esa-sceva/satcom-chunk-collection huggingface dataset:
# Instantaneous Networking Service Availability for Disaster Recovery
## 2 Related Work
### Recovery of Communication Failures  
There are various types of communication failures. We can classify the failure recovery methods according to failure causes: bit error failure recovery, packet delivery failure recovery, route failure recovery, and infrastructure failure recovery. IP/TCP networking technologies involve the protocols and methods for the bit error failure recovery and packet delivery failure recovery [15]. Route recovery has also been examined in a number of research studies in IP networks and other network such as mobile ad hoc networks and software-defined networks (SDN) [15; 16]. For instance, [17] addressed the problem of service function chaining (SFC) using the concept of network function virtualization (NFV) in software-defined networks with reliability. The authors proposed a fault-aware routing architecture for the SFC problem with energy consideration. Mathematical study has been carried out to examine failure recovery and fault minimization. The proposed scheme optimizes the probability of failure in networking devices and in the event of failure of (a) Fog node(s).  
In the disaster situation, the network infrastructure may be broken down and may fail to work. In this paper, we address the problem of network restoration (infrastructure failure recovery) for disaster resilience. Communication support systems and information support systems are essential for disaster resilience. The communication means in a disaster situation are based on the extended utilization of conventional communication technologies such as WLAN, LTE, Bluetooth, satellite communications, etc. The combination of short-range and long-range communication facilitates disaster responses such as telemedicine [3; 18].  
Various communication systems have been developed for disaster situations.  
Two studies by [PERSON] et al. ([1; 19]) introduce the movable and deployable resource unit (MDRU) and van-type MDRU. The idea of the MDRU is to enable rapid transportation of a communication resource unit to the disaster areas. The MDRU constructs an alternative network to the damaged network, providing networking services such as telephone services and Internet connections. A van-type MDRU was introduced in [1]. A van-type MDRU can be connected with a satellite or LAN in addition to the wireless access interfaces.  
[PERSON] et al. [7] introduced a spectrum- and energy-efficient route management scheme for MDRU-based networks. The network considered in this study consists of a number of deployed gateways that connect to the Internet through the MDRU. The study proposed a scheme of finding a routing path from multiple nodes to multiple forwarders with consideration of both energy efficiency and spectrum efficiency in an MDRU-based network.  
There are also other types of networks for disaster areas. Mobile ad hoc networks are used for situation awareness in disaster areas. Relay by smartphone allows users to directly communicate, and it supports multihop relay. Smartphone relay networks are introduced in [4].
# Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
## 2 Background
### Scale and Move Transformation-Based Interpolation
#### 2.1.1 \(N\) Closest Rules Selection  
Without losing generality, suppose that a rule \(R_{i}\) and an observation \(O\) are respectively represented by:  
\[R_{i}: \text{if }x_{1}\text{ is }A_{i1},\cdots,x_{j}\text{ is }A_{ij},\cdots,x_{M}\text{ is }A_{iM}\] \[\text{ then }y\text{ is }B_{i}\] \[O: \text{$x_{1}$ is }A_{1}^{*},\cdots,x_{j}\text{ is }A_{j}^{*},\cdots,x_{M}\text{ is }A_{M}^{*}\]  
where \(A_{ij}\) denotes the \(j\)-th antecedent fuzzy set of rule \(R_{i}\), \(A_{j}^{*}\) denotes the observed fuzzy set of variable \(x_{j}\) and \(B_{i}\) denotes the consequent fuzzy set of rule \(R_{i}\) with \(j\in\{1,\ldots,M\}\), \(M\) being the number of antecedent variables.  
The distance \(d_{ij}\) between the fuzzy set pair of \(A_{ij}\) and \(A_{j}^{*}\) for a given \(j\) can be calculated as follows:  
\[d_{ij}=d(A_{ij},A_{j}^{*})=d(\text{Rep}(A_{ij}),\text{Rep}(A_{j}^{*})) \tag{2}\]  
The distance \(d_{i}\) between the rule \(R_{i}\) and the observation \(O\) is deemed to be the average of all antecedent variables' distances:  
\[d_{i}=\sqrt{\sum_{j=1}^{M}d_{ij}^{\prime\prime}{}^{2}},\;\;d_{ij}^{\prime}= \frac{d_{ij}}{\text{max}_{j}-\text{min}_{j}} \tag{3}\]  
where \(\text{max}_{j}\) and \(\text{min}_{j}\) are the maximum and minimum values of variable \(j\), \(j\in\{1,\ldots,M\}\), and each distance measure \(d_{ij}\) is normalised into the range \([0,1]\), denoted by \(d_{ij}^{\prime}\), to make the absolute distances compatible with each other over different variable domains.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs
### CV-QKD Implementation Technique
#### 4.2.2 Laser Source  
When LO or pilot is multiplexed with QKD signals in the time domain, a pulsed laser must be used. The pulsed laser utilized in CV-QKD does not require a high repetition rate or ultra-narrow pulse widths. Consequently, continuous-wave (CW) lasers modulated by an MZM are commonly used to generate the required pulses [39; 40; 43; 44; 45]. In TLO-based CV-QKD systems, the LO power must be significantly higher than that of the QKD signal. Typically, an LO signal contains \(10^{4}\)-\(10^{8}\) photons per pulse, whereas a QKD signal contains fewer than 20 photons [49]. Due to this large disparity, the extinction ratio (ER) of the pulse must be extremely high to minimize LO-induced interference with the QKD signal. An ER of 80 dB ER has been reported using a cascaded dual-MZM configuration [50]. However, achieving such a high ER becomes increasingly difficult as the repetition rate increases. If QKD and TLO (or pilot) are not time-multiplexed, CW lasers can be used directly, eliminating the need for pulse generation and thereby enhancing the secret key rate of CV-QKD.
## 4 Experimental Results and Analyses
### Experiment on Shot Boundary Detection
#### 4.2.1 Open Video Scene Detection (OVSD) Dataset
Therefore, the large gradual shots that were brought on by the abundance of activities could make the shot boundary hard to detect.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Taxonomy of IFA
#### 3.2.1 Source of the Attack  
The origin of IFAs can be categorized according to the number and coordination of attacking entities. In single-source attacks, a lone adversary dispatches a high volumeof malicious interest packets, aiming to overwhelm the network's resources. In contrast, distributed attacks involve multiple compromised nodes, often orchestrated as a botnet, to flood the network from various points. The distributed nature of the botnet makes detection and mitigation more challenging due to the dispersed attack vectors and the increased volume of malicious traffic, which can originate from either single or multiple sources, each presenting unique challenges to network security.
* _Single-Source Attacks:_ In these scenarios, a single adversary sends a large number of malicious-interest packets with the intent of overconsuming network resources. This method resembles traditional DoS attacks, in which a single system inundates a target with excessive traffic, leading to service degradation or unavailability [18].
* _Distributed Attacks:_ Distributed attacks involve multiple compromised nodes, often orchestrated as a botnet, to flood the network from various points [28]. The botnet approach mirrors DDoS attacks, where numerous systems coordinate to overwhelm a target, complicating detection and mitigation due to the dispersed nature of attack vectors and the increased volume of malicious traffic [29].
## 4 Results and Discussions
### Experimental Findings
#### 4.2.2 Multi-Class Classification
The macro-average F-score \(F1\)-\(score_{M}\) is computed using Equation (23)  
\[F1\text{-}score_{M}=\frac{2*{Sen_{M}}*{Pre_{M}}}{{Sen_{M}}+{Pre_{M}}}, \tag{23}\]  
Figure 6 depicts the confusion matrix obtained by ConvNet-24 model. True positives and true negatives are indicated inside the green color box. The numbers inside pink box represents false negatives and false positives. From the experimental findings, it is evident that Conf1 correctly classifies 170 fresh meat images, 152 half-fresh meat images, and 113 spoiled meat images. This results in an overall accuracy of 96.5%. The sensitivities of fresh, half-fresh, and spoiled classes are found to be 95.5%, 95.6%, and 99.1%, respectively. The precision of fresh, half-fresh, and spoiled cases in Conf1 is 97.7%, 95  and 96.5%, respectively. In Conf2, 433 of 451 images are correctly identified, resulting in an accuracy of 96%. This configuration yields a sensi \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Configurations/** & & & & & & **Training** & **Testing** \\
**Performance** & _Acc_ & _Sen\({}_{M}\)_ & _Pre\({}_{M}\)_ & **F1-Score\({}_{M}\)** & **Time** & **Time** \\
**Metrics** & & & & & & **in s** & **in s** \\ \hline Conf1 & 96.5 & 96.7 & 96.4 & 96.5 & 202.34 & 7.61 \\ Conf2 & 96 & 96.4 & 95.9 & 96.1 & 204.56 & 7.86 \\ Conf3 & 97.3 & 97.5 & 97.1 & 97.3 & 201.87 & 7.42 \\ Ensem & 96.6 & 96.8 & 96.5 & 96.6 & 615.27 & 16.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics obtained using ConvNet-24. Figure 6: Confusion matrix for multi-class classification task. tivity of 94.4%, 95.6%, and 99.1% in fresh, half-fresh, and spoiled cases, respectively. The precision of fresh, half-fresh, and spoiled cases is found to be 97.6%, 93.8%, and 96.5%, respectively. Among 178 fresh meat images, 172 images are correctly predicted in Conf3. This configuration predicts 154 half-fresh and 113 spoiled meat images correctly. This leads to an accuracy of 97.3%. \(\mathit{Sen}_{F}\), \(\mathit{Sen}_{HF}\), and \(\mathit{Sen}_{SP}\) are found to be 96.6%, 96.8%, and 99.1%, respectively. This configuration results in a precision of 98.8%, 96.2%, and 96.5% in fresh, half-fresh, and spoiled classes, respectively. Ensembling the models can improve the overall performance [30]. Furthermore, it improves the robustness. This work employs the average ensemble technique, where the metrics obtained by the three configurations are averaged. Thus, the final ensemble model yields an overall accuracy of 96.6%.
## 1 Introduction
Furthermore, in [29], active filter-based damping techniques are summarized and analyzed in the literature, and through simulations and experiments, the trap filter solution proves to be the most flexible and effective active damping method, but the robustness of the scheme may face challenges when parameter variations are taken into account, which severely limits the application of the method. In addition, unlike the relatively constant frequency in grid systems, the frequency of the motor varies considerably during operation, so the frequency-tracking performance of the notch filter is critical [30]. In this paper, a high-frequency harmonic suppression method based on the LC filter and the adaptive notch filter (ANF) is proposed. The LC filter is used to suppress the high-frequency harmonic components of the stator current, and its parameter design method is given after considering the applicable range and voltage drop. The ANF is set to eliminate the consequent resonant peak. Noteworthily, the least mean square (LMS) algorithm is applied as an adaptive law to the notch filter, whose characteristics can vary with harmonics and noise, thus making the system adaptable. Moreover, through the speed feedback, the notch filter is able to track the shift of the system resonant frequency and achieve frequency adaption. The robustness and dynamic response capability of the HPMSM are improved. The remainder of the article is organized as follows. In Section 2, the mathematical model of the HPMSM is established, and the high-frequency current harmonics are analyzed. In Section 3, the HPMSM system with LC filter is introduced, and the damping technology of resonance peak suppression is discussed. Section 4 describes the modified ANF structure and the implementation of the control system. In Section 5, the simulation results are analyzed. In Section 6, experiments are carried out, and the practicability and effectiveness of the scheme are proved. Finally, Section 7 concludes this article.
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 3 Simulation Results
### Effect of Channel Taps  
The effect of increasing the taps of the channel on the proposed system appears in Figure 7. Increasing the channel taps from 9 to 12 leads to a little bit increase in the performance of the proposed system at SNR greater than 10 dB. This is in a fair agreement with the work of [PERSON] and [PERSON] in [14] as the OTDM is designed especially to work better with the frequency selective channels, which is the case in most broadband systems.  
Figure 6: BER of STC OTDM at different subcarriers and L = 9 for: (**a**) QPSK; (**b**) 64-QAM; and (**c**) 256-QAM.  
The procedure is repeated for all modulation schemes at all values of N with channel taps of 9, 12, and 15. The obtained results at BER = \(10^{-3}\) are summarized in Table 2.
## 4 Numerical Simulation Results
### Settlement and Deformation Law  
Changes in the pore-water pressure caused changes in the shear strength. Figure 8 shows the deformation vector arrows of the subgrade protected by a capillary barrier. The subgrade with a capillary barrier was basically not affected by rainfall, while settlement still occurred. This was mainly because the slight change in the pore-water pressure at the bottom of the expressway shoulder slope caused the strength in the uplift area of the slip surface to decrease, which led to the shear deformation of the subgrade.  
Figure 9 shows the change trend in the expressway's pavement AB settlement with horizontal displacement. In the nonaffected area, the settlement of the conventional subgrade was relatively small, which was only \(1\) cm. While in the affected area, the settlement was large, which was up to \(16\) cm. Large different settlements occurred at the shoulder and in the subgrade. The different settlement was up to \(15\) cm. However, the subgrade using a capillary barrier had a ground settlement of approximately \(1\) cm in the affected zone and the noninfluenced zone. The different settlement was also relatively small. Surprisingly, even if the pore-water pressure in the subgrade did not change, the subgrade still had a certain amount of settlement. This was mainly caused by the shear deformation of the foundation.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results
### Classification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Method** & **Model** & **Dataset** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** & **ROC AUC** & **PR AUC** \\ \hline Original & EFN7 & VinDr-Mammo & 0.86 & 0.83 & 0.88 & 0.81 & 0.92 & 0.90 \\ Fs-ROI & EFN7 & VinDr-Mammo & 0.87 & 0.85 & 0.89 & 0.83 & 0.93 & 0.91 \\ Prediction & EFN7 & VinDr-Mammo & 0.90 & 0.88 & 0.92 & 0.86 & 0.96 & 0.94 \\ Original & CNX1 & VinDr-Mammo & 0.85 & 0.82 & 0.87 & 0.80 & 0.91 & 0.89 \\ Fs-ROI & CNX1 & VinDr Mammo & 0.87 & 0.84 & 0.89 & 0.82 & 0.93 & 0.90 \\ Prediction & CNX1 & VinDr-Mammo & 0.89 & 0.87 & 0.91 & 0.85 & 0.95 & 0.93 \\ Original & EFN7 & MiniDSM & 0.84 & 0.81 & 0.86 & 0.80 & 0.90 & 0.88 \\ Fs-ROI & EFN7 & MiniDSM & 0.85 & 0.83 & 0.87 & 0.81 & 0.91 & 0.89 \\ Prediction & EFN7 & MiniDDSM & 0.88 & 0.86 & 0.90 & 0.84 & 0.94 & 0.92 \\ Original & CNX1 & MiniDSM & 0.83 & 0.80 & 0.85 & 0.79 & 0.89 & 0.87 \\ Fs-ROI & CNX1 & MiniDSM & 0.84 & 0.82 & 0.86 & 0.80 & 0.90 & 0.88 \\ Prediction & CNX1 & MiniDSM & 0.87 & 0.85 & 0.89 & 0.83 & 0.93 & 0.91 \\ Original & EFN7 & CMMD & 0.87 & 0.84 & 0.89 & 0.83 & 0.90 & 0.89 \\ Prediction & EFN7 & CMMD & 0.91 & 0.89 & 0.93 & 0.88 & 0.97 & 0.96 \\ Original & CNX1 & CMMD & 0.86 & 0.83 & 0.88 & 0.82 & 0.92 & 0.90 \\ Fs-ROI & CNX1 & CMMD & 0.87 & 0.85 & 0.89 & 0.83 & 0.93 & 0.91 \\ Prediction & CNX1 & CMMD & 0.92 & 0.90 & 0.94 & 0.89 & 0.98 & 0.97 \\ Original & EFN7 & CDD-CSEM & 0.87 & 0.84 & 0.89 & 0.83 & 0.93 & 0.91 \\ Fs-ROI & EFN7 & CDD-CSEM & 0.88 & 0.86 & 0.90 & 0.84 & 0.94 & 0.92 \\ Prediction & EFN7 & CDD-CSEM & 0.92 & 0.90 & 0.94 & 0.89 & 0.98 & 0.97 \\ Original & CNX1 & CDD-CESEM & 0.86 & 0.83 & 0.88 & 0.82 & 0.92 & 0.90 \\ Fs-ROI & CNX1 & CDD-CESEM & 0.87 & 0.85 & 0.89 & 0.83 & 0.93 & 0.91 \\ Prediction & CNX1 & CDD-CESEM & 0.92 & 0.90 & 0.94 & 0.89 & 0.98 & 0.97 \\ Original & EFN7 & BMCD & 0.87 & 0.84 & 0.89 & 0.83 & 0.93 & 0.91 \\ Fs-ROI & CNX1 & BMCD & 0.88 & 0.86 & 0.90 & 0.84 & 0.94 & 0.92 \\ Prediction & EFN7 & BMCD & 0.92 & 0.90 & 0.94 & 0.89 & 0.98 & 0.97 \\ Original & CNX1 & BMCD & 0.86 & 0.83 & 0.88 & 0.82 & 0.92 & 0.90 \\ Fs-ROI & CNX1 & BMCD & 0.87 & 0.85 & 0.89 & 0.83 & 0.93 & 0.91 \\ Prediction & CNX1 & BMCD & 0.92 & 0.90 & 0.94 & 0.89 & 0.98 & 0.97 \\ Original & EFN7 & RSNA & 0.86 & 0.83 & 0.88 & 0.82 & 0.91 & 0.89 \\ Fs-ROI & EFN7 & RSNA & 0.87 & 0.85 & 0.89 & 0.83 & 0.92 & 0.90 \\ Prediction & EFN7 & RSNA & 0.87 & 0.85 & 0.89 & 0.83 & 0.92 & 0.90 \\ Original & CNX1 & RSNA & 0.85 & 0.82 & 0.87 & 0.81 & 0.90 & 0.88 \\ Fs-ROI & CNX1 & RSNA & 0.86 & 0.84 & 0.88 & 0.82 & 0.91 & 0.89 \\ Prediction & CNX1 & RSNA & 0.86 & 0.84 & 0.88 & 0.82 & 0.91 & 0.89 \\ Prediction & CNX1 & RSNA & 0.86 & 0.84 & 0.88 & 0.82 & 0.91 & 0.89 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different methods and models for breast cancer classification on mammography data sets using various metrics.
## Materials and Methods  
### Volatiles Reference Mix (VRM)  
The VRM was created from a combination of custom mixtures and individual compounds. Mix 1 contained 2-ethyl-1-hexanol, 1-propanol, 2-propanol, 2-butanone, cyclohexane, and 2-methylfuran in P&T methanol/water (90:10), each at a concentration of 1000 \(\upmu\)g/mL (GC grade, Restek Corporation, Bellefonte, PA, USA). Mix 2 contained styrene, 2-methylpentane, 3-methylpentane, 2,4-dimethylheptane, 2-methylhexane, naphthalene, and 1,2,3-trimethylbenzene in P&T methanol, each at a concentration of 1000 \(\upmu\)g/mL (GC Grade Quality, Restek Corporation). Mix 3 was a commercially available standard containing benzene, ethylbenzene, toluene, m-xylene, o-xylene, and p-xylene in P&T methanol, each at a concentration of 2000 \(\upmu\)g/mL (certified reference mixture grade, Restek Corporation). Individual compounds used were heptanal, dimethyl trisulfide, hexanal, and isoprene (analytical standard grade, Sigma-Aldrich, St. Louis, MO, USA). The solvent used for dilution was HPLC-grade methanol (J.T. Baker, Center Valley, PA, USA).  
From the commercial mixes, custom mixes, and individual standards, a series of mixed standards were made for SPME Arrow evaluation. VRM 1 contained mix 1, mix 2, heptanal, dimethyl trisulfide, hexanal, and isoprene each at a concentration of 100 \(\upmu\)g/mL, and mix 3 at a concentration of 200 \(\upmu\)g/mL. VRM 2 contained mix 1, mix 2, heptanal, dimethyl trisulfide, hexanal, and isoprene each at a concentration of 10 \(\upmu\)g/mL, and mix 3 at a concentration of 20 \(\upmu\)g/mL. VRM 3 contained mix 1, mix 2, heptanal, dimethyl trisulfide, hexanal, and isoprene each at a concentration of 1.1 \(\upmu\)g/mL, and mix 3 at a concentration of 2.2 \(\upmu\)g/mL.  
In addition to the VRM, a commercial saturated alkanes mix (SAM\({}_{\text{C}}\)) containing heptane, octane, nonane, decane, dodecane, tridecane, tetradecane, pentadecane, heptadecane, octadecane, nonadecane, eicosane, heneicosane, docosane, tricosane, tetracosane, pentacosane, hexacosane, heptacosane, octacosane, nonacosane, and triacentane was used, each at a concentration of 1000 \(\upmu\)g/mL in hexane (certified reference material grade, Supelco, Bellefonte, PA, USA). The solvent used for dilution was hexane (CHROMASOLV\({}^{\upmu}\) Plus, Sigma Aldrich, St. Louis, MO, USA).  
From the commercial mix, a series of diluted standards were made for SPME Arrow evaluation. SAM 1 contained SAM\({}_{\text{C}}\) at a concentration of 100 \(\upmu\)g/mL. SAM 2 contained SAM\({}_{\text{C}}\) at a concentration of 10 \(\upmu\)g/mL. SAM 3 contained SAM\({}_{\text{C}}\) at a concentration of 1.1 \(\upmu\)g/mL.  
All mixtures were prepared fresh in glass vials (within one month of use) and stored in a freezer between analyses.
## 4 Results and Discussion  
### Thermal-Physical Properties of Non-Newtonian Nanofluids  
Figure 5 shows the variation of the viscosity \(\eta\) of base fluid and nanofluids with the shear rate \(\gamma\) at 25 \({}^{\circ}\)C. As can be seen from Figure 5, the viscosities of all working fluids decreases with the increasing of the shear rate, indicating that all the samples are typical non-Newtonian fluids with shear thinning behavior (\(n<1\)). For a given shear rate \(\gamma\), the viscosity increases slightly with the increase in weight fraction of nanoparticle.  
Thermal-physical properties of base fluid and non-Newtonian nanofluids including consistency index \(K\), power law index \(n\), thermal conductivity \(\lambda\), density \(\rho\) and specific heat \(c_{\rm p}\) were measured and their correlations to temperature are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fluid**} & \multicolumn{5}{c}{**Correlations (298.15 K \(<\)\(T\) \(<\) 338.15 K)**} \\ \cline{2-7}  & \(K\) **(m\({}^{2}\)-g\({}^{2}\))** & **m** & \(\lambda\) **(W-m\({}^{-1}\)-K\({}^{-1}\))** & \(\rho\) **(kg-m\({}^{-3}\))** & \(c_{\rm p}\) **(kJ\(\cdot\)kg\({}^{-1}\)-K\({}^{-1}\))** \\ \hline \multirow{2}{*}{Base fluid} & \(-0.01317+4.6322\) & \(0.0043T-0.9469\) & \(0.0043T-0.6265\) & \(-0.0073T^{2}+\) & \(-2.7149\times 10-5T^{2}\) \\  & \((R^{2}=0.999)\) & \((R^{2}=0.988)\) & \((R^{2}=0.979)\) & \(4.2840T+382.8\) & \(-0.01189T+4.8763\) \\  & \((R^{2}=0.999)\) & \((R^{2}=0.988)\) & \((R^{2}=0.979)\) & \((R^{2}=0.991)\) & \((R^{2}=0.996)\) \\ \hline \multirow{2}{*}{\begin{tabular}{c} 0.2 wt \% \\ MWCNTs/XG \\ \end{tabular} } & \(-0.0124T+4.4211\) & \(0.0040T-0.8517\) & \(0.0035T-0.4168\) & \(-0.0067T^{2}+\) & \(-5.4948\times 10-5T^{2}\) \\  & \((R^{2}=0.999)\) & \((R^{2}=0.989)\) & \((R^{2}=0.987)\) & \(3.8902T+451.2\) & \(+0.04071T-3.5919\) \\  & \((R^{2}=0.991)\) & \((R^{2}=0.991)\) & \((R^{2}=0.991)\) & \((R^{2}=0.991)\) \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} 0.5 wt \% \\ MWCNTs/XG \\ \end{tabular} } & \(-0.01117+4.1513\) & \(0.0025T-0.3962\) & \(0.0026T-0.1468\) & \(-0.00527T^{2}+\) & \(-3.9137\times 10-5T^{2}\) \\  & \((R^{2}=0.992)\) & \((R^{2}=0.982)\) & \((R^{2}=0.995)\) & \((R^{2}=0.995)\) & \((R^{2}=0.990)\) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} 1.0 wt \% \\ MWCNTs/XG \\ \end{tabular} } & \(-0.0130T+4.8356\) & \(0.0021T-0.3016\) & \(0.0025T-0.1473\) & \(-0.0016T^{2}+\) & \(6.9542\times 10-5T^{2}\) \\  & \((R^{2}=0.996)\) & \((R^{2}=0.998)\) & \((R^{2}=0.988)\) & \(0.6362T+965.2\) & \(0.04154T+9.6727\) \\  & \((R^{2}=0.987)\) & \((R^{2}=0.972)\) & \((R^{2}=0.987)\) & \((R^{2}=0.972)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Expression of thermal-physical properties.  
Figure 5: Variation of the viscosity with the shear rate at different concentrations at 25 \({}^{\circ}\)C.