esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
Simulations of the bulk wave are shown in Figure 11. Clearly, the laser array enhances the intensity of the bulk wave. As expected, the amplitude of the acoustic wave generated by the laser array is larger than that generated by a single laser source with the same energy density. In Figure 11a-c, the difference between the arrival times of the peak of the bulk wave in Model 1 and Model 2 at Points 5, 6, and 7 (The points on the \(z\)-axis that are 1, 2, and 3 mm away from the central heat source B) are 0.22, 0.10, and 0.06 us, respectively, after the excitation time. This is because, for detection points further from the excitation source, the propagation paths of bulk waves originating from spots A and C (symmetrically located on either side of spot B) gradually approach the propagation path of the wave originating from spot B. Because there are two sources, the dominant bulk wave propagating along the \(z\)-axis results from a superposition of the waves excited at spots A and C. In Figure 11a, the smaller peak is the acoustic wave excited by source B. In Figure 11b,c, this peak adds constructively to the waves excited by A and C, which increases the width of the wave. Comparing the peak amplitudes of waves at locations 1, 2, and 3 mm from spot B on the \(z\)-axis, it can be seen that the amplitudes of the bulk waves generated in Model 2 are, respectively, 1.1, 1.9, and 2.4 times of those excited by Model 1. In the far-field, the bulk wave excited by a distributed laser array is enhanced by a factor of more than 2 compared to a single spot. This phenomenon is analogous to the mechanism of the widely used synthetic aperture method for the enhancement of the detection SNR [33]. Figure 10: Normalized sound pressure field (k-wave) and displacement field (FEM) detected at Point 1 for (**a**) Model 1, (**b**) Model 2, and (**c**) Model 3. Figure 12 shows the attenuation curve, obtaining the amplitude of the z-direction displacement at six points along the \(z\)-axis: 1, 1.5, 2, 2.5, 3, and 3.5 mm away from source B. It can be seen that the slope of the attenuation curve for Model 1 is steeper than that for Model 2. This phenomenon arises because the wave has a plane component. Figure 11: Displacement in the z-direction as a function of time for Model 1 (single laser source) and Model 2 (laser array) at (**a**) Point 5, (**b**) Point 6, and (**c**) Point 7. Figure 12: The peak z-direction displacement of the wave for different locations for the cases of single laser and laser array sources.
# Global GNSS-RO Electron Density in the Lower Ionosphere
###### Abstract
An improved spatiotemporal coverage is critical for studying the lower ionosphere because of substantial and complex diurnal variations and influences from both the lower atmosphere and upper ionosphere. As in the algorithm developed previously for the \(E\)-region by [PERSON] [31], the new retrieval method removes \(F\)-region contributions in the excess phase profile with a linear function fit to the data before they are input for the \(D\)/\(E\)-region \(N_{e}\) retrieval. The GNSS-RO \(N_{e}\) retrievals tend to produce a lower \(N_{e}\) in the \(E\)-region compared to the IRI-2016 but in a general agreement with FIRI [32]. Similar results are found in the GNSS-RO \(N_{e}\) retrieval from this study through a brief comparison with ionosonde observations at the Hermanus site. The paper is organized to describe the improved algorithm of \(N_{e}\) retrieval with examples in Section 2, followed by the retrieval results and climatology of the GNSS-RO \(D\)/\(E\)-region \(N_{e}\) in Section 3. Brief evaluations of the GNSS-RO \(N_{e}\) with ionosonde observations at the Hermanus site and a novel retrieval technique for total electron content (\(TEC\)) are discussed in Section 4, before it is concluded in Section 5.
## 4 Experimental Evaluation
### Scenario 3: Multiple STAs
#### 4.4.1 Experimental Environment of Scenario 3  
Figure 13 illustrates a measurement environment for evaluating the impact of communication quality by multiple STAs. The two STAs are located at different distances. The distance between AP and STA1 is fixed at 10 m, and between AP and STA2 it is set to 10, 30, 50, and 70 m. That is, we have the following four measurements:  
Measurement 1 (M1): STA1 at 10 m and STA2 at 10 m;  
Measurement 2 (M2): STA1 at 10 m and STA2 at 30 m;  
Measurement 3 (M3): STA1 at 10 m and STA2 at 50 m;  
Measurement 4 (M4): STA1 at 10 m and STA2 at 70 m.  
As in the previous experiments, the operation PC is connected to the AP and the two STAs via a wired line and ad hoc connections.
## 3 Results and Discussion
### Gelling Behavior  
The gel-sol and sol-gel transition temperatures were rheologically determined at the cross-over point from the heating and cooling curves, respectively (Figure 3c,d). At low temperatures, these lipidic matrixes exhibited higher G\({}^{\prime}\) values than those of G\({}^{\prime}\) showing a dominant elastic property. During the heating phase G\({}^{\prime}\) and G\({}^{\prime\prime}\) values showed a more pronounced decrease for CLW, CRW, and OKW matrixes, whereas the BEW matrix showed a linear (constant) decrease with increasing temperature. A plateau followed by one abrupt transition (slope) is observed within the range between 50 and 65 \({}^{\circ}\)C and it corresponded to the T\({}_{\text{G-S}}\) phase. The wax composition was responsible for the differences in the matrix state transition. Once the cross-over point was surpassed, these curves were comparable due to the complete melting of the matrix network. Further, the T\({}_{\text{G-S}}\) matched the T\({}_{\text{m}}\) (56.7 \({}^{\circ}\)C) only for the BEW matrix, indicating that these two processes occurred almost simultaneously or one event is a consequence of the other. Thus, the T\({}_{\text{G-S}}\) of this matrix was not preceded by the extensive microstructure disruption, in contrast to the other matrixes formed by waxes containing high and mid-melting components [31].  
Regarding the cooling phase, below 80 \({}^{\circ}\)C, the solubility of the wax in LEO decreased, and a phase separation begins, and further cooling results in a complete crystallization at the cross-over point (T\({}_{\text{S-G}}\)) (Figure 3d). A further cooling increases G\({}^{\prime}\) due to the three-dimensional (3D) matrix formed by random aggregation and mutual entanglement of wax crystals within LEO. In all matrixes, early nucleation occurred between 68.9 and 70.4 \({}^{\circ}\)C, as indicated by DSC measurements, but a true gelation started at much lower temperatures. On comparing the cross-over points the BEW matrix had a T\({}_{\text{S-G}}\) very closed to the CRW matrix indicating extensive microstructure development, in contrast to the matrixes having a high content of HCs, such as CLW and OKW [32]. Further, there was a large difference between T\({}_{\text{S-G}}\) and Tc of these matrix systems demonstrating structuration since crystal formation started. At the end of the cooling phase (25 \({}^{\circ}\)C), CRW and CLW matrixes achieved a strong gelling behavior (G\({}^{\prime}\)-G\({}^{\prime\prime}\) > 1 decade), whereas OKW and BEW matrixes behaved like weak matrixes (G\({}^{\prime}\)-G\({}^{\prime\prime}\) < 1 decade). Interestingly, the OKW matrix showed two transition temperatures indicating that the crystalline species took longer to develop in a sequential crystallization, probably occurring first for the large fraction of cerevisiae and resins followed by another cross-over point due to the mineral oil fraction.
# A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020
## 2 Characteristics and Impacts of Air Pollution  
### Main Pollultants  
#### 2.1.1.1. _Sulphur_ Dioxide (SO2)  
Sulphur dioxide (SO2) is an inorganic chemical compound from the group of sulphur oxides. It is a colourless gas with a pungent and suffocating odour, highly irritating to the respiratory tract, poisonous to humans, animals, and harmful to plants. When oxidised to sulphur trioxide (SO3) and combined with water, it forms sulphuric acid, which is the main cause of acid rain, which contributes to the acidification of water bodies and soils, causing a decrease in their fertility [2]. This precipitation, known as acid rain, results from the amalgamation of water droplets with absorbed gases from the air or atmosphere, including sulphur oxide, nitrogen oxide, carbon dioxide, hydrogen chloride, and other environmentally harmful substances. Such pollutants impede plant growth and contribute to plant mortality. However, the issue extends beyond rain alone, encompassing other atmospheric phenomena such as acid fog and acid snow.  
Environmental contamination through acid precipitation poses a threat not only to the flora but also to the fauna inhabiting the affected areas, including humans, leading to various health problems. Exposure to these compounds can cause irritation of the upper respiratory tract, throat irritation, coughing, and may contribute to the development of inflammation and diseases such as bronchitis. Prolonged exposure to air pollution amplifies the risk of respiratory and cardiovascular diseases. Children, the elderly, and individuals with conditions such as asthma and those with cardiovascular issues are particularly susceptible to the adverse effects of sulphur dioxide.  
The impact of acid precipitation is not confined to living organisms; it also extends to building infrastructures and various materials. The acceleration of corrosion in metal structures and building materials, including steel, copper, and zinc, is a notable consequence of acid rain, affecting the integrity of structures and materials over time.
## 4 Result  
### Cross-Validation of PS and SBAS Method Results  
The average settlement amount of each settling area at each time was calculated, and the average settlement amount of each settling area of PS-InSAR and SBAS-InSAR in 40 periods was analyzed for correlation. The correlation coefficients of PS and SBAS are above 0.88 in the time series of each sedimentation zone and reach 0.98 in subsidence zone C. The \(p\) of subsidence zone F is less than 0.01. Hence, the correlation is very significant. See Figure 8 and Table 2 for details. Combined with the characteristics of PS-InSAR and SBAS-InSAR, PS is more suitable for dense building areas with high scattering rates and high stability. The SBAS method has better performance in lower-frequency deformation and nonlinear deformation. Based on the monitoring results of PS and SBAS, the results of subsidence zones B and E were monitored by SBAS, and the results of other subsidence zones were monitored by PS.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Region** & **Linear Fitting Equation** & **R2** & **Significance Influence** \\ \hline Subsidence zone a & Y = 0.7585x \(-\) 1.5397 & 0.94 & ** \\ Subsidence zone b & Y = 0.5245x \(-\) 0.0798 & 0.88 & ** \\ Subsidence zone c & Y = 0.8686x \(-\) 0.9393 & 0.98 & ** \\ Subsidence zone d & Y = 1.0332x \(-\) 1.4303 & 0.92 & ** \\ Subsidence zone e & Y = 2.0254x \(-\) 2.4997 & 0.88 & ** \\ Subsidence zone f & Y = 1.5544x \(-\) 5.8799 & 0.98 & ** \\ \hline \end{tabular} Note: Significance influence levels are represented by the number of *s in the analysis: \(p<0.01\) is denoted as ** (very significant).  
\end{table}
Table 2: Correlation of PS and SABS.
# Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes
## 4 Results and Discussion
### Limitations of the Proposed Approach  
The UAV photogrammetry technology and analytic hierarchy process adopted in this study show good applicability in the early identification and stability evaluation of dangerous rock masses of high slopes, which can quickly and accurately determine potential risk areas, helping to narrow the detailed investigation scope and improve the efficiency of dangerous rock identification. However, it is also important to note the limitations of these approaches. The operation of drone photogrammetry technology in adverse weather conditions may be limited, which may affect data acquisition and accuracy. In the process of determining index weights, the analytic hierarchy process relies heavily on expert experience, which may be subjective and uncertain. Whether the early identification model and stability evaluation index system of dangerous rock masses established in this paper are applicable to other areas and under different conditions still needs to be discussed. Therefore, in practical application, it is necessary to consider a variety of factors and combine geological prospecting and monitoring methods to further improve the accuracy and reliability of the evaluation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Failure Mode of Dangerous Rock** & **Unstable** & **Basically Stable** & **Stable** \\ \hline Slump type dangerous rock & \textless{}1.0 & 1.0–1.3 & \textgreater{}1.3 \\ Tipping rock & \textless{}1.0 & 1.0–1.5 & \textgreater{}1.5 \\ Falling dangerous rock & \textless{}1.0 & 1.0–1.5 & \textgreater{}1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Criteria for evaluating the stability of dangerous rock.
## 3 Analysis of Test Results
### Acceleration Response
and 68.0% (Y-direction), respectively; under the action of the Taft wave (XYZ direction, 0.75 g), the isolation efficiency of the lead-core rubber bearing is 33.2% (X-direction) and 48.5% (Y-direction), respectively. Figure 5: Comparison of peak acceleration in the X direction of the isolation storage tank: (**a**) Taft wave (XYZ direction, 0.50 g); (**b**) Artificial wave (XYZ direction, 0.50 g); (**c**) Wolong wave (XYZ direction, 0.75 g); (**d**) Taft wave (XYZ direction, 0.75 g). Comparing spectral characteristics of the acceleration response, it can be seen that the spectral curve of the seismic storage tank has two obvious peaks, while the spectral  
Figure 6: Taft wave (XYZ direction, 0.75 g): (**a**) acceleration time history curves in the X-direction; (**b**) spectrum characteristic curve in the X-direction; (**c**) acceleration time history curves in the Y-direction; (**d**) spectrum characteristic curve in the Y-direction. Figure 7: Wolong wave (XYZ direction, 0.75 g): (**a**) acceleration time history curves in the X-direction; (**b**) spectrum characteristic curve in the X-direction; (**c**) acceleration time history curves in the Y-direction; (**d**) spectrum characteristic curve in the Y-direction. characteristic curve of the isolation storage tank has only one peak. Under the action of the Taft wave (XYZ direction, 0.75 g), the two peaks of the seismic storage tank are located around 13 Hz and 32 Hz, respectively, and the peak value of the isolation tank is located around 6 Hz. Under the action of the Wolong wave (XYZ direction, 0.75 g), the two peaks of the seismic storage tank are located around 10 Hz and 35 Hz, respectively, and the peak value of the isolation tank is located around 7 Hz. This shows that the lead-core rubber bearing can significantly suppress the high-frequency components in seismic waves.
## Numerical Simulation and Parametric Analysis
### Discussions
#### 4.4.2 Effect of Thickness of the Damping Layer  
Figure 15 shows the influence of the thickness of the damping layer on the ultimate compressive resistance and DI ratios of SDC walls. Seven different thicknesses (\(\mathrm{t_{r}}\) = 1, 2, 3, 5, 8, 10, and 15 mm) of damping layers were used in the parametric studies. It can be seen that increasing the thickness of the damping layer leads to a linear decrease of the ultimate compressive resistance of SDC walls. This is  
Figure 14: The deformation of three layers in the SDC wall.  
Figure 13: The lateral stress (S22) of the concrete core of SDC walls.  
caused by the degressive lateral confinement on the concrete core provided by the steel plates due to the incremental thickness of the rubber layer and lessened interaction between concrete core and steel plate. Meanwhile, the ductility of the walls improves significantly as the damping layer becomes thicker. Specifically, augmenting the thickness of the damping layer from 0 to 2 mm and 5 mm, the DI ratio was increased by 18% and 35%, respectively. This is mainly because the thicker rubber layer which can provide more deformable space of concrete core contributed to more energy consumption and better ductility of the SDC wall. However, the increase of the DI ratio becomes steady for the thickness of the rubber layer more than 5 mm. This indicates that a thickness of 5 mm is the threshold of lateral deformation of the concrete core for such SDC walls under compression loads when the walls failed. When the thickness of the rubber layer exceeds this threshold, the ductility of the wall resulted from the high ductility of the rubber marginally increases. Thus, in terms of ductility, the thickness of the rubber layer of SDC walls should not exceed 5 mm in the design phase. It is also worthwhile to mention that the SDC wall with a 2 mm rubber layer has the equivalent loading capacity of the SCS wall with the same cross-section area, which is fundamental to the bearing wall.
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 2 Characteristics of the Detected Signal in Fracture Zones
### Characteristics of Seismic Wave Data in Fracture Zones
This method offers heightened precision in depicting subsurface structures by accentuating the energy and coherence along the identical phase axis of weakly reflected waves. It emphasizes the region of reflected energy in the fracture zones.
## 3 Simulation System
### Ray-Tracing Simulation  
First, as a learning phase of the fingerprinting method, the radio propagation characteristics in the simulated environment were analyzed. In this study, radio propagation simulations were conducted using the ray-tracing method. The ray-tracing method has the following characteristics [16]:  
* It traces radio waves emitted from a transmitting point as rays of light and searches for a path;
* It geometrically calculates paths with reflection, diffraction, and transmission;
* It can take into account multipath effects caused by obstacles;
* It requires much less memory and computation than the FDTD method, a well-known theoretical approach.  
A ray-tracing simulation was performed using the radio propagation simulation software Wireless Insite. The simulation terrain model is shown in Figure 5 and the arrangement of the transmitter and sensor candidate points is shown in Figure 6.  
An urban environment was assumed in this study, and a terrain that reproduced the Tokyo Institute of Technology Ookayama campus was used as the simulation terrain model.  
Training units in the learning phase and targets in the estimation phase were placed at a height of 2 m, assuming that illegal radio transmission sources are on the road. Candidate points for UAV sensors were placed in a grid at heights of 50/75/100/125/150 m. The grid was designed to be as close as possible to the road surface. In this paper, it is assumed  
Figure 4: Simulation system overview.  
that the transmitter parameters in the learning and estimation phases are ideally matched (see [17] for bandwidth and frequency interpolation methods).  
The parameters are summarized in Table 2. Note that the antennas are assumed to be omni-directional for both the transmitter and the receiver and that polarization matching is ideally achieved where vertical polarization is assumed.
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 3 Assessment of Available Generation Capacity (AGC)
### Calculation of the AGC of BSSs  
As described above, the BSS can inject the extra power of an FB into the grid when the distribution network suffers an outage. This process is defined as V2G service. From this perspective, BSS can be viewed as a backup resource. To research an enhancement of reliability of the distribution network with the penetration of BSS, an assessment of available generation capacity, which means discerning how much power the BSS can inject into the grid when the distribution network fails, is necessary. The following describes the calculation method of AGC.  
As mentioned above, each battery has to go through a \"swapping-charging-queuing-discharging-swapping\" cycle repeatedly. Suppose that the initial time when the battery \(k\) is fully charged in the \(c^{k}th\) cycle is \(t_{ck,k}^{full}\), the time when it is swapped is \(t_{ck,k}^{swap}\), and the time when the power available for V2G is released completely is \(t_{ck,k}^{end}\). Then, the indicator variable \(n_{k,t}\) that the battery \(k\) can perform V2G service at time \(t\) can be obtained by Equation (5):  
\[n_{k,t}=\left\{\begin{array}{l}0\ \textit{else}\\ 1\ t_{c^{k},k}^{full}\leq t\leq\min\biggl{(}t_{c^{k},k}^{swap},t_{c^{k},k}^{ end}\biggr{)},k\in\Omega_{1,t}\cup\Omega_{2,t}\end{array}\right. \tag{5}\]  
Among them, when \(n_{k,t}\) is 1, it means that V2G service is available, and when it is 0, it cannot; \(t_{ck,k}^{end}\) can be calculated by Equation (6):  
\[t_{c^{k},k}^{end}=t_{c^{k},k}^{full}+\frac{E_{rated}(0.95-0.8)\eta^{d}}{p_{ rated}^{dch}},\ k\in\Omega_{1,t}\cup\Omega_{2,t} \tag{6}\]  
where \(P_{rated}^{dch}\) denotes the rated discharging power of batteries; and \(\eta^{d}\) denotes discharging efficient of EV batteries.  
After calculating \(n_{k,t}\) at each time, the AGC at each time \(t\) can be obtained by Equation (7):  
\[P_{t}^{AGC}=\sum_{k=1}^{N}n_{k,t}P_{ratat}^{dch},k\in\Omega_{1,t}\cup\Omega_{2,t} \tag{7}\]  
Figure 4: Probability density function (PDFs) with respect to electric vehicles (EV) flow in a typical day.  
where \(P_{t}^{ACC}\) denotes the AGC that can be obtained in each time \(t\) and \(N\) denotes the number of all batteries in the BSS.  
After introducing the calculation method of AGC and the working process of V2G, the whole procedure of AGC assessment can be demonstrated as shown in Figure 5.