esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Photocatalytic Activity
#### 3.5.3 Simultaneous Degradation of Bentazon and Nile Blue  
The efficiency of TiO\({}_{2}\) was also assessed in a mixed solution of BZ and NB. The experiment was carried out in similar experimental conditions, both under UV and sunlight irradiation. Figure 7a,b shows the absorption spectra of the dye and herbicide mixture under UV and sunlight for 160 min. The absorption spectra show a simultaneous decrease at \(\lambda\) = 334 nm and \(\lambda\) = 634 nm for BZ and NB, respectively. Figure 7c shows the percentage degradation of BZ and NB in the mixture under the two irradiations. The percentage degradation of BZ and NB was 78% and 68% under UV light and 60% and 95% under sunlight. The degradation percentages are lower with respect to the single components for both organic pollutants. However, the removal percentage of BZ is lower as compared to NB under UV and sunlight. This might be due to the complex structure of NB which affects the degradation of BZ herbicide. \(\ln(C_{0}/C_{t})\) was plotted as a function of time (t) in Figure 7d. The values of rate constants (k) and R\({}^{2}\) are listed in Table 3. To the best of our knowledge, there are no experimental studies on the photocatalytic behavior of TiO\({}_{2}\) for the solution of BZ and NB under UV and sunlight. [PERSON] et al. synthesized TiO\({}_{2}\) by the combustion method and used it for the degradation of individual solutions and a mixture of metamitron herbicide and rhodamine B dye. They observed the complete degradation of dye and herbicide under UV light [38]. [PERSON] reported the degradation of two dyes Rhodamine B and methylene blue using ultrasound-assisted photocatalytic activity and obtained 63% and 97% degradation in the mixture solution [39].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Catalyst**} & **Catalyst** & **Irradiation** & **Nile Blue** & **Degradation** & **Ref.** \\  & **(gL\({}^{-1}\))** & **Source** & **(mgL\({}^{-1}\))** & **Percentage** & **Ref.** \\ \hline CuO-SiO\({}_{2}\) & 0.1 & UV lamp & 20 & 90\% & [34] \\  & & 250 W & & 95\% in & [35] \\ FeMnO\({}_{3}\) & / & mercury & 10 & 80 min & [35] \\  & & lamps & & 93\% in & \\ CuFe\({}_{2}\)O\({}_{4}\) & 0.03 & Hg lamp & 10 & 120 min & [36] \\ NiO-ZnO & 0.03 & sunlight & 5 & 97\% in & [37] \\  & & & & 140 min & Present \\ TiO\({}_{2}\) & 0.3 & UV light & 5 & 160 min & work \\ TiO\({}_{2}\) & 0.3 & Sunlight & 5 & 100\% in & Present \\  & & & & 160 min & work \\ \hline \end{tabular}
\end{table}
Table 4: The comparison study of photodegradation efficiency of metal oxide-based nanocomposites at different experimental conditions for Nile blue.
## 4 The Proposed ECSO-OBL Algorithm
### WSN Node Coverage Model
\[v_{i}^{t+1}=\lambda v_{i}^{t}+l_{1}r_{1}\big{(}o_{i}^{t}-p_{i}^{t}\big{)}+l_{ 2}r_{2}\big{(}g^{t}-p_{i}^{t}\big{)} \tag{6}\]  
\[p_{i}^{t+1}=p_{i}^{t}+v_{i}^{t+1} \tag{7}\]  
where \(l_{1}\) and \(l_{2}\)\(\rightarrow\) learning factors (they represent the social awareness and self-awareness of an individual nest); \(\lambda\)\(\rightarrow\) inertia weight; and \(o_{i}^{t}\)\(\rightarrow\) optimal value of the \(i^{th}\) nest from the \(1^{st}\) to the \(t^{th}\) generations. \(g^{t}\)\(\rightarrow\) optimal value among all nests in the \(t^{th}\) generation; \(v_{i}^{t}\) and \(v_{i}^{t+1}\)\(\rightarrow\) velocities of the \(i^{th}\) nest in the \(t^{th}\) and \((t+1)^{th}\) generations. \(p_{i}^{t}\) and \(p_{i}^{t+1}\)\(\rightarrow\) positions of the \(i^{th}\) nest in the \(t^{th}\) and \((t+1)^{th}\) generations. \(r_{1}\) and \(r_{2}\)\(\rightarrow\) random numbers \(\in\)[0, 1]. The \"variance\" of the population is updated adaptively, and it is decreased when the algorithm starts converging. Inertia weight (\(\lambda\)) gives how well the speed of the current generation influences the speed of the next generation. As the algorithm continues to execute, after sufficient iterations, the population distribution starts to concentrate, and the diversity reduces. In that case, the \(\lambda\) value is increased to overcome premature convergence, and it is adaptively updated as per Equation (8). \[\lambda_{t}=\lambda_{min}+(\lambda_{max}-\lambda_{min})\times F_{t} \tag{8}\]  
where \(\lambda_{t}\)\(\rightarrow\) inertia weight in the \(t^{th}\) generation, and \(\lambda_{min}\) and \(\lambda_{max}\) are the minimum and maximum values of the inertia weight (\(\lambda\)), respectively. \(F_{t}\) is the population diversity function in Equation (9). \[F_{t}=1-\frac{1}{\big{(}\frac{\gamma}{2}\big{)}}tan^{-1}(V) \tag{9}\]  
\[V=\frac{1}{S}\sum_{i=1}^{S}\left[h(p_{i})-\frac{1}{S}\sum_{i=1}^{S}h(p_{i}) \right]^{2} \tag{10}\]  
where \(V\)\(\rightarrow\) variance of the fitness values of an individual nest and \(h(p_{i})\)\(\rightarrow\) adaption value of the current \(i^{th}\) nest, as mentioned in Equation (10). **Step 5:**: Apply \"opposition-based learning\" for the best 10% possible current solutions (whose fitness values are the highest), and generate their corresponding opposite solutionsby using Equation (11). Accordingly, update the current nest positions, and retain the nests with the highest fitness values. \[\overline{S}_{i,j}=\rho\big{(}min(B_{i,j})+max(B_{i,j})\big{)}-s_{i,j} \tag{11}\]  
where \(s_{i,j}\rightarrow\) search space boundary in the \(j^{th}\) dimension; \(\rho\rightarrow\) multiplication factor in the range [0, 1]; \(S_{i}=\{s_{i,1},s_{i,2},\,s_{i,3},\,\ldots\,s_{i,N}\}\rightarrow\) set of the best current solutions; and \(\overline{S}_{i}=(\overline{s}_{i,1},\overline{s}_{i,2},\overline{s}_{i,3}, \,\ldots\ldots\overline{s}_{i,N})\rightarrow\) set of the inverse solutions. **Step 6:** The algorithm stops executing once it meets the termination condition, and it gives best possible solution as output; otherwise, it moves to step 3.
## 3 Materials and Methods
### Acid Hydrolysis  
In order to analyze bound volatile organic compounds, the free VOCs had to be removed from the sample. The removal of free VOCs was carried out by heating the intact grape skins powder at 60 \({}^{\circ}\)C. To test the time needed for removal of free volatiles, the times 1, 2, 3, and 4 h were selected, along with control (no heating 0 h). The heating was carried out on a magnetic stirrer (RTC basic, IKA, Staufen, Germany). To confirm the removal of free volatiles, the sample was analyzed by SPME-Arrow-GC/MS.  
The conditions of acid hydrolysis were based on the work of [PERSON], et al. (2018) with minor adjustments. In short, 100 mg of grape skin powder (free VOCs removed) was placed in 20 mL headspace screw-top vials. Acid hydrolysis was carried out by reconstituting the dry sample in 4 mL of citric acid buffer (pH 2.4). The vials were sealed with PTFE/silicone septum containing caps and placed in a thermostated bath adjusted at 95 \({}^{\circ}\)C for 2 h in the nitrogen atmosphere. Immediately after hydrolysis, the samples were cooled down in an ice bath and salted out with 2 g of NaCl.  
To optimise the SPME-Arrow extraction conditions Box-Behnken experimental design (BBD) and response surface methodology were used. Sample weight and SPME-Arrow coatings were constant and were 100 mg and DVB/CWR/PDMS. The variables selected for SPME-Arrow optimization were the extraction temperature, incubation time, and exposure time (Table 6). All applied statistical methods were the same as described in the Section 3.4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & \multicolumn{3}{c}{**Factor Levels**} \\ \hline Coded levels & \(-1\) & 0 & 1 \\ A: Extraction temperature (\({}^{\circ}\)C) & 40 & 50 & 60 \\ B: Incubation time (min) & 10 & 20 & 30 \\ C: Exposure time (min) & 30 & 45 & 60 \\ D: Desorption time (min) & 5 & 7.5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Independent factors and their levels used in the BDD for optimization of SPME-Arrow extraction for determination of free volatile compounds.
# Comparison of Root Parallelism in Extraction Cases Treated with Clear Aligners vs. Fixed Appliances
###### Abstract  
In the process of orthodontic treatment, root parallelism is related to the relapse of extraction spaces, black triangles, and periodontal health. However, there are few studies on root parallelism in extraction cases with different types of appliances. The objective was to compare the root parallelism in extraction cases treated with clear aligners vs. those treated with fixed appliances by measuring the differences per extraction site and to access the root-movement control capacity of both orthodontic appliances. A retrospective study was conducted on cases in which the first premolers were extracted, treated by clear aligner appliance (\"Invaslagin\" system, 28 patients) or fixed appliance (30 patients). The angulations of the tooth axis (canines, the second premolers, the first molars, and the second molars) were measured to analyze and compare the difference in root parallelism between the two orthodontic appliances. The percentage of root parallelism between the canine and second premoler in both groups had no significant difference. However, judging from the average angle difference value, the fixed appliance might have a greater range in root angulation adjustment than the aligner. In the angulation categories, the aligner mainly showed root apical divergence, while the fixed appliance mainly showed root apical convergence. The dental crown of the molars was mainly non-tipping in both groups, but it could be observed that the dental crown of the maxillary molars had a tendency toward mesial inclination. In conclusion, the same root parallelism and root inclination were obtained in tooth extraction cases whether treated by clear aligners or fixed appliances. This study provides clinicians with more information on the performance of clear aligners and fixed appliances.  
Keywords:tooth extraction; clear aligner appliances; fixed appliance; orthodontics +
Footnote †: journal: Applied Sciences
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 6 RL-ChoA Engineering Example Application Analysis
### Extension/Compression Spring Optimization Design Problem  
The optimization goal of the extension/compression spring design problem [59] is to reduce the weight of the spring, the schematic diagram of which is shown in Figure 8. Constraints include subject to minimum deviation (\(g_{1}\)), shear stress (\(g_{2}\)), shock frequency (\(g_{3}\)), outer diameter limit (\(g_{4}\)), and decision variables include wire diameter \(d\), average coil diameter \(D\), and effective coil number \(P\), \(f(x)\) to minimize spring weight. The objective functions and constraints of these three optimization variables are as follows:  
\[\begin{split}&\text{Consider :}\\ &\mathbf{x}=[x_{1}\ x_{2}\ x_{3}]=[d\ D\ P];\\ &\text{Minimize}f(\mathbf{x})=x_{1}^{2}x_{2}(2+x_{3});\end{split} \tag{25}\]  
subject to :  
\[\begin{split}& g_{1}(\mathbf{x})=1-\frac{x_{2}^{3}x_{3}}{71785x_{1} ^{4}}\leq 0;\\ & g_{2}(\mathbf{x})=\frac{4x_{2}^{2}-x_{1}x_{2}}{12,566(x_{2}x_{1} ^{3}-x_{1}^{4})}+\frac{1}{5108x_{1}^{2}}\leq 0;\\ & g_{3}(\mathbf{x})=1-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0; \\ & g_{4}(\mathbf{x})=\frac{x_{1}+x_{2}}{1.5}-1\leq 0;\\ &\text{with}\\ & 0.05\leq x_{1}\leq 2.0,0.25\leq x_{2}\leq 1.3,\text{and}\ 2.0\leq x_{3}\leq 15.0.\end{split} \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & \(\boldsymbol{h}\) & \(\boldsymbol{l}\) & \(\boldsymbol{t}\) & \(\boldsymbol{b}\) & Mean \\ \hline GA & 0.2455 & 6.1986 & 8.1264 & 0.2247 & 2.4414 \\ PSO & 0.2024 & 3.5441 & 9.048 & 0.2057 & 1.7315 \\ ChOA & 0.2214 & 3.5358 & 8.9115 & 0.2127 & 1.7737 \\ WChOA & 0.2021 & 3.4707 & 9.0366 & 0.2057 & 1.7249 \\ RL-ChoA & 0.2036 & 3.4715 & 9.0286 & 0.2058 & 1.7227 \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of welded beam design.  
Figure 7: Design principle of the welded beam.  
It can be observed from Table 11 that RL-ChoA shows superior performance on the optimization problem of welded beams.
## 4 Numerical Examples
### Test Problems  
**Test Problem 1** (TP1) We consider the following system given in [13]:  
\(F(x_{1},x_{2})=0\), where \(F:(4,6)\times(5,7)\rightarrow\mathbb{R}^{2}\) and  
\[F(x_{1},x_{2})=(x_{1}^{2}-x_{2}-19,\quad x_{2}^{3}/6-x_{1}^{2}+x_{2}-17).\]  
The Jacobian matrix is given by \(F^{\prime}(x)=\left(\begin{array}{cc}2x_{1}&-1\\ -2x_{1}&\frac{1}{2}x_{2}^{2}+1\end{array}\right).\) The starting vector is \(x^{(0)}=(5.1,6.1)^{T}\) and the exact solution is \(\alpha=(5,6)^{T}\).  
**Test Problem 2** (TP2) We consider the following system given in [3]:  
\[\left\{\begin{array}{l}\cos x_{2}-\sin x_{1}=0,\\ x_{3}^{x_{1}}-\frac{1}{x_{2}}=0,\\ \exp x_{1}-x_{3}^{2}=0.\end{array}\right.\]  
The solution is \(\alpha\approx(0.909569,0.661227,1.575834)^{T}\). We choose the starting vector \(x^{(0)}=(1,0.5,1.5)^{T}\). The Jacobian matrix has \(7\) non-zero elements and it is given by  
\[F^{\prime}(x)=\left(\begin{array}{ccc}-\cos x_{1}&-\sin x_{2}&0\\ x_{3}^{x_{1}}\ln x_{3}&1/x_{2}^{2}&x_{3}^{x_{1}}x_{1}/x_{3}\\ \exp x_{1}&0&-2x_{3}\end{array}\right).\]  
**Test Problem 3** (TP3) We consider the following system given in [3]:  
\[x_{2}x_{3}+x_{4}(x_{2}+x_{3})=0,\] \[x_{1}x_{3}+x_{4}(x_{1}+x_{3})=0,\] \[x_{1}x_{2}+x_{4}(x_{1}+x_{2})=0,\] \[x_{1}x_{2}+x_{1}x_{3}+x_{2}x_{3}=1.\]  
We solve this system using the initial approximation \(x^{(0)}=(0.5,0.5,0.5,-0.2)^{T}\). The solution of this system is \(\alpha\approx(0.577350,0.577350,0.577350,-0.288675)^{T}\). The Jacobian matrix that has \(12\) non-zero elements is given by  
\[F^{\prime}(x)=\left(\begin{array}{cccc}0&x_{3}+x_{4}&x_{2}+x_{4}&x_{2}+x_{3 }\\ x_{3}+x_{4}&0&x_{1}+x_{4}&x_{1}+x_{3}\\ x_{2}+x_{4}&x_{1}+x_{4}&0&x_{1}+x_{2}\\ x_{2}+x_{3}&x_{1}+x_{3}&x_{1}+x_{2}&0\end{array}\right).\]Table 1 shows the results for the test problems (TP1, TP2, TP3), from which we conclude that the \(10^{th}HM\) method is the most efficient method with least number of iterations and residual error.  
In Table 2, we have given CPU time for the proposed methods and some existing methods.  
Next, we consider the \((2r+4)^{th}\)_HM_ family of methods for finding the least value of \(r\) and thus the value of \(p\) in order to get the number of iteration \(M=2\) and \(err_{min}=0\). To achieve this, TP1 requires \(r=6\) (\(p=16\)), TP2 requires \(r=18\) (\(p=40\)) and TP3 requires \(r=8\) (\(p=20\)). Furthermore, it is observed that the order of convergence \(p\) depends on the test problem and its starting vector.
# Evaluation of PID-Based Algorithms for UGVs
## 3 Methodology
### PID Controller
#### 3.4.3 PID CTE + H
```
IF No_Goals = TRUE RETURN FALSE Flag_Goal_Reached = FALSE Flag_Rotation = TRUE WHILE Flag_Goal_Reached = FALSE IF detect_people = TRUE: \(V_{L}=0\) and \(V_{K}=0\) (STOP MACHINE) ELSE: IF Flag_Rotation = TRUE IF Not_Aligned AND 10_Seconds_Passed_Without_Changing_Position: \(v=0\) and \(\omega\
eq\ 0\) (ROTATE MACHINE) ELSE:  Original_Position = Actual_Position Flag_Rotation = FALSE ELSE: \(e_{Position}=\sqrt{\left(T_{x}-A_{Px}\right)^{2}+\left(T_{y}-A_{Py}\right)^{2}}\) \(e_{CTE}=Actual_{DistanceFromLine}-Desired_{DistanceFromLine}\) \(\phi=K_{CT}*e_{CTE}\) \(\theta_{Desired}=\theta_{path}-\phi\) \(e_{Heading}=\theta_{Desired}-\theta_{Real}\) IF\(e_{Position}\leq R\): \(V_{L}=0\) and \(V_{K}=0\) (STOP MACHINE) Flag_Goal_Reached = TRUE Flag_Rotation = TRUE ELSE: \(\omega=K_{p}^{\phi}e_{Heading}+K_{l}^{\phi}\int e_{Heading}\ dt+K_{d}^{\phi}\frac{d\epsilon_{Heading}}{dt}\)  Linear PID \(v=K_{p}^{CTE}e_{CTE}+K_{l}^{CTE}\int e_{CTE}\ dt+K_{d}^{CTE}\frac{d\epsilon_{ CTE}}{dt}\) \(V_{L}=v-\frac{l}{2}\omega\) AND \(V_{K}=v+\frac{l}{2}\omega\)
```  
**Algorithm 4** PID CTE + H controller. The pseudocode outlines a systematic approach for the UGV's navigation, incorporating multiple factors to ensure effectiveness and safety. The process begins with a check for \"No_Goals\", returning FALSE if no goal is set, thus preventing the execution of the algorithm. Following this, two flags, \"Flag_Goal_Reached\" and \"Flag_Rotation\", are initialized, with the first set to FALSE, indicating that the goal has not yet been reached, and the second set to TRUE, signifying that rotation is required. At the core of the algorithm is a loop that continues until the goal is reached (\"Flag_Goal_Reached\" = FALSE). During this loop, person detection takes priority, halting the UGV immediately if someone is detected. In scenarios where rotation is necessary (\"Flag_Rotation\" = TRUE), the algorithm evaluates the trajectory alignment and the elapsed time without any positional change. If trajectory adjustment is needed, the UGV is directed to rotate. Otherwise, the original position is updated, and the \"Flag_Rotation\" is deactivated. When no further rotation is needed (\"Flag_Rotation\" = FALSE), the algorithm checks whether the goal has been achieved. If the goal is met, the machine stops, and \"Flag_Goal_Reached\" is activated. If the goal has not been reached, a series of error calculations are performed, involving the \"Cross_Track_Error\" (CTE) for orientation control, the position error for linear control, and the heading error for angular control.
## 3 Materials and Methods
### Dynamic Graph Generation  
The GCN-based prediction models rely on the graph structure. However, the static graph structure predefined based on prior knowledge is currently difficult to capture the true connectivity between nodes, due to various factors such as weather and social activities. To address this issue, we design a dynamic graph generation module (DG), which contains three parts, namely Initial Dynamic Graph Generation (Initial DG), Spatio-Temporal Correlation Matrix Generation (ST Matrix), and Graph Convolution Kernel Generation (GC Kernel). These three parts are described in detail below.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Confidentiality
#### 3.1.1 Encrypted Storage  
The Encrypted Storage pattern [15] provides a second line of defense against unauthorized disclosure of data since, even if other controls are bypassed, the most sensitive data will remain safe.  
Examples of this pattern include (i) the mechanisms integrated in Operating Systems that allow encryption of the system's files (e.g., Microsoft's Bitlocker ([[https://docs.microsoft.com/en-us/windows/security/information-protection/bitlocker/bitlocker-overview](https://docs.microsoft.com/en-us/windows/security/information-protection/bitlocker/bitlocker-overview)]([https://docs.microsoft.com/en-us/windows/security/information-protection/bitlocker/bitlocker-overview](https://docs.microsoft.com/en-us/windows/security/information-protection/bitlocker/bitlocker-overview))) (accessed on 2 December 2020)) integrated into the Windows operating system); (ii) web sites that use encryption to protect the most sensitive data that must be stored on their backend databases (e.g., databases storing credit card numbers).  
An overview of the pattern is provided in Figure 3. As can be seen in the figure, the Data generator creates the unencrypted data and sends it for storage. Right before the Storage component, the Encryption Module transforms the clear-text version of the sensitive data. Finally, the sensitive data are stored in the Storage unit in an encrypted form.
## 2 Materials and Methods  
In this study, simulation is performed in Lumerical FDTD. Various parameters, such as the lattice constant and radius of nanodisk PC were varied. This model structure has five rectangular layers with disk-shaped PC between the absorber/ETL. The layout of this PC is hexagonal because the spacing between unit cells is minimal, which leads to more photons in the slow photon mode [30]. The finite-difference time-domain (FDTD) method is used to simulate the optical properties of the structures made by solving [PERSON]'s equations. A perfectly matched layer (PML) boundary condition is applied to the top and bottom sides to calculate the absorption spectra in the range of wavelength used in this simulation. In addition, the periodic limits are used on edge boundaries as the periodic 2D PC is introduced in this structure. Furthermore, a plane wave with a 300-1000 nm wavelength range is used as an electromagnetic source.  
The material in each PSC layer and the parameters referred to in this study are presented in Table 1. Variation of photonic crystal parameters was selected based on the optimal design of [PERSON] et al. [29]. The material refractive index data for each layer is obtained from the [PERSON] data [31] for Au layer, from [PERSON] et al. [32] for MAPbI3, from [PERSON] et al. [33] for Spiro-OmeTAD, and from [PERSON] et al. [34] for TiO\({}_{2}\). These data will be one of the simulation's inputs, besides the light wavelength range and more previously mentioned conditions.  
The absorption per unit volume, \(A(r,\lambda)\), is obtained using FDTD, and the light absorption is obtained using the Poynting theory, which corresponds to Equation (1) [35]:  
\[A(r,\lambda)=\frac{1}{2}\frac{Re\bigg{(}\overrightarrow{\
abla}P\bigg{)}}{P_ {in}}\]  
\[A(r,\lambda)=\frac{1}{2}\frac{\omega Im(\varepsilon(r,\omega))|E(r,\omega)|^{ 2}}{P_{in}} \tag{1}\]  
where \(E\) is the electric field, \(r\) is position, \(P_{in}\) is the incident light source power, \(\omega\) is the angular frequency, and \(\varepsilon\) is the dielectric constant. The absorption spectra \(abs(r,\lambda)\) are obtained by integrating the positions corresponding to Equation (2).  
\[abs(r,\lambda)=\int A(r,\lambda)dV \tag{2}\]  
with \(\lambda\) is the wavelength of light-source and \(dV\) is the volume element.  
Then, the photocurrent density (\(J_{sc}\)) is obtained from the incident light according to Equation (3).  
\[J_{sc}=e\int\frac{\lambda}{hc}abs(\lambda)I_{A\text{M1.5}}(\lambda)\ d\lambda \tag{3}\]  
where \(e\) is the electron charge, \(h\) is [PERSON]'s constant, \(c\) is the speed of light, and \(I_{A\text{M1.5}}\) is the 1.5 AM spectrum of solar radiation. This FDTD calculation was performed accurately inside the code of Lumerical FDTD.
# GPR Data Interpretation Approaches in Archaeological Prospection
## 6 CNN Application to GPR Data  
In this example, the application of CNNs using AlexNet architecture [48] is presented and evaluated as a tool that provides useful insights, contributes to data interpretation, and classifies ancient, buried structures from GPR C-scans.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 4 Performance Evaluation
### Effect of Initial Data Distribution  
Figure 5 shows the effect of the G-fairness index on the average utilization of the contact time with GS. We consider three LEO satellites and evaluate their performance with the average results when the orbits of 100 episodes are randomly generated. From Figure 5, it can be found that the contact time utilization decreases as the G-fairness index decreases (i.e., the initial data distribution is biased). This is because biased data distributions require more time to offload data from over-burdened satellites to under-utilized ones. Moreover, it is difficult to guarantee sufficient offloading time in dynamic satellite networks.  
Figure 4: Illustration of a simulation environment.  
Figure 5: G-fairness index vs. average contact-time utilization.  
It can be also seen that DRL-CD always exhibits higher contact-time utilization than other schemes. Specifically, in the case of balanced data distributions (i.e., \(\mathcal{I}(\mathbf{m})=0.9\)), DRL-CD utilizes the contact time 16.4% and 8.2% more efficiently than RANDOM and No-COOP, respectively. Meanwhile, for biased data distributions (i.e., \(\mathcal{I}(\mathbf{m})=0.1\)), DRL-CD shows improved contact-time utilization by 15.2% and 17.8% compared to RANDOM and No-COOP, respectively. This is because DRL-CD can successfully train the cooperative downloading policy that makes all satellites offload appropriate amounts of data and efficiently utilize the given contact time regardless of the initial data distribution.