esa-sceva/satcom-chunk-collection huggingface dataset:
# Lead Monoxide Nanostructures for Nanophotonics: A Review
## 2 Synthesis of PbO Nanostructures  
In recent years, the controlled synthesis of PbO nanocrystals in a series of shapes, including 0D NPs, 1D nanostructures (e.g., NWs, NRs, etc.), 2D NSs, and even 3D hierarchical nanostructures, has achieved significant progress. The synthetic methods, such as liquid-phase exfoliation (LPE) and hydrothermal or solvothermal methods, are generally divided into two categories: the \"top-down\" strategy and the \"bottom-up\" strategy. The \"top-down\" strategy is usually used to exfoliate layered bulk PbO crystals into 0D NPs or 2D single- or few-layered NSs under driving forces, such as sonication and scotch tape, due to their weak van der Waals interaction between neighboring stacked layers. The typical \"top-down\" strategies include LPE and mechanical cleavage. Note that the \"bottom-up\" strategy usually depends on the rational design and controlled synthesis of PbO nanostructures from the precursors under certain conditions, while the typical  
Figure 1: Schematic illustration of the morphology control of PbO nanostructures and their optoelectronics/photonics applications.  
\"bottom-up\" technique for preparing high-quality PbO nanostructures is the hydrothermal method or the solvothermal method. In this section, the PbO nanostructures with different dimensionalities are briefly summarized. It is noted that only the widely used approaches are presented in this section rather than all the synthetic approaches.
## 5 Results
indicating high bias between the datasets and poor agreement between SOLCAST and McClear at these two locations.  
Figure 5 shows that three stations, Marrakech (5%), Tangier (3%), and Agadir (5%), had rMAEs less than 5%, indicating low bias between the datasets and very good agreement between McClear and SOLCAST at these three locations. In Fes and Ouarzazate, there was good agreement, with rMAEs less than 10% and 15%, respectively, whereas Tantan (23%) had poor agreement as indicated by high bias.  
Table 5, which indicates the coefficient of determination (R\({}^{2}\)) at all six stations, shows that R\({}^{2}\) > 0.99 in three stations, namely Agadir (0.995), Marrakech (0.999), and Tangier (0.999), indicating excellent performance based on Table 4 model benchmarks. At the Fes station, the coefficient was (0.985), indicating good correlation between SOLCAST and the McClear clear sky model. At Ouarzazate (0.96) and Tantan (<0.97), the corelation was less than 0.97; this shows poor correlation between SOLCAST and the McClear clear sky  
Figure 4: Hourly relative root mean square error (rRMSE) of modelled clear sky GHI against McClear clear sky GHI.  
Figure 3: Hourly relative mean bias error (rMBE) of modelled clear sky GHI against McClear clear sky GHI.  
model. In Figure 6, SOLCAST was closer to McClear, indicating good agreement but with variations between stations. The performance of SOLCAST model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S6.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Transform Domain
#### 2.1.3 Gradient Domain (GD)-Based Methods  
The gradient domain (GD)-based method is to fuse the gradient representation of the source image, limiting the gradient of the fused image within a particular threshold. Therefore, it is crucial to obtain gradient information of the image for this method. [PERSON] et al. [43] input the gradient of the image component at each image pixel. They solved the Poisson equation at each resolution to achieve boundary continuity in the gradient domain. [PERSON] et al. [44] proposed a gradient domain image fusion method based on the structure tensor, in which source images were stacked into a multi-valued image, and the structure tensor of each source image was calculated according to its gradient graph.  
The method of gradient domain image fusion can improve the image's visual effect, retaining the details and structural information of the source image. This method can be applied not only to multi-focus image fusion but also to multi-exposure image fusion.  
Despite the above fusion methods, many other multi-focus image fusion methods based on transform domain, such as independent component analysis (ICA) [17], high order singular value decomposition (HOSVD) [45], discrete cosine transform (DCT) [18], compressed sensing (CS) [46], cartoon-texture decomposition (CTD) [47] and other methods have been successfully applied to multi-focus image fusion.  
Figure 6: A multi-focus image fusion framework based on sparse representation. Image A and Image B are tow images captured of the same scene; The right area of Image A is focused while the left area of Image B is focused.
## 3 FE Model
\[\left\{\frac{\sigma_{n}}{\sigma_{n}^{C}}\right\}^{2}+\left\{\frac{\sigma_{s}}{ \sigma_{s}^{C}}\right\}^{2}+\left\{\frac{\sigma_{t}}{\sigma_{t}^{C}}\right\}^{ 2}=1 \tag{1}\]  
where \(\sigma_{n}^{C}\), \(\sigma_{s}^{C}\), and \(\sigma_{t}^{C}\) represent the critical values of the nominal stress \(\sigma_{n}\), \(\sigma_{s}\), and \(\sigma_{t}\) for induction in the adhesive under the normal, first, or second shear modes. The latter, instead, permits us to include within the simulation the effects of the interaction of the energies spent for the adherends debonding under mixed-mode conditions, Equation (2). \[\left\{\frac{G_{n}}{G_{n}^{C}}\right\}^{\alpha}+\left\{\frac{G_{s}}{G_{s}^{C}} \right\}^{\alpha}+\left\{\frac{G_{t}}{G_{t}^{C}}\right\}^{\alpha}=1 \tag{2}\]  
Figure 3: Cohesive traction—separation law [44]. Figure 2: FE model (**a**) and cohesive element layer (**b**). where \(G_{n}^{C}\), \(G_{s}^{C}\), and \(G_{t}^{C}\) are the critical fracture energies required to cause the debonding under normal, the first, and the second shear opening modes, respectively. Specifically, the adhesive mechanical properties implemented within the FE model are summarized in Table 6. The critical fracture energy values represent, for each opening mode, the area subtended to the cohesive traction--separation law. For example, if the traction--separation law is represented by a perfect isosceles triangle (Figure 3), \(G_{i}^{C}\) can be computed as in Equation (3). \[G_{i}^{C}=\sigma_{i}^{C}\delta_{0i} \tag{3}\]  
when the thickness of the adhesive is \(t\) = 1 mm, \(\delta_{0i}\) can be computed with Equation (4). \[\delta_{0i}=\frac{\sigma_{i}^{C}}{E_{i}} \tag{4}\]  
In the presented model, the traction--separation law, for each mode, is expressed by a triangle with the B-C side longer than the A-B side, which typically occurs in reality. For material properties of all adherends introduced in the FE model, refer to Table 1. Boundary conditions are defined to reproduce the real ones, Figure 4. According to Figure 4, ux, uy, and uz represent the three translational degrees of freedom with respect to the reference system depicted in the same picture. All degrees of freedom of the orange highlighted nodes are fully constrained except for the ux ones of the right end, where a displacement is imposed to simulate the tensile test. In detail, the FE analysis has been carried out according to the implicit and large displacement schemes.
# Nondestructive Evaluation of Aluminium Foam Panels Subjected to Impact Loading
## 2 Materials and Methods
### Low-Velocity Impact Tests  
The low-velocity impact (LVI) tests were performed with a Coast Fractovis Plus drop test machine, equipped with a system for the elimination of multiple impacts. The LVI tests were carried out at different energies, ranging from low-level impact energy (35 J) to high-level impact energy (400 J). A desired impact energy could be obtained by changing the drop height and/or the impactor mass. The machine was provided with a spring system, which was automatically activated when the tower height was insufficient to obtain the requested potential energy. An impactor with conical shape (6.35 mm conical radius and 025.4 mm base) was used for the tests on single-layer panels (Types I-V), whereas an impactor with a spherical shape (10 mm radius) was used for the tests on double-layer panels (Type VI). The use of an impactor with spherical shape for double-layer panels was due to practical reasons. Even though the drop test machine was equipped with an adjustable support system for a wide range of thickness, the double-layer panels did not fit in the space available between the support and the reference point of the conical nose, which corresponded to the zero height of the impact; thus a spherical nose was chosen instead of the conical one, used for the single-layer panel.  
The impact energy of 35 J was chosen for the repeated impact tests on AF panels without and with skins made of aluminium and GFRP. The panels were simply supported during the impact test on a fixed ring with an internal diameter of 40 mm, without any clamping system.  
During the test, the sample was hit by an impactor of mass M at the certain initial velocity v0 and three variables were recorded during this impact event: the force F, the time t and the initial velocity v0. The force signal F(t) was recorded during the impact test by the impactor, which was instrumented by means of a 90 kN piezoelectric force transducer. The drop test machine was equipped with a photocell that triggered the digital acquisition system at the passage of the impactor and measured its velocity v0 at the beginning of the contact before the impact. The acceleration signal a(t) was given by the ratio between the force signal F(t), subtracted by the impactor weight P (P = Mg) and the mass M. The displacement signal w(t) was obtained by double integration of the acceleration signal a(t) knowing the initial impact velocity v0, which was measured by the photocell.  
Figure 2 shows the impact setup test and an AFS specimen.  
Figure 1: Investigated AF panels: Type I (**a**), Type II (**b**), Type III (**c**), Type IV (**d**), Type V (**e**) and Type VI (**f**).
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
###### Abstract  
The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 \(\upmu\)s. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.  
LiDAR; laser diode array; drive circuit; nanosecond pulse +
Footnote †: journal:  
_Applied_  
_sciences_
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 1 Introduction
Then, Section 3 discusses different architectural solutions to provide 5G connectivity in maritime environments. Section 4 then gives a description of the field trials that are, among others, planned on the ferry route(s) across the Baltic Sea between the ports of Helsinki/Vuosaari in Finland and Tallinn/Muuga in Estonia in the 5G-ROUTES project, focusing on the overall 5G trial architecture that integrates satellite and multi-hop connectivity. Finally, the paper concludes in Section 5 by summarizing the achieved findings and, also, providing a vision for applying the results of these investigations more generally to oceanic traffic.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Unsupervised Methods
#### 3.2.4 Outlier Detection  
Outlier detection methods involve the analysis of inconsistent and unusual values in a database that typically follows a pattern of behavior [16; 77].  
In [72], a Gaussian distribution is employed to detect NTLs. This algorithm returns the probabilities of different consumers being fraudulent or not, using variables such as social class stratification, climatic variations, and energy consumption, achieving a success rate of 75%. Moreover, in [49], a group of samples is modeled as a Gaussian distribution. The probability of belonging to each distribution is calculated. The highest probability is compared with a threshold to decide whether the new sample is an anomaly or not.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 4 Validation Using the International Parallel Test Benchmarks  
The capability of GeoHyTE to converge to correct shear modulus estimates is studied in this section. The main objective of this validation campaign is to coin GeoHyTE as a mainstream interpretation method, along with the time and frequency domain methods.  
A particularly problematic testing configuration reported in the International Parallel Test [12], namely the dry \(K_{0}\)-consolidated Toyoura sand with a vertical applied pressure of 50 kPa is taken as a reference. For this configuration, the International Parallel Test reports 45 measurements involving void ratios between \(e=0.680\) and \(e=0.800\) (typically \(e=0.690\) and \(e=0.770\), also used in this study), with \(G_{0}\) readings ranging from 3.40 MPa to 108 MPa, with an average value of the shear modulus of 44.10 MPa. For each void ratio, the experiments are repeated eight times in full, i.e., the sample is removed from the envelope, dried and set up again. This strategy secures enough data to compare the shear moduli obtained using various time and frequency domain interpretation methods with those predicted by GeoHyTE and check the latter for consistency. A statistical analysis of these data is also performed.
## 3 Experimental Scenario
### Performance Analysis of OST-SMC at Different Desired Speed  
In this subsection, the robustness of the proposed algorithm at variable desired longitudinal speed is validated. The experiment shows that the proposed method guarantees robustness for variable desired speed. The algorithm is validated at the desired speed of 10 m/s, 20 m/s, and 30 m/s, respectively.  
Hence, as clearly shown in Figure 9 the result proves that the target speed could be achieved with high tracking performance based on the proposed method. At the same time, it is found that a bit of oscillation is seen during the declined road when the speed increases; nevertheless, the result confirms that the OST-SMC is indeed robust for such non-linearity.  
Figure 8: Proposed method performance analysis at different friction coefficients.
# Analysis and Ranking of IT Risk Factors Using Fuzzy TOPSIS-Based Approach
## 3 Methods and Results
### Fuzzy TOPSIS Method
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline A1 & 0.638,0.864,0.991 & 0.593,0.819,0.982 & 0.534,0.763,0.954 & 0.562,0.790,0.973 & 0.572,0.805,0.981 & 0.553,0.786,0.972 \\ \hline A2 & 0.575,0.801,0.964 & 0.548,0.774,0.982 & 0.580,0.808,0.963 & 0.562,0.790,0.963 & 0.544,0.777,0.991 & 0.591,0.823,0.981 \\ \hline A3 & 0.593,0.819,1.000 & 0.557,0.783,0.982 & 0.525,0.753,0.918 & 0.507,0.735,0.945 & 0.479,0.712,0.907 & 0.526,0.758,0.953 \\ \hline A4 & 0.529,0.756,0.937 & 0.566,0.792,0.973 & 0.580,0.808,0.982 & 0.553,0.781,0.963 & 0.544,0.777,0. The positive as well as negative ideal solutions are presented in the following Table 6. \begin{tabular}{l l l} \hline  & **Positive Ideal** & **Negative Ideal** \\ \hline C1 & (0.107,0.144,0.167) & (0.088,0.126,0.155) \\ \hline C2 & (0.105,0.143,0.167) & (0.091,0.129,0.161) \\ \hline C3 & (0.104,0.143,0.167) & (0.088,0.126,0.153) \\ \hline C4 & (0.100,0.138,0.167) & (0.085,0.123,0.158) \\ \hline C5 & (0.103,0.142,0.167) & (0.080,0.119,0.151) \\ \hline C6 & (0.099,0.137,0.167) & (0.083,0.122,0.158) \\ \hline \end{tabular} 
5. Step 5: Determine the difference in range among each alternative and the fuzzy positive ideal alternative solution \(A^{*}\), and the range among every alternative and the fuzzy negative ideal solution \(A^{-}\). The range among every alternative and FPIS as well as among every alternative and FNIS is calculated by using the following:  
\[S_{i}^{*}=\sum_{j=1}^{n}d(\widetilde{v}_{ij},\widetilde{v}_{j}^{*})\ i=1,\ 2, \ldots,\ m \tag{7}\]  
\[S_{i}^{-}=\sum_{j=1}^{n}d(\widetilde{v}_{ij},\widetilde{v}_{j}^{-})\ =1,\ 2, \ldots,\ m \tag{8}\]  
\(d\) is the range among two fuzzy figures, when assumed two triangular fuzzy numbers \((a_{1},b_{1},c_{1})\) and \((a_{2},b_{2},c_{2})\), e distance among the two can be estimated as follows:  
\[d_{v}\Big{(}\widetilde{M}_{1},\widetilde{M}_{2}\Big{)}=\sqrt{\frac{1}{3}\Big{[} \big{(}a_{1}-a_{2}\big{)}^{2}+\big{(}b_{1}-b_{2}\big{)}^{2}+\big{(}c_{1}-c_{2 }\big{)}^{2}\Big{]}} \tag{9}\]  
Note that \(d\Big{(}\widetilde{v}_{ij},\widetilde{v}_{j}^{*}\Big{)}\) and \(d\Big{(}\widetilde{v}_{ij},\widetilde{v}_{j}^{-}\Big{)}\) are crisp numbers. The range from positive as well as negative ideal solutions is shown in the following Table 7. \begin{tabular}{l l l} \hline  & **Distance from Positive Ideal** & **Distance from Negative Ideal** \\ \hline A1 & 0.037 & 0.057 \\ \hline A2 & 0.046 & 0.05 \\ \hline A3 & 0.077 & 0.017 \\ \hline A4 & 0.063 & 0.03 \\ \hline A5 & 0.052 & 0.042 \\ \hline A6 & 0.045 & 0.049 \\ \hline A7 & 0.046 & 0.046 \\ \hline A8 & 0.043 & 0.05 \\ \hline \end{tabular}   
6. Step 6: Determine the closeness coefficient as well as rating the options. Every alternative's closeness coefficient can be determined as described in the following:  
\[CC_{i}=\frac{S_{i}^{-}}{S_{i}^{+}+S_{i}^{-}} \tag{10}\]
## 4 Results and Discussion
### Evaluation Metrics  
For the traditional binary classification problem, the samples can be divided into four cases: true cases (TP), false-positive cases (FP), true-negative cases (TN), and false-negative cases (FN). The confusion matrix of the classification results is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Number** & **Mood** & **Number of Samples** \\ \hline
1 & Great satisfaction & 4020 \\
2 & Satisfaction & 4320 \\
3 & Neutral & 4670 \\
4 & Disappointment & 3830 \\
5 & Great disappointment & 3400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True Label**} & \multicolumn{2}{c}{**Predicted Label**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of classification indexes.  
The text emotion recognition task in this paper is a multi-classification task. In this task, Accuracy, Precision, Recall, and F1 values are commonly used evaluation indexes. Accuracy is the percentage of the total sample that predicts the correct outcome. Accuracy is taken as the most original evaluation index, and its calculation formula is as follows [55]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{10}\]  
Accuracy effectively reflects the probability that all predicted sample values are true. It can be calculated by the following formula [56]:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]  
Recall is the probability that a sample that is actually positive is predicted to be positive. The calculation formula of Recall rate is given as follows [57]:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{12}\]  
The F1 value represents the weighted average of Accuracy and Recall. When the numerical difference between Accuracy and Recall rate is large, the F1 value can effectively combine the two indicators. The formula for calculation of the F1 value is as follows [58]:  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{13}\]
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Experimental Validation
#### 3.3.1 Datasets  
Since RRA-Net is designed to dehaze realistic images, we train and test RRA-Net on real-world hazy image datasets. We take the images from the I-Haze [33] dataset, O-Haze [34] dataset, and NH-Haze (2020) [2] dataset together to form a training set. The I-Haze dataset contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. The O-Haze dataset consists of 45 pairs of hazy and corresponding haze-free outdoor images. In practice, the hazy images have been captured in presence of real haze, generated by professional haze machines. The NH-Haze (2020) dataset is the first non-homogeneous image-dehazing dataset and contains 55 outdoor image pairs, among which 50 pairs are usually used for training and 5 pairs are for testing. The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates real conditions of hazy scenes. In our experiments, the constructed training set consists of 130 image pairs in total, including 35 pairs from the I-Haze dataset, 45 pairs from the O-Haze dataset, and 50 pairs from the NH-Haze (2020) dataset.  
As for the test dataset, we use the NH-Haze (2021) [35] dataset and the Dense-Haze [36] dataset. The NH-Haze (2021) dataset is a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. Additionally, the Dense-Haze dataset is characterized by dense and homogeneous hazy scenes.