esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Constraint Pillar Test and the Application in Gob-Side Entry Retaining
## 2 Experimental Study on Bearing Capacity of Restrained Pillars
### Analysis of the Influence of Key Parameters on the Bearing Capacity of Pillars  
#### 2.3.1 Filling Medium  
The load-displacement curves of different filling media under unconstrained conditions are shown in Figure 5. Based on the test results, the following conclusions can be drawn:  
1. In the whole process of loading and deformation, the load-displacement curve can be divided into three stages: elastic stage (linear rising curve), elastic-plastic stage (nonlinear rising curve), and failure stage (falling curve). In the early stage of loading, the axial displacement of each specimen is proportional to the load, and the load  
Figure 4: Failure forms of high-water-material pillar specimens under different external constraints.  
displacement curve of the specimen basically increases linearly from the beginning of loading to the stage of 70-80% of the ultimate load. With the continuous increase in load, the specimen appears to undergo plastic deformation, and the slope of load-displacement curve gradually decreases, showing a nonlinear rise. After loading to the peak load, the axial displacement of the specimen increases rapidly, and the bearing capacity decreases rapidly.
2. The bearing capacities of the unconfined high-water material, C30 concrete, and C50 concrete are 1107 kN, 2608 kN, and 3565 kN, respectively. Compared with high-water-material pillars, the bearing capacity of concrete pillars is increased by 2.4-3.2 times. After the specimen reaches the ultimate load, the higher the strength of the filling medium, the faster the bearing capacity of the specimen decreases, and the worse the ductility. This is mainly because the higher the strength of concrete, the more obvious the brittle characteristics, which affect the overall ductility of the specimen.
3. Under the same external constraint conditions, the bearing capacity of concrete columns is generally higher than that of high-water-material columns, especially when the external constraint strength is weak (metal mesh and polyester-fiber mesh external constraint), but the deformation capacity of concrete columns is less than that of high-water-material columns.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 5 Simulation and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{3}{c}{**Average**} & \multirow{2}{*}{**Learning Time**} \\ \hline \multicolumn{2}{c}{**1-Layer**} & \multicolumn{1}{c}{**2-Layer**} & \multicolumn{1}{c}{**3-Layer**} & \multirow{2}{*}{**3-Layer**} & \multirow{2}{*}{8.0} & \multirow{2}{*}{259.8 s} \\ \hline \multirow{4}{*}{Layer} & 16 & 16 & 4.7 & 258.9 s \\  & 32 & 32 & 32 & 8.0 & 259.8 s \\  & 64 & 64 & 64 & 8.78 & 262.9 s \\  & 128 & 128 & 128 & 9.78 & 317.9 s \\  & 128 & 64 & 32 & 7.12 & 294.3 s \\  & 64 & 32 & 16 & 9.0 & 282.5 s \\  & 32 & 64 & 32 & 7.32 & 288.3 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison based on layer combinations. errors and processing times that occurred during 1000 episodes were averaged. As a result, it was verified that the nearest RP to the UE was identified in approximately 0.03 s. The positioning error achieved was less than 13 cm, demonstrating good performance. In Table 4, the performance of the proposed method is compared with existing methods. At this time, since the proposed method and KNN depend on the number of reference points, the same number of reference points was used. A total of 676 reference points were used, as shown in Table 2. Triangulation was determined using three APs that provide a strong RSS. As shown in the table, all three techniques achieve accuracy within 1 m. Among them, Triangulation has the lowest positioning precision at 0.3281 m, while the proposed method shows the highest precision at 0.13 m. At this time, it was confirmed that a fast processing time of 0.03 s was achieved. This shows that the proposed scheme rapidly converges to the optimal value by limiting the initial state of the agent based on Cell-ID. Hence, based on these results, the effectiveness of the proposed method was verified.
# Water and Wastewater Management in Agriculture
## 1 Introduction  
The growing world population requires highly efficient food production methods. Modern agricultural pracises such as soilless cultivation, livestock farming, and aquaculture rely heavily on environmental resources such as water and nutrients and therefore have an increasing impact on the natural environment, resulting in problems such as eutrophication, soil fertility decline, and soil contamination [1; 2; 3]. However, the negative impacts of these pracises can be mitigated through the use of modern crop production technologies; sustainable water and wastewater management; and the implementation of innovative approaches relative to wastewater treatment, reuse, and nutrient recovery [4; 5; 6]. The solutions most useful in rural conditions include nature-based solutions (NBSs), which are described as natural processes that can help improve wastewater and water management. There is a growing interest in the urban applications of NBS [7; 8; 9], especially constructed wetlands (CWs) for the treatment of domestic and agricultural wastewater [10; 11; 12]. This alternative water source is increasingly recognised as a vital source of irrigation as agriculture is the sector most affected by water scarcity, especially in semi-arid regions [13]. However, its utilisation must be accompanied by the strict monitoring of the natural environment in order to protect it and promote sustainable development. The overarching goal of these endeavours is sustainable agriculture [14].  
The \"Farm to Fork\" of the European Green Deal is a new strategy based on the assumption that agriculture is the most influential economic sector for the environment and climate [15]. At the same time, agriculture must remain a sector that produces healthy food with high nutritional quality and the lowest possible impact on the natural environment. We will not be able to implement the \"Farm to Fork\" strategy of the European Green Deal without finding new sources of water and fertilizers. Examples of such activities are solutions for the treatment of wastewater from soilless plant cultivation and the recovery of water and fertilizer components from treated wastewater [16].  
Many countries have already introduced solutions to reduce water consumption [17]. At the same time, system solutions are being sought for the reuse of treated wastewater. The EU has focused on the reuse of wastewater in agriculture [18]. Currently, over 1.2 billion m\({}^{3}\) of wastewater-derived water is used in European agriculture, which corresponds to around 2.5% of treated municipal wastewater. By 2025, this figure will be over 6.5 billion m\({}^{3}\)[19].  
This Special Issue presents the latest developments in water and wastewater management in agriculture and the environmental technology of aquaculture. Topics examined include innovative technological approaches to water and wastewater treatment in agriculture and aquaculture, strategies for treating nutrient-laden runoff from agricultural land, examples of effective water and wastewater management pracises, and the impact of agriculture on surface water quality.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 2 Methodology  
### Working Principle  
To study the heat and mass transfer of SCE in both laboratory and scalable application scenarios, two 3D models in COMSOL Multiphysics are built. The model for laboratory device is built according to the SCE device in the previous literature [37], including aluminum foil, absorber-emitter, insulation foam, bulk water and a container wall, as shown in Figure 2a. The absorber-emitter is the key energy conversion component in this device, which absorbs the sunlight on top and emits infrared thermal radiation on bottom. The infrared thermal radiation will further be absorbed by the water in the container. To create the air gap separating the water and the absorber-emitter, the absorber-emitter is set on top of the container wall, while the water level is lower than the container wall. To avoid the extra solar heating of container wall, a thin layer of aluminum foil is covered on top of the container wall, surrounding the square absorber-emitter. To reduce the heat loss from the absorber-emitter to the ambient environment, thermally-insulated foam is set beneath the aluminum foil. The model for a scalable device is built according to its application on solar evaporation pond with large surface area, without aluminum foil, insulation foam and walls, as shown in Figure 2b. The absorber-emitter used in both models are considered as a copper plate with selective absorption coating on top and emittance-enhancing coating on bottom, as shown in Figure 2c. This is also consistent with the previous literature [35; 37]. Straight holes are equidistantly drilled on the absorber-emitter for vapor escaping. In both devices, solar energy is absorbed by the absorber-emitter to heat up the water near air-water interface. The water vapor generated will then escape from the holes on absorber-emitter. However, heat losses will also exist from absorber-emitter and water to the ambient. In general, the two models share the same working principle. In this case, the effectiveness of both models could be ensured, if one model is validated.  
The detailed geometrical parameters of the laboratory and scalable devices are listed in Table 1. The size of the absorber-emitter in the laboratory device was set as 35 mm, 35 mm, 1.5 mm for depth, width and height, respectively, determined by the experiment setup. The straight holes distribution of laboratory device was set as 3 \(\times\) 3. Since the scalable model is to study the conjugate heat and mass transfer in a single hole for scalable application scenarios, its geometric structure was scaled down equally according to the laboratory device, with A, B and C of 11.67 mm, 11.67 mm, 1.5 mm, respectively.
## 3 Materials and Methods
### Measuring Land Use Conflicts
The equation for the FI is shown in the following formula:  
\[FI=\sum_{i=1}^{n}F_{i}\times\frac{a_{i}}{S}n=6 \tag{8}\]  
where \(F_{i}\) is the fragility degree of the land use type \(i\); \(a_{i}\) is the area of various landscapes in a unit; \(S\) is the total area of space units in the landscape; \(n\) is the total number of spatial land use types. The fragility degree of six land use types is ranked in ascending order: built-up land (1), forest (2), grassland (3), arable land (4), water (5), and unused land (6). Lastly, landscape stability is a representative indicator of the risk effect. As the main impact of land use conflict on regional landscape patterns, landscape stability fragments landscape patches. Here we defined the landscape fragmentation index (_LFI_) by patch density to show the degree of landscape stability in a certain region [47]. The stability index (_SI_) of land use type can be expressed as:  
\[SI=1-LFI,LFI=\frac{n_{i}}{A} \tag{9}\]  
where \(n_{i}\) is the patch number of a landscape unit \(i\). \(A\) is the grid size of the fishnet. The greater the patch density, the lower the degree of landscape stability in the region. Considering the scale of the study area and data availability, a 2.5 km fishnet was selected as the basic spatial analysis unit [46], while the cell size of all raster data was set to be 30 m \(\times\) 30 m. In order to enable the aggregation of various indices, the calculation results of the LUCI, CI, FI, and SI were normalized to the range from 0 to 1.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 2 Methodology
### Characteristics of Fracture and Movement in Heavy Layer
#### 2.2.2 Fracture Patterns of Heavy Layer Induced by Mining
The fracture process of heavy layer involves phenomena such as initial rock fracture, continuous crack propagation, internal structural rotation, and structural sliding, all of which can potentially trigger heavy layer-type mining-induced earthquakes [30]. As shown in Figure 7a, during the initial fracture of the heavy layer, the internal units of the rock layer are subjected to triaxial stress, accumulating significant elastic energy, which results in a high-energy mining-induced earthquake. In Figure 7b, as cracks continue to develop, new fracture surfaces gradually form in the middle and upper sections of the heavy layer, also inducing mining-induced earthquakes. The fractures propagate upward until they reach the top of the rock stratum. Under mining-induced stress, when the articulated structures within the heavy layer undergo rotational or sliding instability (as shown in Figure 7c and Figure 7d, respectively), energy is released and transferred, triggering mining-induced earthquakes [22; 23]. At this stage, the presence of fracture surfaces allows the rock units to be approximated as being in a biaxial stress state. Compared to triaxial stress, biaxial stress results in less accumulated elastic energy within the rock layer, leading to lower seismic energy release than during the initial rupture [24].
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 2 Scenario and Problem Description
The core problem studied in this paper is to maximize the total revenue of TT&C tasks while minimizing the total time fragments after the execution of TT&C tasks without exceeding the capacity of network resources.
## 3 Developing of the Model for Mass Transfer Through GO Membranes
Taking into account Equation (8) and the aforementioned assumptions, the total flux of molecules passing through some effective cross-section of the slit-like pore \(\sigma_{\rm e}=Hl\), where \(l\) is the length of the line on the flat pore wall which is directed perpendicularly to the direction of mass transfer, can be written as  
\[J_{\rm it}=-Hl\psi_{\rm i}iD_{\rm i,Kn}\frac{dn_{\rm i}}{dx}-2 ID_{\rm is}\frac {dn_{\rm ia}}{dx}=-HlD_{\rm i,Kn}\biggl{(}\psi_{\rm i}+\frac{1}{2H}\frac{D_{ \rm is}\psi_{\rm i}\tau_{\rm i}\alpha_{\rm i}}{D_{\rm i,Kn}}\biggr{)}\frac{dn _{\rm i}}{dx}, \tag{14}\]  
where \(x\) is the longitudinal coordinate directed along the slit-like pore in the direction of mass transfer and \(D_{\rm is}\) is the coefficient of surface diffusion which, for the model of localized surface diffusion on the graphene sheets, is given by [4]:  
\[D_{\rm is}=D_{\rm i0}\exp\biggl{(}-\frac{E_{\rm is}}{kT}\biggr{)}, \tag{15}\]  
where the value of \(D_{\rm i0}\) is usually assumed to be constant. Let us introduce the dimensionless value \(j_{\rm i0}=j_{\rm it}/j_{\rm i,Kn}\), where \(j_{\rm i,Kn}\) is the flux of molecules passing through the effective cross-section of the slit-like pore \(\sigma_{\rm e}\) by the classic Knudsen diffusion  
\[J_{\rm i,Kn}=-HlD_{\rm i,Kn}\frac{dn_{\rm i}}{dx}. \tag{16}\]Taking account Equations (14) and (16), we can write the following equation for \(J_{\rm i0}\)  
\[J_{\rm i0}=\psi_{\rm i}+\gamma_{\rm i}, \tag{17}\]  
where \(\gamma_{\rm i}=D_{\rm is}\gamma_{\rm i}\tau_{\rm i}\alpha_{\rm i}/(2 HD_{\rm i,Kn})\). We assume that parameters related to the interaction of gas molecules with the slit-like pore walls (in particular \(\psi_{\rm i}\), \(\alpha_{\rm i}\), \(\tau_{\rm i}\) and \(D_{\rm is}\)) are the surface-average values in relation to graphitic areas and oxidized regions of the GO sheets. Equation (17) gives the possibility of analyzing the influence of different factors, related to the interaction of gas molecules with the walls of the slit-like pore, on mass transfer through the pore. Let us consider the joint influence of surface diffusion and the Maxwell diffuse-specular scheme of the molecule scattering (when \(\psi_{\rm i}=(2-f_{\rm i})/f_{\rm i}\)) or the backscattering scheme for molecules (when \(\psi_{\rm i}=f_{\rm i}/(2-f_{\rm i})\)) on mass transfer in the slit-like pore with the flat parallel walls. Figure 1 shows the dependence of the value of \(J_{\rm i0}\) on \(f_{\rm i}\) for the different values of parameter \(\gamma_{\rm i}\) in Equation (17) for the Maxwell diffuse-specular scheme of the gas molecule scattering and the backscattering scheme. It is seen from Figure 1 that in the case of a diffuse-specular scheme of molecule scattering the value of \(J_{\rm i0}\) increases with an increase in \(\gamma_{\rm i}\) and decreases with an increase in \(f_{\rm i}\) and in the case of a backscattering scheme \(J_{\rm i0}\) increases with an increase in both \(\gamma_{\rm i}\) and \(f_{\rm i}\).
## 2 Urban Pipeline Gas Leak Sensor Placement Method
### Optimization Method Based on Maximum Diffusion Range  
Figure 2 illustrates the optimization method based on the maximum diffusion range. Points A, B, and C denote three adjacent inspection wells. Each point serves as the center of a circle with radius R, indicating the maximum diffusion radius of a gas leak. These circles intersect the gas pipeline network, and the resulting chords represent the pipeline sections that each inspection well, acting as a monitoring point, can cover.  
Figure 2: Optimization method based on maximum coverage range. (**a**) Optimal monitoring method. (**b**) Critical monitoring method. (**c**) Redundant monitoring method.  
As depicted in Figure 2a, if a segment of the pipeline within point B's monitoring range is not covered by adjacent points, then point B must remain a monitoring point to maximize pipeline coverage. In Figure 2b, as the distance between points A and C decreases, point B's monitoring range becomes fully covered by points A and C, placing B at the critical threshold where its necessity as a monitoring point is uncertain. In Figure 2c, with further reduction in the distance between A and C, point B's monitoring range is entirely encompassed by points A and C, indicating that point B no longer needs to be a monitoring point as its area is adequately monitored by points A and C.
## 3 Results  
To illustrate the effect of using MOO algorithms to solve the parameter selection problem for our AAFO, we shall first show the performance of parameter sets chosen at random from Table 1. Five sets of parameters were generated from uniform distributions for each variable in the corresponding interval. We refer to this collection of parameter sets as 5-RS, and each parameter set is labeled from RS-0 to RS-4. Figure 5 shows the ankle angle output (left) and power output (right) resulting from the evaluation of 5-RS for one of the initial conditions (10 degrees of ankle angle). Note that parameter set RS-3, when evaluated in simulation, resulted in a tracking failure after around 100 time samples.  
As Figure 5 shows, the performance of random sets of parameters is low and can even result in gross tracking errors. This introduces the need to apply some optimization procedure that ensures satisfactory performance of the AAFO. To this end, we configured our main simulation such that the parameters from Table 1 could be selected before execution, and the costs from Section 2.2.2 were obtained as outputs for each simulation run. We then used MOO algorithms to obtain solution sets that minimized the objectives. To ensure that the resulting solution sets consisted of parameters that provide certain robustness to varying initial conditions, in each simulation, the AAFO attempted to track the reference ankle angle signal five times, changing the initial ankle angle over a range of 7 to 13 degrees. Hence, the costs computed for each simulation run are actually the sum of those from the five tracking tasks.  
The initial configuration for the NSGA-II implementation was set to 50 individuals and 300 generations, and for RVEA, we used 10 partitions and 300 generations. The search space for the MOO algorithms follows Table 1, and the objectives are those defined in Section 2.2.2. In this section, we will show the shape of the solution set, based on the costs for each objective, per individual in each MOO algorithm. Then, we will present the best solutions, according to each objective and for each MOO algorithm, both in a table and in graphs of ankle angle and energy expenditure. Some random parameter sets were also used to establish a baseline of performance, which will be compared to the solutions from the MOO algorithms to better illustrate their effectiveness in this application. Finally, we show the robustness of the solutions from the MOO algorithms under different initial conditions of the gait simulation.
## 3 Design Specification  
### Design Objectives  
The design specification is based on the literature review, evaluation of our previous work, and feedback provided by medical professionals. Based on our previous work, there are at least two main concerns: ResQbot's inability to safely load the casualty's entire body and safety regarding the robot's initial contact with the casualty's head during casualty extraction procedures. Therefore, in this study, we focus on three main design objectives:  
* Optimising the design mechanism to safely load a casualty's entire body onto the robot's stretcher bed.  
Figure 3: Two critical concerns of the ResQbot 1.0 design: (**a**) ResQbot 1.0 can only load and secure half of the casualty’s body (i.e., upper body). (**b**) The initial contact between the robot and the casualty’s head during the casualty loading process remains a critical safety concern (the potential cause of head or neck injury).  
* Designing a mechanism that provides more protection to the casualty's head and neck during the extraction process.
* Optimising the robot's compact design and manoeuvrability in narrow environments.