esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 3 System Model and Problem Description
### Problem Description  
As shown in Figure 2, before forwarding the packet to the next hop routing node, the sender needs to construct a CFS within its communication range and copy the packet to all nodes in this set. However, if too many forwarders are selected, the number of packet replicas in the network will increase, resulting in higher communication energy consumption for the nodes. The surviving nodes will quickly die due to energy depletion, thereby exacerbating the intermittent connectivity problem of the post-disaster network. On the contrary, if the number of forwarders is too small, the packet delivery rate of the post-disaster network cannot be guaranteed, resulting in lower reliability of the post-disaster data transmission. Therefore, the size of the constructed CFS will affect the transmission performance of the entire post-disaster network. In the multi-hop routing network of Mine Internet of Things (MIoT), the post-disaster data transmission in the planar accident area has a directional characteristic towards the sink. In order to reduce the transmission energy consumption of residual nodes and improve the success rate of packet forwarding, the CFS should be constructed by selecting nodes with high residual energy, more neighboring nodes, and that are closer to the sink. After the CFS is determined, it is necessary to further optimize the packet forwarding strategy and select an optimal forwarder from the CFS as the relay node for the current sender. Since the energy of the surviving nodes in the accident area is limited and cannot be replenished after the disaster, energy conservation is a main consideration in post-disaster data transmission. In order to reduce the transmission energy consumption generated by forwarding packets, candidate nodes that are farther away from the sink and have lower residual energy should be avoided by selecting the correct relay nodes. In addition, in the accident rescue scenario of the fully mechanized mining face, surviving nodes will be damaged by the environment factor in the accident area and fail at any time, leading to an increase in the probability of invalid nodes being selected as relay nodes. During post-disaster data transmission, when invalid nodes are selected as relay nodes, data packets will be discarded by the nodes, which not only increases the network energy consumption but also reduces data transmission efficiency. In Figure 2, there are no available relay nodes within the forward communication range towards the sink for the invalid node. Therefore, it is necessary to find new recovery relay nodes on the reverse transmission path towards the sink and continue to forward packets.
## 4 Conclusions  
This paper presented a hybrid cooling system for the thermal regulation of photovoltaic panels (PV). The innovative solution consisted of the design and construction of a box, applied on the back of the panel, containing a combination of fins/graphene/PCM. Aluminum fins were mounted on the rear surface of the panel and immersed in the PCM (RT 35 HC), working as a \"thermal bridge\". The graphene nanoparticles, equally distributed in the PCM, increased the thermal conductivity of the PCM. The procedure for preparing samples was presented in the paper. The proposed system was compared to a reference simple panel to investigate the performance improvements.  
Indoor tests, under a solar simulator, aimed at comparing the front temperature of the two panels. Results showed that the enhanced panel was averagely 6 \({}^{\circ}\)C colder than the reference case, resulting in an increase in the electric yield of about 3%.  
Outdoors, the proposed solution and the simple panel were monitored simultaneously for two days. A detailed analysis in the function of the PCM state was performed. Results showed:  
* During the solid phase, the two systems showed a similar temperature, the efficiency was slightly higher in the case of the hybrid panel (0.5%).
* The solid-liquid phase change provided the best results. The hybrid panel was about 5 \({}^{\circ}\)C colder, with an efficiency increase of about 2.5%.
* The liquid PCM did not absorb large amounts of heat, and the panel overheated. The hybrid panel showed higher temperatures, and the efficiency was reduced.  
In addition, outdoor tests proved that the liquid-solid phase change occurred both days, under atmospheric conditions. This ensured that, the day after, the PCM was completely solid and could start a new melting cycle. Further research can optimize this innovative solution, including an extraction system that would allow the PCM to not overheat once fully melted and to exploit, cyclically, the phase change process. This way, the system always works in its best conditions. Future works will be provided in this direction.  
Conceptualization, D.C. and P.P.; Investigation, D.C. and P.P.; Methodology, D.C. and P.P.; Software, D.C.; Validation, D.C. and P.P.; Writing--original draft, D.C.; Writing--review & editing, P.P. Both authors have read and agreed to the published version of the manuscript.  
Figure 16: Monitoring of temperatures of the enhanced panel during the outdoor test.
## 2 Kirchhoff Pre-Stack Depth Migration Imaging Principle
### Pre-Stack Depth Migration Imaging Process  
In the process of pre-stack depth migration, we should pay attention to the following points: Firstly, we must obtain an accurate time migration velocity field, which lays a good and solid foundation for the establishment of the initial layer velocity model. Secondly, in order to obtain a high-quality time migration velocity field, the velocity point can be encrypted to improve the accuracy of velocity picking. Thirdly, the pre-stack time migration data layer can be picked up, paying attention to how the required layer can be reasonably selected and ensuring that the established layer model can conform to the geological law. This layer can also act as a reasonable and effective constraint in the process of velocity modeling. Fourthly, in the process of optimizing the layer velocity model, it should be taken into account that the inaccuracy of the interpretation model will directly affect the effects of migration imaging. Therefore, in this data processing method, the first choice is to use the horizon constraint to iterate 2-3 times to optimize the velocity model and then use the grid tomography to iterate 1-2 times until the ideal depth domain layer velocity body is obtained, and finally, the data are offset as a whole. Fifth, the appropriate migration parameters are selected, including migration aperture and anti-aliasing parameters, to ensure that migration imaging obtains good results. The specific process is shown in Figure 1.
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Overvoltage Simulation Model Including Variable Capacitance
#### 3.3.2. Arc Burning Detection Module  
The function of the arc burning detection module is to output the logical signal \(C_{2}\) that indicates gap breakdown and power frequency current zero crossing. As shown in Figure 8, the module's input parameters include pantograph-catenary distance \(L\), pantograph-catenary voltage \(U\), and arc current \(I\). The breakdown threshold voltage \(U_{0}\) is calculated based on \(L\). When \(U>U_{0}\), the breakdown condition is satisfied and the comparator module outputs a logic signal of \"1\". Then the arc current \(I\) is filtered to \(I_{1}\) (50 Hz), which is put into the zero-crossing detection module. When the power frequency current crosses zero, the zero detector module outputs a logic signal of \"1\". These conditions for voltage breakdown and current crosses zero are combined through an AND gate. Gap breakdown occurs only when both the voltage breakdown condition is satisfied and the power frequency current  
Figure 7: Pantograph–catenary distance control module.  
has not crossed zero. Otherwise, no breakdown occurs under any other conditions. The output signal is denoted as \(C_{2}\).
## 2 Calculation Model and Method
### Space-Time Neutron Diffusion Model
#### 2.2.3 Macroscopic Cross Section Generation  
Taking into account the Doppler effect and the coolant feedback effect, the parameters of macroscopic cross sections (\(D\), \(\Sigma_{a}\) and \(\
u\Sigma_{f}\)) are mainly related to fuel temperature \(T_{f}\) and coolant density \(\rho_{c}\) in Equation (1). The calculation of macroscopic cross sections is performed using a lattice physics code at different temperature points [26]. Then the values of these parameters \(D(T_{f},\rho_{c})\), \(\Sigma_{a}(T_{f},\rho_{c})\) and \(\
u\Sigma_{f}(T_{f},\rho_{c})\) could be obtained by a linear interpolation method. Finally, the one-dimensional equivalent parameters are obtained from flux volume weighting in three-dimensional space:  
\[M_{i}=\frac{\int\limits_{V_{i}}M(\mathbf{r})\phi(\mathbf{r})dV}{\int\limits_{V_{i}} \phi(\mathbf{r})dV} \tag{6}\]  
where \(M_{i}\) is the average value of the macroscopic cross-section (\(D\), \(\Sigma_{a}\) and \(\
u\Sigma_{f}\)) in axial node \(i\), \(\mathbf{r}\) is the position vector in three-dimensional space, \(\mathbf{r}\) is the volume of axial node \(i\).
## 1 Introduction  
Rubbery materials with viscoelastic properties exhibit complex behaviour owing to their deformability, stress softening, and time-dependent attributes; thus, they are widely used in various industrial applications such as seals, dampers, bushings, and tires, among others [1, 2, 3, 4, 5]. As rubber products are exposed to different environmental conditions and subjected to cyclic loading, they often fail owing to nucleation and the presence of defects [6, 7]. In addition, the stress-strain curve of a rubber material subjected to cyclic loading exhibits a hysteresis loop, indicating energy loss owing to its viscoelasticity. Most of this energy loss is eventually dissipated into heat. When the heat generated by cyclic loading does not exit the material and accumulates, the temperature of the material increases, resulting in fatigue failure or changes in the material properties [8, 9, 10, 11, 12, 13].  
The unique rheological properties of magnetorheological elastomers (MREs) can be easily tuned when exposed to an external magnetic field, making them suitable for advanced rubber products, such as adaptive dampers and stiffness-tuneable mounts. Natural rubber (NR) exhibiting high mechanical properties can contain high content of magnetic particle to improve the MRE's performances, while soft carbonyl iron particles (CIPs) are widely used because of their high permeability, high magnetic saturation, and low remnant magnetization. On the other hand, MRE containing a large amount of filler has a relatively low fatigue resistance. There have been ample studies on various approaches to improve the performance of MREs. Recently, researchers have reported the fatigue behaviour of MREs under a magnetic field [14; 15; 16; 17; 18; 19; 20; 21]. [PERSON] et al. [22] reported that the fatigue life of an MRE increases when fatigue is induced under an external magnetic field. [PERSON] et al. [23] demonstrated the effect of repetition of a magnetic field on the fatigue behaviour, hysteresis loss, and storage modulus of MREs. Meanwhile, most studies on the durability of MRE have focused on monitoring the change of tensile strength and compressive load [24; 25; 26]. However, the effect of an external magnetic field on the property changes of MRE during cyclic fatigue needs to be further investigated for the actual application as engine mount.  
In this study, an anisotropic MRE was fabricated using NR, which is widely used as the matrix for MRE, and CIPs. Cyclic shear fatigue and strain amplitude sweep tests were performed to investigate the effect of the magnetic field on the prepared samples.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
###### Abstract  
The finite element technique has been accepted as a tool for modeling geotechnical complex processes. In this study, finite element (FE) modeling of various stages of the soil-nailing process, i.e., construction stages and overburden pressure stages, is carried out considering different soil parameters, simulating with in-house developed laboratory models. The soil-nailing process built in laboratory models is idealized as a plain strain problem and modeled in PLAXIS software. The laboratory models of the soil-nailing process consist of a Perspex sheet box containing a sandy soil slope, a Perspex sheet facing, steel bars as reinforcement and a steel plate as foundation. The stress-strain relationship of the sand is represented by a Hardening-Soil model. The interface at the soil and nail is described by the Coulomb friction model. The behavior of the soil-nailing process, during the construction stage and under varying overburden pressure and varying soil density, are investigated in terms of displacements of slope and stress conditions in slope soil mass. The slope displacements and stress conditions in slope soil mass are all well presented by the FE modeling and compared with laboratory model test data. The sensitivity analysis of the laboratory models' dimensions is carried out by three-dimensional modeling of the nailed-soil slope. It can be concluded that the developed finite element model has the potential to simulate the performance of a field nailed-soil slope during construction and working stages and could provide guidance for the construction/maintenance of soil-nailed cut slopes in granular soils/weathered rocks.  
soil-nailing process; finite element models; overburden pressure; nailed-soil slope; sandy soil; nail spacing; nail inclination
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 2 Model
### Integral Model of the Vent Valve  
The whole structure is modeled by the working principle of the valve controlled by two-stage partial pressure and the analysis results of each component in the valve. The valve opening process was simulated with a slope signal of 1-7.5 in 0-4 s, and the closing action was simulated with a slope signal of 7.5-1 in 4-8 s. The overall model of the vent valve was obtained as shown in Figure 8. The damping hole is a small hole or a micro-hole which has the functions of throttling, regulating pressure, buffering and shock-proofing in the system. The damping hole is extremely important for adjusting the opening and closing performance of the two-stage pressure control valve. As shown in Figure 8, \(Z_{1}\) is the diameter of damping hole A, Z\({}_{2}\) is the diameter of damping hole B, and Z\({}_{3}\) is the diameter of damping hole C. Different sizes of nozzle diameters will also produce system damping, which has an important impact on the pressure transfer of the valve. T\({}_{1}\) is the diameter of the middle nozzle 1, and T\({}_{2}\) is the diameter of nozzle 2. The parameterized simulation method was used to simulate the different sizes of damping holes and nozzle diameters, and the parameters in the opening and closing process of the valve were obtained.  
Figure 7: The variation law of equivalent damping hole area with pressure ratio: (**a**) Fitting curves under different pressure ratios; (**b**) Residual diagram at different pressure ratios.
* The SMC approach to obtain the power sharing in parallel inverters has been done in many literatures, but in this study, in order to possess more freedom of degree in control coefficients and reduce the conventional SMC errors, a novel higher order SMC is proposed. * Since the conventional droop control integrated with SMC could not be able to implement in complex MG considering multiple DGs, the higher order SMC approach is capable to overcome these problems especially feeder impedance variations.
## Materials and Methods
### Experimental Sampling and Analysis
#### 2.3.3 Denitrogenation Functional Gene Abundance Sampling and Analysis  
After the wetland operation, the upper, middle and lower substrates were sampled from the substrate sampling ports of the three groups of wetlands. The upper, middle and lower substrates of each group of wetlands were weighed with the same quality and mixed evenly, and sent to the Zhengzhou Research Center of the Cotton Research Institute of the Chinese Academy of Agricultural Sciences. Enzymes encoded by genes for nitrification, anammox, denitrification and dissimilatory nitrate reduction to ammonium (DNRA) and their specific catalytic functions are shown in Table 2. Real-time quantitative PCR was used to determine the abundances of denitrogenation function genes (_amoA_, _nxrB_, _hzo_, _nirK_, _nirS_, _nrfA_) in the wetland substrate of each group. The primer sequences of related genes were shown in Table 3, and the specific experimental steps referred to the method of [PERSON] et al. [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nitrogen Cycle Step** & **Gene** & **Enzyme** & **Catalytic Function** \\ \hline Nitrification & _amoA_ & Ammonia monooxygenase & NH4+-NO2- \\ Nitrification & _nxrB_ & Nitrite oxidoreductase & NO2--N-3 \\ Anammox & _hzo_ & Hydrazine oxidase & N2H2-N-2 \\ Denitrification & _nirK_ and _nirS_ & Nitrite reductase & NO2-N-NO \\ DNRA & _nrfA_ & Nitrite reductase & NO2-N-H4+ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Enzymes encoded by genes for nitrification, anammox, denitrification and DNRA and their specific catalytic functions.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 2 Methodology
### Detection of 2D Modules in VHR Imagery
Specifically, these methods lack the capacity to integrate broader contextual information, such as varying lighting conditions, shadows, occlusions caused by nearby objects, or partial module visibility, all of which significantly affect PV module detection accuracy. Deep learning techniques, in contrast, leverage extensive contextual cues and spatial relationships within the data, thus providing superior adaptability and robustness for detecting PV modules even under challenging conditions, as will be shown in the results section of this paper. Moreover, the mask obtained in Figure 3d does not provide any instance-based detection of individual PV modules, and this has to be further performed, for example, with a region filling algorithm. In order to have a more accurate estimation of module sizes, it is highly advised to use orthorectified and georeferenced VHR images (i.e., true orthophotos), wherein objects are viewed in a top-down orthographic projection. Most VHR imagery is already readily georeferenced, where, for each pixel, a precise geolocation is known. Since the airborne LiDAR point cloud is in many cases also already georeferenced, this allows precise spatial correspondence between VHR pixels with the LiDAR point cloud. In some cases, remote sensing data can be georeferenced with different geographic coordinate systems; hence, coordinate re-projection must be performed prior to ensure that all datasets have the same coordinate system. Producing a true orthophoto from VHR images is beyond the scope of this paper; however, there are several methods for this purpose, as detailed in [41,42]. It should also be noted that the ground truth data used for semantic segmentation models do not need to be orthorectified, which allows for potentially larger training datasets. The width \(PVM_{w}\) and height \(PVM_{h}\) of each given module \(PVM\) can then be estimated from the pixels of the modules detected in VHR. This provides important information in creating an accurate 3D reconstruction of the modules. To estimate the tilt and aspect angles, 3D information is necessary, which can be derived from airborne LiDAR data, as will be described in the next subsection. Figure 3: Steps to create ground truth data for module detection: (**a**) input VHR image, (**b**) image preprocessing, (**c**) detection of contours, and (**d**) filtering and thresholding for creating module masks, which are further manually processed.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 2 The Model of Single Base Station 3D Sensing
### Model and Key Parameters Design
The direction of the line segment OA was defined as the positive direction of the X-axis, and the direction of the  
Since HB = OA = \(d\) and HC = OX = \(s\), the relationship between the vertical FOV \(\alpha\) of the base station towards the air, the maximum vertical coverage height \(h_{1}\), and the coverage blind spots can be expressed as follows:  
\[\alpha=\arctan\frac{h_{1}}{s}, \tag{9}\]  
The horizontal distance ratio of the neighboring station's coverage over the top of the local station tower (HC/OA) can be expressed as follows:  
\[\omega=\frac{h_{1}}{d\cdot\tan\alpha}\times 100\%, \tag{10}\]  
which also represents the relationship between the maximum coverage blind spots and the inter-station distance. It further clarifies: (1) the coverage capability of single base stations, (2) the performance degradation caused by vertical FOV limitations, and (3) references for subsequent designs where adjacent stations compensate for the tower-top coverage blind spots of local stations. In summary, it can be concluded that the main lobes of neighboring stations cover the tower top blind spots of the local base station. Specifically, the coverage area of the local base station's main lobe and the neighboring stations' main lobes exhibit a proportional relationship, as expressed in Equation (10). This relationship is jointly determined by three key parameters: the base station coverage height, the vertical FOV of the base station, and the station spacing. These parameters interact and are interrelated, collectively influencing the coverage characteristics and complementary relationships between base stations. Further analysis revealed that when station spacing and coverage height remained constant, a larger vertical FOV of the base station implied a broader coverage range of the local base station, thereby reducing the reliance on neighboring stations for covering the tower-top area. The model emphasizes optimizing antenna design and base station coverage areas, particularly in low-altitude sensing networks. Effective optimization methods are proposed by analyzing the relationships among antenna radiation angles, base station height, FOV, and station spacing. Combined with hexagonal grid planning, the model provides theoretical support for network coverage in low-altitude areas, optimizes base station design, and enhances sensing performance.