esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Non-Dischargeable Volume of Water (Difference between Pumped and Discharge Water)
#### 3.1.1 TOP Scenarios (Influence of k)  
Figure 5 displays the evolution of the non-dischargeable volume of water (a) and the accumulated volume of the water exchanges in both directions (i.e., inflows and outflows) (b) for the TOP scenarios. In Figure 5b, positive values refer to water inflowing to the underground reservoir, while negative values refer to water outflowing from it. The results show that the \(K\) of the surrounding medium played a relevant role in the volume of water that could not be discharged into the underground reservoir. When the piezometric head was located at the top or near the top of the chambers, the hydraulic head inside the mine was usually below it during the operation of the plant.  
Therefore, the total volume of water that inflows into the underground reservoir was larger than that flowing out (Figure 5b). Consequently, the underground reservoir was filled partially, and a portion of the pumped water could not be discharged. As expected, the water exchanges and, therefore, the volume of non-dischargeable water increased with \(K\) (Figure 5b). The accumulated volume of non-dischargeable water over a year varied between \(2.1\times 10^{7}\) and \(2.2\times 10^{5}\) m\({}^{3}\) for the scenarios TOP-6 and TOP-3.  
Considering that non-dischargeable water should be discharged into surface water systems, the environmental impacts would be considered as increasing as the surrounding medium becomes more permeable. Environmental impacts on the underground medium would be also higher for scenario TOP-3, since water exchanges between the mine and the surrounding groundwater system are also higher, i.e., changes in the hydraulic head elevation or in the water chemistry are easily transmitted to the groundwater in the surrounding medium.
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 1 Introduction
However, the previous techniques are found to have certain limitations in handling problems with uncertainties and incomplete information due to unexpected risks and disruptions, thus, resulting in inconsistent findings. Therefore, a better technique is proposed. Generally, the purposes of this study are:  
1. To incorporate the element of resilience into supplier selection;
2. To calculate the weights of resilience criteria with a higher consistency;
3. To rank the performances of the resilient suppliers while taking into account uncertainties and incomplete data. Hence, a Grey-MCDM model which is a new integrated Grey Relational Analysis (GRA)-Best Worst Method (BWM)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model is proposed for resilient supplier selection. This new technique is a serial integration between three different techniques to generate better outcomes. It could be applied to determine the importance level of each criterion to be considered in resilient supplier selection. At the same time, it could provide highly consistent results of criteria weights and suppliers' ranks by considering the problems with uncertainties and incomplete information. The paper is structured as follows: the literature review and research gaps are presented in Section 2. The research methodology is explained and defined in Section 3. The case study and results are presented in Section 4. Section 5 presents the validation of the proposed model, followed by Section 6 which presents the managerial implications. Finally, the conclusions and recommendations for future work are provided in the last section.
## 2 Materials and Methods
### Cropping Intensity Identification  
The determination of cropping intensity relied on the extraction of phenological characteristics from smoothed EVI time series. Throughout a crop growing season, EVI values take a distinctive pattern: a gradual increase during the vegetative growth stage, reaching a peak when the canopy closes, and declining through the reproductive growth stage until harvest. Within one year, single-cropping systems will exhibit one distinctive cycle, whereas double-cropping systems will display two complete cycles. In contrast, non-cropping areas or fallow lands tend to have consistently low EVI values or significantly lower EVI peaks during the growing season. Therefore, the count of EVI peaks serves as a robust indicator of cropping intensity (Figure 6b). In this study, potential peaks in the EVI time series were identified as local maxima using a moving window spanning nine composite dates. Peaks with an EVI value lower than 0.35 were disregarded as spurious peaks [17]. Furthermore, peaks occurring after late October were excluded because winter wheat could exhibit a peak before entering dormancy. Cropping intensity was quantified by tallying the total count of peaks observed within the calendar year. Finally, we obtained the annual cropping intensity map for Shaanxi during 2019-2023.
## 2 Hybrid Tractor Energy System of Fuel Cell/Power Battery/Ultracapacitor
### Research on the Hydrogen Fuel Cell System  
Several articles have introduced mathematical modeling methods for hydrogen fuel cell systems, and this section will refer to building a simple PEMFC model [38; 39; 40].  
In this article, the fuel cell simulation model is built based on the parameters of FC_ANL50H2 proton exchange membrane fuel cell provided in the automotive simulation software Advisor. Figure 1 shows the actual voltage variation curve of the PEMFC.  
Open-circuit voltage is calculated as follows [38]:  
\[E_{\text{OC}}=K_{\text{C}}E_{\text{n}} \tag{1}\]  
\[E_{\text{n}}=1.229+(T-298)\frac{-44.43}{2F}+\frac{RT}{2F}\ln\Bigl{(}P_{\text{H }_{2}}P_{\text{O}_{2}}^{0.5}\Bigr{)} \tag{2}\]  
where \(E_{\text{OC}}\) is the open circuit voltage, \(K_{\text{C}}\) is the voltage constant, \(E_{n}\) is the energy thermodynamic voltage, \(F\) is the Faraday constant, \(P_{H_{2}}\) and \(P_{\text{O}_{2}}\) are the partial pressures of hydrogen and oxygen into the reactor, respectively, \(T\) is the reaction temperature.  
As can be seen from Figure 1, there are voltage losses in the PEMFC reaction process, respectively, as follows:  
\[V_{a}=\xi_{1}+\xi_{2}T+\xi_{3}T\ln(C_{O_{2}})+\xi_{4}T\ln(I) \tag{3}\]  
\[C_{O_{2}}=\frac{P_{O_{2}}}{5.08\times 10^{6}\exp(-498/T)} \tag{4}\]  
where \(V_{a}\) is the activation voltage loss, \(\xi\) is the empirical factor, \(I\) is the fuel cell current, and \(C_{O_{2}}\) is the oxygen concentration.  
\[V_{0}=1R \tag{5}\]  
\[R=\frac{r_{m}l}{A} \tag{6}\]  
\[r_{m}=\frac{181.6[1+0.03i+0.0062(T/303)^{2}l^{2}5]}{[\lambda-0.634-3i\exp(4.18( T-303)/T)]} \tag{7}\]  
\[i=\frac{I}{A} \tag{8}\]  
where \(V_{0}\) is the ohmic voltage loss, \(R\) is the fuel cell internal resistance, \(A\) is the Tafel slope, \(i\) is the exchange current, and \(l\) is the thickness of the proton exchange membrane.  
\[V_{C}=-B\ln(1-\frac{i}{i\max}) \tag{9}\]  
In summary, the hydrogen fuel cell output voltage \(E_{FC}\), power \(P_{FC}\), and efficiency \(\eta_{FC}\) are as follows:  
\[E_{FC}=E_{OC}-V_{a}-V_{0}-V_{C} \tag{10}\]  
Figure 1: The actual voltage variation curve of the PEMFC.  
\[P_{FC} =E_{FC}I \tag{11}\] \[\eta_{FC} =\frac{E_{FC}}{E_{OC}} \tag{12}\]  
The fuel cell system selected for the tractor in this article has a rated power of 16.8 kW and a peak power of 25 kW. The relationship between the efficiency and power of the PEMFC is shown in Figure 2. It can be seen that hydrogen fuel cells are efficient in specific power regions.
## 5 Simulation Results
### Case One: Weak Shading Pattern  
In this case, six connected panels receive uniform radiation condition (1000 W/m\({}^{2}\) radiation), while one receives 800 W/m\({}^{2}\) and one receives 600 W/m\({}^{2}\). The arrangement is illustrated in Table 2. Multiple maxima occur in the P-V characteristics. The location of the GMPP is shown in the characteristics in Figure 7. Figure 8 illustrates the simulation results for this case with a performance comparison between the proposed technique and the conventional P&O and IC techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Irradiation of the First** & **Irradiation of the Second Four** & **Power at GMPP** \\  & **Four Series Modules** & **Series Modules** & **Power at GMPP** \\ \hline One (Weak shading) & [1000,1000,1000,800] & [1000,1000,1000,600] & 490.9 W \\ Two (Moderate shading) & [1000,1000,800,800] & [1000,1000,500,500] & 435.5 W \\ Three (Strong shading) & [1000,1000,600,400] & [800,600,400,200] & 257.4 W \\ Four (Strong shading) & [1000,1000,600,400] & [600,400,400,400] & 263.8 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Incident irradiation for the PV system array with the corresponding power at the global maximum power point (GMPP).  
Figure 6: Simulink model of the MPPT controller using a boost converter.  
Figure 8: _Cont._  
Figure 7: Scenario of Case One (Weak shading pattern).
## 4 Extension of the Formalism When the Quantum Emitter Has Both an Electric and a Magnetic Dipole  
A current density \(\mathbf{j}_{f}(\mathbf{r},\omega)\) of a particle can be described in terms of its moments in a multipole expansion [63]. A small particle, however, can often be described using only the multipole moments of the lowest orders. In the case of a metallic nanoparticle, its response is dominated by the electric dipole moment. Nevertheless, it is known that in some cases it is necessary to go beyond the electric dipole approximation and consider higher-order moments [6]. In particular, it has been shown that silicon nanoparticles with size between the tens and hundreds of nanometers can have strong responses in the infrared and visible spectra due to higher order moments [4; 5; 6; 23; 64], with the magnetic dipole moment contributing the most, even though the particles are not magnetic by themselves. This motivates us to generalize the formalism of the previous sections to the case of a point-like nanoparticle (or quantum emitter) with both electric and magnetic dipole moments. Although the Green's functions technique has been used before in this problem [6; 15; 38], some details regarding the behavior of the Green's functions at coincidence, that is, when \(\mathbf{r}^{\prime}=\mathbf{r}\), have been overlooked. Therefore, we carefully present the full formalism, that is, accounting for both electric and magnetic dipole contributions, below.  
Figure 6: Real (**a**) and imaginary (**b**) renormalized polarizability of a gold nanoparticle in close proximity to a plasmonic graphene-based grating. The solid red line represents the \(xx\) component of the polarizability in the presence of graphene, the dashed brown line represents the \(yy\) component, and the black dotted line represents the \(zz\) component. The dashed blue line is the \(xx\) component of the polarizability in the absence of graphene. Note that \(\alpha_{xx}\
eq\alpha_{yy}\), due to lack of rotational symmetry in the \(xy-\)plane introduced by the ribbon structure. The parameters of the grating are \(L=0.5\,\mu\)m and \(w=L/2\). The parameters for the graphene conductivity and for the Drude dielectric function of gold are the same as in Figure 3.
## 3 Results
### Damage Evolution Behavior
#### 3.2.1 Effect of Confining Pressure on Damage Evolution  
Taking the specimen with a laminar inclination of 0\({}^{\circ}\) as an example, the evolution of the damage variable (\(D_{\rm V}\)) of rock specimens with the same inclination under different confining pressure loading is illustrated in Figure 3. When the inclination angle is constant, the \(D_{\rm V}\)-\(\varepsilon\) curve of the specimen shifts to the right as confining pressure increases. This shift indicates a more pronounced stage of slow damage rise. For specimens with the same inclination, a higher confining pressure corresponds to a larger strain value for the same damage value. This suggests that confining pressure exerts a binding effect on the deformation of the rock specimen, inhibiting the development of new microfractures and controlling damage progression. Consequently, greater axial deformation is required to produce the same damage under higher confining pressure.  
As confining pressure increases, the initial damage of specimens with the same inclination also increases. The rate of change of the \(D_{\rm V}\)-\(\varepsilon\) curve during the damage decline stage becomes larger. This is because higher confining pressure increases the crack closure wave velocity, causing microfractures within the rock to close faster and the wave velocity growth rate to accelerate. Additionally, the increase in confining pressure significantly enhances the damage during the slow rise stage, while the overall rate of change of the curve decreases gradually during the rapid rise stage. This phenomenon occurs because confining pressure promotes the development of plastic deformation in the specimen, weakening its brittle characteristics and making the trend of wave speed decline more evident.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 3 Fault Diagnosis Process
### Lstm  
CNN has a strong feature extraction ability, which can extract the digital features of vibration signals. However, because CNN trains and fits the weights of neural networks in batches with batch_size as the unit [38], it splits the characteristics of vibration data in terms of time, resulting in incomplete feature extraction. In addition, since CEEMDAN decomposes vibration data in multiple dimensions, there is a strong correlation between each IMF. Therefore, it is necessary to adopt the Long Short-Term Memory Network (LSTM) to capture the correlation information between each component, enhance the recognition ability of the network, and improve the accuracy of fault diagnosis of building frame structures. The LSTM structure is shown in Figure 7.  
Compared with RNN, LSTM is more flexible in state updating, and can adopt forgetting gate to lose part of memory information randomly. The update formula of LSTM is as follows.  
\[i_{t}=\sigma(\;W_{i}\;[h_{t-1},x_{t}]+b_{i}) \tag{13}\]  
\[o_{t}=\sigma(\;W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{14}\]  
\[f_{t}=\sigma\Big{(}\;W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{15}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes tanh(\;W_{c}\;[h_{t-1},x_{t}]+b_{c}) \tag{16}\]  
\[h_{t}=o_{t}\otimes tanh(C_{t}) \tag{17}\]  
where \(x_{t}\) is the input data at time \(t\), \(h_{t}\) is the output data of LSTM, \(W_{i}\), \(W_{o}\), and \(W_{f}\) represent the weight coefficients of input gate, output gate, and forgetting gate, respectively, \(b_{i}\), \(b_{o}\), and \(b_{f}\) are bias vectors. With such a gate structure, the LSTM neural network has the ability to maintain long-term storage information and can effectively increase the length of memory. Therefore, it is suitable for extracting correlation features between reconstructed signals.  
In this paper, a two-layer LSTM was adopted to replace the full connection layer of WDCNN and a dropout layer was added between the two-layer LSTM, so that the time data transmission of the two-layer LSTM can be lost immediately according to a certain probability, so as to increase the generalization ability of the network. At the same time, the Adam adaptive optimization algorithm [39] was adopted to optimize weight parameters of the overall network.  
Figure 7: LSTM structure.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 4 Experimental Results and Analysis  
### Datasets and Experimental Settings  
The experiments are performed on the ESB dataset, which comprises 867 comprehensive and high-quality point clouds [22]. Additionally, we include a supplementary collection of point clouds sourced from PU-GAN [17], along with other mainstream datasets such as ModelNet40 [23] and PU1K [14]. Representative samples from these datasets are presented in Figure 5.  
To ensure consistency across experiments, we reference other upsampling methods and normalize the point cloud models to the standard range of \([-1,1]\) as the input to our network. The proposed network is trained and tested on a server equipped with an NVIDIA Tesla K80 GPU. The network optimization is carried out using the Adam optimizer, and the proposed loss function detailed in the main paper is employed. The key parameters for SSPU-FENet are outlined in Table 1.  
The evaluation of the upsampled point clouds is conducted using four commonly used metrics: Chamfer distance (CD), Hausdorff distance (HD), point-to-face distance (P2F), and Jensen-Shannon divergence (JSD) [17]. The results obtained from these metrics in our experiments are reported in the units of \(\times 10^{-3}\), providing a quantitative measure of the performance and accuracy of our approach.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Window sizes of 2D conv layers & 3/3 \\ Values k in two k-max pooling layers & 2/2 \\ Learning rate & 0.001 \\ Batch size & 28 \\ Max epoch & 120 \\ Decay rate & 0.70 \\ Default upsampling ratio & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters in SSPU-FENet.  
Figure 5: Part point cloud models in the datasets.
## 1 Introduction
The transition takes place only for helium plasmas. Similar experiments on argon and neon plasmas [1; 2; 19; 20; 21; 22; 23; 24] show that the radial theory developed by the authors that takes into account the temperature of the ions, but does not take into account the mass of the ions or the ion mean free path, is successful in predicting the positive ion current. Neither radial nor orbital models consider collisions in the calculation of the positive ion current of a negatively biased cylindrical Langmuir probe relative to the plasma. The most frequent collision of the ions in their fall towards the probe in low-pressure, low-temperature plasmas is the ion-neutral charge-exchange collision (INCEC) with the neutral atoms of the background gas [29; 30]. In INCEC, an electron transitions from the neutral atom to the ion, effectively interchanging the moment between them. In radial models, this loss of moment is similar to a friction force in the fluid, so that the positive ion current is reduced. In the orbital models, the ions may lose their orbital velocity, so that, after the last collision, the ions will lose their angular momentum and fall radially towards the probe [1; 2; 5; 7; 17; 18; 26; 28], increasing the positive ion current collected by the probe. The effect of collisions is opposed in both models and will affect not only surface technology but plasma diagnosis methods, both depending on the positive ion current. Electropositive plasmas can be studied by means of three parameters--that is, the ion to electron temperature ratio, the ion mean free path to Debye length ratio and the ion mass [22; 31]. The effect of the ion to electron temperature ratio was studied in previous works [4; 22; 24; 26; 28]. The INCEC mean free path is recognized as an important parameter to discriminate between the ABR and the OML behavior in recent theoretical works [29; 30; 31; 32]. This article shows an experimentally observed transition from the ABR to the OML behavior as a function of the INCEC mean free path, \(\lambda_{+}\), to the Debye length,\(\lambda_{D}\), ratio \(\Lambda=\lambda_{+}/\lambda_{D}\)[26; 28]. This study allowed us to recognize the mass of the ion, \(m_{+}\), as the critical parameter in the presence of the observed transition in helium plasmas. A theoretical justification of the transition as a function of the \(\beta\), \(\Lambda\) and ion mass parameters is proposed. In the Section 2, after this introduction, the experimental setup and measurement method are briefly cited. The Section 3 states the experimental measurement conditions and the methodology. The Section 4 presents the results. In the Section 5, supported by a set of novel measurements, a theoretical discussion about why the transition takes place only for the helium plasmas is proposed. Finally, the Section 6 is an exposition of the conclusions.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 2 Mining Conditions and Methods  
### Mining Conditions  
The Maoping lead-zinc mine is located in the northeastern part of Yunnan Province, under the jurisdiction of Yiliang County, Zhaotong City. The geographical coordinates are from 103\({}^{\circ}\)59\({}^{\prime}\) to 104\({}^{\circ}\)04\({}^{\prime}\) east longitude and 27\({}^{\circ}\)28\({}^{\prime}\) to 27\({}^{\circ}\)32\({}^{\prime}\) north latitude. The area of the mine is approximately 45 square kilometers, and its distribution area is 0.4 square kilometers. The strata exposed in the mining area include the Upper Devonian Zaig Formation (D33), Carboniferous Fengning Formation (C1), Weining Formation (C2), Lower Permian Formation (P1), and Quaternary Formation (Q). Except for the Carboniferous Fenning Formation Wanshushan Group and Lower Permian Liangshan Group, which are coal-bearing clastic rock systems, all others are carbonates.  
In the main mining zone, the bottom plate of the I mining zone is dark gray tuff and dolomite with shale, and the top plate is medium to fine crystalline dolomite, with a small amount of shale. The orebody is 144 m long, with a tendency of 110 m downward from the middle section at an elevation of 896 m. A layer measuring 16 m in thickness, with an average grade of 4.52% Pb and 10.13% Zn, contains 102,936 tons of proven Pb-Zn metal. This accounts for 46.3% of the total reserves in the mine area. The I ore zone consists of thick, greyish-white, coarse-crystalline limestone from the Middle Carboniferous period.
## 5 Results
### Comparison Experiment Results
In contrast, the performance of the 2 DU-Net+CLSTM model in the CCD region declined more significantly, with an F1-score of only 66.55%, indicating that its cyclic structure-based temporal modeling faces bottlenecks under conditions of cloud cover and incomplete time-series data. To further investigate the source of the model's performance improvement, we replaced the original PVM decoder with the classic U-Net decoder for a comparative experiment. Although the variant model still performed relatively stably on the CCD Set (F1-score of 68.24%, IoU of 54.26%), the F1-score and IoU dropped by 3.69% and 1.68%, respectively,  
Figure 10: Comparison experiment results on the Hunan dataset. From left to right, the four regions are: Original Image, PVM, MTAF-TST, 2 DU-Net+CLSTM, and PVM (U-Net). PVM, MTAF-TST, 2D U-Net+CLSTM, and PVM (U-Net) represent the classification results of different models on this dataset. As shown in Table 6, overall, the PVM model outperforms the comparison models in all evaluation metrics. The F1-score reaches 74.84%, and the IoU is 61.38%, which are 2.16% and 2.94% higher than the MTAF-TST model, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Precision (\%)**} & \multicolumn{4}{c}{**CCD Set**} \\  & & **Recall (\%)** & **F1\_Score (\%)** & **IoU (\%)** & **mPA (\%)** \\ \hline PVM & 79.16 & 75.65 & 71.93 & 55.94 & 66.14 \\ MTAF-TST & 77.06 & 75.28 & 69.14 & 56.15 & 65.04 \\
2 DU-Net+CLSTM & 74.42 & 70.76 & 66.55 & 51.83 & 62.87 \\ PVM (U-Net) & 76.38 & 74.19 & 68.24 & 54.26 & 64.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of PVM and mainstream models (CCD Set). compared to the complete PVM. This result further confirms that the decoder structure we designed has stronger expressive power in adapting to the deep spatiotemporal features output by the Transformer, especially in cross-regional scenarios, where it more accurately restores complex object boundaries and details. In the experiments on both the HN-NG and CCD regions, the PVM model consistently demonstrated superior accuracy and stability compared to mainstream methods, validating the effectiveness and robustness of the proposed phenology modeling and multi-source fusion mechanisms in large-scale non-food crop semantic segmentation.