esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Impingement Hole
#### 3.3.1 Effect of Impingement Hole Diameter
Figure 7: Comparison of the average Nusselt number of (**a**) the upper surface and (**b**) the lower surface under various impingement hole diameters. Figure 8: \(Nu\) distribution of **(A)** the upper surface and **(B)** the lower surface under different impingement hole diameters at \(Re=2\times 10^{4}\). Figure 9: \(Nu\) distribution curves of **(a)** the upper surface and **(b)** the lower surface under impingement hole diameter of 7.2 mm at \(Re=2\times 10^{3}\). Enhanced heat transfer is accompanied by a corresponding pressure drop, so the pressure drop should also be considered when realizing a better heat transfer effect. Figure 10 is a comparison of frictional resistance under various impingement hole diameters. The larger the impingement hole diameter, the greater the flow resistance. The \(Re\) has little effect on the flow resistance when the impingement hole diameter is small. The pressure drop of the impingement cavity with the impingement hole diameter of 4.8 mm is the smallest. Compared with it, the flow resistance with the hole diameter of 6.0 mm, 7.2 mm, 8.4 mm and 9.6 mm is increased by 39.7%, 123.5%, 251.7% and 459.3%, respectively. The average Nusselt number of the lower surface with the impingement holes diameter of 7.2 mm increases by 46.2% compared with the hole diameter of 4.8 mm. That of 7.2 mm decreases by 28.1% compared with that of 9.6 mm. The flow resistance of the impact cavity with a hole diameter of 7.2 mm is 123.5% higher than that of 4.8 mm. That of 7.2 mm is 59.1% lower than that of 9.6 mm. Considering the heat transfer and pressure loss, the impingement holes diameter can be selected as 7.2 mm.
## 2 Materials and Methods
### Characterization Methods of Microapsules  
Optical microscopy (OM, Carl Zeiss AG, Oberkochen, Germany, Model: LSM 900) and scanning electron microscopy (SEM, Thermo Fisher Scientific Inc., Waltham, MA, USA, Model: APREO-2S) were used to observe the reaction conditions in the emulsion under different process parameters and preparation stages and to capture surface images of the microcapsules. Nano Measurer 1.2.5 software was employed to collect particle size data for both the microcapsules and small oil droplets. A Fourier transform infrared spectrometer (FT-IR, Thermo Fisher Scientific Inc., Waltham, MA, USA, Model: IS50) was used to analyze the monomers of the shell material, crosslinkers, core material, polymer wall material, and microcapsules. The testing range was 500-4000 cm\({}^{-1}\), with 32 scans and a resolution of 4 cm\({}^{-1}\) to obtain the FT-IR spectra of each material. Thermogravimetric analysis (TGA, Nippon Seiko Kabushiki Kaisha, Tokyo, Japan, Model: 6300) was conducted on the core material, polymer wall material, and microcapsules. The analysis was performed in an air atmosphere with a heating rate of 10 \({}^{\circ}\)C/min, within a temperature range of 25 \({}^{\circ}\)C to 600 \({}^{\circ}\)C, to obtain the TGA curves. In a comparative experiment, epoxy coatings containing 8 wt% microcapsules and those without microcapsules were uniformly applied to carbon steel substrates. The coatings were subjected to scratch testing, and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **PVA-205 (g)** & **MMA (g)** & **DVB (g)** & **EPR (g)** & **BA (g)** & **APS (g)** & **Emulsification Speed (r/min)** \\ \hline
1 & 2.55 & 0.73 & 0.075 & 2 & 4.4 & / & 2000 \\
2 & 2.55 & 1.46 & 0.15 & 4 & 8.8 & / & 2000 \\
3 & 2.55 &  
self-healing performance of the microcapsules was verified by comparing the self-repair effects at the scratched areas of both coatings.
## 3 Calculation Results and Analysis  
### Influence of Different Rotational Speeds on Internal Fluid Velocity  
Figure 4 shows the influence of different rotational speeds on the internal fluid velocity of the vortex pump when the flow rate of the vortex pump is \(0.6Q_{\mathrm{d}}\). For the convenience of observation, four axial sections within the impeller basin are selected successively along the \(Z\)-axis direction from the outside to the internal at the same speed, namely, \(z=0\), \(0.7\), \(1.4\), and \(2.0\) mm. As shown in the figure, under the same speed, the fluid flows in the radial direction from the inlet of the vortex pump during the rotation of the impeller, through the internal flow channel to the outlet. In this process, the fluid velocity gradually increases along the flow channel. Except that the fluid flow velocity in the impeller blade groove basin is relatively chaotic, the velocity flow lines in other basins are basically similar. This is mainly due to the special structure of the vortex pump, which causes the higher velocity of the fluid to be mainly concentrated at the bottom of the flow channel and the outlet area. For the axial direction, at the \(z=0\) mm section, the velocity displayed at each point of the section is \(0\) m/s, which is due to the selection of no-slip conditions on the walls of the flow channel and tongue area. At the sections of \(z=0.7\), \(1.4\), and \(2.0\) mm, the internal fluid of the three sections flows in from the inlet and gradually increases in velocity, and the internal fluid flow velocity distribution is consistent. At the same time, under the rotation of the impeller, the fluid velocity gradually increases along the flow channel, pushing the fluid flow to the outlet, and the velocity reaches its maximum at the outlet. The fluid velocity gradually increases from both sides towards the center along the axial direction, and the closer it is to the outer edge of the impeller, the greater the fluid velocity. In addition, it can be observed that as the rotational speed increases from \(2700\) r/min to \(3300\) r/min, the fluid velocity in the flow channel continuously increases. This is due to the increasing rotational speed of the fluid impeller, so that more mechanical energy is converted into kinetic energy of the fluid, which leads to the gradual increase in the fluid velocity that continues to obtain energy in the flow channel. Compared to lower speeds, the maximum velocity of the fluid is closest to the outlet when the speed reaches \(3300\) r/min. The main reason for this phenomenon is that as the rotational speed of the impeller increases with the motor speed, the fluid velocity in the flow channel gradually increases with the increase in rotational speed. Moreover, as the flow velocity continues to increase, the maximum velocity of the fluid flowing towards the outlet gradually approaches the outermost edge of the flow channel.
# A Classification Tree for Modeling Ground Fractures from Subsidence
## 3 Modeling of Ground Cracking  
The investigation focuses on one of the municipalities of Mexico City that has experienced the strongest and most dangerous ruptures in the last 50 years, namely the region of Tlahuac. In the 85 km\({}^{2}\) that make up this municipality, very particular areas are recognized where this phenomenon is forcefully manifested, particularly the Del Mar neighborhood (approx. 2 km\({}^{2}\)) (Figure 3), where cracks of tens of centimeters with openings of 4 m deep and huge steps approximately 120 cm high have been recorded. Agreeing with previous studies [55; 56; 7], ground fracturing is caused by the interaction of different factors, between the most important, geological preexisting discontinuities (because of variations in the depositional environment), slope of the basement underlying the compressible layers, thermal cycles, heterogeneity in compressibility and permeability, and the extensive exploitation of aquifers. This last aspect is considered the trigger factor of subsidence due to the corresponding vertical and horizontal tensile stresses on certain soil strata [57; 58].  
When the water level of the aquifer system drops, it induces a gradual compaction of the sedimentary filling, causing superficial sinking. In T\(\rm\mathring{a}\)huac, this sinking is uneven, so superficial cracks and scarps are formed. There are intrinsic (natural) factors, such as material heterogeneity and basement characteristics, that control the trace, shape, and size (Figure 4) [22; 59]. It is also recognized that anthropic settings, such as foundations, buried installations, loads (imposed by houses and buildings), and covert anomalies (e.g., pre-Hispanic structures) are also responsible for the extent and spatial definition of these cracks [60].  
For training the CART model, the selected physical variables (natural and anthropic) are integrated as a spatial database, to construct the training matrix. Natural information (geological and geotechnical) and man-made conditions (depletion of groundwater levels, location of dwelling arrangement, and communication routes) were compiled and related to cracks, fissures, and steps in the studied area.  
Figure 4: Scheme to represent the environment and mechanism. Left, a typical arrangement of the region with a steeply sloping irregular basement which underlies layers of material susceptible to compression and a superficial water table; right, after years, the pumping process lowers the water level and, in a certain area, on or near the steep slope of the basement, subsidence and deformations are manifested, the most alarming expression of which are cracks and steps.  
Figure 3: T\(\rm\mathring{a}\)huac, in southern Mexico City, and the Del Mar neighborhood, the subject of this study.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 5 Assessment of the Usefulness of the Collected Data
### Identification of the Probability Distribution of an Exemplary Technical Task  
Exemplary calculations for a single maintenance task are presented below. For this purpose, 20 measurements of the duration of the maintenance procedure were used: the operation test of the ice detection system.  
The data from Table 4 were arranged in a non-decreasing sequence \(t_{1}\ \leq\ t_{2}\ \leq\ldots\ \leq\ t_{n}\), and the distribution coordinates for the four considered distributions were calculated using the equations presented in Table 3. The results obtained are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**Duration [min:sec]** & 24:12 & 34:00 & 27:25 & 25:36 & 33:30 & 52:06 & 28:24 & 22:12 & 43:18 & 27:00 \\ \hline
**No.** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** \\ \hline
**Duration [min:sec]** & 29:13 & 45:24 & 39:30 & 31:11 & 31:05 & 30:30 & 41:49 & 27:18 & 21:36 & 33:24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured duration of performing sample maintenance task.  
After substituting the data from Table 5 into Equation (7), the following results were obtained:  
\[\hat{R}_{norm}^{2}=92.8\%;\ \hat{R}_{log-norm}^{2}=97.2\%;\ \hat{R}_{wieib}^{2}=89.5\%;\ \hat{R}_{wyk}^{2}=97.1\%\]  
Summarizing the obtained results, the log-normal distribution was selected as the distribution best suited to the sample of data. Based on Equations (5) and (6) and formulas for log-normal distribution parameters from Table 3, the resulting function (Figure 7) was obtained with the following parameters:  
* Scale parameter \(\mu\) = 3.45;
* Shape parameter \(\sigma\) = 0.256.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{6}{c}{**Distribution**} \\  & & & & & & & & & & \\ \cline{5-10} \(i\) & \(t_{i}\) & \(F(\textbf{t}_{i})\) & \multicolumn{2}{c}{**Normal:**} & \multicolumn{2}{c}{**Log-Normal:**} & \multicolumn{2}{c}{**Weibull:**} & \multicolumn{2}{c}{**Exponential:**} \\ \cline{5-10}  & & & & \(x_{i}\) & \(y_{i}\) & \(x_{i}\) & \(y_{i}\) & \(x_{i}\) & \(y_{i}\) & \(x_{i}\) & \(y_{i}\) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Calculation of coordinates for four considered probability distributions.  
Figure 7: Maintainability function—duration of performing “Operation test of the ice detection”.  
To confirm that the selected distribution fits the collected data, the Kolmogorov test of consistency was calculated at the significance level \(\alpha=0.05\)[49; 50]. The calculated value of \(D\) (i.e., the largest deviation between the theoretical and empirical distribution function) is smaller than the value of the tabulated critical value (\(D_{crit}=0.294\)). Thus, there is no reason to reject the hypothesis that the data distribution is log-normal.  
\[D=\max_{1<i<n}\lvert K(t_{i})-F(t_{i})\rvert=\]
## 4 Current Status and Identified Needs
### Level 2 Land Surface Reflectances
#### 4.2.1 SR Assessment within ACIX Initiative
ACIX-II is now completed, and the relevant papers are now published for the aquatic reflectances [121] and in preparation for the land domain and for the cloud mask. As a result of ACIX-II, additional recommendations and needs were identified on how to pursue the effort in assessing the uncertainty budget of satellite-derived SR products. We recall here the recommendations, which are relevant in the land domain. The use of AERONET-derived SR is a pragmatic solution, and it provides a robust and fair protocol for inter-comparing the different AC codes. On the other hand, there is a clear need for having a direct validation approach, using independent ground-based surface reflectance measurements. The RadCalNet network [107], which was primarily designed for assessing TOA radiometry, cannot entirely fulfil this need, mainly because the RadCalNet stations are generally located over non-vegetated, flat and spatially homogeneous areas, except for La Crau station, with very limited cloud coverage and aerosol loading. This scenario represents the almost ideal conditions and is thus not suitable to properly challenge the performances of the AC codes for the majority of conditions. The requirement for SR validation is to have vegetated sites representative of a wide range of surface and atmosphere realistic conditions and potentially also spatial heterogeneous sites to study the adjacency effects, which are difficult to properly model within the AC algorithms. The adoption of the 6 SV RTM [119] for generating the \"ground-truth\" over AERONET stations could potentially induce biases when assessing the accuracy of those codes, which use different RTMs for estimating and subtracting the atmospheric contribution from the TOA signal. Typical RTMs being used by the ACIX community include LibRadTran [122] and MODTRAN [123]. Overall, there is a need of inter-comparing the accuracy of different RTMs against a community-agreed benchmark algorithm. While similar exercises were done in the past [124], the benchmarking should now be repeated to assess how much RTM discrepancies propagate in the uncertainty budget of the output SR products, with focus on the current and future optical land imaging sensors. To this purpose, ESA in coordination with the EC, is currently supporting and promoting the RAdiation transfer Model Intercomparison for Atmosphere (RAMI4 ATM) initiative [125]. The primary goal of RAMI4 ATM will be to document the variability between coupled surface-atmosphereRTMs under well-controlled, but realistic, conditions. Within RAMI4 ATM, the surface properties will be defined by the simple homogeneous scenes, as defined within the RAMI-V exercise [126].
## 2 Related Work
Recently, the task offloading and resource allocation optimization for a mobile edge computing environment has been proposed in [36; 37; 38]. More specifically, [PERSON] et al. [36] proposed a cooperative task-offloading approach for UAV-aided MEC systems, where the nearest mobile device of a UAV can be served as an assisted hot to transfer the tasks of far mobile devices to UAV for remote processing. In addition, a block coordinate descent-based algorithm is proposed to optimize the trajectory of UAVs and decrease the overall energy consumption of mobile devices and UAVs. Meanwhile, in a different contribution, a multi-task offloading and resource allocation approach was proposed for MEC systems in satellite IoT [37], in which the task allocation and scheduling are initially handled by multiple unmanned aerial vehicle-based air base stations, with edge computing provided by satellites. Moreover, the directed acyclic graph is utilized to drive the main dependencies between tasks, and then, an attention mechanism and proximal policy optimization collaborative-based algorithm is proposed to obtain the offloading strategy. Whereas, in [38], an energy and delay-optimized trajectory planning framework is proposed for a multi-UAV multi-IoT network. More specifically, [PERSON] et al. designed a multi-UAV multi-IoT system environment in hexagonal cells, in which each cell involves a set of IoT devices and a single UAV. In addition, each cell has IoT devices grouped into clusters that UAVs hover over to collect and delegate tasks when needed. Moreover, the major objective of this study is the optimization of energy consumption and transition times between hovering points. Finally, to identify the Pareto-optimal front and choose the optimal solution, a multi-objective optimization technique is applied. Table 1 provides a concise summary of the literature reviewed in terms of the main objective, solution, application and tier environments as well as highlights the main weakness. It is observed from the above summary of related works that numerous efforts and approaches have been investigated for addressing the task-offloading issues for multi-user MEC systems with single and/or multiple edge servers either using the cloud or not. Nevertheless, most of these solutions allow mobile users to only transmit their tasks to the connected MEC server, which implies an unbalanced Consequently, some mobile users may find it difficult to complete their computation tasks within the acceptable latency threshold. Meanwhile, implementing an optimal strategy for multi-users in dynamic and complex systems such as a multi-users MEC system is another challenging issue that should be carefully addressed. Motivated by these considerations, an efficient load-balancing model for multi-user and multi-edge systems is presented in this paper in order to balance the loads among base stations and reduce system costs. Furthermore, a novel task-offloading approach based on deep learning techniques is proposed to efficiently derive the offloading solution.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Empirical Model  
Empirical models, also known as global kinetics models, usually describe the laws of reaction through power exponential equations, which only involve the concentration changes of reactants rather than intermediates. Kinetics research is very important for the design and operation of the SCWO process. However, since the actual pollutants are usually the mixture of many compounds, it is difficult to identify all the intermediate compounds involved and establish their complex reaction mechanisms. Therefore, the kinetic equations based on COD or TOC removal rate are used to describe the degradation characteristics of complex pollutants and model compounds [82].  
Among the current mainstream methods for establishing empirical models, various expressions are different in assumptions or calculation methods adopted in the experimental data processing. Additionally, there is a multi-step kinetic equation that divides the SCWO process into several reaction process distributions to establish the kinetic equation, and a response surface method that can optimize the experimental conditions through polynomial fitting.
## 5 Numerical Results
### The Convergence of the DDPG-Agent  
To test the influence of coefficient \(\beta\) on the convergence speed and the final performance of the DDPG-Agent, \(\beta\) was set to 0, 0.5, 1, 2, and 3, respectively. The convergence process of DDPG-Agent under the different values of \(\beta\) is shown in Figure 4. The DDPG-Agent converges quickly in the first 500 episodes by exploring the action space. Then, the DDPG-Agent gradually approaches the optimal policy in the following training. Agents with different \(\beta\) show similar convergence speeds. Figure 4a shows that the larger the \(\beta\), the smaller the corresponding reward value tends to be. To compare the influence of different \(\beta\) on the performance of QoS guaranteed, we plotted the value of the reward with the penalty item removed in Figure 4b, that is, the BS-level QoS satisfaction rate. As can be seen, agents with \(\beta\
eq 0\) show better performances than with \(\beta=0\). Increasing the \(\beta\) performance has an obvious marginal effect when \(\beta\) is larger than 1. If the reward only has a load balancing mechanism, which is marked as \(\beta=10,\alpha=0\), the DDPG-Agent will be unable to judge how to achieve a better QoS satisfaction rate after a full load. In the following experiment, \(\beta\) is set to 3.
# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
###### Abstract  
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.  
transfer learning; reinforcement learning; adaptive operator selection; artificial bee colony +
Footnote †: journal: _Algorithms_  
0000-0001-999-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-000  
outcomes. However, since this has been done within the boundaries of a single problem domain, significant changes in data and domain will necessitate the duplication of the same process. Recent machine learning studies suggest that learning how to handle a case can be transferred across domains, and a certain level of success can be achieved using deep learning [5]. This article proposes a reinforcement learning-based transfer learning approach to aid search algorithms with adaptive operator selection schemes while transferring gained experience from one case/run/benchmark to another. Although it is widely acknowledged that deep learning approaches facilitate the use of pre-trained tools in solving new problems, shallow learning processes, particularly the use of building adaptive operator selection schemes within a dynamic and extremely unknown environment, such as metaheuristic search processes for problem solving, are less well understood. This is the first attempt, to the best of the authors' knowledge, to apply transfer learning in building adaptive operator selection processes designed with reinforcement learning and implemented in swarm intelligence algorithms, such as the artificial bee colony algorithm.  
The rest of the paper is organised as follows: Section 2 introduces the approaches on which the proposed method developed, while Section 3 details the proposed transfer learning approach. Extensive experimental results are provided in Section 4, and the article is concluded in Section 5.