esa-sceva/satcom-chunk-collection huggingface dataset:
# Photovoltaic Solar Cells: A Review
## 2 Solar Cells  
### The Working Principle of PV Cells  
A PV cell is the essential unit of a solar energy generation system in which sunlight is promptly converted to electrical energy. The solar cell is a p-n junction device. n-type refers to the negatively charged electrons donated by donor impurity atoms and p-type refers to the positively charged holes created by acceptor impurity atoms, referring to Figure 1 of a PV structure [15; 16; 17].  
The working principle of solar cells is based on the photovoltaic effect. The PV effect can be divided into three essential procedures [18; 19; 20].  
1. Absorption of photons in a p-n junction electronic semiconductor to generate the charge carriers (electron-hole pairs). The absorption of a photon with energy (E = hv) higher than the gap energy 'E\({}_{\text{g}}\)' of the doped semiconductor material means that its energy is used to excite an electron from the valence band 'Ev' to the conduction band 'E\({}_{\text{c}}\)' leaving a void (hole) at the valance level. Additional kinetic energy is given to the electron or hole by the excess photon energy (hv-hv\({}_{0}\)). 'hv\({}_{0}\)' is the minimum energy or work function of the semiconductor required to generate an electron-hole pair. The work function here represents the energy gap. The excess energy is dissipated as heat in the semiconductor [21; 22].
2. Consequent separation of the light-generated charge carriers. In an external solar circuit, the holes can flow away from the junction through the p-region, and electrons can flow out across the n-region and pass through the circuit before they recombine with the holes.  
Figure 1: A p-n junction PV cell.  
3. Finally, the separated electrons can be used to drive an electric circuit. After the electrons passed through the circuit, they will recombine with the holes.  
The n-type must be designed thinner than the p-type. Thus, the electrons can pass through the circuit in a short time and generate current before they recombine with the holes. Besides, an anti-reflective coating is applied over the n-layer to reduce surface reflection and enhance the transmission of the light to the semiconductor material.  
All the aspects presented in this section will be discussed in more detail in the next sections.
# Genetic Algorithm-Based Model Updating in a Real-Time Digital Twin for Steel Bridge Monitoring
## 5 Model Updating and Results
Initially, the MAC values were relatively low, particularly for the first mode of Span 2, where the initial correlation was only 0.19. This indicated a significant discrepancy between the numerical and experimental modal shapes. After the calibration process, which involved optimizing the elastic modulus of the steel components using a genetic algorithm, the MAC values increased significantly \begin{table}
\begin{tabular}{l c} \hline \hline  & **Initial \(E_{s}\)(GPa)** & **Calibrated \(E_{s}\) (GPa)** \\ \hline \hline Braces & \(186\) \\ \hline \hline Tracks & \(200\) \\ \hline \hline Top and bottom chords & \(178\) \\ \hline Verticals & \(191\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial and calibrated elastic modulus of various structural members of the bridge. Figure 8: Adopted framework for developing digital twin via genetic algorithm and model updating. across all modes, with a minimum value reaching 0.93. This improvement confirms that the updated model better captures the true dynamic behavior of the bridge, ensuring a more reliable digital twin representation for structural health monitoring applications. The enhanced correlation underscores the effectiveness of the model updating approach in refining both global and local structural response characteristics.
## 3 Results
### Implications on the Regional Carbon Cycle  
The variations in atmospheric CO\({}_{2}\) are mostly due to fluctuating carbon uptake by terrestrial ecosystems [51]. The GOSAT detrended XCO\({}_{2}\) average value was used to investigate the changes of atmospheric CO\({}_{2}\) during this strong El Nino event in Southeast Asia. The whole area of Southeast Asia was averaged owing to the coarse spatial resolution of XCO\({}_{2}\) data. The XCO\({}_{2}\) showed a consistent negative correlation with SIF and EVI (Figure 8)  
The correlation coefficient between SIF and XCO\({}_{2}\) (R = \(-\)0.53, \(p\) < 0.01) was more significant than that between EVI and XCO\({}_{2}\) (R = \(-\)0.40, \(p\) < 0.01). As mentioned before, this El Nino peaked in late 2015 and ended in May 2016. SIF showed better capability to capture the response of vegetation to the drought. Figure 8 displayed that in 2016 the CO\({}_{2}\) emission rose drastically from March along with SIF and EVI went down to the lowest value, which was 5 months behind the fire event. During this period, SIF was highly correlated to XCO\({}_{2}\), much more relevant than EVI.  
OCO-2 is the first NASA satellite designed to measure and characterize CO\({}_{2}\). The relationship between the OCO-2 SIF and XCO\({}_{2}\) was also investigated based on the OCO-2 products. The average values of OCO-2 XCO\({}_{2}\) and SIF from 2015 to 2017 were shown in Figure 9. SIF  
Figure 8: Monthly average of XCO\({}_{2}\), EVI and SIF for the Southeast Asia from April 2009 to 2016. The shaded area showed the drought years.  
Figure 7: Normalized anomalies of aerosol optical depth (AOD) for Southeast Asia from 2007 to 2016. The shaded area showed the drought years.  
during this period, indicating that vegetation status was relevant with CO\({}_{2}\) concentration. OCO-2 XCO\({}_{2}\) and SIF products had a strong negative correlation (R = \(-0.59,p<0.01\)), which could show the high potential of OCO-2 data for the drought research in the future.  
The net fluxes of production and consumption processes from both natural and anthropogenic sources can be reflected by dynamics of column CO\({}_{2}\). And our results confirmed that SIF, as a proxy for vegetation status, can indirectly reflect the regional CO\({}_{2}\) variations, especially when the water deficit occurred. Water deficit usually results in the closure of stomata, which then reduces the absorption of CO\({}_{2}\) and makes photosynthesis slow down [52]. Thus, a strong carbon emission occurred along with the decreased SIF in Southeast Asia from the end of 2015 to mid-2016 because of the water deficit. Atmospheric CO\({}_{2}\) variability is driven by a wide range of processes, such as temperature change, drought/flooding, fire emissions, and land use changes, which all play significant roles in changing the carbon land sink at global and regional scales [53]. Using satellite SIF observations, we can better understand the underlying processes and improve the performance of biochemical models, which still contained many uncertainties for the simulations of carbon cycle [54].
## 5 Conclusions  
A paralleling algorithm PCFMIS3 with GS and EDS is proposed in MapReduce framework to parallel CFMIS to mine frequent subtrees efficiently. The GS method divides the subtrees into different groups, which solves the data division problem in parallel computing. It avoids inter-group communication while mining frequent subtrees in each group to reduce parallel computing time. In PCFMIS3, load balancing is achieved by using EDS. Additionally, the compression stage in mining frequent subtrees is optimized by avoiding all candidate subtrees of a compression tree, which reduces the calculation time of nodes. Experiments demonstrate that the PCFMIS3 algorithm performs best on the comparison of load balancing and running time on both the real data set and synthetic data set. The maximum load is 1.3 times the minimum load, while it is up to 7 times without EDS. The time ratio of PCFMIS3 is only about 1.1, while the time ratio exceeds 2.0 without EDS. PCFMIS3 also performs best on different support values, saving half the computational time compared to PCFMIS1. The PCFMIS3 achieves the optimal speedup which is closest to the linear one on both small and large number of nodes.  
Serial computing technology is difficult to meet the needs of massive data processing. Parallel computing can take advantage of multi-node computing resources to reduce problem resolution time. The proposed GS and EDS methods solve the two important issues of data partitioning and load balancing in parallel computing. To apply GS and EDS method in the mining of other frequent item sets is our future work.  
Conceptualization, [PERSON] and X.L.; methodology, [PERSON] and X.L.; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; writing--original draft preparation, [PERSON]; visualization, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Development Plan of Jilin Province (Jing Wang 2022) and the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: The speedups of four algorithms for four data sets on a large number of nodes.
## 5 Conclusions  
In this paper, we have proposed a novel hybrid method to implement an efficient BCH decoder for different finite field extensions by having the SRU module in hardware and the rest implemented in GPU kernel routines. By using this method, we have given flexibility on two parameters: first, the flexibility over different finite fields \(GF(2^{m})\); second, the flexibility over different bit error support. The flexibility over \(GF(2^{m})\) was achieved by splitting the syndrome into the SRU unit and the syndrome kernel. The SRU module resided on the Euclidean domain of \(GF(2)\) polynomials, thus making it programmable across multiple finite fields. The syndrome kernel was executed only when an error was encountered. The latency taken by our method, without error, was superior to the CPU and GPU implementations and was equal to the performance observed in [20]. Besides, we had two-fold area savings in the SRU unit to achieve flexibility over \(GF(2^{m})\) and bit errors. Therefore, this hybrid approach is a pragmatic solution to have a flexible error correction for modern NAND flash devices.  
Conceptualization, A.S. and T.O.; methodology, A.S.; software, A.S.; validation, A.S.; formal analysis, A.S. and T.O.; investigation, A.S. and T.O.; resources, T.O.; data curation, T.O.; writing, original draft preparation, A.S.; writing, review and editing, A.S. and T.O.; visualization, A.S.; supervision, T.O.; project administration, T.O.  
This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Raw bit error vs. throughput (sectors/s).
## 6 Description of the Datasets  
The datasets used in this research include images taken from a Reflective Optics System Imaging Spectrometer (ROSIS). More specifically, the Pavia University and Pavia Center datasets were considered [32]. Both datasets came from the ROSIS sensor during a flight campaign over Pavia in southern Italy. The number of spectral bands is 102 for the Pavia Center and it is 103 for Pavia University. The selected Pavia Center and Pavia University images have an analysis of \(1096\times 1096\) pixels and \(610\times 610\) pixels respectively. Ultrasound imaging consists of 115 spectral channels ranging from 430 to 860 nm, of which only 102 were used in this research, as 13 were removed due to noise. Rejected specimens which in both cases contain no information (including black bars) can be seen in the following figure (Figure 6) below.  
The available samples were scaled down, so that every image has an analysis of 610 \(\times\) 610 pixels and geometric analysis of 1.3 m. In both datasets the basic points of the image belong to nine categories which are mainly related to land cover objects. The Pavia University dataset includes nine classes, and in total, 46,697 cases. The Pavia Center dataset comprises nine classes with 7456 cases, whereas the first seven classes are common in both datasets (_Asphalt, Meadows, Trees, Bare Soil, Self-Blocking Bricks, Bitumen, Shadows_) [33].  
The Pavia University dataset was divided into training, validation and testing sets, as is presented in the following table (Table 2) [32].  
The Pavia Center dataset was divided into training, validation and testing sets, as is presented analytically in the following table (Table 3) [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class No**} & \multirow{2}{*}{**Class Name**} & \multirow{2}{*}{**All Instances**} & \multicolumn{2}{c}{**Sets**} \\ \cline{3-5}  & & & **Training** & **Validation** & **Test** \\ \hline
1 & Asphalt & 7179 & \(\surd\) & - & - \\
2 & Meadows & 19,189 & \(\surd\) & - & - \\
3 & Trees & 3588 & \(\surd\) & - & - \\
4 & Bare Soil & 5561 & \(\surd\) & - & - \\
5 & Self-Blocking Bricks & 4196 & \(\surd\) & - & - \\
6 & Bitumen & 1705 & \(\surd\) & - & - \\
7 & Shadows & 1178 & \(\surd\) & - & - \\
8 & Metal Sheets & 1610 & - & \(\surd\) & - \\
9 & Gravel & 2491 & - & - & \(\surd\) \\ \hline  & **Total** & **46,697** & **42,596** & **1610** & **2491** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pavia University dataset.  
Figure 6: Noisy bands in Pavia Centre and University datasets.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 1 Introduction
In summary, the contributions of this work are as follows:  
1. An end-to-end network for single-image de-defocusing is presented. Wavelet transforms are also incorporated into the encoding stage of the proposed network, reducing the feature map size while ensuring a wide receptive field. 2. IFA and GCN modules are introduced to increase the network's depth, thereby enhancing the ability to reconstruct clear images. 3. A proprietary dataset is curated. In contrast to previous datasets, the proposed collection included a higher proportion of images with extensive defocus blur, alongside their corresponding all-in-focus images.
## 3 Results
### Fabrication of the NPs
#### 3.3.4 The Influence of an Organic Solvent  
All the neutral AABB polymers selected for the present study--**1F6**, **1L6**, **4F4**, and **8L6**, five of eight surfactants, and four organic solvents--Were involved in these experiments. All the AABB polymers were soluble in the selected polar organic solvents such as DMI, DMF, and DMSO, and **8L6** and **1L6** were also soluble in acetone (for the solubility of the polymers see Table 2). The parameters (invariable and variable) of the NPs fabrication process are given in the table below: \begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Parameters of the Process**} \\ \hline \hline \(\bullet\) & solvent--DMI & \(\bullet\) & polymers (two)--**1F6** and **4F4** \\ \(\bullet\) & concentration of the polymer in DMI--6.0 mg/mL & \(\bullet\) & surfactants (four)--Ween 20, Tween 80, \\ \(\bullet\) & O/W ratio--1:10 & Triton X-100, Brij 010 \\ \(\bullet\) & organic phase dropping rate--12 drops/min & \(\bullet\) & surfactant’s concentration in the water \\ \(\bullet\) & stirring rate of the water phase--700 rpm & \(\bullet\) & phase--2.0, 5.0, 10.0 and 20.0 mg/mL \\ \hline \hline \end{tabular}   
The conditions of the NPs fabrication were as follows: Polymer concentration in an organic solvent: 6.0 mg/mL; surfactant concentration in the water phase: 5.0 mg/mL; the O/W ratio: 1:10; the organic phase dropping rate: 12 drops/min; and the stirring rate of the water phase: 700 rpm. The results obtained for the selected AABB polymers **1F6** and **4F4** using four surfactants in terms of the MPD and PDI of the NPs are given in Figure 5.  
Figure 5: The influence of the surfactant concentration in the water phase on the formation of the **1F6** (**A**) and **4F4** (**B**) NPs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Organic Solvent**} & \multicolumn{5}{c}{**Surfactant**} \\ \cline{2-6}  & **Tween 20** & **Tween 80** & **Kollibror P188** & **Brij 010** & **Mowiol 4-88** \\ \cline{2-6}  & \multicolumn{5}{c}{**MPD (nm) \(\pm\) SD**} & \multicolumn{1}{c}{**[PDDI] \(\pm\) SD**} \\ \hline \multirow{2}{*}{DMI} & **158 \(\pm\) 7** & **155 \(\pm\) 7** & **111 \(\pm\) 1** & **163 \(\pm\) 1** & **173 \(\pm\) 9** \(\blacklozenge\) \\  & [0.111 \(\pm\) 0.006] & [0.091 \(\pm\) 0.003] & [0.054 \(\pm\) 0.012] & [0.299 \(\pm\) 0.013] & [0.117 \(\pm\) 0.009] \\ \hline \multirow{2}{*}{DMF} & **138 \(\pm\) 3** & **124 \(\pm\) 2** & **135 \(\pm\) 3*** & **123 \(\pm\) 2** & **204 \(\pm\) 2** \(\blacklozenge\) \\  & [0.116 \(\pm\) 0.011] & [0.126 \(\pm\) 0.004] & [0.132 \(\pm\) 0.004] & [0.119 \(\pm\) 0.032] & [0.103 \(\pm\) 0.008] \\ \hline \multirow{2}{*}{DMSO} & **93 \(\pm\) 4** & **60 \(\pm\) 1** & **100 \(\pm\) 3*** & **56 \(\pm\) 2** & **98 \(\pm\) 1** \(\blacklozenge\) \\  & [0.302 \(\pm\) 0.023] & [0.240 \(\pm\) 0.001] & [0.260 \(\pm\) 0.001] & [0.132 \(\pm\) 0.078] & [0.110 \(\pm\) 0.010] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The influence of the organic solvent on the formation of **8L6** NPs in the presence of five surfactants.
## 3 System Architecture
### End-Edge-Cloud Architecture
The surface sink node recovers the monitoring data according to the received prediction parameters and correction data based on the edge prediction algorithm. Figure 1: End–edge–cloud architecture of underwater wireless sensor networks.
## 3 DTN Solutions for IoT
### Taxonomy of DTN Solutions for IoT
#### 3.2.1 BP-Based  
BP-based solutions target DTN architecture and especially the BP (Bundle Protocol), showing its efficiency for IoT [19; 21] and WSNs [22]. BP is a specific protocol proposed for DTNs [7] that supports the store-carry-forward principle where messages are stored locally at each node then they are carried and forwarded to the destination when access is available. Most solutions in this class are BP implementations like IBR-DTN [23], \(\mu\)DTN [24] and nanoDTN [25]. Another important topic in DTN architecture is the Convergence Layer (CL) that represents a network overlay to assure the adaptation between the bundle layer and the underlying protocols [6]. Security is a major feature of the DTN Architectural Description [6]. Security specification for the BP is provided in [26] but it still presents an interesting research area, particularly for IoT contexts.
## 3 Problem Solution Procedure  
Concerning the fact that the formulated problem using Equations (1)-(25) has a mixed integer nonlinear programming (MINLP) structure, this model includes nonlinear formulation [74; 75; 76]. The CSA has been adopted to address the problem and find solutions thanks to the advantages of CSA such as high speed and reliable solutions, particularly the low amount of standard deviation [77]. The details of the algorithm can be referred to in Algorithm 1.  
Constraints of the problem in the form of penalty functions are involved in the objective function [78], in which \(\mu\).max(0, \(a-b\)) is used for constraint \(a\leq b\). Also note that \(\mu\geq 0\) denotes a Lagrangian multiplier. Constraints (19), (23), and (24), and \(\mu\) help find the optimal decision variables using the CSA. These variables include \(y^{+}\), \(y^{-}\), \(PG\), and \(QG\). Equality constraints of the problem assist to find the rest of the variables. This article adopts the forward-backward approach and solves AC power flow equations [79]. Eventually, the variables are used to formulate the fitness function.  
```
1 Find those parameters that can be set, this includes the population size or number of crows (\(N\)), maximum iterations (\(iter_{max}\)), awareness probability (\(AP\)), and flight length (\(f\))
2 Initialize the position and crows' memory in a random way
3 Assess the value of the objective function as per Step 2
4 Update the crows' positions
```  
\[\begin{array}{l}\text{for}\;iteration=1\text{:}iter_{max}\\ \text{for}\;i=1\text{:}N\\ \text{Select a row randomly (e.g., crow }j)\\ \text{Specify a random number between [0, 1] for }j\text{th crow, i.e., }r_{j}\\ \text{if}\;r_{j}\geq AP\\ \text{position (}i,\;iteration+1\text{)}=\text{position (}i,\;iteration)+r_{j}\times fl \times\text{\{memory}}\\ \text{(}i,\;iteration\text{)}-\text{position(}i,\;iteration\text{)}\text{)} \\ \text{Check the feasibility of positions of individual crows}\\ \text{else}\\ \text{position (}i,\;iteration+1\text{)}=\text{a random value for the position between its}\\ \text{minimum and maximum values}\\ \text{end}\\ \text{end}\\ \text{Evaluate the objective function based on the new position date }\\ \text{Update the crows' memory }\\ \text{if fitness (position (}i,\;iteration+1\text{)) is better than}\\ \text{fitness (position (}i,\;iteration\text{))}\\ \text{memory (}i,\;iteration+1\text{)}=\text{position (}i,\;iteration+1\text{)}\\ \text{else}\\ \text{memory (}i,\;iteration+1\text{)}=\text{memory (}i,\;iteration\text{)}\\ \text{end}\\ \end{array}\] ```  
**Algorithm 1**Steps of the CSA
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 4 Results and Analysis
### Results and Analysis
#### 4.3.2 Motions of the Floating VAWT Considering the Wind and Regular Wave Loads  
Motions of the floating VAWT were calculated considering the wind loads and regular wave loads in this section. The parameters of wave and wind are shown in Table 4.  
The time histories and the corresponding spectra (obtained by the Fast Fourier Transform) of the floating VAWT motions are shown in Figures 8-10, and the mean displacements and the corresponding standard deviations (STD) are shown in Table 5.  
Table 5 shows that, for the three cases, the max STDs of the surge, heave and pitch motions of the floating VAWT are 1.55 m and 0.60 m and 2.64 degrees, respectively; the mean values of heave are all small, and the max mean values of the surge and the pitch motions of the three cases are 2.95 m and 3.27 degrees, respectively. Therefore, the floating VAWT has good motion performance. Compared with LC3.2, the pitch STD of the floating VAWT in LC3.3 increases by approximately 77%, and the corresponding mean value increases by approximately 25%.  
In Figures 8-10, we can find that the motion frequencies of the floating VAWT are dominated by the wave frequencies for the three cases. For the pitch frequencies of the floating VAWT, there are components of the rotor rotational frequency (2P response). However, they are not significant compared with the wave-frequency responses.