\pm 3.54\) & \(19.31\pm 4.88\) & \(32.09\pm 7.27\) & \(17.44\pm 4.71\) & \(19.71\pm 6.31\) & \(18.55\pm 4.82\) & 0.283 \\ \hline IL-18 (pg/mL) & \(136.10\pm 48.45\) & \(159.41\pm 55.21\) & \(163.36\pm 76.23\) & \(112.23\pm 29.89\) & \(111.33\pm 13.87\) & \(119.65\pm 41.65\) & \(99.90\pm 16.12\) & \(130.33\pm 27.99\) & 0.974 \\ \hline IL-12 (pg/mL) & \(40.30\pm 13.65\) & \(48.85\pm 11.20\) & \(34.60\pm 4.77\) & \(48.33\pm 5.69\) & \(51.25\pm 5.78\) & \(35.09\pm 0.76\) & \(65.66\pm 3.36\) & \(53.98\pm 5.35\) & 0.804 \\ \hline IL-17 (pg/mL) & \(43.72\pm 8.31\) & \(50.58\pm 10.12\) & \(33.37\pm 6.78\) & \(43.40\pm 7.56\) & \(53.26\pm 4.60\) & \(33.08\pm 5.47\) & \(41.89\pm 10.50\) & \(34.59\pm 4.41\) & 0.428 \\ \hline IL-33 (pg/mL) & \(612.27\pm 68.81\) & \(598.94\pm 95.12\) & \(622.62\pm 9\) & \(516.23\pm 60.95\) & \(650.36\pm 42.59\) & \(577.06\pm 80.56\) & \(584.99\pm 46.44\) & \(617.76\pm 58.09\) & 0.850 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Analysis of inflammatory markers in systemic circulation.
## 1 Introduction
Nevertheless, it is conceivable that specific modifications or revisions could be implemented to the packet headers for diverse objectives that could potentially result in packet corruption or spoofing. This suggests that if an adversary has the capability to exploit the given timeframe and carry out an attack, they will have the chance to do so. The traditional intrusion detection system (IDS) does not hinder the store-and-forward operation. Therefore, the potential for a lack of mechanisms to identify occurrences within these operations may arise. The rationale for formulating this research problem is rooted in the need to mitigate false negatives. The main challenges in detection revolve around generating elements that encompass credible attack signatures or constructing a signature that encompasses all possible permutations of the pertinent attack [14]. The primary focus lies in the significance of detection accuracies, wherein the optimal outcomes are achieved through leveraging the features of the detected elements. This implies that the data input obtained from external sources necessitates adequate training or learning in order to be effectively processed or predicted [14; 15]. In light of the aforementioned issues in the store-and-forward context, the present study introduces a novel approach titled \"Hybrid IP Packet header modifications detection\" to tackle these challenges. The aim of this study is to integrate diverse machine learning prediction techniques in order to effectively detect any modifications that transpire during a store-and-forward operation. This implies that the detection mechanism employs a hybridized approach, incorporating various techniques to detect alterations made to packet headers. One possible approach to resolving the issue at hand could be the incorporation of multiple classifiers into the system.
# Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
## 2 Nonlinear Model with Creep
### Performance Prediction  
In practice, the parameters of a known speaker (e.g., \(R_{e}\), \(L_{e}\), \(M_{m}\), \(K_{m}\), \(Bl\), and \(R_{m}\)) can be easily obtained through measurement, while \(K_{2}\) and \(R_{2}\) can be derived by fitting the measurement data [16]. In this case, the state variables \(\mathbf{X}=[i,x,v,x_{1}]^{T}\) of the NL-C model can be obtained by Equation (11).  
Subsequently, the sound pressure response of the miniature loudspeaker can be calculated using the following formula:  
\[p(t,r)=\frac{\rho_{0}A}{2\pi r}\frac{dv}{dt}e^{-\frac{\beta\pi ft}{\rho_{0}}}, \tag{13}\]  
where \(r\) denotes the distance between the sound emission point of the miniature loudspeaker and the test point, \(A\) is the effective radiating area, \(\rho_{0}\) is the air density, \(c_{0}\) is the sound velocity in air, \(j\) is the imaginary unit, and \(f\) is the frequency of the excitation signal. Due to the nonlinear factors within the loudspeakers, nonlinear distortions will be generated in the sound pressure output response. Total harmonic distortion (THD) is commonly used to quantify the amount of harmonic distortion present in the output signal when a pure input signal is applied. It can then be calculated as follows:  
\[\text{THD}=\frac{\sqrt{\sum_{n=2}^{\infty}\left|P(nf)\right|^{2}}}{\left|P_{t }(f)\right|}\times 100\%, \tag{14}\]  
where \(P_{t}(f)\) and \(P(nf)\) represent the total sound pressure and the \(n_{th}\) harmonic component in the complex sound pressure signal, respectively. Generally, a lower THD indicates that the loudspeaker more accurately reproduces the original signal, resulting in clearer and more precise sound.  
It is worth noting that the NL-C model degrades to the NL-NC model when \(K_{2}\) and \(R_{2}\) tend toward infinity and the secondary branch describing creep becomes open-circuited. Accordingly, the nonlinear performance of the NL-NC model can also be evaluated.