esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Environmental Applications of Organoclays
### Water Pollution
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clay** & **Modifier** & **Methodology** & **Application** & **Efficiency** & **Reference** \\ \hline Montmorillonite (Qatar) & Iron (III) chloride hexahydrate & Cation exchange & Heavy metal Removal & 0.191 mg g\({}^{-1}\) & [82] \\ \hline Montmorillonite (China) & (11-Ferrocenylundecyl) trimethyl ammonium bromide (FTMA) & Cation exchange & Phenol Removal & 19.3 mg g\({}^{-1}\) & [83] \\ \hline Na- & Didodecyl di- & & Phenol Removal & 81.68 mg g\({}^{-1}\) & \\ Montmorillonite (China) & methyl hydroxypropyl-multi amine & Cation exchange & 2-CP & 336.59 mg g\({}^{-1}\) & [84] \\ (China) & bis quaternary ammonium salt & & 2,4,6-TCP & 535.49 mg g\({}^{-1}\) & \\ \hline Bentonite & & & & \\ Montmorillonite (Ethiopia) & Aluminum axi-hydroxide (AO) & Cation exchange & Fluoride Removal & 28\% & [85] \\ (Ethiopia) & & & Removal & 45\% & [85] \\ \hline Montmorillonite (Algeria) & 1-butyl-3-methylimidazolium chloride & Cation exchange & Dye Removal & 1.918 mg g\({}^{-1}\) & [86] \\ \hline  & & 2019 & & \\ \hline  & dioctadecyl tetrahydrooxyethyl & & & \\ Na- & dibromopropane diammonium & & & \\ Montmorillonite (DTDD) & Cation exchange & Dye Removal & 250.63 mg g\({}^{-1}\) & [87] \\ (China) & octadecylmethylidihydroxyethyl & & & \\ ammonium bromide (OMDAB) & & & & \\ \hline Montmorillonite & & & & \\ Montmorillonite (Iran) & alkyl dimethyl benzyl ammonium chloride & Cation exchange & Dye Removal & 227.3 mg g\({}^{-1}\) & [88] \\ \hline  & sodium eicosenoate & & & \\ Montmorillonite (Iran) & cetyltrimethylammonium chloride & & & \\ (Iran) & Fe\({}_{3}\)O\({}_{4}\)MNPs (magnetic na- & Cation exchange & & \\  & norparticles) & & & \\ \hline Montmorillonite (Argentina) & Fe(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O & Cation exchange & Heavy metal Removal & 6.3 g kg\({}^{-1}\) & [90] \\ (2\(\cdot\)) & Chitosan & Cation exchange & Heavy metal Removal & 0.185 mg g\({}^{-1}\) & [91] \\ (Janpan) & & & & \\ \hline Na- & & & & \\ Montmorillonite (China) & L-lysine & Cation exchange & Heavy metal Removal & 48.89 mmol & [92] \\ (China) & Gemini surfactant, butane-1,4 & & & \\ (China) & bis(dodecyl dimethyl ammonium bromide) & Cation exchange & & \\ \hline Acid Montmorillonite & chlorophyll trimethoxy silane & & & \\ \multicolumn{1}{c}{rimolite (Iran)} & imidazole & Cation exchange & Phenol Removal & 95\% & [94] \\ \hline  & Hexadecyltrimethyl ammonium & & & & \\ Montmorillonite (China) & poly-4- & Cation exchange & & \\ (China) & Vinylpyridine-co-styrene & & & \\ \hline  & & 2020 & & \\ \hline Montmorillonite & & & & \\ and zeolite (Korea) & Hexadecyltrimethylammonium & Cation exchange & Phenol Removal & 23.8 mmol kg\({}^{-1}\) & [96] \\ (Korea) & & & Removal & 59.4 mmol kg\({}^{-1}\) & [97] \\ \hline Montmorillonite & & & & \\ (Spain) & octadecylamine & Cation exchange & Phenol Removal & 64 mg g\({}^{-1}\) & [97] \\ (Spain) & octadecylamine & Cation exchange & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 3 The Proposed Method
### Re-Detection Module  
The re-detection module is a crucial component to improve the robustness and long-term tracking ability of the tracker. It is used to find the target quickly after it has been lost. Our method is based on the LCT+ [30] method, and an online support vector machine (SVM) classifier is used as the detector. The difference is that another confidence parameter (Section 3.4) is used as the criterion in conjunction with the predefined re-detection threshold \(T_{r}\). The re-detector only trains the translated samples to reduce the computational burden. The feature representation of the sample is based on the multiple experts using the entropy minimization (MEEM) method [40], namely the quantized color histogram. For a training set \(\{(\mathbf{v}_{i},c_{i})i=1,2,\ldots,N\}\) with \(N\) samples in a frame, the objective function of solving the SVM detector hyperplane \(\mathbf{h}\) is:  
\[\begin{array}{l}\underset{h}{\min}\frac{\lambda}{2}\|\mathbf{h}\|^{2}+\frac{ 1}{N}\underset{i}{\sum}\,\ell(\mathbf{h};(\mathbf{v}_{i},c_{i})),\\ \text{where }\ell(\mathbf{h};(\mathbf{v}_{i},c_{i}))=\max\{0,1-c\langle \mathbf{h},\mathbf{v}\rangle\},\end{array} \tag{8}\]  
where \(\mathbf{v}_{i}\) represents the feature vector generated by the \(i\)-th sample, and \(c_{i}\in\{+1,-1\}\) represents the class label. The notation \(\langle\mathbf{h},\mathbf{v}\rangle\) represents the inner product of the vectors \(\mathbf{h}\) and \(\mathbf{v}\). A passive-aggressive algorithm is used to update the hyperplane parameters:  
\[\mathbf{h}\leftarrow\mathbf{h}-\frac{\ell(\mathbf{h};(\mathbf{v},c))}{\left\| \
abla_{\mathbf{h}}\ell(\mathbf{h};(\mathbf{v},c))\right\|^{2}+\frac{1}{2T}} \
abla_{\mathbf{h}}\ell(\mathbf{h};(\mathbf{v},c)), \tag{9}\]  
where the gradient of the loss function \(\mathbf{h}\) is denoted by \(\
abla_{\mathbf{h}}\ell(\mathbf{h};(\mathbf{v},c))\), and \(\tau\in(0,+\infty)\) is a hyper-parameter used to control the \(\mathbf{h}\) update rate. Similar to the long-term filter \(\mathbf{f}_{\text{Long}}\), we use Equation (9) to update the classifier parameters only when \(C_{t,\text{Long}}\geq T_{a}\).
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 3 Proposed Framework
### Embedding Network  
Figure 3 shows the flow chart of the watermark embedding network, the detailed process is as follows:  
1. DCT layer: A DCT is performed on the attention feature map generated by the attention network. This is because embedding the watermark in the DCT is consistent with the frequency-masking effect of the HVS, and the human eye has a large acceptable range of changes.
2. Watermark embedding: The network structure is a \(1\times 1\) Conv and four \(2\times 2\) Conv. Each Conv includes convolution, batch normalization, and ReLU activation functions, and the number of convolution kernels is 64. The input is the frequency-domain information after the DCT transformation and watermark information, and the output is the information after the feature fusion. The \(1\times 1\) convolutional layer is to change the dimension of the tensor. The \(2\times 2\) convolutional layers embed the watermark data into bottom features. The \(2\times 2\) convolutional layers are designed as residual structures, with each residual structure corresponding to two convolutional layers, that is, a shortcut connection is added every two layers.
3. Inverse DCT (IDCT): an inverse DCT is performed on the tensor generated by the watermark embedding network to transform it from the frequency domain back to the spatial domain.
4. Embedding strength factor \(\alpha\): The embedding strength factor \(\alpha\) controls the embedding strength of the watermark, achieving the balance between imperceptibility and robustness. The tensor generated in step (3) is multiplied by \(\alpha\) and then added to the attention feature map to generate the watermarked tensor. The experimental results showed that with the increase of \(\alpha\), the robustness was enhanced. To enhance the robustness of the algorithm, \(\alpha\) was taken as 1.0 in the training phase. In the actual application phase, \(\alpha\) can be appropriately adjusted according to application requirements.
5. A \(3\times 3\) Conv: The number of convolution kernels is 1. The input is the output of step (4), and the output is the watermarked image with a size of \(M\times N\), the same as that of the cover image.
## 2 Related Work
### Fl  
FL was first proposed by Google. FL is a distributed learning method that does not send the data of each user to a centralized server; instead, it trains on the local device and shares only the results with the server. While the existing centralized learning model gathers data in one place to train a model, FL performs training with the data distributed across devices or on the local server. This protects data privacy, and the central server only integrates model updates from individual devices to learn a global model [9; 36].  
Figure 4 shows the structure and operation of FL. The FL process starts with the central server distributing the initial model to individual devices. Each device then trains the model using its local data and sends the trained results to the central server. The central server aggregates this information to create a new global model, which is then distributed back to the devices, and the process is repeated. During this process, the client's data are never sent to the server; only model updates are sent back and forth, minimizing the risk of data leakage [37]. FL has three main features. First, local devices train models using their own data, so the data never leave the device. Second, a central server integrates model updates from all devices to create a new global model and redistributes this model to the devices. Third, the communication between the devices and the server only involves model updates, and various protocols are required for efficient communication [38].  
Figure 4: FL process.  
Several unresolved challenges remain: The first challenge is asynchronous learning. Since all devices cannot participate in learning at all times, depending on the availability of devices or network conditions, some devices may not be able to participate in learning, thus creating an unbalanced learning environment. The second challenge is data imbalance, where the amount or nature of data held by each device is different, affecting model performance. To address this, the recently proposed Federated Feature Augmentation (FedFA) approach improves model performance by sharing the feature space between clients. FedFA uses each client's local data to extract feature vectors, which are then combined with data from other clients to form a richer feature space. This process mitigates the problem of data imbalance and enables more accurate model training, especially for clients with small datasets [39]. The third problem is communication cost; there is a need for research to reduce the cost of devices periodically sending weights to the server. Finally, privacy and security concerns need to be addressed. To prevent attacks based on model updates, techniques such as differential privacy and homomorphic encryption can be applied to enhance the security of personal data [40; 41; 42]. FL has great potential for applications in various fields, such as smartphone applications, healthcare, autonomous vehicles, and IoT, and in the future, with the advancement of network technology and artificial intelligence (AI) technology, more diverse applications of FL are expected [43].
## 3 Residual Dense Convolutional Graph-U-Net-Based GAN for Target Speech Enhancement
### Graph Construction
#### 3.3.2 Discriminator Network  
The discriminator is similar to the encoding stage of the generator network. Thus, it consists of a series of densely connected residual convolution blocks. Leaky ReLU (IReLU) is applied following the convolutional layers in the discriminator. After the down sampling process, the feature maps will pass through a fully connected layer whose output is a confidence value representing the similarity between the generated mask and true mask.  
The functionalities of the generator and discriminator can be concisely expressed by the following steps:  
Update discriminator such that (x, y)-pairs are classified as real i.e., \(D(x,y)=1\).  
Update discriminator such that the pair comprising generated samples \(x_{pred}\) and true labels \(y\) are classified as fake i.e., \(D(x_{pred},y)=0\).  
Freeze the discriminator and update the generator such that the discriminator classifies the \((x_{pred},y)\)-pair as real i.e., \(D(x_{pred},y)=1\).
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 3 Results and Discussions  
### LA-MoS\({}_{2}\) Synthesis and Structural Characterization  
MoS\({}_{2}\) was obtained by hydrothermal synthesis and was characterized by Raman spectroscopy to confirm the nano-sheets formation. The spectrum of MoS\({}_{2}\) shows two characteristic peaks, the out-of-plane vibration of sulfur atoms (A\({}_{1g}\)) at 405.6 cm\({}^{-1}\) and a peak characterizing the in-plane vibration of molybdenum and sulfur atoms (\(E_{2g}^{1}\)) located at 382.5 cm\({}^{-1}\) (Figure 1a). The pic-to-pic difference of 23.1 cm\({}^{-1}\) is consistent with obtaining one monolayer of MoS\({}_{2}\)[35]. To introduce functional group on the surface of MoS\({}_{2}\), the nano-sheets were treated with lipoic acid, which forms strong interaction between molybdenum and thiol group. This will provide functional acid group on the surface of MoS\({}_{2}\) for further aptamer immobilization.  
To confirm the presence of lipoic acid moieties on the MoS2 surface, FT-IR analysis was performed. The spectra displayed in Figure 1b show the characteristic bands attributed to LA such as the band located at 3500 cm\({}^{-1}\) corresponding to O-H vibrations of carboxylic group, the band at 2925 and 2850 cm\({}^{-1}\) corresponding to CH2 and CH stretching vibration and the band 1670 cm\({}^{-1}\) and 1434 cm\({}^{-1}\) corresponding to C=O and C-O vibrations, respectively of carbonyl group [36]. All above mentioned bands are absent in the control spectrum of MoS2. Furthermore, the broad band located at 3392 cm\({}^{-1}\) corresponds to O-H stretching vibration of residual solvent in the case of unmodified MoS2 nano-sheets [37].
## Appendix A  
This appendix illustrated the proving process to the controllability of the linear system of Equations (13) and (14).  
**Proof.** Given \(rank(E)=rank(S^{-1}U^{H}EV)\), the rank of \(E_{I}\) (Rank) is equivalent to discussing the rank of the following Equation (A1):  
\[\Big{[}I_{n^{2}}\quad 0_{n^{2}\times n(m-1)}\Big{]}+\sum_{j=1}^{n}\epsilon_{j}F _{j}\] (A1)  
where \(F_{j}=S^{-1}U^{T}E_{j}V,j=1,2,\cdots\overline{n}\). If the matrix has at least one nonsingular \(n^{2}\times n^{2}\) submatrix, then the matrix whose rank is at least the above formula has a rank of \(n^{2}\) and the sufficient condition is that the following formula is a nonsingular matrix:  
\[G=I_{n^{2}}+\sum_{j=1}^{n}\epsilon_{j}\widetilde{F}_{j}\] (A2)  
where \(\widetilde{F}_{j}=S^{-1}U^{T}E_{j}V\Big{[}I_{n^{2}}\quad 0_{n^{2}\times n(m-1)} \Big{]}^{T}\).  
According to the lemma proposed by [PERSON] and [PERSON] [39].  
Let \(\|\cdot\|\) represent the norm of the induction matrix defined on \(C^{n\times n}\). For \(A\in C^{n\times n}\), the following properties of the matrix measure \(\mu_{\text{measure}}(\cdot)\) are all true:  
1. \(\mu_{\text{measure}}(\pm I)=\pm 1\), for the identity matrix I;
2. \(-\|A\|\leq-\mu_{\text{measure}}(-A)\leq Re(\lambda(A))\leq\mu_{\text{measure}}( A)\leq\|A\|\);
3. \(\mu_{\text{measure}}(A+B)\leq\mu_{\text{measure}}(A)+\mu_{\text{measure}}(B)\), for any two matrices \(A,B\in C^{n\times n}\);
4. \(\mu_{\text{measure}}(\alpha A)=\alpha\mu_{\text{measure}}(A),\forall\alpha>0, \alpha\in R\),  
where \(\lambda(A)\) represents the eigenvalues of the matrix \(A\), and \(Re(\lambda(A))\) represents the real number of \(\lambda(A)\). The following formula is obtained:  
\[\begin{split}&\mu_{\text{measure}}\Big{(}-\sum_{j=1}^{n} \epsilon_{j}\widetilde{F}_{j}\Big{)}=\mu_{\text{measure}}\Big{(}-\sum_{j=1}^{n} \epsilon_{j}\Big{(}S^{-1}U^{T}E_{j}V\Big{[}\begin{array}{cc}I_{n^{2}}\quad 0_{n^{2}\times n(m-1)} \end{array}\Big{]}\Big{)}\Big{)}\\ &\leq\sum_{j=1}^{n}\mu_{\text{measure}}\Big{(}-\epsilon_{j}\Big{(}S^{-1}U^{T}E _{j}V\Big{[}\begin{array}{cc}I_{n^{2}}\quad 0_{n^{2}\times n(m-1)} \end{array}\Big{]}\Big{)}\Big{)}=\sum_{j=1}^{n}\epsilon_{j}\varphi_{j}<1\end{split}\] (A3)Therefore, according to the lemma proposed by [PERSON] [40], let \(A\in C^{n\times n}\). If \(\mu_{\text{measure}}(-A)\)\(<1\), then \(det(I+A)\
eq 0\), and the following formula is obtained:  
\[det(G)=det\Bigg{(}I_{n2}+\sum_{j=1}^{n}\varepsilon_{j}\widetilde{F}_{j}\Bigg{)}\
eq 0 \tag{10}\]  
Therefore, the matrix of Equation (10) is nonsingular. It can be observed that the matrix \(E_{I}\) is full rank \(n^{2}\). Furthermore, according to the lemma proposed by [PERSON] [41], the uncertain linear systems of Equations (13) and (14) are robust and controllable. End of proof. \(\square\)
### Problems in Relay Selection  
Generally, a relay is used to enhance performance of cooperative D2D communication networks. The selection of a relay that maximizes throughput of D2D communications become difficult when transmission power of the relay device is variable that is, when the relay is capable of choosing or adjusting its transmission power. This is because the signal-to-noise-ratio (SNR) of D2D communication depends upon the length of links from the source to the relay and that from the relay to the destination as well as transmission power of the relay. Recent studies [5, 7, 8] have put forward the idea of utilizing social information of users in the network for relay selection.  
In our previous relay selection schemes [9, 10, 11] and other state-of-the-art schemes [7, 12, 13], it is assumed that a relay device always transmits at a power proportional to the social trust it has for the source. However, mobile devices being power constrained devices, may not always have full battery power that is, the battery power on mobile devices may vary at different times. When the battery power on a relay is low, the transmission power of the relay may not be proportional to the social trust. This makes the relay selection more challenging. Even in co-operative networks, it is possible that a relay having a low battery power may preserve its battery power by using a low transmit power while relaying data for other devices. In such scenarios, throughput of existing relay selection schemes may vary as compared to their performance when relay devices have full battery power. It is important to analyze how the transmission behaviour of a relay with varying battery power impacts the throughput of relay selection schemes in D2D communications. Additionally, designing a relay selection scheme that enhances throughput in such network scenario is necessary.
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 3 Materials and Methods
### Modeling and Optimization of System Output
In the application of the GA, the fitness function was determined by comparing the speaker output with the ideal contour. The fitness function was calculated using both the correlation and the RMSE values. Genetic algorithms inherently strive to minimize the fitness function. Therefore, if the correlation value was positive, the fitness function was calculated as the inverse of the correlation (\(1/correlation\)); if the correlation was negative, a penalty value was added. This approach ensures that the genetic algorithm works to increase the correlation, thereby decreasing the fitness value as the correlation becomes more positive. Additionally, the RMSE value was incorporated into the fitness function, ensuring that a smaller RMSE results in more similar outcomes. This method has helped the speaker output to become closer to the ideal contour, thereby enhancing the performance of in-vehicle audio systems. The genotype representation is achieved by encoding the speaker filter parameters as a series. Each genotype is a vector containing the values of center frequency, gain, and Q factor. The genotype is coded to include three parameters for each filter, as shown in (5). \[genotype=[f_{1},\ G_{1},Q_{1},\ f_{2},\ G_{2},Q_{2},\ldots,f_{10},\ G_{10},Q_{1 0}] \tag{5}\]  
The genotypes used in this study were determined within the lower and upper bounds, specified by the ISO 226:2003 standards and listed in Table 1. These bounds aim to bring the filter parameters closer to the ideal contour. The lower bounds (\(l_{b}\)) and upper bounds (\(u_{b}\)) for the first to the tenth filter are provided in Table 3.  
The computational complexity of the genetic algorithm depends on the population size and the number of generations. The parameters used in this study were carefully  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Center Frequency Values (Hz)** & **Gain Values (dB)** & **Q Factor Values** \\ \hline \(l_{b}\) & \(u_{b}\) & \(l_{b}\) & \(u_{b}\) & \(l_{b}\) & \(u_{b}\) \\ \hline
52 & 64 & 8.0 & 12.0 & 0.7 & 1.1 \\ \hline
64 & 102 & 8.0 & 12.0 & 0.5 & 0  
selected to maintain control over the computation time while ensuring sufficient diversity and performance. These parameters maximize the flexibility and adaptability of the GA, creating an ideal environment for optimizing the frequency response of in-vehicle audio systems. Consequently, a higher quality and more comfortable audio experience is achieved for both drivers and passengers.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### SMT-Based Scheduling Optimization  
#### 7.3.1 Problem Formulation  
* Objective: Minimize the total fault probability while ensuring job deadlines and machine utilization constraints are met.
* Task order and job precedence.
- Machine availability and mutual exclusivity.
- Deadline compliance.
- Priority level consideration.
- Minimize fault probability for every task execution.
# Classification of Valvular Regurgitation Using Echocardiography
## 4 Experimental Results
### Output of Proposed Methodology
#### 4.3.3 Classification Output
of** & \multirow{2}{*}{\%} & \multicolumn{2}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\
**Fold** & \multirow{2}{*}{\%} & **P0** & **P1** & **P2** & **R0** & **R1** & **R2** & **F0** & **F1** & **F2** \\ \hline \multirow{6}{*}{2-folds} & 10 & 80.95 & 64.51 & 48.27 & 79.07 & 50.00 & 73.68 & 79.99 & 56.33 & 58.87 \\  & 20 & 76.59 & 72.00 & 58.82 & 81.81 & 56.25 & 66.66 & 79.11 & 63.15 & 62.49 \\  & 30 & 88.88 & 71.42 & 100 & 95.23 & 86.95 & 80.48 & 91.94 & 78.42 & 89.18 \\  & 40 & 75.00 & 77.41 & 31.42 & 62.50 & 57.14 & 68.75 & 68.18 & 65.74 & 43.12 \\  & 50 & 81.08 & 55.17 & 42.50 & 62.50 & 59.25 & 61.29 & 70.58 & 57.13 & 50.19 \\ \hline \multirow{6}{*}{3-folds} & 10 & 87.50 & 81.25 & 86.95 & 87.50 & 68.42 & 100 & 87.50 & 74.28 & 93.01 \\  & 20 & 48.14 & 45.00 & 58.33 & 54.16 & 39.13 & 58.33 & 50.97 & 41.86 & 58.33 \\  & 30 & 78.26 & 63.66 & 84.61 & 69.23 & 73.68 & 84.61 & 73.46 & 68.28 & 84.61 \\  & 40 & 65.51 & 71.42 & 66.66 & 70.37 & 62.50 & 70.00 & 67.85 & 66.66 & 68.28 \\  & 50 & 83.33 & 71.42 & 66.66 & 85.71 & 52.63 & 82.35 & 84.60 & 60.60 & 73.67 \\ \hline \multirow{6}{*}{5-folds} & 10 & 77.77 & 66.66 & 66.66 & 77.77 & 61.53 & 72.72 & 77.77 & 63.99 & 69.55 \\  & 20 & 82.35 & 58.33 & 75.00 & 77.77 & 63.63 & 75.00 & 79.99 & 60.86 & 75.00 \\ \cline{1-1}  & 30 & 85.71 & 80.00 & 88.88 & 80.00 & 72.72 & 100 & 82.75 & 76.18 & 94.11 \\ \cline{1-1}  & 40 & 60.00 & 62.50 & 36.84 & 52.94 & 35.71 & 63.63 & 56.24 & 71.61 & 46.66 \\ \cline{1-1}  & 50 & 62.50 & 69.23 & 84.61 & 83.83 & 52.94 & 84.61 & 71.61 & 59.99 & 84.61 \\ \hline \multirow{6}{*}{8-folds} & 10 & 66.66 & 71.42 & 40.00 & 60.00 & 50.00 & 66.66 & 63.15 & 58.82 & 49.99 \\ \cline{1-1}  & 20 & 55.55 & 71.42 & 60.00 & 55.55 & 55.55 & 75.00 & 55.55 & 62.49 & 66.66 \\ \cline{1-1}  & 30 & 77.77 & 62.50 & 77.77 & 87.50 & 62.50 & 70.00 & 82.34 & 62.50 & 73.68 \\ \cline{1-1}  & 40 & 81.81 & 90.00 & 80.00 & 90.00 & 81.81 & 80.00 & 85.70 & 85.70 & 80.00 \\ \cline{1-1}  & 50
# Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources
## 1 Introduction
### Literature Survey
#### 1.2.1 Steam Methane Reforming (SMR)  
SMR is currently the most widely used method of hydrogen production, accounting for more than 75% of global hydrogen production. The cost of producing hydrogen through SMR varies according to the price of natural gas, the main raw material. This process involves the reaction of natural gas with water vapour to produce hydrogen and carbon dioxide. Carbon dioxide can be captured and stored, helping to reduce greenhouse gas emissions. The cost of hydrogen produced using SMR ranges from USD 1.5 to USD 3.50 per kilogram of hydrogen.