esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Study Limitations  
The case selection for this study was from patients with ANB of more than 3\({}^{\circ}\). In other words, we restricted the study to skeletal Class I or II and excluded Class III patients, on the grounds that molar intrusion is unsuitable for skeletal Class III treatment [30]. Patients with normal mandibular body length would present skeletal Class I and II malocclusions, while those with anterior-posterior disharmony display skeletal Class III malocclusion. Thus, the results of this study do not apply to skeletal Class III patients.  
In addition, the sample size in this study satisfied the calculation, although it was less than that of similar studies. This may be due to the small sample size of the preliminary study used in the power analysis.  
In our treatment, the method of molar intrusion using skeletal anchorage is not standardized--cases vary in having TADs implanted in the buccal alveolar region or palate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **AUC** &
\begin{tabular}{c} **Cutoff** \\ **Point** \\ \end{tabular} & _p_**-Value** & **95\% CI** & **Sensitivity** & **Specificity** \\ \hline FMA & 0.87 & 30.0 & 0.03 * & 0.52 & 0.67 & 0.70 \\ UM height & 0.75 & 25.3 & 0.04 * & 0.47 & 0.67 & 0.75 \\ UI height & 0.60 & 32.0 & 0.46 & 0.90 & 0.67 & 0.50 \\ \hline \hline \end{tabular}  
* NS: Not significantly different;
* \(p\) < 0.05: statistically significant difference.  
\end{table}
Table 4: Results of the ROC curve analysis for Group I and Group E.  
Figure 7: (a) The ROC curve of FMA measurement (between anterior teeth extrusion and molar intrusion); (b) the ROC curve of upper molar height measurement (between anterior teeth extrusion and molar intrusion); (c) the ROC curve of upper incisor height measurement (between anterior teeth extrusion and molar intrusion).  
In addition, even with molar intrusion, some percentage of the amount of improvement in overbite may also result in anterior tooth extrusion, which is considered a limitation of this study.  
This study consisted of cases with stability one year after active treatment. [PERSON] et al. reported a three-year follow-up in open bite molar intrusion cases. According to their study, 80% of the amount of molar relapse occurred in the first year [1]. Therefore, we set the follow-up period to one year, but a longer follow-up period is desirable as a future research prospect.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
###### Abstract  
Multi-temporal InSAR (MT-InSAR) methods have been widely used in remote sensing monitoring of ground subsidence, which occurs at many places around the world. Land subsidence, caused by excessive extraction of groundwater, has always been a problem to be solved in Tianjin, China. Although the subsidence in the urban area has been controlled at a low rate, the subsidence issue has not been effectively solved in the suburban area recently, which should be paid much attention. This paper aims to present two multi-temporal differential interferometry techniques, persistent scatterer (PS) and small baseline subset (SBAS), for monitoring the latest surface subsidence in a Tianjin study area on the basis of 20 Sentinel-1A images obtained from March 2017 to March 2019. Our research showed that the average velocity map obtained from the SBAS method closely followed the outcomes of the PS technique from the perspective of identifying similar subsidence patterns. Subsidence rate gradually increased from the urban area of Tianjin to the suburbs and high subsidence zones were mainly distributed at the junction of the Wuqing, Xiqing and Beichen districts. In the past two years, the annual average subsidence rate in the high settlement area mostly exceeded \(-50\) mm/year, which caused serious damage to local infrastructures. Besides, high-resolution remote sensing images combined with field investigations further verified the successful application of MT-InSAR technology in Tianjin's subsidence monitoring. Effective ground subsidence control measures need to be taken as soon as possible to prevent the situation from getting worse.  
ground subsidence; PS-InSAR; SBAS-InSAR; Sentinel-1A; Tianjin +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Quantititative Microstructure Assessment  
The cyclic softening of ferritic-martensitic steels in fatigue loading is caused by the rearrangement of the initially high dislocation density to a dislocation cell structure of lower internal stress [7, 8]. Furthermore, the inability of coarsening precipitates to pin the martensitic lath boundaries contributes to recrystallization [7, 8]. Degradation of a tempered martensite lath structure because of prolonged fatigue loading thus is a well-documented phenomenon. For this reason, dislocation density and precipitate size evolution of P91 is not covered by our study. Nevertheless, it should be mentioned that sub-grain activity during fatigue loading of ferritic-martensitic steel is ambiguous: While in isothermal fatigue subgrain coarsening is reported [8, 9, 10], several authors documented, what we like to call polygonization rather than recrystallization in thermomechanical fatigue [4, 11, 12, 13, 14]. Obviously, the manifestations of sub-grain activity do depend on the type of fatigue cycle, which governs the balance of dislocation creating (plasticization) and annihilating (temperature, time) parameters.  
The differences in TMF behavior, outlined in Section 3.1, can only be explained by detailed microstructure analysis with regard to the characteristic experimental phases. In the case of P91, the examination was focused on the question if polygonization could be utilized for material damage and lifetime assessment, i.e., if it correlates to the initial drop in stress range or rather to damage initiation and final failure. In the case of the ferritic Crofer(r)22 H, no microstructure information was available to explain the shape of the fatigue curve, especially the peak after initial strengthening (Figure 3a,b) and the minimized decrease in stress range per cycle in the damage regime. Investigation thus concentrated on the description of the strengthening mechanisms and deriving recommendations for potential further optimization of this alloy type.
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 4 Analysis of the Impact of IIDG Access Location on Stage-Based Protection
### Setting of Current Interruption Protection  
In the event of a fault occurring on line AB, it is anticipated that Protective Device 2 will respond without delay. Similarly, if the fault occurs on line BC, Protective Device 1 should act expeditiously. It is essential to consider the issue of selective action when designing the protective device. In particular, it is anticipated that current flow interrupting Protection 2 will be triggered in the event of a short circuit at point K1, while Protection 2 should remain deactivated in the case of a short circuit at point K2. To accomplish this, the configuration of current flow interrupting Protection 2 must consider the prevailing current conditions in order to prevent a short circuit at the exit of the downstream line. Consequently, a reliability factor is introduced: \(K^{\prime}_{rel}\).  
The magnitude of the short circuit current can generally be expressed as follows:  
\[I_{K}=\frac{K_{K}E_{S}}{Z_{S}+\beta Z_{L}} \tag{9}\]  
where \(K_{K}\) is the fault type factor; \(Z_{S}\) is the system impedance; \(Z_{L}\) is the line impedance, which affects the distribution of fault currents in the line; and \(\beta\) is a scaling factor indicating the ratio of the distance from the fault point to the start of the line to the length of the entire line.  
The efficacy of the flow-disconnect protection is reflected in its rapid response to faults within the line and is typically quantified by the percentage of the total line length covered. It is of paramount importance to evaluate the coverage of the protection system when the grid is operating at its least loaded state and a two-phase short circuit occurs [18]. If the protective device in question is able to meet or exceed the predefined value, even under such extreme conditions, it can be considered to have an adequate level of sensitivity to faults. This guarantees that even in unfavorable circumstances, the current failure protection is capable of effectively isolating the fault and maintaining the safety and stability of the grid.  
Following the connection of an inverter-based distributed generation system (IIDG) to the distribution network, theoretical analysis indicates that the impact of varying IIDG output levels on the current of the protection device is insignificant. This suggests that the IIDG has a minimal effect on the functionality of the upstream current flow interruption protection [19]. Therefore, irrespective of the output power of the IIDG, it does not significantly impinge upon the fault identification and response capability of the protection device, thereby ensuring the reliability of the grid protection system [20].
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 7 Conclusions
### The Interface Mobility \(L\)  
The interface velocity \(V\) is typically expressed as product of a factor involving the thermodynamic driving force for dissolution and a kinetic factor involving the interface mobility [103], i.e.,  
\[V=V_{0}\bigg{[}1-\exp\bigg{(}\frac{\Delta G}{RT}\bigg{)}\bigg{]}, \tag{10}\]  
where the kinetic factor \(V_{0}\) corresponds to the limiting velocity under infinite driving force (forward reaction rate), \(\Delta G\) is the Gibbs free energy difference, between the free energy of solid and liquid which is responsible for the interface displacement. The driving force of dissolution \(\Delta F\) is given by \(\Delta F=f^{L}\big{(}c_{L}^{\prime}\big{)}-f^{S}\big{(}c_{S}^{\prime}\big{)}- \big{(}c_{L}^{\prime}-c_{S}^{\prime}\big{)}f_{S}^{S}\big{(}c_{S}\big{)}\), \((-\Delta G=\Delta F)\). Thus, Equation (10) is obtained by Taylor series expansion and approximated as:  
\[V\cong-V_{0}\frac{\Delta G}{RT}=V_{0}\frac{\Delta F}{RT}. \tag{11}\]  
Using the derivation in the KKS model, Equation (23) can be expressed in terms of \(\Delta F\) as follows:  
\[\frac{\partial\phi}{\partial t}=-L[\
abla k\
abla\phi+h\prime(\phi)\Delta F] -\omega g\prime(\phi). \tag{12}\]  
Under 1D instantaneous steady state, \(\phi\) is derived for the position as follows:  
\[\frac{d\phi}{dx}=-\frac{L}{V}\bigg{[}k\frac{d^{2}\phi}{dx^{2}}+h\prime(\phi) \Delta F-\omega g\prime(\phi)\bigg{]}. \tag{13}\]  
Under the thin interface limit condition, Equation (13) combined with the equilibrium phase expression (\(dx/d\phi_{0}=-\sqrt{k}/\sqrt{2\omega}[1/\phi_{0}(1-\phi_{0})]\)) and Equation (10) modifies into:  
\[\frac{RT}{V_{0}}=\frac{\sigma}{Lk}. \tag{14}\]  
It should be noted that this equation only holds if the diffusion potential of solute in the interface region is constant. In addition, if the dissolution process is controlled by diffusion, \(V_{0}\) is usually approximated as \(D_{\text{L}}/l_{0}\); where \(D_{\text{L}}\) is the diffusion coefficient of the solute in solution [31]. In general, the determination of \(V_{0}\) is very difficult [104]. In summary, \(L\) can be derived from Equation (14):  
\[L=\frac{D_{\text{L}}\sigma}{l_{0}RTk}, \tag{15}\]
## 1 Introduction
The optimal parameter setting for the data-driven SIF retrieval algorithm was determined according to the specifications of the SIFIS onboard TECIS-1, and end-to-end retrievals were conducted to show the precision of the optimal combination of parameters (Section 3.2). The mechanism of the influence of empirical parameters on the retrieval is explained in the Discussion section. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Spectral Resolution (nm)** & **Sampling Interval (nm)** & **Spectral Range (nm)** & **Signal-to-Noise Ratio** \\ \hline Value & 0.3 & 0.1 & 670–780 & 322 \({}^{\star}\) \\ \hline \end{tabular} \({}^{\star}\) The reference radiance is 10 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\).  
\end{table} 
Table 1: The spectral characteristics of the solar-induced chlorophyll fluorescence Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1).
## 4 Nonlinear Analysis (Pushover)
### Seismic Analysis by Ductility  
The ductility analysis should be conducted using an ultimate strength approach, specifically employing an incremental load (pushover) method. The resistance coefficient Cr is defined as the ratio between the ultimate lateral load capacity of the platform prior to collapse determined through this analysis and the reference lateral load, which is calculated based on seismic resistance analysis. This evaluation was performed for 20 different directional occurrences. The analysis procedure is structured as follows:  
* The gravity load is incrementally applied in steps of 10% until reaching the total design gravity load (100%).
* The seismic load is then applied in 5% increments until reaching 100% of the prescribed seismic load. Subsequently, the incremental application of the seismic load continues at 5% intervals until the collapse mechanism of the structure is fully developed.  
This methodology ensures a systematic assessment of the platform's ductile behavior, providing critical insights into its ultimate capacity and failure mechanisms under seismic excitation.
## 2 Materials and Methods  
### Study Area  
The study area was located in the experimental base of Dryland Agriculture Research Institute of Hebei Academy of Agriculture and Forestry Sciences in Shenzhou City (SZ). The coordinates of the site are 37\({}^{\circ}\)54\({}^{\prime}\)15.63\({}^{\prime\prime}\)N, 115\({}^{\circ}\)42\({}^{\prime}\)29.32\({}^{\prime\prime}\)E. It is located in a warm temperate semi-arid region with monsoon climate. It has distinct continental climate characteristics, with distinct seasons and simultaneous occurrence of rain and heat. The average annual temperature is 13.4 \({}^{\circ}\)C, and the annual precipitation is 486 mm, which is suitable for the growth of grain crops. The soil in this area is light loam, which is rich in mineral nutrients but deficient in organic matter and available nitrogen and phosphorus nutrients. In addition, Zhoukou City (ZK) in the southern part of North China Plain and Huaibei City (HB) in the eastern part were used to verify the applicability of the selected vegetation indices in different regions. ZK is located at 33\({}^{\circ}\)03\({}^{\prime}\)-34\({}^{\circ}\)20\({}^{\prime}\)N and 114\({}^{\circ}\)05\({}^{\prime}\)-115\({}^{\circ}\)39\({}^{\prime}\)E, with a total area of about 11,959 square kilometers, and is one of the major grain production bases in the country. HB is located at 116\({}^{\circ}\)24\({}^{\prime}\)-117\({}^{\circ}\)03\({}^{\prime}\)E and 33\({}^{\circ}\)16\({}^{\prime}\)-34\({}^{\circ}\)10\({}^{\prime}\)N, with an average annual precipitation of 760 mm to 840 mm. At the same time, both regions were subjected to multiple artificial irrigations during the winter wheat growing season, and the growth conditions of winter wheat differed significantly from SZ.  
The experiment was conducted in a split-plot model in a randomized complete block design in 2023, with three blocks. The field plots received the different levels of irrigation (IS--Table 1), and 11 common winter wheat varieties in the North China Plain were distributed in the subplots: T1 (Chang8744), T2 (Shimai22), T3 (Luyuan472), T4 (Shimai15), T5 (HengH1603), T6 (Xinmai28), T7 (Jimai418), T8 (Shannon28), T9 (Nongda212), T10 (Heng4399), and T11 (Jimai22).  
Therefore, a total of 231 ground plots were set up, with each plot measuring 1.5 \(\times\) 6 m, with an area of 9 square meters per plot, and 11 winter wheat varieties were randomly planted in these plots. The distribution of all plots and the distribution of each variety observed for each IS from left to right are shown in Figure 1.  
The winter wheat seeds in all plots were sown on 15 October 2022, at a planting density of 375 plants per square meter. The basal fertilizer applied during souring consisted of pure nitrogen fertilizer (225 kg ha\({}^{-1}\)), P\({}_{2}\)O\({}_{5}\) (112.5 kg ha\({}^{-1}\)), and K\({}_{2}\)O (112.5 kg ha\({}^{-1}\)). No additional fertilizer was applied during the growing season.
# Slot Waveguide Enhanced Bloch Surface Waves
## Materials and Methods
### Materials  
Considering the existing fabrication processes, it is important to define the materials constituting the high and low refractive index layer. The low index material is intended to be silicon dioxide (SiO\({}_{2}\), \(n_{\rm L}\) = 1.46 at \(\lambda\) = 488 nm) and the high refractive index is amorphous titanium dioxide (\(n_{\rm H}\) = 2.50 at \(\lambda\) = 488 nm). These two materials can be deposited conformally on the polymer basis using atomic layer deposition (ALD) [13; 14]. The high refractive index contrast between the two materials allows for a smaller number of periods (N = 5, here), relaxing the fabrication but also reducing the total width of the waveguide.  
Figure 1: Schematic representation of the proposed waveguide structure for BSW excitation and confinement. **(a)** Top view of the entire device including the input waveguide, the Y-junction splitter, and the slot waveguide. The solid thick black curve represents the multilayer; **(b)** Cross section of the polymer slot waveguide coated with a multilayer. The inset is a close up of the multilayer, which is a periodic arrangement of a low and high refractive index material terminated by a finely tuned layer (high refractive index).  
The polymer, Ormocore(r) in this case, is chosen for its perfect compatibility with nano-imprinting techniques and its relatively high thermal stability (up to T = 270 \({}^{\circ}\)C). This second property is extremely important for the deposition of the ALD layers at a process temperature T = 120 \({}^{\circ}\)C [15].
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology
### Model Framework
#### 3.2.3 Enconv1d Model  
The single-differenced pseudorange \(\Delta\rho\) is input into both the temporal and spatial feature extraction modules. The features from the two modules are then integrated using a dense layer, which ultimately outputs the positioning result of the Enconv1d model. The dense layer merges \(X_{out}^{Spatial}\) and \(X_{out}^{Temporal}\) into spatio-temporal features, which are then passed through multiple fully connected layers to output the final positioning values. The overall configuration was as follows: the dimensionality m was 36, the time feature extraction module had a step size T = 6, and the batch size was 64. The dimensions of \(X_{out}^{Spatial}\) and \(X_{out}^{Temporal}\) were both batch_size \(\times\) (1 \(\times\) 512). The target variables were normalized to accelerate model training, and the model was trained using the Mean Squared Error (MSE) as the loss function with the Adam optimizer. The learning rate was set to 0.0002.  
The sliding window time step selection in the time feature extraction module was based on two key considerations. Firstly, the positioning accuracy required in the tunnel was at the decimeter level. Therefore, when learning time correlation, the distance moved within the time step T should ideally be kept under 1 m to accurately capture the vehicle's position changes within the tunnel. Secondly, the device sampling frequency was 100 Hz, and the speed limit within the tunnel was 80 km/h, resulting in a sample point spacing between 0.1 and 0.2 m. To ensure that the vehicle's movement within the time step T does not exceed 1 m while still covering adjacent time intervals, we set the T range within 5-7. In the validation set, we found that variations in T within this range did not lead to significant changes in accuracy, so we selected T = 6.  
Figure 4: One-dimensional CNN with multi-scale convolutional kernels.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3D CNN & 3D Convolutional Neural Networks [68] \\
3D CNN3D & Convolutional Neural Networks [68] \\ ANN & Artificial Neural Network [69] \\ BPNN & Back-Propagation Neural Network [70] \\ BRNN & Bidirectional RNN [71] \\ CARS & Competitive adaptive reweighed sampling [72] \\ CARS-PLSR & Least Squares Regression \\  & (with Partial Competitive Adaptive Reweighted Sampling) [72; 73] \\ CMT & Cubist model tree [74] \\ CR & Continuum removal [75; 76] \\ DT & De-trending [77] \\ EL & Ensemble Learning [78] \\ EvalML & Automated Machine Learning for model evaluation and selection [79] \\ FDR & First-order derivative of reflectance [80] \\ GA & Genetic Algorithm [81] \\ GRU & Gated Recurrent Unit [82] \\ IM & Inception Module [83] \\ IRIV & Iteratively retaining informative variables [84] \\ KELM & Kernel-based extreme learning machine [85] \\ LDA & Linear Discriminant Analysis [86] \\ LSTM & Long Short-Term Memory [87] \\ LR & Logistic Regression [88] \\ MLP & Multilayer Perceptron [89] \\ MPLS & Modified Partial Least Squares Regression [90] \\ MSC & Multiplicative Scatter Correction [91] \\ MVRMZ & Multivariate Regression Models [92] \\ NB & Naive Bayes [93] \\ OSTD & Optimal-Spec-TD Model \\ PCA & Principal Components Analysis [94] \\ PCR & Principal Components Regression [95] \\ PLS-DAP & Partial Least Squares Discriminant Analysis [96] \\ PLSR & Partial Least Squares Regression [73] \\ R\({}^{2}\) & Metrics of determination coefficient [97] \\ R\({}^{2}\)cv & Coefficient of Determination for Cross Validation R\({}^{2}\) (CV) [97] \\ Relieff & Feature Selection Algorithm [98] \\ \end{tabular}
## 6 Discussion
### Limitations  
The evaluation strategies for each detection method differ substantially. Some methods are evaluated using imbalanced datasets, while others use balanced datasets. Additionally, the sample sizes used for evaluation also differ between the detection methods. To address these variations, we used precision, recall, and F\({}_{1}\) as performance metrics instead of accuracy. However, variations in evaluation strategies can still impact the validity of the results. For example, EVulHunter reported an F\({}_{1}\)-score of 93%, but other studies found its F\({}_{1}\) score to be 17% and 23% [18; 65]. However, using the results from these complementary studies may further threaten the validity of the results as they may contain implementation bugs.  
Many cryptojacking detection methods do not distinguish between cryptomining and cryptojacking. Cryptomining refers to the use of a user's resources to mine cryptocurrency with the user's explicit consent. This has been used as an alternative revenue source by organizations such as UNICEF [77]. Cryptojacking, on the other hand, refers to the use of a user's resources to mine cryptocurrency without their explicit consent. Currently, only MinerRay is able to differentiate between these two activities. This differentiation may result in a 1-2% increase in false positives [47].  
The datasets used for evaluation can impact the results of the evaluation. Cryptodejacking websites often modify or move their scripts to different domains to avoid being blacklisted. Additionally, the CoinHive shutdown [16; 78] resulted in a decrease in cryptojacking activity. As a result, the accuracy of cryptojacking detection methods may vary depending on when the dataset was collected.