esa-sceva/satcom-chunk-collection huggingface dataset:
# Damping of Liquid Sloshing Using Hydrophobic Walls in the Off-Impulse Regime
## 3 Forcing  
As previously described, the excitation frequency was gradually increased over six values between 0 and 1.20 Hz. Figure 4 shows the time history of the six signals for \(\lambda=1.00\). The horizontal position of the container is indicated by the black line, where the maxima correspond to the position of the container on the right and are divided by 3.4 for a better visualization. In the same graphics, the green and red curves show the water surface history for the containers with hydrophobic coating and no coating, respectively.  
Figure 3: (a) A particular frame of both containers in RGB bands for the container with \(\lambda=1.00\) and (b) the image binarization of the same frame. The column from which the data were extracted is indicated by the vertical red lines. Note the opacity of the container with hydrophobic walls (green color in (a)).  
These graphics point out that the water level oscillates with the same frequency as the forcing one but with several harmonics. As the system is excited with a frequency different from the natural one, this is to be expected. Moreover, as the forcing frequency \(f\) is higher, the oscillation amplitude \(\eta_{0}\) of the water level is greater; this behavior is predictable since the system approaches its resonance. It is also important to note that for all the tested forcing frequencies, the oscillation amplitude \(\eta_{0}\) is higher for the uncoated containers (red lines in Figure 4) than for the hydrophobic containers.  
To quantify this difference, the left-hand vertical axis of Figure 5 shows the evolution of the amplitude \(\eta_{0}\) as a function of the forcing frequency. The green bars are always lower than the red ones, as mentioned before. Now, the right-hand axis represents the difference in amplitude in % between the waves in the coated and uncoated containers. The solid blue line shows that the reduction in amplitude varies from 15% for higher frequency values to 40% for low frequency values, with an average value of approximately 33%.  
It is interesting to note that our observations are different from the results reported by [13], who found an increment of the elevation of the free surface with hydrophobic walls. The resulting free-slip condition in the latter case should indeed promote faster motions of the water at the walls with higher waves. However, this was not the case in our experiments. This may be due to the development of a boundary layer despite the hydrophobic coating, as explained by [17]. Moreover, recent experiments have shown that greater friction coefficients can be generated by hydrophobic coated walls in comparison with hydrophilic coated walls [18].  
Figure 4: Time history for the cells with \(\lambda=1.00\). The external forcing is represented by the black line divided by 3.4, while the green and red lines indicate the water surface for the containers with hydrophobic coating on the walls and no coating, respectively. (**a**) \(f=0.64\) Hz; (**b**) \(f=0.73\) Hz; (**c**) \(f=0.83\) Hz; (**d**) \(f=0.94\) Hz; (**e**) \(f=1.04\) Hz; (**f**) \(f=1.16\) Hz.
## 2 Calculation Methods of Surrounding Rock Pressure
### China's Specifications for Design of Highway Tunnels (SDHT)
#### 2.3.2 Surrounding Rock Pressure of Shallow Tunnels  
[PERSON] proposed a method for calculating stratum pressure in shallow tunnels [26]. The calculation schematic diagram is shown in Figure 4.  
Figure 3: Sketch of tunnel outline and collapse outline.  
It was assumed that in shallow tunnels, the support strength is insufficient and the W1 will sink. Due to the internal shear and friction of soil, W1 would drive W2 on both sides to move down together, which makes W2 and soil form a breaking slip surface with a \(\beta\) angle to the horizontal plane. During the moving process of the soil W2 on both sides, the rock stratum produces resistance F to the wedge body on both sides W2, and the wedge body on both sides W2 transmits resistance to W1. According to the static equilibrium condition, the vertical force \(Q\) on AA\({}^{\prime}\) was obtained.  
\[Q=\gamma Bh-\gamma(H+h)^{2}\lambda tan\theta \tag{12}\]  
where,  
\[\lambda=\frac{tan\beta-tan\varphi_{c}}{tan\beta[1+tan\beta(tan\varphi_{c}-tan \theta)+tan\varphi_{c}tan\theta]} \tag{13}\]  
\[tan\beta=tan\varphi_{c}+\sqrt{\frac{(tan^{2}\varphi_{c}+1)tan\varphi_{c}}{tan \varphi_{c}-tan\theta}} \tag{14}\]  
Considering that the pressure of surrounding rock is slightly higher, it is beneficial to structural safety, so enlarging load,  
\[Q=\gamma Bh-\gamma(H+h)^{2}\lambda tan\theta<\gamma Bh-\gamma h^{2}\lambda tan \theta=\gamma h(B-h\lambda tan\theta) \tag{15}\]  
In shallow tunnel, the vertical uniform load is as follows,  
\[q_{shallow}=Q/B=\gamma h\bigg{(}1-\frac{\lambda htan\theta}{B}\bigg{)} \tag{16}\]  
when the tunnel depth \(h\) is less than the equivalent height, \(\theta\) is 0.
# Habitat Loss and Other Threats to the Survival of _Parnasius apollo_ ([PERSON], 1758) in Serbia
## 3 Results
### Vegetation Structural and Functional Dynamics: LAI and FPAR Trends (2010-2024)  
Long-term temporal analysis of the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) provide a robust assessment of vegetation structure and photosynthetic potential across study sites from 2010 to 2024. LAI reflects the density and depth of foliage available for gas exchange and transpiration, while FPAR captures the proportion of incoming solar radiation effectively utilized by the canopy for photosynthesis.  
Descriptive analysis revealed interannual variation and spatial heterogeneity in both indices. Sites such as _Raska_ and _Luka_ exhibited relatively high and stable values, indicative of structurally consistent and productive vegetation. In contrast, _Brzece_ and _Bucje1_ demonstrated marked declines in both LAI and FPAR during the latter part of the observation period (2020-2024), suggesting increased vulnerability to environmental stressors.  
To quantify these patterns, we performed site-specific linear regression analyses using year as the independent variable. The results confirmed statistically significant negative trends at the more sensitive sites. At _Brzece_, LAI decreased at a rate of \(-0.0167\) units per year (\(p=0.021\), \(R^{2}=0.193\)), and FPAR declined by \(-0.0114\) units annually (\(p=0.039\), \(R^{2}=0.145\)). Similarly, _Bucje1_ showed reductions of \(-0.0142/\)year for LAI (\(p=0.030\), \(R^{2}=0.171\)) and \(-0.0098/\)year for FPAR (\(p=0.045\), \(R^{2}=0.138\)). These declining trends support the interpretation of progressive canopy thinning and diminishing photosynthetic light absorption capacity, likely driven by thermal and hydric stress. Conversely, _Raska_ and _Luka_ presented nonsignificant trends in both LAI (_Raska_: \(p=0.422\); _Luka_: \(p=0.912\)) and FPAR (_Raska_: \(p=0.536\); _Luka_: \(p=0.874\)), with near-zero \(R^{2}\), suggesting resilience or buffering against climate fluctuations. The observed changes are consistent with broader climatological dynamics, including increases in potential evapotranspiration (PET) and decreases in the Temperature Condition Index (TCI), both of which are known to reduce vegetation growth efficiency. Such patterns have been recently emphasized in studies using high-resolution remote sensing data to explore how multiple environmental stressors jointly shape vegetation trends [80; 102].  
Overall, the integration of LAI and FPAR trends with climatic parameters such as TCI and PET reveals a coherent signal of ecosystem response to increasing abiotic stress. The data highlight the importance of vegetation structural indicators in long-term environmental monitoring and underscore the need for localized management strategies in vulnerable montane and transitional landscapes.
## 5 Conclusions  
A detailed investigation on the beam quality of a pulsed electric-discharged non-chain DF laser with a compact confocal positive-branch resonator was performed. The inner cavity with a convex mirror axially mounted on the output mirror was used. Four groups of unstable optical resonators with magnifications \(M=1.65\), 1.85, 2.05, and 2.25 were designed, and the intensity, energy distribution, and divergence angles were calculated. The experimental results of the divergence angles had a range of 1.08-0.66 mrad from \(M=1.65\) to \(M=2.25\), and the corresponding laser energy varied from 2.76 to 3.32 J, with the maximum energy achieved at \(M=1.85\). The far field energy density had a monotonic increase with \(M\), and the maximum energy density was 82.8 mJ/m\({}^{2}\) at \(M=2.25\). The beam quality in the experiment was 2.25-1.95 times higher than in the calculation. The reasons for this difference were analyzed. In future research, we will eliminate the influence of electrode diffraction by properly selecting the size of the back mirror, reduce the thermal deformation of the output mirror by improving the refrigeration mode, and investigate the influence of mirror tilt on the beam quality.  
Investigation, C.S.; methodology, Q.P.; supervision, F.C. and J.G.; writing--original draft, L.Z.; writing--review and editing, P.R. All authors have read and agreed to the published version of the manuscript.  
This study was supported by National Key R&D Program of China (2018 YFE0203202), Natural Science Foundation of Jilin Province (20220101207 JC), Science and technology cooperation project between Jilin Province and Chinese Academy of Sciences (2021 SYHZ0028), State Key Laboratory of Laser Interaction with Matter Project (SKLLIM2115), Youth Innovation Promotion Association CAS (2021216).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Experiments and Analysis
### Evaluation Metrics
#### 5.3.3 Comparison Experiments with Different Models on Deformable Images  
To validate the classification performance of the model for deformable objects, data enhancement methods were used for the images. The deformable images were obtained through random rotation, stretching, translation, etc. These data were used for different models with the same experimental conditions as before, and the obtained results are shown in Table 4. From the data in the table, we can see that the classification accuracy of the method in this study was improved by 6.2% compared with the ResNet_0 without adding the DSDCN module. Results show that the deformable convolution had a good classification effect, which fully illustrates that the DSDCN module designed in this paper has excellent feature extraction performance for deformed objects. Compared with other models, the classification results of the DSDCN-ResNet in this study had improved in accuracy, recall, and F1 scores, with average increases of 5.3%, 5.6%, and 5.8%, respectively, which shows that the proposed method has better performance for underwater objects.  
Figure 11: Classification accuracy of the model with DSDCN at different positions. * is the accuracy of each model.
## 2 Materials and Methods
### Microstructure Observation  
The microstructure of the coating was investigated by an OLYMPUS BX51 optical metallographic microscope (OM) (OLYMPUS Corporation, Tokyo, Japan) and Quanta FEG650 scanning electron microscope (SEM) (FEI Company, Hillsboro, OR, USA). Element distributions were analyzed by energy disperse spectroscopy (EDS) (FEI Company, Hillsboro, OR, USA) and an electron probe microanalyzer (EPMA) (SHIMADZU Corporation, Kyoto, Japan). The phase identification of the coatings was carried out on a Shimadzu XRD-7000 X-ray diffractometer (XRD) (SHIMADZU Corporation, Kyoto, Japan). The detailed parameters of XRD were as follows: Cu-K\({}_{\alpha}\) radiation at 40 kV and 200 mA as an X-ray source. Specimens were scanned in the 20 range of 20\({}^{\circ}\)-80\({}^{\circ}\) in a step-scan mode (0.02\({}^{\circ}\) per step).
## 3 Results and Discussion
### Characterization of the Zeolite Samples
#### 3.1.5 XPS Analysis
The O 1s XPS spectra of P-adsorbed iron-Z-A shifted to the higher energy side, but in the case of P desorbed, iron-Z-A shifted back towards the lower energy side, and the peak intensity remained the same. In the case of the XPS spectra of Na 1s, there is a big decline in its peak intensity than peak shift. The decrease in intensity of Na 1s of iron-Z-A than Z-A is probably due to the dispersion of Fe into the Z-A framework and replacement of Na\({}^{+}\) with Fe\({}^{3+}\) ions in the framework. In the acidic pH during the adsorption phase, the Na\({}^{+}\) ion might have been replaced with H\({}^{+}\) ion. So, there is a sharp decline in the peak intensity of P-adsorbed Iron-Z-A as compared to fresh Iron-Z-A. The revival in peak intensity of Na 1s of P desorbed Iron-Z-A suggests the replacement of Na\({}^{+}\) ion onto the Iron-Z-A surface. The results of XPS of all the samples suggest that Fe and Al exhibited a pivotal part in the adsorption of phosphate, whereas in the case of desorption, Al only actively desorbed phosphate. As already mentioned, Fe cannot desorb PO\({}_{4}\)\({}^{3-}\) easily due to the strong attraction force existing between them. Therefore, Al plays a key role in the regeneration of Iron-Z-A for further use.
## 5 Conclusions  
In this work, a new disturbance observer-based terminal sliding mode control was proposed to control a class of nonlinear systems under the influence of both matched and mismatched disturbances. Although matched disturbances were suppressed by the robust nature of the terminal sliding mode control, mismatched disturbances were compensated by the enhancement in disturbance observation. The employment of a nonlinear disturbance observer enabled the mismatched disturbances to be estimated, and then compensated, by the proposed control algorithm. A general guideline on the appropriate selection of parameters for this observer was provided, and the influence of parameter selection was later illustrated by simulations. Other aspects of the proposed controller, namely, chattering attenuation, sign function utilization, and nominal control performance preservation, were also discussed and demonstrated in this work. Although the proposed control scheme was first designed to stabilize a class of nonlinear second-order systems, it was also shown to be extendable to an nth-order formulation for wider applications. The formulation extension was subsequently used to control a third-order electro hydrostatic actuator system, which, along with a numerical example, revealed the characteristics and verified the effectiveness of the proposed algorithm. Comparative simulation results ultimately demonstrated the satisfactory performance of the proposed controller.  
Throughout the study, system states were assumed to be available, and thus different sensors were required. However, there are numerous cases in practice in which only one sensor is implemented for several reasons, such as reducing the installation cost and space. Therefore, future research can be undertaken to design a disturbance-state observer to estimate both disturbances and unmeasured states in a fast-changing, noisy measurement environment.  
[PERSON] was the supervisor and administrator of the project and reviewed the manuscript. [PERSON] undertook the theoretical conceptualization, carried out simulation studies and examined the obtained results. [PERSON] checked the manuscript and provided valuable insights into the project. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, South Korea, under Grant NRF 2020R1A2B5B03001480.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SMC & Sliding Mode Control \\ TSMC & Terminal Sliding Mode Control \\ MAGLEV & Magnetic Levitation \\ PMSM & Permanent Magnet Synchronous Motor \\ EHA & Electro Hydrostatic Actuator \\ DOB & Disturbance Observer \\ ESOSMC & Extended State Observer based Sliding Mode Control \\ DOBTSMC & The proposed controller \\ \end{tabular}
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 2 Methodology
### Particle Swarm Optimization (PSO)  
Particle swarm optimization is a simplified algorithm proposed by [PERSON] and [PERSON] inspirited by the regularity of foraging behavior of birds [30]. The PSO uses massless abstract particles to simulate individuals in a flock of birds and uses globally optimal solutions to simulate the location of food. Each particle has only two attributes, velocity and direction, and finds the individual optimal solution in its respective range. The current global optimal solution is found by multiple interactions and information sharing among the particles. Using the individual optimal solutions and the current global optimal solution, the unique properties of each particle are updated and each particle reaches the global optimal solution through continuous iterations. In the computation of the PSO algorithm, the particles are randomly generated by initialization. In the iterative process, each particle finds the individual's historical optimal solution and the swarm'shistorical optimal solution from the fitness value, and then updates its velocity and position according to the following equations in Ref. [30]:  
\[v_{i}(t+1)=\omega v_{i}(t)+c_{1}R_{1}\Big{[}R_{i}^{b}(t)-x_{i}(t)\Big{]}+c_{2}R_{2 }\Big{[}R_{g}^{b}(t)-x_{i}(t)\Big{]} \tag{9}\]  
\[x_{i}(t+1)=x_{i}(t)+\varphi v_{i}(t+1) \tag{10}\]  
where \(t\) is the iteration number; \(\omega\) is the inertia weight; \(v_{i}(t)\) is the \(i\)-th particle speed; \(c_{1}\) and \(c_{2}\) are learning factor, which determine the step size of the particle flight toward individual optimal solution and global optimal solution; \(R_{1}\) and \(R_{2}\) are random uniform distribution numbers between 0 and 1; \(x_{i}(t)\) is the \(i\)-th particle position; \(R_{i}^{b}(t)\) is the \(i\)-th particle optimal position; \(R_{g}^{b}(t)\) is the group optimal position; and \(\varphi\) is a contraction factor.
# Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks
## 3 Methodology  
This paper analyses the effect of the massive connection of EVs in low-voltage rural networks by simulating different scenarios in a village in Aragon, Spain. The grid model has been developed in DlgSILENT PowerFactory, and it is based on the data provided by EDISTRIBUCION Redes Digitales, S.L., the main DSO in the region (Figure 1).  
The results have been obtained using quasi-dynamic simulation. This simulation method is particularly beneficial for medium and long-term planning analyses, where variable demand and generation profiles are established, and the network's evolution is modeled in various expansion stages. During these phases, multiple load flow simulations are executed with user-defined time intervals, ranging from minutes to hours [51].  
The analysis is focused on the effect of the charging process on the network, specifically examining the following:  
* Voltage drops;
* Network saturation;
* Unbalanced currents and voltages;
* Increase in network losses.  
Various options exist for measuring the imbalance of three-phase networks [30], with the most commonly used being the Unbalance Factor (UF), defined by decomposing the network into its symmetrical components. In this way, considering only positive and negative sequences, UF is typically expressed as:\[\text{UF}=\frac{\left|G_{-}\right|}{\left|G_{+}\right|} \tag{2}\]  
where \(G\) can be either voltages or currents, \(G_{-}\) is the value corresponding to the negative sequence, and \(G_{+}\) is that of the positive sequence.  
In the case of four-wire three-phase networks, the most commonly used is the unbalance factor UF\({}_{0}\), defined as:  
\[\text{UF}_{0}=\frac{\sqrt{\left|G_{-}^{2}\right|+\left|G_{0}^{2}\right|}}{ \left|G_{+}\right|} \tag{3}\]  
where \(G_{0}\) is the zero-sequence or homopolar component. In the rest of the paper, CUF and CUF\({}_{0}\) will refer to Current Unbalance Factors, and VUF and VUF\({}_{0}\) will refer to Voltage Unbalance Factors.  
The ANSI standard [52] suggests that the voltage unbalance factor (VUF\({}_{0}\)) values should be kept below 3%, whereas the International Electrotechnical Commission (IEC) [53] and the International Council on Large Electric Systems (CIGRE) [54] related standards establish a limit of 2%, permitting it to reach 3% in networks with a high concentration of single-phase customers to guarantee supply quality.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 5 Conclusions  
In this study, we proposed an LQE to apply to industrial wireless sensor networks with high reliability. Based on the burstiness property of wireless links, we estimated the number of transmissions required to reach the PRR target by using the burstiness distribution list proposed in Section 3. We proved by simulation in Cooja that our approach estimated the number of transmissions that can reach the target PRR, as we expected. Based on the simulation in which we compared our approach with some RNP-based methods (ETX and PRR), our proposal can be used as the input metric to calculate the number of transmissions in scheduling for industrial wireless sensor networks. We conclude that our approach is highly suitable for industrial wireless sensor networks that require high reliability for data transmissions. In future work, we will evaluate our approach in regards to the real devices for monitoring and controlling systems in industrial environments.  
Probe packets distribution makes increasing the overhead of the network. However, it can be helpful in guaranteeing the reliability of data transmission when the link quality is not very good. Especially in real applications, we cannot guarantee that the link quality can be stable every time. Every link in the network may have a very different link quality. Therefore, estimating the link quality to guarantee the reliability of data transmission is more important than energy consumption in the industry application.  
Conceptualization, N.H.N. and M.K.K.; methodology, N.H.N.; software, N.H.N.; validation, N.H.N. and M.K.K.; formal analysis, N.H.N.; investigation, N.H.N.; resources, M.K.K.; data curation, N.H.N.; writing--original draft preparation, N.H.N.; writing--review and editing, N.H.N. and M.K.K.; visualization, N.H.N.; supervision, M.K.K.; project administration, M.K.K.; funding acquisition, M.K.K. All authors have read and agreed to the published version of the manuscript.  
This work (Grants No. S2665996) was supported by project for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Ministry of SMEs and Startups in 2020.  
The authors declare no conflict of interest.