esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of a Closed Cycle Power Takeoff for a Shore-Based Wave Energy Device
###### Abstract  
A promising class of devices for ocean wave energy conversion is called the oscillating water column in which the wave power is transferred to an airflow that rotates a turbine. A closed cycle power takeoff (CCPTO), in which air is forced through two valves and a turbine, has two main benefits for such a system: it allows a unidirectional turbine, and it smooths the large variations in pressure due to irregular (i.e., polychromatic) seas. This paper presents the design of a CCPTO for a shore-based installation, with particular attention paid to the turbine. The entire system is simulated with a reduced order model for a range of sea states to estimate the operating conditions of the turbine. It is found that the pressure drop range is modest but strongly dependent on the tide and sea chamber geometry. The geometry of the turbine is initially developed with a 1D preliminary design of the blades and then the turbine performance is analysed and the design is refined using Reynolds Averaged Navier-Stokes simulations. The design process is conducted based on the geometry and the sea climate of a real-life wave energy installation located in Mutriku (Spain). A turbine that displays efficient performance over a wide range of sea states is obtained. The overall performance of this turbine as part of the entire CCPTO system is assessed and leads to an energy output of \(\sim\)1500 kWh for one month in sea conditions at Mutriku wave power plant in Spain. It is concluded that the CCPTO deserves further development in any fixed oscillating wave column system.  
closed cycle power takeoff; oscillating water column; wave energy conversion; turbine design; computational fluid dynamics +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
In this work, the temperature rise characteristics of mold induction heating were explored at different heating depths, currents, and frequencies, during which the specialized coils that are different from conventional spiral-shaped ones were adopted. With maximum mean temperature and temperature uniformity as target results, COMSOL Multiphysics was introduced to conduct simulation experiments. The influence of these three parameters on temperature rise characteristics on the mold surface was summarized. Results show that these characteristics do not change with different shapes of coils.  
There are three factors selected by Taguchi method: heating depth, current, and frequency, each of which has three levels. On this basis, the L9 orthogonal experiment was designed to explore the extent to which each factor affects the maximum mean temperature and temperature uniformity. It can be verified that the heating depth and current have a greater impact on both target results than the frequency. Therefore, the maximum mean temperature was most affected by current, which is followed by heating depth and frequency successively, and the temperature uniformity is most affected by heating depth, which is followed by current and frequency successively. The analysis of S/N ratio indicates that in order to maximize the maximum mean temperature, the heating depth, current, and frequency shall be set at 5 mm, 1200 A, and 40 KHz, respectively. The optimal temperature uniformity was realized when the three factors are 7 mm, 800 A, and 20 KHz, respectively. Then, ANOVA was conducted on these factors to summarize their contribution rates.  
Finally, the synergistic effect of any two factors on target results was discussed using RSM and the corresponding response surface diagrams were plotted. As stated earlier, frequency is involved to the least extent. Therefore, in the combination of current and frequency or heating depth and frequency, it is the former that plays a leading role. By contrast, current and frequency have similar contribution rates to temperature uniformity. Therefore, the change in this uniformity results from the interaction of these two parameters. As shown by response surface diagrams, the temperature uniformity is optimal at the current of about 850 A and the heating depth of about 7 mm, which conforms to the optimal combination obtained by Taguchi method.  
Conceptualization, Y.Z. and J.L.; methodology, J.L.; software, J.L.; validation, J.L., P.Z. and J.H.; formal analysis, J.L.; investigation, J.L.; resources, J.L.; data curation, J.L., P.Z. and J.H.; writing--original draft preparation, J.L.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 5 Solution Modeling
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## 1 Introduction
### Landscape Preference Theory and Eye-Tracking Technology
used eye-tracking technology to study the extent of people's preference for native greenery plants from the perspective of visual preference using pictures of plant organs such as leaves,flowers, and fruits as stimulating materials to improve rural greening techniques, with a study conducted by students from the Central South University of Forestry Technology and villagers in Changkou Village, Fujian Province [47]. Some studies explore the application of eye-tracking technology in the landscape from different populations. For example, [PERSON] et al. divided 90 participants into two groups of outsiders and insiders to view photos of Han Buddhist temples. Consensus and differences in their visual preferences and eye movement metrics were assessed [48]. [PERSON] et al. divided the experimental group into 21 landscape experts and 21 amateurs and asked them to observe 74 landscape photographs to explore whether expertise in landscape-related issues affects the way landscapes are observed [49]. From this, the following conclusions can be drawn: (1) More and more studies of landscape preference have introduced eye-tracking technology and obtained reliable experimental results, and the application of eye tracking in landscape preference has great potential. (2) Most of the studies on landscape space using eye-tracking technology are on urban space, and the studies on rural public space only focus on elements such as plants, or the mountains and forests on the periphery of the countryside, and relatively little on the use of eye-tracking technology in rural internal public space. Currently, as rural public space in China is undergoing urbanization changes, the public attributes of the countryside are constantly changing. As an important tourist village, local residents and visitors are two important types of user groups in Beijing's Huangshandian Village, and if the visual landscape preferences of local residents and visitors in using rural public space are unknown, it will be challenging to make effective recommendations for landscape enhancement as well as resource management in Huangshandian Village. Therefore, this paper aims to combine subjective assessment and eye tracking to explore the perceptions of local residents and visitors on different rural public spaces, and to enrich the application of eye-tracking technology in landscape preference studies. It also provides a quantitative index and a powerful guide for the optimization of rural public space landscape, which is more conducive to the optimization and construction of rural public space landscape.
## 5 Discussion  
### Practical Use at the Underground Mine Site  
The truck transport time data, which could be collected through the log data collection system developed in this study, could be used to analyze, diagnose, and predict the truck transport routes of the mine. It was possible to determine whether the newly collected truck transport time data for a specific transport route was measured within the error range (P10 to P90) of the previously accumulated data and to diagnose whether the truck travel in the section was operating normally. Moreover, it would be possible to predict the likelihood of problems occurring in truck haulage operations. Problems that could be predicted through this include poor maintenance of mines, congestion in tunnels, poor maintenance of vehicles, and the proficiency of truck drivers.  
In this study, the transport routes were diagnosed and predicted using newly acquired log data for the section of beacon IDs 13 to 12, with one showing stable truck transport times as a result of log data analysis for 15 weeks. The average transport time and percentiles (P90, P10) of the truck were recalculated using the most recently collected 5 weeks of data (19 January 2021 to 21 February 2021) for the remaining 15 weeks of data. The newly collected log data were divided by date and truck, as shown in Figure 11. As a result, it was found that in the 16 th week of haulage operations, two trucks operated the section and transported the limestone 42 times (truck A: 32 times, truck B: 10 times). When looking at the targeted error range of the truck transport time (P10-P90) in the corresponding section, the number of trips within the error range was found to be 27 (truck A: 20 times, truck B: 7 times). The remaining 15 times (truck A: 12 times, truck B: 3 times) were found to be out of the error range. In particular, in the case of truck A, there were two cases in which the transport time was measured to be abnormally large, as compared to the existing data.  
To determine the cause of this abnormally large truck transport time, the production logs were checked, and interviews with field staff were performed. As a result, it was confirmed that vehicle congestion occurred due to the failure and maintenance of trucks in the tunnel. Problems such as the disruption or delay of trucks due to poor maintenance of the tunnel or the closing of the tunnel due to equipment failure may have increased the transport time of the truck or caused an abnormal travel pattern. Consequently, it is necessary to diagnose the condition of the transport route using the past collected data and the new collected data to predict anticipated problems. It is believed that the efficiency and productivity of truck transport operations in mines could be improved if tunnels are repaired, expanded, or opened in advance by finding sections where problems are expected to occur and if vehicles used are repaired in advance.
## 5 Experimental Result
### Case Study: Samoonjin Bridge  
To illustrate an application of the proposed algorithm, we present a case study of Samoonjin Bridge in Korea with actual data. Before applying the algorithm, we created a 3D model by analyzing the satellite photograph and specifications of the bridge; this 3D model is shown in Figure 8.  
The length, width, and height of the bridge are 780 m, 10.9 m, and 13.5 m, respectively. The minimum distances for cameras and target grid points (\(w_{s}\) and \(w_{o}\), respectively) are 10 m and 2 m, respectively. The other parameters are set to the same values as in the previous simulation tests (shown in Table 3).  
After the visibility test phase, the total number of surveillance target grid points is 7852 and the number of candidate camera placements is 37,963. Based on the result, we applied GA and ULA, which showed a better performance in the previous experiments, to find a solution to the defined camera placement problem; the experimental results are compared in Table 7.  
From the results, ULA outperforms GA in terms of cost, coverage, and computation time. The total number of cameras used is 12, and the total cost is approximately USD 108,000. A camera placement using ULA is depicted in Figure 9.  
The yellow region represents the simplified 3D model of Samoonjin Bridge, and the blue regions are the visualization of the FoV of the cameras. We observe that the cameras covering the central area of the bridge form a major portion of the covered target grid points owing to the geographical characteristics of the bridge.t  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Result types** & **GA** & **ULA** \\ \hline Computation Time (min) & 25.15 & 17.42 \\ Number of Cameras & 14 & 12 \\ Total Cost (1000 USD) & 134 & 108 \\ Coverage (\%) & 80.15 & 80.07 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results summary of Samoonjin Bridge (Minimum coverage 80%).  
Figure 8: 3D model of Samoonjin Bridge. The blue area represents feasible locations for the camera and Samoonjin Bridge is modeled as the yellow area.
## 2 Materials and Methods
### Running a Simulation
#### 2.7.2 Simplification Assumptions  
In order to be able to take into account the hydrological characteristics of different types of land use and to be able to integrate rainfall as a relevant input variable, simplification assumptions have been made concerning the model. These simplifications are explained here so that the results can be correctly interpreted.  
**Assumption 1**.: _The first assumption is the following. It is assumed that rain falls instantaneously at the beginning of the simulation and that runoff (if any) does not start until after this time. Rain and runoff are therefore not simultaneous. If, for example, we want to simulate a rainy episode with an intensity of 30 mm/h, at the beginning of the simulation the 30 mm of rain will fall instantaneously on the basin. This rainfall is materialized by the creation of agentdrops on each land cell. All of these newly created drops will have a water stock value equal to 30 mm. This simplified hypothesis avoids the creation of too many drops. It is also sufficient to highlight the spatial connectivity at the watershed scale._  
**Assumption 2**.: _A second simplifying assumption comes in parallel to the first assumption mentioned above: the useful reserves of each land cell do not empty throughout the simulation. This assumes that for a field agent belonging to a surface with an infiltration rate equal to 10 mm/h, the model will assign to this field agent a useful reserve equal to 10 mm. This useful reserve may decrease as the simulation progresses (due to the infiltration of drip agents), but in no case will it be renewed._  
**Assumption 3**.: _These two assumptions can only be accepted if a third one is respected: the duration of the simulated event does not exceed 1 h. This implies that the use of the model, in the present version, is only fully suitable for small catchment areas with a concentration time not exceeding one hour._  
**Assumption 4**.: _The last hypothesis concerns the infiltration capacities. [PERSON] (2009) proposed a parameterization in the Ruciells cellular automaton Delahaye (2009) during the simulation of a spring flood on a catchment of the Seine-Maritime department (Villers). Our simulation is set up with the same parameters. (For crops, it was based on the Benkhadra model [PERSON] (2009) combined with field observations; for grasslands on experimental data [PERSON] (2009); and for buildings, on the literature concerning sparsely populated areas [PERSON] (2010))._  
The values of the infiltration parameters to be considered are therefore the following: infiltration rate of grasslands and woods: 50 mm/h; infiltration rate of habitats (building and garden): 5 mm/h; infiltration rate of crops: 1 mm/h (in this configuration, the agricultural soils are degraded with a completely formed surface crust).
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 4 Selected Studies on CPS Water Supply Network Management
### CPS Leak Monitoring Applications  
[PERSON] and [PERSON] [105] proposed an RFID-based architecture for pipe leak monitoring. The application of the proposed system was demonstrated on a small laboratory test involving simple piping runs, however, the feasibility of the system on a real water distribution system is a major concern. Most water piping runs are installed underground, however, RFID system suffers from poor signal strength in underground environments. [PERSON] et al. [106] proposed a test for leak monitoring in water networks using integrated flow and pressure sensors. In [PERSON] et al. [15], a CPS framework for leak localization in multibranch pipelines was proposed. Several sensing devices arranged in a network mode were utilized to acquire data related to the pipe internal pressure, flow rate, and temperature. Also, communication between the sensor network and a base station was provided via Wi-Fi, ZigBee, and 3G communication technologies. Due to the fact that the pipe internal pressure varies and is nonstationary, during data processing, a wavelet packet was employed to decompose and analyze the pressure signal for leak localization. The framework was simulated in MATLAB and Flowmaster software environments with some level of success regarding leak localization. However, a real-time application is a major concern. Elsewhere, several computational tools have been used to analyze the nonstationary pressure wave signals. [PERSON] et al. [80] proposed a hybrid method employing independent component analysis and the support vector machine for analyzing pressure traces with relatively good accuracy. Also, in [107], an interactive self-organizing data analysis algorithm was utilized for the pressure traces analysis in the pipes. Studies on the use of artificial neural networks (ANN) and fuzzy ANN in this domain may also be found in the literature [108; 109; 110] with some level of success. Unfortunately, most of these techniques cannot efficiently handle some variations that occur in real-time pipe environments, such as the state of flow within the pipe (transient operations in most cases), state of the surrounding soil (underground pipe applications), and the leak characteristics (hydraulic nature of leaks discharge). Therefore, their adoption for real-time applications is uncertain. Also, small leaks cannot be analyzed and detected, therefore, it is quite clear that they cannot be used for background leak detection, in which there is a small, continuous discharge running along the water distribution networks.
## 2 Theoretical Model and Simulation
### Numerical Simulation
Figure 2b shows the propagation of acoustic waves when excitation of the central source B is delayed. If the delay in exciting the central source B is \(\delta_{\text{t}}\), the delayed acoustic wave from B adds constructively to the acoustic waves from the symmetrical sources A and C at a time t, which can be calculated from  
\[[\text{v}\times(\text{t}+\delta_{\text{t}})]^{2}=(\text{v}\times\text{t})^{2 }+\text{L}^{2} \tag{3}\]  
when the delay time is chosen to be 0.1 us, constructive wave superposition occurs 0.5 us after the first excitation at a distance on the \(y\)-axis of about 1.2 mm, which is less than 10% of the distance without any time delay. When the delay time is increased to 0.2 us, the time for constructive superposition is shortened to 0.325 us with a propagation distance of only 0.375 mm. Figure 2: (**a**) Schematic diagram of laser array excitation with no time delay between pulses. (**b**) Schematic diagram of laser array excitation with a central pulse (B) delayed. To ensure that the superposed propagating waves do not separate again, \(\delta_{\rm d}\) is required to be always less than the acceptable error value. \[\delta_{\rm d}={\rm d}_{2}-{\rm d}_{1}=\sqrt{{\rm L}^{2}+\left[{\rm v}\times({ \rm t}+{\rm t}_{0}-\delta_{\rm t})\right]^{2}}-{\rm v}\ \times\ ({\rm t}+{\rm t}_{0}-\delta_{\rm t})\ <\ \varepsilon\ \times\ {\rm v} \tag{4}\]  
where \({\rm t}\) is the propagation time after constructive interference, \({\rm t}_{0}\) is the time that constructive interference first occurs, and \(\delta_{\rm t}\) is the delay time. For all times \({\rm t}>0\), \(\delta_{\rm d}<\varepsilon\ \times\ {\rm v}\), that is, there is no solution when \(\delta_{\rm d}\geq\varepsilon\ \times\ {\rm v}\). We can see that when the delay time is 0.1 or 0.2 \(\mu\)s, the solution of the inequality is an empty set. Therefore, after constructive superposition occurs, the enhanced wavefront propagates as a complete new wavefront without separating again. In the results and discussions section, we will confirm that the enhanced wavefront propagates as a complete wavefront by fitting and reconstructing the wavefront.