esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Two Models for Calculating the Satellite Repeat Period  
Prior to the introduction of these two models, the definition of the satellite repeat period, satellite operation period, and the satellite repeat shift time are given. First, the satellite repeat period refers to when the satellite returns to the same position on the sky after a period of running, denoted as \(T_{SRP}\). Since the ground track repeats for a circle in this process, it is also named the revisiting period [23]. As for the satellite operation period, it means the period when the satellite moves around the orbit of itself once in time, denoted as \(T_{\text{SOP}}\)[19]. Lastly, the satellite repeat shift time is the interval between the precise satellite repeat period and the approximate satellite repeat period by solar days, denoted as \(T_{SRST}\)[3].  
In terms of the approximate satellite repeat period united in solar days, mainly two methods are used to calculate it: One is based on the orbit offset coefficient model [24] and the other is based on the simplest fraction model [23]. The specific algorithms are given as following.
# Acoustic Propagation Characteristics of Unsaturated Porous Media Containing CO\({}_{2}\) and Oil
## 3 Numerical Analysis
### Wave Modes Characteristics
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameters** & **Value** \\ \hline Nonwetting fluid—CO\({}_{2}\), (\(p\) = 10.3 MPa, \(T\) = 45 \({}^{\circ}\)C) & density, \(\rho_{1}\) (kg/m\({}^{3}\)) & 539.07  \\ Nonwetting fluid—CO\({}_{2}\), (\(p\) = 10.3 MPa, \(T\) = 45 \({}^{\circ}\)C) & bulk modulus, \(K_{1}\) (MPa) & 30.6  \\ viscosity, \(\mu_{1}\) (\(\mu\)Pa \(\cdot\) s) & 38.9  \\ CO\({}_{2}\) gravity, \(G\) & 1.5281  \\ \hline Wetting fluid—oil, & density, \(\rho_{0}\) (kg/m\({}^{3}\)) & 799  \\ (\(p\) = 0.101 MPa, \(T\) = 25 \({}^{\circ}\)C) & viscosity, \(\mu_{0}\) (mPa \(\cdot\) s) & 2.76  \\ \multicolumn{1}{c}{(\(p\) = 0.101 MPa, \(T\) = 25 \({}^{\circ}\)C)} & \multicolumn{1}{c}{Molar Weight, \(M_{\rm IV}\) (g/mol)} & 223  \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 1: Effects of frequency and CO\({}_{2}\) saturation on the phase velocities of four waves: (**a**) the first compressional wave (P1); (**b**) the shear wave (S); (**c**) the second compressional wave (P2); (**d**) the third compressional wave (P3). Figure 2: Effects of frequency and CO\({}_{2}\) saturation on the attenuations of four waves: (**a**) P1; (**b**) S1; (**c**) P2; (**d**) P3. From Figures 1 and 2, we chose a typical seismic frequency (\(f=100\) Hz) to ensure each mode wave propagates more clearly. Figure 3 displays the results for the P1 wave and S wave. It can be observed in Figure 3 that when CO\({}_{2}\) saturation increases, the S velocity increases, while the P1 velocity decreases. At high CO\({}_{2}\) saturation, the change in P1 velocity is not obvious. This results from both the density and velocity of the pore fluid mixture. The pore fluid velocity significantly impacts the P1 velocity, which decreases as the fluid velocity decreases. Only the pore fluid density affects S velocity, which increases as the fluid density decreases. Due to the dissolution effect, the critical saturation marks the point at which the pore fluid changes from a single phase to two phases. When the free CO\({}_{2}\) appears, the attenuations of P1 and S increase rapidly. With the above results, we can expect that [PERSON]'s model can be used for the seismic monitoring of CO\({}_{2}\) migration.
## 4 The NLOS Communication Scheme Based on CP/CS-SC-FDE
### The Channel Estimation Algorithm of CP/CS-SC-FDE for UAV PC
```
0:\(y^{\prime}_{n}\), \(P_{k}\);
0:\(\hat{\mathbf{h}}_{d}\);
1: Perform an \(M\)-point DFT transformation on \(y^{\prime}_{n}\);
2: Obtain the channel frequency response Using LS estimation \(\hat{H}_{k}=\frac{Y^{\prime}_{n}}{P_{k}}\);
3: Obtain \(\hat{h}_{n}\) by performing an \(M\)-point IDFT transformation on \(\hat{H}_{k}\);
4: Set \([\hat{\mathbf{h}}_{d}]_{n}=0,n=0,...,N-1\);
5:for all\(n=0,1,\cdots,N-1\)do
6:if\(n\leq C-1\)then
7:\([\hat{\mathbf{h}}_{d}]_{n}=\hat{h}_{n}\);
8:else if\(n\geq N-D\)then
9:\([\hat{\mathbf{h}}_{d}]_{n}=\hat{h}_{M-(N-n)}\);
10:endif
11:endfor
12:return\(\hat{\mathbf{h}}_{d}\). ```  
**Algorithm 1** Channel estimation algorithm of CP/CS-SC-FDE
## 2 Multi-UAV Cooperative Persistent Monitoring Path Planning Model
### Discretization of the Graph  
Persistent monitoring needs to consider UAV movement synchronization. In order to solve the problem, a discrete approximation operation is introduced on the graph \(G\). Several virtual nodes are inserted in an approximately uniform way to the edges of \(G\) leading to a discretized graph that includes many more edges of equal intervals, denoted by \(\delta\). This operation encourages good behavior in which any UAV will certainly move forward from its current node position to its neighbor node in \(G\) instead of staying between nodes at time step \(k\). This is called UAV movement synchronization. Consequently, nodes can be divided into two categories, one is the **task node** set, \(V\), which requires monitoring and the other is the **virtual node** set, \(U\), which is generated during discrete approximation operation and does not to be monitored. The complete node set, called a **generalized node** set, is denoted as \(V^{\prime}=V\cup U=\{1,2,\ldots,N+|U|\}\). It should be emphasized that all virtual nodes in \(U\) are not real monitoring tasks, so they do not need to record their monitoring delays. The final adjacency matrix of \(G\) is \(A\in\mathbb{R}^{(N+|U|)\times(N+|U|)}\), where any element \(a_{ij}\) is binary. \(a_{ij}=1\) indicates that node \(i\) and \(j\) are adjacent to each other, otherwise \(a_{ij}=0\).
## 3 Results and Discussion
### Antibacterialrial Experimental Results  
Figure 11 depicts the results of the anti-microbial property on the Cu0, Cu1, Cu2, Cu3 and Cu4 samples. The Cu/TiO\({}_{2}\) 2 mm heat treated tablets were fabricated by a method consistent with the thin film to perform this antibacterial characterization. The Cu0 and Cu1 almost formed no anti-microbial zone, and this may be attributed to the insufficiency of the doped Cu. With the increase of the Cu doped in the TiO\({}_{2}\) films, the anti-microbial zone formed and became larger. (Anti-microbial zone size: Cu4 \(>\) Cu3 \(>\) Cu2 \(>\) Cu1). Copper ions--which are positively charged, are easy to bind with negatively charged bacterial protein--further combine with sulfhydryl groups (-SH) of protein causing inactivation of the active enzyme, which leads to bacterium death [49]. The results above proved that the Cu doped TiO\({}_{2}\) thin film possessed antibacterial properties, which was positively correlated with the amounts of doped Cu; more doped Cu contributed to better antibacterial properties. Therefore, Cu4 possessed the best antibacterial function in this work.  
Figure 11: Antibacterial experiment of _E. coli_ on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces after 24 h of incubation.  
Figure 10: The SEM characterization of macrophages on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces after 24 h of incubation.
## 5 Ultimate Capacity Test Results and Discussion
### Influence of Interlayer Thickness on Ultimate Capacity and Ultimate Deflection  
By comparing \(q_{\text{b}}\) and \(\Delta\) of the specimens with identical cold-bending curvatures and different interlayer thicknesses, it was found that both parameters of cold-bent SGP laminated glass panels decreased with greater interlayer thickness. This situation occurred because the initial stress (cold-bending stress) of the glass panels was greater with the thicker interlayers, and the \(q_{\text{b}}\) and \(\Delta\) of the glass panels with thicker interlayers in the ultimate capacity tests decreased. When the interlayer thickness increased from 1.14 mm to 1.52 mm, the \(q_{\text{b}}\) and \(\Delta\) values decreased by 4.2% and 2.1% on average, respectively. Consequently, the influence of interlayer thickness on \(q_{\text{b}}\) and \(\Delta\) was much less significant than that of the cold-bending curvature.  
Besides, it can be seen from Figure 11 that the two curves of different interlayer thicknesses showed very similar shapes and a relatively close mutual distance. Therefore, the interlayer thickness was demonstrated to show a slight influence on \(q_{\text{b}}\) and \(\Delta\). Moreover, the two curves exhibited a tendency to converge as the cold-bending curvature rose. At a cold-bending curvature of 1.0%, the two curves almost overlapped, indicating that if the cold-bending curvature were to exceed 1.0%, the interlayer thickness would have a negligible influence on the \(q_{\text{b}}\) and \(\Delta\) values of the cold-bent SGP laminated glass panels.
## 4 Experimental Results and Analysis
### Implementation Details  
During the experiments, the samples of the two datasets were randomly divided into a training dataset and a test dataset. The training dataset was used to train the proposed model SSAMMA to obtain the best projection matrix. The test dataset was used to verify the effectiveness of the proposed model SSAMMA for dimensionality reduction. To reduce the chance, 10 replicate trials were performed under the same set of parameters. The effect of the algorithm under this set of parameters was expressed by calculating the average result of the 10 replicate trials. In addition, to quantitatively evaluate the effectiveness of the proposed method, the classification result graph, overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kappa) were used as evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category Number** & **Category Name** & **Sample Number** & **Color** \\ \hline
1 & Asphalt & 6631 & \\
2 & Meadows & 18,649 & \\
3 & Gravel & 2099 & \\
4 & Trees & 3064 & \\
5 & Painted metal sheets & 1345 & \\
6 & Bare Soil & 5029 & \\
7 & Bitumen & 1330 & \\
8 & Self-blocking bricks & 3682 & \\
9 & Shadows & 947 & \\ \hline Total & & 42,776 & \\ \hline \end{tabular}
\end{table}
Table 2: Ground object category for the UP dataset.  
Figure 3: UP dataset: (**a**) false-color composite image; (**b**) ground-truth map.
# A Survey of Multi-Focus Image Fusion Methods
## 5 Conclusions  
This paper describes four kinds of multi-focus image fusion methods: transform domain method, boundary segmentation method, deep learning method, and combinatorial fusion method. Each method is deeply classified and the advantages and disadvantages of each method are compared. For different scenarios, it is necessary to choose the appropriate method. In addition, the commonly used evaluation indicators are listed, and the objective evaluation is more accurate than the subjective evaluation, which takes less energy and time. Finally, the solution is discussed based on the analysis of the shortcomings of current applications and methods.  
Multi-focus image fusion can effectively solve the depth of field problem in optical lens areas and has a wide application space in many fields, such as medicine, security, photography, etc. It has been successfully applied in the areas of microscopic imaging [63], image deblurring [64], focusing shape [65], and information forensics [66].  
To sum up, multi-focus image fusion needs further development. Solving the problem of image registration will improve the universality of the method and expand the scope of fusion. In the pursuit of fusion quality, it is necessary to pursue time efficiency, taking real-time fusion as the ultimate goal.  
Conceptualization, Y.Z. ([PERSON]) and L.Y.; methodology, Y.Z. ([PERSON]); software, C.Z.; validation, C.H., S.W. and M.Z.; formal analysis, Y.Z. ([PERSON]); investigation, Z.K.; resources, Z.G.; data curation, S.F.; writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, L.Y.; funding acquisition, Y.Z. ([PERSON]) and S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the financial support from the National Natural Science Foundation of China (Grant No. 51903199), Outstanding Young Talents Support Plan of Shaanxi Universities (2020), Natural Science Basic Research Program of Shaanxi (No. 2019)Q-182 and 2018)Q5214), Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 18 JS039), Innovation Capability Support Program of Shaanxi (Program No. 2022 KJXX-34), Science and Technology Guiding Project of China National Textile and Apparel Council (No.2020044 and No.2020046), the Open Project Program of Key Laboratory of Eco-textiles, Ministry of Education, Jiangnan University (No. KLET2010), the Graduate Scientific Innovation Fund for Xi'an Polytechnic University (chx2021004), and the Work and Research Project of University Laboratory of Zhejiang Province (YB202110).  
Not applicable.  
The authors declare no conflict of interest.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 4 Results
### Determining a UAV Grouping Method
The object is branching (Figure 5), so checkpoint 6 is not directly connected to checkpoint 7. Checkpoint 3 is an intersection point; thus, the cell with a value of 9.60 is coloured. Thus, the highest monitoring frequency of the whole object per day is 4.00 when using eight drones. It should also be noted that seven drones will be sufficient to achieve the highest monitoring frequency. This is because the \(P\)5-_P_6 section is the longest, and it is longer than the sum of the lengths of the two shortest adjacent sections \(P\)3-_P_7 and \(P\)7-_P_8. \begin{table}
\begin{tabular}{r c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Landing**} \\ \cline{2-10}  & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
**0** & \(-\) & 4.70 & 2.25 & 1.51 & 1.14 & 1.00 & 0.80 & 1.33 & 1.22 \\
**1** & 4.70 & \(-\) & 4.27 & 1.48 & 1.13 & 0.99 & 0.79 & 1.29 & 1.18 \\
**2** & 2.25 & 4.27 & \(-\) & 4.62 & 2.34 & 1.81 & 1.25 & 3.12 & 2.60 \\
**3** & 1.51 & 1.48 & 4.62 & \(-\) & 4.73 & 2.97 & 1.89 & 9.60 & 5.39 \\
**Take-off** & **4** & 1.14 & 1.13 & 2.34 & 4.73 & \(-\) & 7.98 & 2.77 & 3.17 & 2.68 \\
**5** & 1.00 & 0.99 & 1.81 & 2.97 & 7.98 & \(-\) & 4.00 & 2.27 & 1.97 \\
**6** & 0.80 & 0.79 & 1.25 & 1.89 & 2.77 & 4.00 & \(-\) & 1.58 & 1.43 \\
**7** & 1.33 & 1.29 & 3.12 & 9.60 & 3.17 & 2.27 & 1.58 & \(-\) & 14.84 \\
**8** & 1.22 & 1.18 & 2.60 & 5.39 & 2.68 & 1.97 & 1.43 & 14.84 & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary table of monitoring frequencies for the different UAV responsibility zone allocation options. The values presented indicate the expected monitoring frequency in a day per drone. Highlighted cells indicate the monitoring frequency between neighbouring take-off and landing points, i.e., the option of using 8 drones in total. The table allows for determining the highest frequency of monitoring of the whole object. Figure 7: Dependence of monitoring frequency (number of flights per day) on the number of drones. It can be concluded that the lowest monitoring frequency would be 0.73 flights per day and the highest monitoring frequency of four flights per day could be achieved with seven drones per day. Thus, the model makes it possible to determine the optimum number of drones for a given task. The cost of operations and investments could also be taken into account for more detailed analyses.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 9 Conclusions  
This paper critically analyzed recent deep-learning-based Arabic-OCR and text-correction works. We conclude with the following remarks and strong recommendations based on our analysis for both researchers and practitioners in the future. (1) For the OCR step, LSTM-CNN-CTC is the standard SOTA architecture because it can handle spatial relations, long text dependencies, and text alignments. However, we recommend the promising Transformer architecture if more data and computational resources become available. (2) For the text-correction step, there is no singular recommended architecture; however, the combination of variations of text-embedding techniques such as BART and FastText along with datasets containing errors and corrections shows significant improvement. Nevertheless, this approach is still in its infancy; therefore, we strongly recommend that multiple embedding techniques and datasets be explored further. (3) There is a noticeable lack of datasets, particularly for OCR; therefore, it is a major priority to make morechallenging, domain-specific, context-aware, high-quality, and font-variant Arabic OCR and text-correction datasets, since deep learning's first pillar is the data. Moreover, we also recommend building more multilingual datasets that include Arabic with the same properties as those described above. (4) Many OCR applications are neither applied nor well developed in Arabic, and as this technique is entering Arabic domains, we strongly recommend that researchers and practitioners attempt build Arabic-based solutions in unexplored areas, such as automatic handwritten-essay scoring, paleographical and historical studies in education, and the digitization of handwritten documents in the legal and healthcare domains, with the ultimate aim of integrating AI-enabled solutions in the era of automation.  
Conceptualization, S.F. and R.N.; methodology, S.F. and R.N.; investigation, R.N.; resources, S.F. and R.N.; writing--original draft preparation, R.N.; writing--review and editing, R.N. and S.F.; visualization, R.N.; supervision, S.F.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deputyship of Research and Innovation, Ministry of Education, Saudi Arabia, through project number 964. In addition, the authors would like to express their appreciation for the support provided by the Islamic University of Madinah.  
Not applicable.
## 4 Inspection Results
### Human Operator  
The performance of the human operator has been evaluated in depth in previous work [9; 10; 11; 87] and the results are summarised as follows. The combined inspection accuracy for screen-based piece-part inspection of engine blades with a variety of defect types, severity levels and part conditions is 76.2% [9; 11; 87]. When operators were given the actual (physical) part and allowed to use their hands to feel the blade and apply their tactile sense, an accuracy of 84.0% was achieved [87]. In borescope operations the inspection is always made based on a digital presentation of the parts on a screen. The inspection accuracy in such situation was determined to be 63.8% [10].  
The inspection time differed between the different inspection types. Screen-based piece-part inspection was the fastest with 14.972 s, followed by the borescope inspection with 20.671 s on average. Allowing the operators to touch and feel the blade led to the longest inspection times of 22.140 s [87].  
The assessment of the inspection consistency and repeatability revealed that operators agreed with themselves 82.5% of the time, and 15.4% of the time with each other [11].  
For a detailed evaluation of the human inspection, please refer to [9; 10]. The authors applied eye tracking technology to assess the search strategies applied by different operators and to determine the inspection errors.
# Training Spiking Neural Networks with Metaheuristic Algorithms
## 3 SNN Model and Learning Method
### Information Coding  
The initial phase of an SNN is the transformation of the original features into spikes to feed the spiking networks. The encoding strategy has a significant effect on network performance. Selecting the best coding strategy depends on the application, hardware limitations, and neuron model [59]. Rate coding is one of the traditional strategies used for encoding information in SNNs, which is based on the spike firing rate. Another popular coding strategy is temporal coding, which encodes information in spike times [60,61]. Figure 2b depicts the schematic of rate and temporal coding. Temporal encoding can be represented mathematically as follows:  
\[Y(x)=\left[\frac{a^{\prime}-a}{m}*x\right]+\left[\frac{(a*N)-(a^{\prime}*n)}{m }\right] \tag{2}\]  
where \(x\) is the original feature value and the maximum and minimum values that \(x\) can take are \(N\) and \(n\). The range between \(N\) and \(n\) is denoted by \(m\), and \(Y\) is the spike temporal transformation over the lower and upper temporal interval bounds \([a,a^{\prime}]\).  
In this work, a type of temporal coding called rank-order encoding is used to transform features into spikes [62]. It assumes that strongly activated neurons will tend to fire earlier than weakly activated ones. As compared to rate coding, rank-order coding provides more information with fewer spikes. Another benefit of rank-order coding, compared to rate coding, is that it requires fewer simulation time steps for training. With the use of rank-order coding, the IF neuron can fire at most one spike: the most-activated neurons fire first, while less-activated neurons fire later. Since the network decision is based on the first spike in the output layer, the earlier spikes carry more information.
## 3 Results and Discussion  
### Equation of State  
We simulated the thermodynamic properties of nitrogen using the molecular dynamics method and then compared the result with the SRK equation of state. The results are shown in Figure 3. The blue square is the results from MD while the green solid line is that of the SRK equation of state. The red dashed line is the ideal gas equation of state for reference purposes. It is obvious that the state equations obtained by molecular dynamics simulation are perfectly in line with the SRK equation of state, both at a low temperature (Figure 3a) and a high temperature (Figure 3b). This coincidence certifies the correctness of the nitrogen molecular model. The conclusion also matches the results of [PERSON] [12]. Also, our results have much smaller errors compared with Reference [12] because we used the harmonic model, in which the length of the molecular bond changes naturally according to the force on it, instead of the RATTLE algorithm, which constrains the length of all the molecular bonds to a constant value. The RATTLE algorithm may bring some unphysical pressure fluctuations because the length of the bonds is forcibly adjusted. For nitrogen, the critical temperature is 126 K and the critical pressure is 3.4 MPa. Near the critical point, the sensitivity of thermodynamic properties can be observed: the density increases sharply along with the increase of pressure.