esa-sceva/satcom-chunk-collection huggingface dataset:
# Review Rating Prediction Based on User Context and Product Context
## 4 Experiments and Evaluations
### The Impact of User Context and Product Context on Our Methods  
To evaluate the impact of user context and product context on RRP, we separately remove user context and product context from RRP + UC + PC and conducted comparative experiments on four datasets. The experimental results of the four datasets are shown in Figures 3 and 4, respectively.  
From Figure 3, we can find the MAE of RRP + UC + PC is greatly improved when the user context information is removed compared to the removal of the product context. For example, when the user context information is removed, the MAE of RRP + UC + PC is increased by 4.49% ((0.8170 \(-\) 0.7803)/0.8170) in the douban2 datasets. However, when the product context information is removed, the MAE of RRP + UC + PC is only increased by 3.44% ((0.8081 \(-\) 0.7803)/0.8081) in the douban2 datasets.  
From Figure 4, we can get the same result. We find the RMSE of RRP + UC + PC is greatly improved when the user context information is removed as compared to the removal of the product  
Figure 3: Impacts of User Context and Product Context on MAE of Our Methods in the Four Datasets.  
Figure 2: Impacts of Number of Reviews Per User on MAE of Our Methods in Yelp2013 Datasets.  
context. For example, when the user context information is removed, the MAE of RRP + UC + PC is increased by 8.29% (\((0.8114-0.7441)/0.8114\)) in the yelp 2013 datasets. However, when the product context information is removed, the MAE of RRP + UC + PC is only increased by 7.27% (\((0.8024-0.7441)/0.8024)\) in yelp2013 datasets.  
The experimental results on four different datasets show that user contexts are more effective than product contexts in RRP. This is because the user context information contains not only user-special words, but also user's personal sentiment bias.
# Secured Real-Time Machine Communication Protocol
## 2 Existing Protocols
### Data Link Layer Protocols  
EtherCAT (Ethernet for Control Automation Technology) is a high-performance Ethernet-based fieldbus system that operates at the transport layer. EtherCAT is known for its extremely low latency and jitter, providing precise and deterministic communication [18]. It is highly efficient and has low hardware costs, often utilizing standard Ethernet components and requiring minimal additional hardware due to its innovative processing of Ethernet frames [18]. However, EtherCAT has limited support for real-time communication, which can be a limitation in some applications [19]. Additionally, vendor-specific implementations can lead to interoperability issues, requiring careful management to ensure seamless operation in multi-vendor environments [20].  
The Controller Area Network (CAN) protocol is widely used in automotive and industrial applications. It operates at the data link layer of the TCP/IP model. CAN is highly reliable and robust in noisy environments, making it ideal for industrial and automotive applications [21]. It is efficient for short messages, providing quick and reliable communication for control signals [21]. However, CAN has a limited data transfer rate, which can be a constraint for applications requiring high data throughput [22], and it is not suitable for large-scale networks without additional infrastructure, limiting its scalability [23].
## 3 Background
### Trust Management in IoV
#### 3.1.2 Components of Trust  
Direct trust: Because of the interactions between the trustor and the target vehicle, this component displays a trustor's direct observations of a target vehicle [26]. Knowledge, in the context of some studies, can mean any information the trustor learns about the trustee [27]. This normally makes use of parameters that are dependent on the nodes and services involved. Although direct trust is more important than indirect trust, when evaluating a vehicle, a combination of the two should be considered [28]. The indirect trust component is used to display the opinions of the trustor's neighbors about the trustee, who is the target node. Indirect trust usually considers the node's previous encounters and  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Trust Properties** & **Description** \\ \hline \multirow{2}{*}{Direct} & This attribute calculates trust value based on trustor-trustee relationships. \\ \hline \multirow{2}{*}{Indirect} & Trust value is calculated from the suggestions and opinions of the trustor’s various neighbors. \\ \hline \multirow{2}{*}{Subjective} & Trust value is calculated from the personal views of the trustor. \\ \hline \multirow{2}{*}{Objective} & The parameters of the observed trustee entity are what determine the value of trust, which is derived from those parameters. \\ \hline \multirow{2}{*}{Local} & The value of trust is exclusively accessible to the trustor and trustee and cannot be accessed by other users in the network. \\ \hline \multirow{2}{*}{Global} & Each entity that is part of the network has its own trust value that is known by every other entity. \\ \hline \multirow{2}{*}{Asymmetric} & This is when one entity gives trust to a second entity but the second entity does not give trust to the first entity. \\ \hline \multirow{2}{*}{History-dependent} & Trust value is given depending on the previous behaviors of the entity under observation. \\ \hline \multirow{2}{*}{Context-dependent} & The importance of trust depends on the surrounding circumstances. \\ \hline \multirow{2}{*}{Composite} & Trust value is based upon various parameters. \\ \hline \multirow{2}{*}{Dynamic} & If the initial trust value was generated with certain parameters and those values later change, the trust value will also change. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trust properties.  
experiences. In some circumstances, researchers employ both experience and reputation to explain the indirect observation. All previous experiences with a target node are included in reputation, which contributes to the formation of a global opinion about the node under observation. Experience, on the other hand, is frequently based on the interaction between the trustor and the trustee, and it strongly relies on the trustor's confidence in the trustee's competence to complete a task [21].
## 5 Model Updating for the Segmental Beam
### Model Updating Based on the Measured Static Data
When the RS model is determined with numerical analysis, the objective function \(\mathrm{H}(\theta)\) described in Equation (6) can be built based on the measured data. Then, the simulated annealing global optimization algorithm is used to minimize the objective function \(\mathrm{H}(\theta)\), and obtain the optimal parameters of the segmental beam model. To validate the accuracy of the updated finite element model, the relative error is used to evaluate the difference in structural responses between the updated model and measured data in the experiment. After the model updating of the segmental beam model, the calibrated model parameters are presented in Table 8. The corresponding predicted structural responses of the updated segmental beam model are listed in Table 9. Table 9 indicates that the updated segmental beam model can accurately predict the structural displacement responses and neutral axis locations with a maximum error less than 5%. This means that the calibrated segmental beam model can reliably be used to predict the structural deflections. From Table 9, the accuracy of the updated model exhibits an appreciable improvement when comparing with the initial model. The relative errors in neutral axis locations and static deflection responses are significantly reduced. After model updating, the relative errors of neutral-axis locations are less than 1%. For the deflections under two different loading levels, although the results are not as good as that of the neutral axis locations, the relative errors are within an acceptable range, which is less than 5%. This means that an accurate FEM is obtained to represent the tested precast segmental beam under the static loads. To further validate the accuracy of the updated segmental beam model, a case with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multicolumn{2}{c}{**Neural Axis Location**} & \multicolumn{2}{c}{**15 kN**} & \multicolumn{2}{c}{**20 kN**} \\ \cline{2-7}  & **MNAL2** & **MNAL3** & **ML1** & **ML23** & **ML4** & **ML1** & **ML23** & **ML4** \\ \hline \multirow{2}{*}{RE (\%)} & Original & \(-2.47\) & \(-1.65\) & \(4.15\) & \(1.92\) & \(11.29\) & \(6.73\) & \(3.86\) & \(13.73\) \\  & Updated & \(0.49\) & \(-0.15\) & \(-3.39\) & \(-0.96\) & \(1.25\) & \(-3.49\) & \(-3.55\) & \(4.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Defined error indices for the updated model. a prestress force of 62 kN is applied to the model, and a comparison between the numerical model analysis and experimental results of the neutral axis locations is summarized in Table 10.  
Table 10 indicates that the relative errors in the neural axis locations of the updated model are 0.99% and 1.34%, compared with the experimental results. Both the relative errors in the neutral axis locations of the updated model at S2 and S3 are below 2%, indicating that the same conclusion as summarized before, that is, the updated model can provide a good representation of the tested beam under different levels of prestressing forces.
## 4 Multi-Criteria Decision-Making Method for Emergency Communication Protocol (MCDM-ECP)
### MCDM-ECP for Hybrid DAWNs  
Two processes are involved in MCDM-ECP: the initialization process, and the data transmission process. The flowcharts of the two processes are shown in Figures 2 and 3, respectively.  
Figure 2: Initialization procedure of multi-criteria decision-making method for emergency communication protocol (MCDM-ECP).  
Figure 2 describes the initialization procedure of MCDM-ECP. Routing discovery and routing maintenance are included in this process. In the rescue tasks, ECVs and Mobile Nodes (MNs) broadcast their own Hello Messages (HM) frequently for routing discovery. The data format of the HM message is illustrated in Table 2.  
The \"Type\" field is a special tag for HM. Followed with the \"Type\" field, there are four decision criteria in DAWNs that determine the crucial factors for decision making. The \"IP\({}_{0}\)\" field shows the IP address of this mobile node. The following fields are neighbors' IP information, which are illustrated as IP\({}_{\rm x}\) (\(\rm x=1,\ldots,n\)). First responders move from one affected area to another to perform rescue tasks, so neighbors are frequently changed during their movements. The time period of broadcasting a Hello message can be calculated by basic distance (e.g., distance of first responder from one affected area to another) to speed (e.g., speed of first responder when he or she is moving from one affected area to another) ratio i.e., (time = distance/speed) [44]. We use the red front to highlight them in the revised version.  
Nodes update their routing tables (RT) as soon as they receive HMs from other nodes. The routing table includes content of source identification (ID), destination ID, the number of nodes connected, hop counts, node positions, next-hop node, etc.  
Figure 3 illustrates the data transmission procedure of a MCDM-ECP. In DAWNs, dynamic nodes may lose connection with their neighbors frequently, so nodes first check if there is a valid route to ECVs in the routing table (RT) before data transmission. Data will be stored temporarily until a valid route to the ECVs exists in the routing table. One hop to ECVs means that data can be directly transferred to ECVs; otherwise, the next-hop node should be chosen through the AHP-TOPSIS method. The MCDM-ECP algorithm is shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type** & EC & CN & HC & DIS & IP\({}_{0}\) & IP\({}_{1}\) & IP\({}_{2}\) & \(\ldots\) & IP\({}_{\rm n}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hello Messages (HM) data format.  
Figure 3: Data transmission procedure of MCDM-ECP.
## 1 Introduction
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**References** & **Solution** & **\(\mathbf{T}\) (K)** & \(\mathbf{P}\) (bar)** & **Nd** \\ \[PERSON] [23] & water & 273.00–313.00 & \(1^{+}\) & 41 \\ [PERSON] [24] & water & 281.20–323.20 & \(1^{+}\) & 22 \\ [PERSON] [25] & water & 298.20–308.20 & \(1^{+}\) & 2 \\ [PERSON] and [PERSON] [26] & water & 278.20–333.20 & 0.20–1.60 & 8 \\ [PERSON] [27] & water & 283.15–363.15 & \(1^{+}\) & 42 \\ [PERSON] [28] & water & 273.15–323.15 & 0.00–1.00 & 109 \\ [PERSON] and [PERSON] [29] & water & 283.20–300.20 & 0.00–3.50 & 29 \\ [PERSON] and [PERSON] [30] & water & 298.15–383.15 & 0.14–4.50 & 55 \\ [PERSON] and [PERSON] [31] & water & 273.00–298.00 & 0.00–1.20 & 38 \\ [PERSON] and [PERSON] [32] & water & 298.20 & 0.40–1.00 & 6 \\ [PERSON] and [PERSON] [33] & water & 298.20–323.20 & 0.00–0.20 & 16 \\ [PERSON] [34] & water & 373.00–423.00 & 1.70–7.20 & 22 \\ [PERSON] and [PERSON] [35] & water & 296.40–386.15 & 0.12–2.11 & 53 \\ [PERSON] [36] & water & 263.00–294.00 & 0.00–0.06 & 42 \\ [PERSON] and [PERSON] [37] & water & 303.15–353.15 & 0.09–1.20 & 43 \\ [PERSON] et al. [38] & water & 293.20–333.20 & 0.04–0.34 & 35 \\ [PERSON] [39] & water & 283.15–313.15 & \(1^{+}\) & 4 \\ [PERSON] and [PERSON] [40] & water & 299.15–363.15 & \(1^{+}\) & 5 \\ [PERSON] and [PERSON] [41] & water & 278.97–303.25 & \(1^{+}\) & 6 \\ [PERSON] et al. [42] & water & 298.15–323.15 & \(1^{+}\) & 2 \\ [PERSON] and [PERSON] [43] & water & 293.14–393.33 & 0.35–25.09 & 66 \\ [PERSON] et al. [44] & water & 291.05–294.45 & 0.02–0.03 & 50 \\ [PERSON] [9] & water & 293.00–333.00 & 0.02–0.21 & 20 \\ [PERSON] et al. [11] & water & 297.05–312.25 & 0.25–1.96 & 3 \\ [PERSON] et al. [13] & water & 298.15–323.15 & 0.00–0.12 & 19 \\ [PERSON] et al. [14] & water & 273.15–373.15 & 10.00–300.00 & 45 \\ [PERSON] et al. [15] & water & 293.15–353.15 & 0.00–12.00 & 45 \\ \multicolumn{4}{c}{\(m_{\text{Nacl}}\) (mol/kg)} \\ \multicolumn{4}{c}{\(\text{Fox}\)[25]} & 0.50–3.22 & 298.00–308.00 & \(1^{+}\) & 6 \\ [PERSON] et al. [45] & 0.01–6.00 & 278.15–298.15 & \(1^{+}\) & 17 \\ [PERSON] et al. [7] & 2.90–6.00 & 313.15–393.15 & 0.30–37.00 & 90 \\ [PERSON] et al. [10] & 0.49–4.28 & 298.15 & 0.00–0.30 & 37 \\ [PERSON] et al. [13] & 1.00 & 298.15–323.15 & 0.03–0.12 & 19 \\ \multicolumn{4}{c}{\(m_{\text{KCl}}\) (mol/kg)} \\ \multicolumn{4}{c}{\(\text{Fox}\)[25]} & 0.00–0.05 & 298.00–308.00 & \(1^{+}\) & 12 \\ [PERSON] [27] & 0.50–4.40 & 298.00–308.00 & \(1^{+}\) & 43 \\ [PERSON] et al. [13] & 0.05–1.00 & 298.15–323.15 & 1.00 & 19 \\ \multicolumn{4}{c}{\(m_{\text{Na}_{2}\text{SO}_{4}}\) (mol/kg)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SO\({}_{2}\) solubility measurements in pure water and aqueous single-salt solutions.
## 5 Simulation Experiment
### Performance Comparison Test
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Example**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{\(B_{KS}\)} & \multicolumn{4}{c}{**Algorithm in This Paper**} & \multicolumn{2}{c}{**CPLEX**} \\ \cline{3-8}  & & & \(t_{B}\) & \(t_{a}\) & \(t_{av}\)/s & \(t_{B}\) & \(t_{av}\)/s \\ \hline Kacem01 & \(4\times 5\) & 11 & 11 & 11 & 8.1 & 11 & 20.28 \\ Kacem02 & \(8\times 8\) & 14 & 14 & 14 & 59.2 & 14 & 242.34 \\ Kacem03 & \(10\times 7\) & 11 & 11 & 11 & 171.6 & 11 & 2468.32 \\ Kacem04 & \(10\times 10\) & 7 & 7 & 7.5 & 258.6 & 8 & 3600 \\ Kacem05 & \(15\times 10\) & 11 & 12 & 12.6 & 394.8 & 13 & 3600 \\ MK01 & \(10\times 6\) & 40 & 40 & 40 & 178.8 & 40 & 2541.24 \\ MK02 & \(10\times 6\) & 26 & 26 & 26 & 181.2 & 26 & 2677.51 \\ MK03 & \(15\times 8\) & 204 & 204 & 206.2 & 333.6 & 243 & 3600 \\ MK04 & \(15\times 8\) & 60 & 60 & 61.5 & 320.4 & 82 & 3600 \\ MK05 & \(15\times 4\) & 172 & 174 & 175.1 & 352.2 & 195 & 3600 \\ MK06 & \(10\times 15\) & 57 & 61 & 61.67 & 417.4 & 76 & 3600 \\ MK07 & \(20\times 5\) & 139 & 146 & 147.2 & 517.8 & 184 & 3600 \\ MK08 & \(20\times 10\) & 523 & 536 & 539.4 & 879.4 & 652 & 3600 \\ MK09 & \(20\times 10\) & 307 & 327 & 332.7 & 957.6 & 378 & 3600 \\ MK10 & \(20\times 15\) & 198 & 238 & 247.4 & 1177.2 & 307 & 3600 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Solution results of test example. Figure 9: Kacem01 instance optimal scheduling Gantt chart. The data in Table 6 show that, when compared with the other four algorithms from the literature, the average relative error, MRE, of this algorithm in 15 standard instances is 3.43, and the number of times the optimal value, \(N_{B}\), is taken is 7. From the results, the relative error of the IDPSO algorithm is the smallest, and the number of known optimal solutions is the largest, which again proves the effectiveness and excellent stability of the algorithm in this paper. Figure 11: MK09 instance optimal scheduling Gantt chart. Figure 10: Kacem03 instance optimal scheduling Gantt chart.
## 2 Numerical Methods
### Rotating Blade (Body Force Propeller) Model
#### 2.3.1 Blade Geometry Generation (2D Outline)  
According to the non-dimensional chord length \(C/D_{P}\), pitch \(P/D_{P}\), and skew \(\theta_{s}\) distribution along the propeller radius, which are generally provided by the propeller offset table, the propeller geometry on an axial plane (\(x\): shaft axis) or a \(y\)-\(z\) plane can be constructed. For an \(N\)-bladed propeller, the mid-chord line would be:  
\[y=\frac{r}{R_{0}}\cos\left[\theta_{s}+k^{\prime}\frac{2\pi}{n_{b}}\right], \tag{9}\]  
\[z=\frac{r}{R_{0}}\sin\left[\theta_{s}+k^{\prime}\frac{2\pi}{n_{b}}\right], \tag{10}\]  
herein, \(n_{b}=1,\,2\,\ldots,\,N.\) Here, \(k^{\prime}=1\) is to produce mid-chord line. The leading and trailing edge can then be generated from the mid-chord line:  
\[y=\frac{r}{R}\cos\left(\theta_{s}+k^{\prime}\frac{2\pi}{n_{b}}\mp\theta\right), \tag{11}\]  
\[z=\frac{r}{R}\sin\left(\theta_{s}+k^{\prime}\frac{2\pi}{n_{b}}\mp\theta\right), \tag{12}\]  
\[\theta=2\pi\frac{C}{D_{P}}/\sqrt{\left(2\pi\frac{r}{R}\right)^{2}+\left(2 \frac{P}{D_{P}}\right)^{2}}. \tag{13}\]  
At certain time \(t\), the coordinate transformation due to blade rotation is:  
\[y^{\prime}=x\cos 2\pi nt+y\sin 2\pi nt, \tag{14}\]  
\[z^{\prime}=-x\cdot\sin 2\pi nt+y\cdot\cos 2\pi nt, \tag{15}\]  
in which \(n\) is propeller rotation rate (RPS, revolution per second).  
If a 3D model of propeller geometry is available, the leading and trailing edge points \((x_{L},y_{L},z_{L})\) and \((x_{T},y_{T},z_{T})\) relative to the propeller center (0,0,0) can be extracted first. Next, the propeller skew (\(\theta_{s}\)), pitch \(P\), and chord length \(C\) can be calculated by Equations (19)-(21), respectively. Finally, the propeller blade outline can be constructed by following the previous Equations (9)-(15).  
\[\theta_{L}=\arctan(y_{L}/z_{L}), \tag{16}\]  
\[\theta_{T}=\arctan(y_{T}/z_{T}), \tag{17}\]  
\[\theta^{\prime}=(\theta_{L}-\theta_{T})/2, \tag{18}\]  
\[\theta_{S}=\theta_{L}+\theta^{\prime}, \tag{19}\]  
\[P=\pi(x_{L}-x_{T})/\theta^{\prime}, \tag{20}\]  
\[C=\sqrt{\left(2\pi\theta\right)^{2}+\left(x_{L}-x_{T}\right)^{2}}. \tag{21}\]
## 2 Materials and Methods
### Fatty Acid Profile  
Fatty acids (FAs) were determined in the assayed oils by gas chromatography FID detection [36], previous preparation of the fatty acid methyl ester derivatives using boron trifluoride/methanol. A Perkin-Elmer Clarus 500 gas chromatograph (Sheldon, CT, USA), equipped with a split-splitless injector, and automatic autosampler, and coupled to a computerized system for data acquisition (TotalChrom, version 6.2.1) was used. It was fitted with a capillary column SPTM-2560 (100 m \(\times\) 0.25 mm I.D. \(\times\) 0.2 \(\upmu\)m). The temperature of the injection port was 250 \({}^{\circ}\)C and 260 \({}^{\circ}\)C for the detector. The oven temperature was programmed to increase from 170 to 200 \({}^{\circ}\)C at a rate of 10.0 \({}^{\circ}\)C/min and then at a rate of 4.0 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C. The carrier gas was hydrogen, 2.15 mL/min. The sample size was 0.5 \(\upmu\)L and the split ratio was 120:1. The identification of the 30 fatty acids analyzed was conducted by the comparison of their retention times with those of pure fatty acid methyl esters. The quantification used heptadecanoic acid methyl ester as an internal standard. Individual methylated standards from Sigma (St. Louis, MO, USA) were used for the saturated, monounsaturated, cis polyunsaturated fatty acids and the trans-t-Palmitoleic C16:1 \(\Delta\)9t, Elaidic C18:1 \(\Delta\)9t, Brasidic C20:1 \(\Delta\)13t. For linoleic acid isomers, the mixture of Linoleic acid cis/trans isomers (50% of C18:2\(\Delta\)9t,12t; 20% of C18:2\(\Delta\)9c,12t and C18:2\(\Delta\)9t,12c; 10% of C18:2\(\Delta\)9c,12c) from Sigma was used. The order of elution in the case of mixtures of isomers (Linoleic acid cis/trans isomers) was also considered [37], and spiking the sample with each standard individually was finally used for confirming the identification. Elaidic acid eluted very closely to other C18:1 trans isomers (possibly \(\Delta\)6-\(\Delta\)12), which are all located before Oleic acid (C18:1 \(\Delta\)9c). Quantification for all these C18:1 trans isomers was conducted as the sum of all of them. After the quantification of the individual FA, the sums of saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), trans, \(\upomega\)-3 and \(\upomega\)-6 were also calculated.
## 1 Introduction
Using a shift in the inter-block coefficients, the authors of [26] described a method for embedding a watermark into data that was both undetectable and extremely secure; this method was based on DCT blocks. Digital watermarks can be created by using these techniques, but their quality is severely restricted. To a large extent, the scaling factor determines the watermark's quality. The scaling factor determines the combination of the cover and watermark images in a specific proportion. The method's robustness, i.e., the quality of the recovered image, will suffer if the scaling factor is set to a low number. The quality of the watermarked image and, therefore, its imperceptibility will suffer, however, if the scaling factor is set to a large number. Accordingly, it is necessary to maximize the scaling factor. To optimize many different types of problems, meta-heuristic algorithms are frequently utilized [25]. This paper proposes a new algorithm for improving the watermarking approach based on the DWT and DCT. The proposed algorithm combines the dipper-throated optimization (DTO) algorithm with the stochastic fractal search (SFS) algorithm and is referred to as DTOSFS. The proposed algorithm's advantage is that it can find the optimal regions in a cover image to implant a target message. The target message in our case is a medical image, which is sensitive for patients who wish to store it securely through transmission and communication with health agencies. The main contributions of this work can be summarized in the following points:  
* A new algorithm is proposed in order to improve the watermarking of sensitive images. * The proposed optimization algorithm is tested in terms of the DWT+DCT watermarking method to prove its effectiveness. * The proposed approach is compared with the original DWT+DCT and the original DTO and SFS, in addition to another watermarking method based on the discrete Fourier transform (DFT) + singular value decomposition, to prove the effectiveness and superiority of the proposed approach. * The proposed approach is tested in terms of various methods of attack, including the average filtering, Gaussian filtering, median filtering, and Wiener filtering methods, to show its robustness. * The proposed approach is tested in terms of various cover images. The structure of this paper is as follows. A review of previous studies addressing recent watermarking approaches is presented in Section 2. The materials and methods employed in the proposed methodology are described in Section 3. The proposed methodology is then illustrated in Section 4. The achieved results and the corresponding analysis are explained in Section 5. Finally, the conclusion and future perspectives are presented in Section 6.