esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 5 Conclusions  
This study's solar array fault diagnostic system utilizes a multi-input model architecture combining Convolutional Neural Networks (CNN) and fully connected layers, enabling simultaneous training with images and feature values. Through MATLAB Simulink, normal and six types of fault current-voltage (I-V) curves for solar strings were simulated. From the original simulated data, ten feature values were calculated, and Gaussian noise was added before converting them into curve images for training the multi-input model.  
In the validation of actual field data, the model correctly identified shading and hot spot faults; however, it misclassified crack faults as hot spot faults. Future improvements might include calculating the slope at the steps in the I-V curves of hot spot and crack faults as a new feature, enhancing the model's accuracy in distinguishing these faults. This study only requires obtaining the current-voltage curves of solar strings to quickly identify faults. Although hot spot and crack faults may be misclassified, shading faults can be correctly identified. Hot spot and shading faults exhibit similar step features, yet this model can distinguish the differences between them. Additionally, the study currently includes only shading, hot spot, and crack faults for field validation. To ensure the model's accuracy in real-world applications, it is hoped that future work will incorporate actual field data from other types of faults to validate the model, training it with new fault categories.  
Conceptualization, C.-C.K. and W.-T.L.; methodology, C.-M.C. and Y.-C.H.; validation,
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## Nomenclature  
Symbols and definitions used throughout the manuscript.  
\begin{tabular}{l l l} Symbol & Definition & Unit \\ \(m\) & Mass of the WEC & kg \\ \(k_{h}\) & Hydrostatic stiffness & N/m \\ \(b_{f}\) & Frictional damping coefficient & N/s/m \\ \(N\) & Gear ratio & rad/m \\ \(J_{d}\) & Rotor (shaft) inertia & kg\(\mathrm{m}^{2}\) \\ \(k_{r}\) & Motor torque constant & N-m/A \\ \(R_{w}\) & Motor winding resistance & \(\Omega\) \\ \(\alpha\) & Phillips parameter (JONSWAP) & \(--\) \\ \(g\) & Gravitational acceleration & m/s\({}^{2}\) \\ \(\omega\) & Angular frequency & rad/s \\ \(\omega_{p}\) & Peak angular frequency & rad/s \\ \(\gamma\) & JONSWAP peak enhancement factor & \(--\) \\ \(\sigma\) & Spectral width parameter & \(--\) \\ \(x[k]\) & State vector at discrete time step \(k\) & \(--\) \\ \(u[k]\) & Input vector at discrete time step \(k\) & \(--\) \\ \(A,B,C,D\) & State-space matrices & \(--\) \\ \(H(z)\) & Discrete-time transfer function & \(--\) \\ \(T_{s}\) & Sampling period & s \\ \(z(t)\) & Vertical displacement of the WEC & m \\ \(z(t)\) & Vertical velocity of the WEC & m/s \\ \(F_{\mathrm{PTO}}(t)\) & Power-take-off force & N \\ \(K_{p}\) & Proportional gain & N-s/m \\ \(K_{l}\) & Integral gain & N\(\mathrm{s}^{2}\)/m \\ \(f_{\mathrm{peak}}\) & Frequency at peak of PSD & Hz \\ \(f_{\mathrm{control}}\) & Spectral centroid & Hz \\ \(F_{\mathrm{flat}}\) & Spectral flatness & \(--\) \\ \(H_{\mathrm{spectral}}\) & Spectral entropy & \(--\) \\ \(P_{\mathrm{band}}\) & Band power in specified frequency ranges & \(--\) \\ \(s\) & CWT scale parameter & \(--\) \\ \(b\) & CWT translation (time shift) & s \\ \(\psi(t)\) & Mother wavelet & \(--\) \\ \(W(a,b)\) & Continuous wavelet coefficient & \(--\) \\ \end{tabular}
## 1 Introduction
Both sets of data are generally consistent with each other: the numerical values of the parameters of the least-square linear fits to the corresponding \(U_{s}\)-\(U_{p}\) data agree to within \(\sim\)15%. Here, \(U_{p}\) and \(U_{s}\) are particle and shock velocities, respectively. The unique feature of both sets of data is that for each substance, except cerium (Ce), the adequate description of the data in the whole \(U_{p}\) region of up to \(\sim\)3.6 km/s requires a two-segment fit; that is, the lower and upper portions of the data sets are described by two distinct linear segments such that the combined two-segment graph is continuous, but not smooth, as the slope of the second segment (\(\sim\)1.2-1.6) is \(\sim\)1.5 times higher than that of the first one (\(\sim\)0.8-1.0). In each case, the change of slope happens at a compression of \(\sim\)1.6; the corresponding transition \(P\) increases from \(\sim\)20 GPa for the light lanthanide members to \(\sim\)40 GPa for the heavy ones [8]. Eu and Yb are two exceptions, for which the transition \(P\) is \(\sim\)10 GPa. Additionally, the Eu data show the existence of a two-wave structure in the shock front, indicating a significant volume change at the transition, whereas in most of other cases, the data indicate that the volume change associated with this transition is either zero or very small [8]. Such \(U_{s}\)-\(U_{p}\) behavior is typical of a solid-solid phase transformation or melting. In any event, the \(U_{s}\)-\(U_{p}\) slope being \(\sim\)50% higher indicates that the compressibility of the emerging phase is changing more rapidly (the value of the \(P\)-derivative of the bulk modulus is higher). Direct evidence of phase transitions and melting along the principle Hugoniot in several lanthanide members using in-situ laser shock diffraction at the Dynamic Compression Sector was recently presented in [21]. In this work, we derive the analytic forms of the principal Hugoniots of Ac and Pm, which have both never been measured or calculated before, as well as those of Tb, Tm, and Lu, the three least studied of the remaining lanthanides. Indeed, the existing Hugoniot data on each of these three substances can only be found in a single literature source, namely, Ref. [8] for both Tb and Tm and Ref. [7] for Lu.
# A Survey of CNN-Based Network Intrusion Detection
## 6 Analytical Investigation
Additionally, it can be seen in this figure that ACC (accuracy), TPR (Recall), PPV (Precision), FPR (false alarm rate), and F1 Score are the most commonly employed metrics in these studies. Few IDS researchers have incorporated evaluation metrics such as AUC, ROC, and TNR. Figures 10-14 outline the accuracy of the CNN-based IDS schemes investigated in this study that employ the NSL-KDD, KDD Cup99, CIC-IDS2017, UNSW-NB15, and CSE-CIC-IDS2018 datasets, respectively. In these figures, the green and red colors show an accuracy higher and lower than 95%, respectively. To enable the findings to be compared, the results for each dataset are presented individually. Researchers can compare these findings with their own results. The maximum value between binary and multiclass classifications has been selected for the plot in all figures. Figure 8: Number of convolutional layers that have been used for IDS schemes. Figure 9: The number of IDS schemes that have applied each evaluation metric. Figure 15 outlines the comparison between the binary and multiclass accuracies of the CNN-based IDS schemes investigated. The green and red colors in these figures show multiclass and binary accuracies. It can be seen in this figure that the binary classification results are mostly higher than the multiclass classification results. Figure 11: Accuracy of the studied schemes on the KDD Cup99 dataset [16,17,19,23,26,39,40,62,65,69,72,74,76,77]. Figure 10: Accuracy of the studied schemes on the NSL-KDD dataset [3,19,20,22,24,25,28–33,35,36,38,39,44,47,49,50,58,59,61,62,64,68,70,75,76,78,111]. Moreover, it can be seen from the literature that:  
17. LSTM is the most frequently used deep learning algorithm to hybridize with CNN in the IDS domain; this hybrid method generates the highest accuracy. 18. CNNs produce better performance when trained by a larger dataset. Figure 12: Accuracy of the studied schemes on the CIC-IDS 2017 dataset [15,18,41,44,46,48,52,66,68,71,73,76]. Figure 13: Accuracy of the studied schemes on the UNSW-NB15 dataset [15,16,20,37,38,42,49,59,61, 62,64,75].
# Parametric and Experimental Modeling of Axial-Type Piezoelectric Energy Generator with Active Base
## 5 Experimental Probe
### Method of Natural Modeling of the Oscillatory Processes of PEG
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**PE N**} & \multicolumn{4}{c}{**Voltage Amplitude (V) across the Electrodes at Oscillation Frequency of 39 Hz**} \\  & \multicolumn{4}{c}{**and the Corresponding Active Electric Load R (k\(\Omega\))**} \\ \hline  & 10 k\(\Omega\) & 51 k\(\Omega\) & 75 k\(\Omega\) & 150 k\(\Omega\) & 300 k\(\Omega\) & 2000 k\(\Omega\) \\
1 & 0.14625 & 0.294872 & 0.355079 & 0.392063 & 0.560649 & 1.317638 \\
2 & 0.06685 & 0.096381 & 0.100432 & 0.222399 & 0.207643 & 0.187802 \\
3 & 0.06505 & 0.085808 & 0.077982 & 0.1905 & 0.183093 & 0.180334 \\  & \multicolumn{4}{c}{**Voltage Amplitude \((\mathbf{V})\) across the Electrodes at an Oscillation Frequency of**} \\  & \multicolumn{4}{c}{**107 Hz and the Corresponding Active Electric Load R (k\(\Omega\))**} \\  & 10 k\(\Omega\) & 51 k\(\Omega\) & 75 k\(\Omega\) & 150 k\(\Omega\) & 300 k\(\Omega\) & 2000 k\(\Omega\) \\
1 & 0.043576 & 0.094412 & 0.061982 & 0.117833 & 0.127969 & 0.467393 \\
2 & 0.032606 & 0.043102 & 0.06355 & 0.136766 & 0.111208 & 0.081129 \\
3 & 0.02439 & 0.082987 & 0.078731 & 0.109417 & 0.106021 & 0.106581 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental measurements of the voltage amplitude on the PE electrodes at the corresponding vibration frequencies and the corresponding active electric load. Figure 12: Visualization of the process of measuring the PEG output characteristics during sweeping. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PE N**} & \multicolumn{4}{c}{**Peak Power (\(\mu\)W) at the PE Electrodes at an Oscillation Frequency of 39 Hz and the Corresponding Active Load R (k\(\Omega\))**} \\  & \multicolumn{4}{c}{**the Corresponding Active Load R (k\(\Omega\))**} \\ \hline  & 10 k\(\Omega\) & 51 k\(\Omega\) & 75 k\(\Omega\) & 150 k\(\Omega\) & 300 k\(\Omega\) & 2000 k\(\Omega\) \\
1 & 2138.9 & 1704.9 & 1681.1 & 1024.8 & 1047.8 & 868.1 \\
2 & 446.9 & 182.1 & 134.5 & 329.7 & 143.7 & 17.6 \\
3 & 423.2 & 144.4 & 81.1 & 241.9 & 111.7 & 16.3 \\  & \multicolumn{4}{c}{**Peak power (\(\mu\)W) at the PE Electrodes at an Oscillation Frequency of 107 Hz**} \\  & \multicolumn{4}{c}{**and the Corresponding Active Load R (k\(\Omega\))**} \\  & 10 k\(\Omega\) & 51 k\(\Omega\) & 75 k\(\Omega\) & 150 k\(\Omega\) & 300 k\(\Omega\) & 2000 k\(\Omega\) \\
1 & 189.89 & 174.78 & 51.22 & 92.56 & 54.59 & 109.23 \\
2 & 106.31 & 36,43 & 53.85 & 124.7 & 41.22 & 3.29 \\
3 & 59.49 & 135.04 & 82.65 & 79.81 & 37.47 & 5.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental measurements of the output power peak on the PE electrodes at the corresponding vibration frequencies and the corresponding active load. Figure 14: _Cont._  
Figure 13: Dependence of the output voltage on the magnitude of the load. (**a**) Piezocylinders and (**b**) piezoplates.
## 2 Theoretical Background
### Fluid-Structure Parameter Exchange  
Using a 1D beam FE model, only the displacement at the node along the centerline of the hull can be obtained from the motion equation. The displacement is converted to a displacement of the hull surface, for which the following third-order polynomial shape functions are used.  
\[\omega_{p}=\omega_{n1}S_{1}(r)+\theta_{n1}S_{2}(r)+\omega_{e2}S_{3}(r)+\theta_ {n2}S_{4}(r) \tag{14}\]  
\[r=\frac{d}{l},\quad l=x_{n2}-x_{n1},\quad d=x_{p}-x_{n1} \tag{15}\]  
\[S_{1}=1-3r^{2}+2r^{3},\quad S_{2}=lr(1-r)^{3} \tag{16}\]  
\[S_{3}=3r^{2}-2r^{3},\quad S_{4}=lr^{2}(r-1) \tag{17}\]  
Here, \(\omega_{p}\) is the translational displacement in the vertical direction; the translational and rotational displacement at the two nodes are given by \((\omega_{n})\) and \((\theta_{n})\), respectively, and \(S_{i}\) is the shape function of the beam element. The displacement between the nodes is interpolated by a third-order polynomial in Figure 2. If the displacement at the centerline is converted to displacement at the surface of the object, it is calculated assuming no deformation within the section.  
The beam models are shown in Figure 3, and the hull structures are represented by some 1D beam elements in the centerline of the ship model. The finite element method solves structural reactions, including both rigid and flexible motions. For the calculation of warping distortion, we followed the Vlasov beam theory, which assumes that the wall behaves like a thin shell. In addition, this beam theory assumes that the contour is not deformed in its own plate and that the in-plane shear strain of the middle surface of the wall is zero. Using a 1D beam model capable of simulating bending torsion and warping distortion coupling, the torsional response of a thin-walled, open-section beam was accurately calculated.  
Figure 2: Coupling the beam nodes of 1D beam model and grids of 3D panel model.
## 3 Results and Discussion
### Results of the Description Models on Textural Features  
The results of the prediction model based on texture features and different machine learning algorithms in the prediction set are shown in Figure 13. Among them, the \(\text{R}_{\text{P}}{}^{2}\) of the SVR prediction model based on image features was 0.805, and the RMSEP was 0.19; the \(\text{R}_{\text{P}}{}^{2}\) of the PLSR model was 0.852, and the RMSEP was 0.268. To a certain extent, the prediction effect based on image features was slightly lower than that of the prediction model based on spectral information. It was because the spectral information has higher sensitivity and more obvious absorption for nitrogen in the green band and red-edge region. From the prediction results, although the prediction effect based on image features is not as good as the spectral data, the \(\text{R}_{\text{P}}{}^{2}\) was above 0.80. Moreover, the texture image features were able to compensate for the deficiency of spectral information to a certain extent, which strongly proved image texture's feasibility in predicting corn LNC and has some research values.
### Organization and Notation  
The organization of this paper is as follows. In Section 2, the system model considered is described and an optimization problem considered is formulated. The methods of the proposed training schemes are described in Section 3. The simulation results for the performance comparison are presented in Section 4. We conclude the paper in Section 5.  
_Notation:_\((\cdot)^{T}\), \((\cdot)^{*}\), \((\cdot)^{H}\), \(\otimes\), \(\text{vec}(\cdot)\), and \(\text{Tr}(\cdot)\) denote the transpose, conjugate, conjugate transpose, Kronecker product, vectorization, and trace operators, respectively. \(||\cdot||_{F}\) denotes the Frobenius norm of a matrix. The notation \(\mathbf{A}\succeq\mathbf{0}\) means that a Hermitian matrix \(\mathbf{A}\) is positive semi-definite. The Cartesian product of two sets \(A\) and \(B\) is denoted as \(A\times B\). The notation \(\text{Diag}(a_{1},\cdots,a_{m})\) and \(\text{Blkdiag}(\mathbf{A}_{1},\cdots,\mathbf{A}_{m})\) denotes a diagonal matrix whose diagonal elements are given by \(a_{1},\cdots,a_{m}\) and a block diagonal matrix whose diagonal blocks are given by the matrices \(\mathbf{A}_{1},\cdots,\mathbf{A}_{m}\), respectively. \(\boldsymbol{\
abla}_{\mathbf{A}}\) and \(\boldsymbol{\
abla}_{\mathbf{A}}^{2}\) represent the gradient and Hessian operators with respect to (w.r.t.) the variable \(\mathbf{A}\). \(\mathbb{E}(\cdot)\) denotes the expectation operator, and a circular symmetric Gaussian random vector \(\mathbf{a}\) with mean \(\bar{\mathbf{a}}\) and covariance matrix \(\mathbf{A}\) is denoted by \(\mathbf{a}\sim CN(\bar{\mathbf{a}},\mathbf{A})\).
###### Abstract  
Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity.  
brain-tumor segmentation; Swin Transformer; CNN; dual encoder; stability +
Footnote †: journal: Medical Imaging
###### Abstract
The main remarks by these authors were that reducing the moisture content of the storage container led to seaweed discoloration and cellular leakage. Additionally, vacuum storage to lower the air content did not result in an extended post-harvest shelf-life. Containers and wraps with high gas exchange rates were found to be less effective in prolonging shelf-life. However, seaweed that was fully immersed in seawater and kept in the dark at 17 \({}^{\circ}\)C remained viable and free from discoloration for over a month [35]. The present work aims to study the impact of MAP on the physicochemical characteristics and food safety of two seaweed species: \"laver\" (_Porphyna umbilicalis_, Rhodophyta) and \"sea-lettuce\" (_Ulw lactuca_, Chlorophyta). For this purpose, a mixture of gases--consisting of 80% argon (Ar) and 20% carbon dioxide (CO\({}_{2}\))--together with vacuum packaging were used. CO\({}_{2}\) was selected due to its antimicrobial properties and its role as a carbon source for seaweed's metabolism. Argon, on the other hand, was chosen for its nitrogen-like properties, along with its superior water solubility and ability to inhibit specific enzymatic reactions. Physical, microbiological, and chemical parameters were evaluated: composition of headspace gases (respiration rate), color, texture (hardness), pathogenic bacteria and marine heterotrophic bacteria counts, and volatile organic compounds (VOCs) through gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS).
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
## 3 Data Processing Results
### Determination of Earthquake Parameters
#### 3.2.2 Earthquake Parameters in the Potential Source Area  
The magnitudes were divided into 6 grades: 4.0-5.4, 5.5-5.9, 6.0-6.4, 6.5-6.9, 7.0-7.4, and above 7.5. When determining the spatial distribution function of the potential source area, the following principles are mainly considered: (1) medium- and long-term earthquake prediction results; (2) the sufficiency of the tectonic conditions where the potential source area is located; (3) the large earthquakes provided by the small earthquake seismic activity risk background; and (4) the randomness of earthquakes, that is, the area factor of each potential source area. According to the division results of potential epicenters and the above-mentioned principles, we obtained the spatial distribution functions of each potential epicenter (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **Region** & \(m_{uz}\)1  & **4.0–5.4** & **5.5–5.9** & **6.0–6.4** & **6.5–6.9** & **7.0–7.4** & \(\geq\)**7.5** \\ \hline
1 & Gangsang Duodi & 8.0 & 0.0443 & 0.0443 & 0.0443 & 0.0445 & 0.1138 & 0.4881 \\
2 & Nima North & 7.0 & 0.0136 & 0.0136 & 0.0136 & 0.0138 & 0 & 0 \\
3 & Qinglong & 7.0 & 0.0054 & 0.0054 & 0.0054 & 0.0056 & 0 & 0 \\
4 & Jiangcuo & 8.5 & 0.0086 & 0.0086 & 0.0086 & 0.0084 & 0.0221 & 0.0949 \\
5 & Namucuo & 7.0 & 0.0088 & 0.0088 & 0.0088 & 0.0089 & 0 & 0 \\
6 & Sangxiong & 8.0 & 0.0052 & 0.0052 & 0.0052 & 0.0051 & 0.0133 & 0.0571 \\
7 & Dangxiong & 8.5 & 0.0122 & 0.0122 & 0.0122 & 0.0123 & 0.0314 & 0.1347 \\
8 & Kangma & 7.5 & 0.0228 & 0.0228 & 0.0228 & 0.0227 & 0.0585 & 0 \\
9 & Rikaze & 7.0 & 0.0142 & 0.0142 & 0.0142 & 0.0145 & 0 & 0 \\
10 & Naqu & 7.0 & 0.0090 & 0.0090 & 0.0090 & 0.0089 & 0 & 0 \\
11 & Jiali & 7.5 & 0.0092 & 0.0092 & 0.0092 & 0.0095 & 0.0236 & 0 \\
12 & Jinda & 7.0 & 0.0036 & 0.0036 & 0.0036 & 0.0037 & 0 & 0 \\
13 & Qushui & 7.5 & 0.0084 & 0.0084 & 0.0084 & 0.0085 & 0.0215 & 0 \\
14 & Qusong & 7.5 & 0.0150 & 0.0150 & 0.0150 & 0.0153 & 0.0385 & 0 \\
15 & Jiacha & 7.5 & 0.0080 & 0.0080 & 0.0080 & 0.0081 & 0.0204 & 0 \\
16 & Yadong & 7.5 & 0.0090 & 0.0090 & 0.0090 & 0.0091 & 0.0092 & 0 \\
17 & Luozha & 7.5 & 0.0087 & 0.0087 & 0.0087 & 0.0088 & 0.0090 & 0.0099 \\
18 & Jiayu & 8.0 & 0.0076 & 0.0076 & 0.0076 & 0.0077 & 0.0080 & 0.0139 \\ \hline \end{tabular}
\end{table}
Table 3: Spatial distribution functions of the main potential source areas.
## 4 Proposed Mechanism under Solar Radiation  
An attempt to explain the mechanism of hydrogen generation is herein proposed and discussed (See Figure 11). As previously reported, cuprous oxide in the p-n heterojunction systems based Cu\({}_{2}\)O/TiO\({}_{2}\) photocatalysts acts as an energy antenna [41], being able to absorb energy within the visible spectrum. Under direct sunlight radiation, in which the UV and the visible components are present, the activation of both semiconductors (i.e., Cu\({}_{2}\)O and TiO\({}_{2}\)) can be considered (See the reactions (2) and (3)):  
\[\text{Cu}_{2}\text{O}\xrightarrow{\text{hy}}\ \text{Cu}_{2}\text{O}(\text{h}^{+} )+\text{Cu}_{2}\text{O}(\text{e}^{-})\] (R2)  
\[\text{TiO}_{2}\xrightarrow{\text{hy}}\ \text{TiO}_{2}\big{(}\text{h}^{+} \big{)}+\text{TiO}_{2}(\text{e}^{-})\] (R3)  
After charge carrier generation, an electric field from TiO\({}_{2}\) to Cu\({}_{2}\)O and band bending are established. Positive holes can react with the adsorbed organic species (mainly on the titania surface), following the reactions (4) and (5), thus forming byproducts and protons, which in turn can react with the photogenerated electrons to form H\({}_{2(\text{g})}\) (through the reaction (6)).  
\[\text{Org}+\theta_{\text{TiO}_{2}}\overset{\text{K}_{\text{ads}}}{\leftrightarrow} \text{Org}_{\text{ads}}\] (R4)  
\[\text{Org}_{\text{ads}}+\text{TiO}_{2}\big{(}\text{h}^{+}\big{)}\overset{ \text{k}_{\text{h}^{+}}}{\rightarrow}\text{O}_{\text{ads}}^{\bullet}+\text{TiO }_{2}\big{(}\text{h}^{+}\big{)}+\text{ H}^{+}\overset{\text{Fast}}{\rightarrow} \text{P}+2\text{H}^{+}\] (R5)  
\[2\text{H}^{+}+2\text{TiO}_{2}(\text{e}^{-})\overset{\text{Fast}}{\rightarrow} 2\text{H}^{\bullet}\overset{\text{Fast}}{\rightarrow}\text{H}_{2}\] (R6)  
Figure 11: Schematic illustration of the proposed mechanism for hydrogen generation under solar light radiation in presence of Cu\({}_{2}\)O/TiO\({}_{2}\) composite material.  
Obviously, electron-hole pairs may recombine to produce heat and light (through reactions (7) and (8)), despite the p-n heterojunction formation boosts photogenerated charge carrier separation, resulting in better photocatalytic efficiencies with respect to those observed with bare materials.  
\[\text{TiO}_{2}\big{(}\text{h}^{+}\big{)}+\text{TiO}_{2}(\text{e}^{-})\xrightarrow{ \text{k}_{\text{g}}}Q+\text{light}\] (R7)  
\[\text{Cu}_{2}\text{O}\big{(}\text{h}^{+}\big{)}+\text{Cu}_{2}\text{O}(\text{e}^{ -})\xrightarrow{\text{k}_{\text{g}}}Q+\text{light}\] (R8)