esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Representation of a Normal Map for Screen-Space Fluid Rendering
## 4 Training Data and Model Architecture
### Datasets and Training Scenes
The marching-cube method has a disadvantage in that the computation time depends on the spatial resolution and the number of particles because a scalar field must be calculated for each particle. Therefore, this method is not often used in real-time rendering, where the computation time is important. In our experiments, a normal map generated using the marching-cube method was used for the target datasets. Because the surface creation time is independent of the computation time of our proposed algorithm, a marching-cube surface extraction algorithm with an anisotropic kernel was used. While creating a normal map, the view-space normal was used, with every normal being a unit vector in the 3D space. Therefore, to store data in the range of 0\(\sim\)1, each channel was multiplied by 0.5, and 0.5 was added. **Scene types:** Several sets of training data were used to investigate the results obtained in a variety of situations (as shown in Figure 6). The training data comprised six scenes in total. The training data and validation data were generated separately for each scene. While training the model, we updated the weights using the training data and checked how the training was proceeding by using validation data at every epoch. We simulated 500 frames for each scene. For each scene, there was a static scene where the camera was held as the motion of the fluid was rendered, and there was also a dynamic scene where the camera moved around as the fluid motion was rendered. In the training process, we used cropped 512 \(\times\) 512 images, applying data augmentation to increase the amount of data. This process was only applied to translation (not to rotation and shear) because the normal is defined in the view space, as shown in Figure 7.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithm Based on the End-to-End Mode
#### 2.1.2 Delay-Based CC Algorithms  
Delay-based CC algorithms perform link congestion control by measuring the change in delay. The Vegas algorithm takes the increase in RTT as a signal of network congestion. As RTT increases, the congestion window decreases; as RTT decreases, the congestion window increases. The FAST TCP [16] algorithm is a delay-based TCP congestion control algorithm that mainly measures the queue delay in the network as a reference for the degree of network congestion. The delay-based CC algorithm can more accurately judge whether the network is congested than the method based on packet loss. This is because when the data packet loss occurs, the queue has already experienced serious congestion. If CC can be implemented when the router queue starts to jam, it is more conducive to reducing the data packet loss and improving the bandwidth utilization. When there is considerable noise packet loss on the link, the loss-based CC algorithm cannot distinguish between noise packet loss and packet loss caused by router buffer overflow. In this case, the loss-based CC algorithm frequently enters the congestion control phase, resulting in low bandwidth utilization.
# A Survey on Blockchain-Based Federated Learning
## 4 Applications
### Internet of Things  
In the realm of IoT, devices are decentralized, and consequently, conducting model training on these devices necessitates both timely and secure data access, as well as robust model generalization capabilities. The research pertaining to the application of BFL within the IoT domain predominantly centers on addressing concerns related to data security, resource allocation, communication protocols, and failure detection [27; 71; 108; 122]. The overarching objective of these efforts is to empower IoT devices to collaboratively train models that exhibit high performance. [PERSON] et al. [89] constructed a distributed multi-party data sharing model that further ensures the authenticity of data through differential privacy, allowing devices to retrieve data securely and accurately. Instead of the common PoWconsensus algorithm, the proof of training quality (PoQ) consensus algorithm in [89] is used to verify training models, aiming to improve the utilization efficiency of computational resources. To help household appliance manufacturers improve service quality and optimize appliance functions, [PERSON] et al. [109] introduced a hierarchical crowdsourcing FL system, utilizing blockchain technology to prevent malicious model updates. To make the 6G network more secure and efficiently apply it to the IoT, [PERSON] et al. [83] proposed a combination of a blockchain and FL, integrating mobile edge computing and device to device (D2D) communication, to address the challenges faced by the 6G network.
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 2 Materials and Methods
### The Proposed Method
#### 2.2.1 Episodic Training Mechanism and Backbone  
Assume we have a dataset \(\mathcal{D}\) and define \(\mathcal{C}\) as the set of all categories in \(\mathcal{D}\). In few-shot learning, we further divide the category set \(\mathcal{C}\) into \(\mathcal{C}_{base}\) and \(\mathcal{C}_{novel}\), where \(\mathcal{C}_{base}\) represents the set of categories in the base dataset, and \(\mathcal{C}_{novel}\) represents the set of categories in the novel dataset. Specifically, \(\mathcal{C}_{base}\cap\mathcal{C}_{novel}=\emptyset\) and \(\mathcal{C}_{base}\cup\mathcal{C}_{novel}=\mathcal{C}\). We divide training set \(\mathcal{D}_{base}\) and test set \(\mathcal{D}_{novel}\) based on \(\mathcal{C}_{base}\) and \(\mathcal{C}_{novel}\). Episodic training mechanism takes a single task as the basic unit of training, and the entire training process can be divided into two stages: meta-training and meta-testing. In the meta-training stage, different tasks are constructed by combining different samples in \(\mathcal{D}_{base}\). In particular, \(M\) categories are randomly selected from \(\mathcal{C}_{base}\), and each category randomly selects \(K\) samples. Thus, a task \(\tau_{i}\) consists of a support set \(S=\{(x_{i},y_{i})\}_{i=1}^{M\times K}\) and a query set \(Q=\{(x_{i},y_{i})\}_{i=1}^{q}\), where \(Q\) is composed of \(q\) samples selected from \(M\) categories. This task construction pattern is also known as the M-way K-shot problem. These episodic tasks utilize the support set \(S\) to build a classifier for training and use the query set \(Q\) to evaluate. During the meta-training stage, the model learns transferable meta-knowledge from the \(\mathcal{D}_{base}\). In the meta-testing stage, the learned meta-knowledge is utilized to identify the few samples from \(\mathcal{D}_{novel}\). The episodic training strategy effectively supports cross-task generalization from \(\mathcal{D}_{base}\) to \(\mathcal{D}_{novel}\).  
In the task of few-shot image classification, we follow the ResNet with parameter \(\varphi\) used in [16] as the backbone. ResNet introduces residual modules to solve problems such as vanishing gradients and exploding gradients, ensuring the stability of the model. ResNet generally consists of several residual modules, with each residual block composed of several convolutional layers. By utilizing these convolutional layers, ResNet obtains image feature embeddings at multiple scales.
## 2 Related Work  
We classify existing research into three groups: supervised, unsupervised, and semi-supervised algorithms. For supervised algorithms [3; 5; 8], the set of all possible traffic flows and output data must be known beforehand. A training data set of sample instances is used to build this classification model. The model that is built can be used to predict the class membership for any unknown instances by analyzing the characteristic values of these flows. Supervised learning mainly helps with organizing new instances into some pre-defined categories by building knowledge structures [3; 8]. We aim to provide a set of sample instances which are pre-categorized to the learning machine. The output generated by the learning process is called a classification model. A classification model is built by first examining and then inferring from the set of given inputs. Therefore, the main focus in the supervised algorithm is mapping the input features to the output class and building a model. The knowledge acquired can be manifested as classification rules, or decision trees, or flowcharts, etc. This acquired knowledge is used in the future to classify new instances [5; 8]. Supervised learning mainly comprises two levels: training and testing. The first level, called training, is a knowledge-acquiring level which examines the sample data, termed the training data set, and then develops a classification model. The second level, called testing, is used as the classifying step in which the model is created after the training is employed to analyze any unknown instances. Unlike supervised machine learning, clustering techniques do not apply any sample instances for training; alternatively, they create clusters in a given data set applying internalized heuristics.
## 3 Results
### Number of Protons per MU  
The beam monitor calibration in terms of the number of protons per MU is derived from the DAP measurement and the Monte Carlo simulation. The beam monitor in the nozzle of different energies was calibrated with both measurement results and the Monte Carlo simulation (Figure 7). Both methods demonstrated consistent results. The error bars derived from MC were bigger than those derived from the BPC at low energy regions. The peak value takes place at 83.8 MeV. The error comes from the uncertainty of DAP measurement and is attributed to the instability of the scanned field. The percentage difference in Table 2 denotes the difference in the number of protons per MU derived from the BPC compared to that from MC. The maximum deviation was 2.61%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Energy (MeV)**} & \multicolumn{3}{c}{**DAP (Gy*mm2/MU)**} & \multicolumn{3}{c}{**Standard Deviation (Gy*mm2/MU)**} & \multirow{2}{*}{**Percent Difference**} \\ \cline{2-2} \cline{5-7}  & **MC** & **BPC** & **Mean Value** & **MC** & **BPC** & \\ \hline
235 & 73.24 & 72.66 & 72.95 & 0.17 & 0.03 & \(-\)0.79\% \\
212.6 & 72.82 & 72.17 & 72.49 & 0.28 & 0.27 & \(-\)0.89\% \\
185.8 & 72.99 & 72.55 & 72.77 & 0.18 & 0.20 & \(-\)0.60\% \\
179.9 & 72.82 & 72.50 & 72.66 & 0.31 & 0.11 & \(-\)0.44\% \\
174.2 & 73.04 & 72.31 & 72.67 & 0.50 & 0.06 & \(-\)1.00\% \\
166.6 & 73.05 & 72.66 & 72.86 & 0.27 & 0.04 & \(-\)0.53\% \\
148.7 & 73.24 & 72.89 & 73.07 & 0.35 & 0.04 & \(-\)0.48\% \\
140.1 & 73.92 & 73.42 & 73.67 & 0.31 & 0.11 & \(-\)0.68\% \\
86.8 & 81.76 & 81.14 & 81.45 & 0.93 & 0.05 & \(-\)0.76\% \\
83.8 & 84.33 & 82.22 & 83.28 & 1.89 & 0.03 & \(-\)2.50\% \\
70.6 & 90.94 & 91.29 & 91.11 & 0.66 & 0.15 & 0.38\% \\ \hline \end{tabular}
\end{table}
Table 1: DAP and the standard deviation measured with Markus chamber and Bragg peak chamber.  
Figure 7: Number of protons per MU as a function of nominal proton beam energy derived from Markus chamber and Bragg peak chamber measurements.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 4 Experiments and Results
### Image Cleaning  
The recognition of an image by the human visual system involves the inference and perception of a defective region based on the contours of the image. As shown in Figure 4c, the image is filled by extending the contour from the edge of the image defect. While an incorrect contour may be perceived by the visual system (Figure 4d), the entire contour of the defective area is an important factor in determining whether the restoration of an image is correct.  
The mural itself may contain noise. The continuous contour extraction of an image can produce many irrelevant contours. Therefore, clear image contours must be extracted to reduce the noise processing of the image. We set different sizes for the convolution kernel and different kernel parameters and used four noise reduction methods to smoothen the image.  
The mean filter used a 3 \(\times\) 3 convolution kernel with the parameters on the kernel set to 1/9 to achieve the smoothing effect; the Gaussian filter uses a 5 \(\times\) 5 convolution kernel to manipulate the image. We normalized the box filter to achieve the same effect as the Gaussian filter. The median filter extracts an area of 5 \(\times\) 5 pixels, and the median is saved to that pixel point after sorting. Figure 5 illustrates the noise reduction effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Restoration Type** & \multicolumn{2}{c}{**Large Regular Defects**} & \multicolumn{2}{c}{**Random Irregular Defects**} \\ \hline Group Number & a1 & a2 & b1 & b2 \\ \hline SSIM Value & 0.7943 & 0.8154 & 0.9331 & 0.9107 \\ \hline \end{tabular}
\end{table}
Table 1: Calculation results of SSIM values.  
Figure 4: Image contours of the defective area ((**a**) original image, (**b**) defective image, (**c**) correct contour, (**d**) error contour).  
Figure 5: Image noise reduction methods (blur means mean filter, box_norm means regularized filter, box_no_norm means non-regularized filter, gaussian means Gaussian filter, mean means median filter).  
By comparing the four filtering methods, we determined that using a median filter result in the loss of more contour details. The normalized box and Gaussian filters exhibited insufficient smoothing effects. We selected the mean filter method for noise reduction in the image.  
The process of contour extraction: First, to prevent distortion, we converted the image to be processed into a grayscale image. Subsequently, the contour values of the image were extracted via binarization. Finally, we redrew the contours from the original image according to the extracted contour values to obtain a contour map. Figure 6 shows the original image, grayscale image, binarized image, image contour, and image contour drawn on the original image. The figure shows the contour extraction of the two pictures and the effect of noise reduction on the contour extraction. Noise reduction reduces the influence of irrelevant contours and makes the extracted picture contours clearer.  
Figure 6: Image contour extraction process and results.
## 4 Results
### Usage of the Internet and Social Media
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Pattern 1**} & \multicolumn{3}{c}{**Pattern 2**} & \multicolumn{3}{c}{**Pattern 3 1**} \\  & **(LINE + Twitter + Instagram)** & **(LINE + Twitter + Instagram + TikTok)** & **(LINE + Twitter + Instagram + TikTok)** & **(LINE + TikTok)** & **(LINE + TikTok)** \\  & **2021** & **2022** & **2023** & **2021** & **2022** & **2023** & **2022** & **2023** \\  & **(\(n\) = 737)** & **(\(n\) = 477)** & **(\(n\) = 276)** & **(\(n\) = 176)** & **(\(n\) = 127)** & **(\(n\) = 95)** & **2021** & **(\(n\) = 119)** & **(\(n\) = 122)** \\ \hline Gender & & & & & & & & \\ \hline Males & 294 & 180 & 109 & 64 & 53 & 30 & 79 & 84 \\ Females & 436 & 292 & 166 & 111 & 73 & 63 & 37 & 36 \\ Others & 7 & 5 & 1 & 1 & 1 & 2 & 3 & 2 \\ \hline Average Usage Time (hours/month) & & & & & & & & \\ \hline LINE & 40.3 & 37.7 & 37.4 & 46.4 & 42.5 & 42.6 & 38.1 & 37.9 \\ Twitter & 55.9 & 46.4 & 44.8 & 45.0 & 47.2 & 50.8 & 76.4 & 63.9 \\ Instagram & 39.3 & 36.9 & 38.9 & 50.5 & 43.9 & 44.5 & 32.5 & 34.9 \\ Discord & & & & & & & 45.9 & 40.3 \\ TikTok & & & 47.4 & 45.6 & 47.7 & & \\ \hline Post Frequency (days/month) 2 & & & & & & & \\ \hline LINE & 20.2 & 17.0 & 17.5 & 19.6 & 18.0 & 19.1 & 18.4 & 20.4 \\ Twitter & 12.5 & 10.1 & 9.3 & 11.8 & 11.0 & 10.7 & 18.4 & 17.4 \\ Instagram & 6.8 & 6.5 & 6.4 & 11.0 & 7.7 & 8.6 & 7.0 & 7.9 \\ Discord & & & & & & 9.2 & 10.0 \\ TikTok & & & 5.4 & 3.1 & 2.5 & & \\ \hline \hline \end{tabular}  
* \({}^{1}\) Discord data started to be collected in the 2022 survey. \({}^{2}\) To calculate the frequency of social media use, we converted the responses as follows: never or seldom = 0 days/month; once per month = 1 day/month; once a week = 5 days/month; several times a week = 15 days/month; and almost every day = 30 days/month.  
\end{table} 
Table 4: Changes over time in university students’ social media use patterns, usage time, and posting frequency (for the top three patterns). three years (Table 5). The results showed that LINE and Discord were used most frequently to post messages in response to friends, and Discord was also used to post messages regarding common hobbies. Twitter was used the most to post information about common hobbies, whereas Instagram was used to upload photos and videos and create posts related to users' daily lives and those of their friends. In contrast, 80% of respondents did not post anything on TiTok.
# An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
## 4 Experiments
### Results
#### 4.3.3 Polbooks  
Table 4 and Figure 4 show the results on the Polbooks dataset. The result differs from the labeled partition by 16 nodes: 0, 4, 6, 18, 28, 46, 48, 52, 58, 64, 65, 67, 68, 76, 77, and 85.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **CN** & **NMI** \\ \hline Labeled partition & 3 & / \\ AMI-NRL & **3** & **0.58** \\ GN & 5 & 0.56 \\ FN & **3** & 0.57 \\ Louvain & **3** & 0.55 \\ IE & 2 & 0.55 \\ EdMot & 5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of AMI-NRL and the other community detection algorithms on Polbooks.  
Figure 3: (**a**) AMI variation graph of adjacent partitions of Dolphins. (**b**) Vectors representing each node of Dolphins. (**c**) Result of community detection on Dolphins.  
Table 5 shows the result of the link analysis on these nodes, taking into account the edges inside communities and between communities in both community detection results and labeled partitions. As shown in Table 5, nodes 0, 4, 6, 18, 28, 46, 48, 58, 64, 65, 76, and 77 had a greater number of edges inside communities than the labeled partition; nodes 52, 67, and 68 had the same internal and external edges as that of the labeled partition. Only node 85 had one more internal edge than the labeled partition. Therefore, we hold the opinion that the AMI-NRL achieves a better result on the Polbooks than the labeled partition.  
Figure 4: (**a**) AMI variation graph of adjacent partitions of Polbooks. (**b**) Vectors representing each node of Polbooks. (**c**) Result of community detection on Polbooks.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Labeled Partition**} & \multicolumn{2}{c}{**AMI-NRL**} \\ \cline{2-5}
**Node** & **Internal Links** & **External Links** & **Internal Links** & **External Links** \\ \hline
0 & 2 & 4 & 6 & 0 \\
4 & 3 & 5 & 5 & 3 \\
6 & 4 & 7 & 11 & 0 \\
18 & 1 & 2 & 3 & 0 \\
28 & 1 & 2 & 2 & 1 \\
46 & 0 & 4 & 1 & 3 \\
48 & 0 & 4 & 4 & 0 \\
52 & 3 & 2 & 3 & 2 \\
58 & 5 & 8 & 7 & 6 \\
64 & 5 & 4 & 7 & 2 \\
65 & 4 & 3 & 7 & 0 \\
67 & 4 & 2 & 4 & 2 \\
68 & 3 & 1 & 3 & 1 \\
76 & 0 & 13 & 11 & 2 \\
77 & 2 & 5 & 5 & 2 \\
85 & 3 & 2 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Link analysis of the result and the labeled community partition on Polbooks.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## Appendix A  
Prove: When the encryption algorithm is \(c=E(m)=m^{e}modn\), the decryption algorithm is \(m=D(c)=c^{d}modn\).  
Assuming that the proof holds, then we have  
\[m=D(c)=c^{d}modn \tag{11}\]  
Obtained from Equation (11)  
\[c^{d}\equiv m(\bmod n) \tag{12}\]  
\[c=E(m)=m^{e}modn \tag{13}\]  
It can be seen from Equation (13), and set its quotient as, then  
\[c=m^{e}-kn \tag{14}\]  
It can be obtained from Equations (12) and (14)  
\[(m^{e}-kn)^{d}\equiv m(\bmod n) \tag{15}\]  
Expand the left side of Equation (15)  
\[\begin{split}(m^{e}-kn)^{d}&=C_{d}^{0}(m^{e})^{d} (-kn)^{0}+C_{d}^{1}(m^{e})^{d-1}(-kn)^{1}\\ &+\cdots+C_{d}^{d-1}(m^{e})^{1}(-kn)^{d-1}+C_{d}^{d}(m^{e})^{0}(- kn)^{d}\end{split} \tag{16}\]  
From Equation (16), except for the first term, the rest of the terms contain the factor, so Equation (15) can be simplified as  
\[m^{ed}\equiv m(\bmod n) \tag{17}\]  
During the encryption process, it is known  
\[(de)\bmod\varphi(n)=1 \tag{18}\]  
Let the quotient be \(h\), we can get  
\[ed=h\varphi(n)+1 \tag{19}\]  
From Equations (17) and (19) we can get  
\[m^{hp(n)+1}\equiv m(\bmod n) \tag{20}\]  
Therefore, to prove that the hypothesis holds, it is only necessary to prove that Equation (19) holds.  
The following is divided into two cases to discuss and prove.  
1 if \(m\) and \(n\) are coprime, and \(m<n\).  
According to [PERSON]'s theorem, we can get  
\[m^{\varphi(n)}\equiv 1\bmod(n)=kn+1 \tag{21}\]  
Multiply the exponents on both sides of Equation (21) simultaneously to get  
\[m^{hp(n)}=(kn+1)^{hp(n)} \tag{22}\]  
Processing with Equation (16), we can get  
\[m^{hp(n)}=(kn+1)^{hp(n)}\equiv 1(\bmod n) \tag{23}\]Multiplying both sides of Equation (A13) by \(m\), we can get  
\[m^{hp(n)+1}\equiv m(\text{mod}n)\] (A14)  
Therefore, when \(m\) and \(n\) are mutually prime, the proof is complete.  
2 if \(m\) and \(n\) are not coprime, and \(m<n\).  
Assume, \(m=l\times p\) and \(n=p\times q\).  
Therefore, necessarily \(m\) and \(q\) coprime, we can get  
\[\gcd(m,n)=p\] (A15)  
And according to [PERSON]'s theorem, we can get  
\[m^{\varphi(q)}\equiv 1(\text{mod}q)\] (A16)  
Since \(q\) is a prime number, we can get  
\[m^{q-1}\equiv 1(\text{mod}q)\] (A17)  
Multiplying the left side of Equation (A17) by \(m^{h(p-1)}\), we get  
\[m^{h(p-1)(q-1)}\text{mod}q=1\] (A18)  
also because  
\[\varphi(n)=(p-1)(q-1)\] (A19)  
Available  
\[m^{hp(n)}\text{mod}q=1\] (A20)  
Substitute Equation (A9) into the above equation to get  
\[m^{ed-1}\text{mod}q=1\] (A21)  
\[m^{ed-1}=l_{1}q+1\] (A22)  
Multiplying both sides of the equation by \(m\), we get  
\[m^{ed}=l_{1}qm+m\] (A23)  
Substitute the assumptions into  
\[m^{ed}=l_{1}q\times lp+lp\] (A24)  
And because \(n=p\times q\), you can get  
\[m^{ed}=l_{1}\times l\times n+lp\] (A25)  
Substitute Equation (A9) into the above equation, we can get  
\[m^{hp(n)+1}=l_{1}\times l\times n+m\] (A26)  
Therefore  
\[m^{hp(n)+1}\equiv m(\text{mod}n)\] (A27)  
Therefore, if \(m\) and \(n\) are not mutually prime, the proof is complete.
## 3 Proposed Solution
### Classification Model
#### 3.4.1 Data Balancing  
A deal imbalanced dataset is quite common in real-time classification problems. Sometimes the number of levels in one observation class is comparatively less than other target class levels. This kind of imbalanced dataset gives significantly inaccurate results in the classification method. There is a solution to this problem called resampling (oversampling and undersampling). The synthetic minority oversampling technique, or SMOTE, is an appropriate technique to oversample the minority class in our case.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### PV Cell Models
#### 2.1.3 PV Module  
In the case of the PV module, the photovoltaic cells are connected in series or in parallel. Equation (3) shows the calculation of the output current when considering a PV module [38].  
\[I=N_{p}\ I_{ph}-N_{p}\ I_{sd1}\left[exp\left(\frac{N_{p}\ V+N_{s}\ IR_{s}}{n_{1}N _{p}\ N_{s}\ V_{t}}\right)-1\right]-N_{p}\ I_{sd2}\left[exp\left(\frac{N_{p}\ V+N_{s}\ IR_{s}}{n_{2}N_{p}\ N_{s}\ V_{t}}\right)-1\right]-\frac{N_{p}\ V+N_{s} IR_{s}}{N_{s}\ R_{sh}} \tag{3}\]  
where \(N_{s}\) is the number of series connections between PV cells, while \(N_{p}\) is the number of parallel PV cell connections. For this study, \(N_{p}\) is set to be equal to 1 because, in general, for industrial modules, the photovoltaic cells are connected in series. The parameters for the PV module will be used to extract both models, which in the SDM requires five parameters to be extracted, and the second bracket from Equation (3) can be considered as 0. On the other hand, in the case of the DDM, to improve the functionality of the module, seven parameters must be extracted.