esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results
#### 4.2.3 Performance Evaluation on Real-World Scene Data  
In Figure 7, we test our method on real-world scenes provided by [PERSON] [54]. Our method demonstrates significant improvements in occlusion removal compared to existing methods such as DeOccNet and ISTY. The evaluation is based on two factors: (1) the preservation integrity of non-occluded areas, and (2) the restoration accuracy in occluded regions. Our method performs particularly well in scenes with thin and repetitive occlusions, while it exhibits limitations in handling larger, more irregular occlusions.  
In the first row (bike_01), our method is particularly effective in removing thin occlusions, such as the basket grid in the foreground. The grid structure, which occludes part of the background (i.e., the cars in the scene), is accurately removed without introducing significant artifacts in the unoccluded areas. The red boxes highlight the areas of focus where our method shows clear improvements. The multi-scale feature extraction and refinement in the BiFPN allow for more accurate distinction between foreground occlusions and background content. Compared with DeOccNet, which loses important background  
Figure 6: Qualitative comparisons on the dense LF dataset, with selected areas marked by red boxes for focused analysis, highlighting distinct differences among the methods.  
information and generates blurred results, our method restores the background with a higher level of detail. This is supported by the higher PSNR (13.51) and SSIM (0.536) values, which indicate better recovery quality. Moreover, ISTY produces slightly better results than DeOccNet but still fails to maintain the clarity of finer details, particularly in regions with complex textures, such as the ground and parked vehicles.  
However, in the second row (Trees_Back), where the occlusions consist of large and irregular objects like tree trunks and branches, the performance of our method decreases. While our approach performs slightly better than both DeOccNet, the results are still not ideal. The occluded background, in this case, is partially recovered, but there are visible occlusion residues, as shown in the red box. The PSNR (17.89) and SSIM (0.535) values reflect this, indicating an improvement over one of the competing methods but still showing that the recovery quality is not optimal. Both our method and DeOccNet face challenges in dealing with dense occlusions, leading to blurred and incomplete reconstructions in the occluded regions. This limitation can be attributed to the complexity and size of the foreground occlusions. Large occlusions obscure significant portions of the scene, causing a serious loss of background information. Despite our method's multi-scale feature fusion strategy, which helps mitigate this issue to some extent, it still struggles to fully recover the background in heavily occluded areas. Future work may address this challenge by enhancing the model's global perception capabilities and potentially leveraging more comprehensive camera array setups for better occlusion removal.
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 3 Force Analysis of Circular Diaphrag Wall  
A body defined by two surfaces is called a shell if the distance between the surfaces is much smaller than the other dimensions of the body. The face formed by the points that are equidistant from the two surfaces is called the middle face. The shell with the cylindrical face as the middle face is called the cylindrical shell. Furthermore, if the middle face of the cylindrical shell is a circular cylindrical face, this is called the circular cylindrical shell [29]. As shown in Figure 3, in the engineering case, the thickness \(\delta\) of the circular diaphragm wall is 1.5 m, the radius \(R\) of the middle face of the circular diaphragm wall is 28.5 m, and the ratio of \(\delta\) to \(R\) approaches 1/20; hence, this can be simplified into a circular cylindrical shell for force analysis [30]. According to the literature [29], for thin circular cylindrical shells, a curvilinear coordinate system can be selected for mechanical calculation. Coordinate \(\alpha\) is placed in the longitudinal direction, which is the direction of the generatrix of the middle face. Coordinate \(\beta\) is placed in the circumferential direction, which is the direction of the directrix of the middle face. Coordinate \(\gamma\) is placed in the radial direction, which is the direction of the thickness of the middle face. As shown in Figure 4, the circular diaphragm wall is mainly subjected to four kinds of horizontal external forces, including the horizontal supporting force of the cap beam, the horizontal supporting force of the lining, the horizontal subgrade reaction on the pit-facing surface of the circular diaphragm wall, and the horizontal earth pressure on the pit-back surface of the circular diaphragm wall.  
Figure 4: Circular diaphragm wall model under normal loads.  
Figure 3: Curvilinear coordinate system for the cylindrical shell and internal forces. (**a**) Membrane internal forces. (**b**) Bending internal forces.
## 4 Impossibility Results
### Synchronization and State Observation Problems
#### 4.1.4 Distributed Deadlock Detection (DD)  
The DD problem is specified as follows.  
* **Safety specification of DD** states that only a process that is part of a deadlock cycle or knot should be aborted/killed as part of deadlock resolution.
* **Liveness specification of DD** states that once a deadlock (cycle or knot in the wait-for graph) occurs in a consistent global state it should be detected and deadlock resolution performed.  
**Theorem 5**: _In a system with even one Byzantine process, the distributed DD problem is subject to the same limitations (exposure to false positives and false negatives) as the CD problem, resulting in safety and liveness violations._  
**Proof.** Solving DD requires satisfying  
\[\Phi_{DD}(e_{i}^{\vartheta})\stackrel{{ def}}{{=}}\bigwedge_{h}(e_{h} ^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}=1)\wedge\phi (e_{i}^{\vartheta}),\]  
where \(e_{h}^{x_{h}}\) is an event where the process gets blocked, \((\forall h)\,y_{h}\leq\hat{y}\), \(\phi\) is a predicate capturing the other requirements of the DD problem (existence of a cycle or knot in the wait-for graph in a consistent global state) besides the first predicate, \(e_{i}^{\vartheta}\) is an event where the deadlock is detected, and \(\to\) is defined on deadlock detection algorithm messages.  
As \(e_{h}^{x_{h}}\) is an internal event, from Theorem 1 for the CD problem, detecting \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}\) is susceptible to false positives and false negatives. Thus, it cannot be guaranteed that the predicate \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}\) in the formula \(\Phi_{DD}\) can be satisfied. Hence, DD cannot be solved.  
A false positive of the CD problem can result in a safety violation--unnecessary abortion--in the DD problem. A false negative of the CD problem is a liveness violation--deadlock not detectable--in the DD problem.
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**HRV**} & \multicolumn{2}{c}{**HRV + PFI**} & \multicolumn{2}{c}{**HRV + RF**} & \multicolumn{2}{c}{**HRV + SpO2**} \\ \hline Adaptive Boosting & 0.852 [0.849, 0.856] & 0.870 [0.867, 0.873] & **0.904 [0.901, 0.906]** & 0.888 [0.885, 0.891] \\ Decision Tree & 0.805 [0.801, 0.809] & 0.801 [0.798, 0.805] & **0.825 [0.821, 0.828]** & 0.803 [0.798, 0.807] \\ Gradient Boosting & 0.919 [0.916, 0.922] & 0.926 [0.923, 0.928] & 0.937 [0.935, 0.939] & **0.952 [0.950, 0.953]** \\ K-Nearest Neighbors & 0.889 [0.886, 0.892] & 0.894 [0.892, 0.895] & **0.927 [0.925, 0.929]** & 0.888 [0.885, 0.891] \\ Logistic & 0.702 [0.698, 0.705] & 0.723 [0.719, 0.727] & **0.731 [0.728, 0.734]** & 0.727 [0.723, 0.731] \\ Noise Bayes & 0.670 [0.665, 0.675] & 0.679 [0.675, 0.683] & **0.723 [0.720, 0.727]** & 0.672 [0.667, 0.676] \\ Random Forest & 0.961 [0.959, 0.962] & 0.960 [0.958, 0.961] & **0.967 [0.966, 0.968]** & **0.969 [0.967, 0.970]** \\ Support Vector & 0.864 [0.861, 0.867] & 0.894 [0.891, 0.896] & **0.921 [0.919, 0.923]** & 0.885 [0.881, 0.888] \\ Machine & & & & & & \\ \hline \multicolumn{10}{l}{Bold is best performance.} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Models trained on the entire length (i.e, \(t_{i}=-48\)), but with separate combinations of features.
## 3 Absorption Characteristics
### Co-polarization and Cross-polarization with Polarization Conversion Ratio (PCR)  
The proposed MMA's PCR value was determined to verify that the proposed MMA is not a polarization converter but an absorber. As shown in Figure 9a, we demonstrated both co-polarization and cross-polarization components with the help of Equation (6). It is clear from Figure 9 that the cross-polarization component is nearly 0 on the linear magnitude scale. Thus, it was ensured that no waves were converted by this design.  
\[\mid S_{11}(\omega)\mid^{2}=\mid S_{TE,\,TE}\,(\omega)\mid^{2}+\mid S_{TE,\,TM} \,(\omega)\mid^{2}=R_{xx}{}^{2}+R_{xy}{}^{2} \tag{6}\]  
The PCR value is considered with the help of Equations (7) and (8), one for y-polarization and the other for x-polarization, respectively. Figure 9b depicts the PCR value of both polarized waves, showing that the suggested design is an absorber but not a polarization converter. The figure clearly shows that the PCR value computed close to 0 due to very little cross-polarization.  
\[\text{Y to x polarized wave, PCR}_{y}=\frac{R_{xy}{}^{2}}{\left(R_{yy}{}^{2}+R_{ xy}{}^{2}\right)} \tag{7}\]  
\[\text{X to y polarized wave, PCR}_{x}=\frac{R_{yx}{}^{2}}{\left(R_{xx}{}^{2}+R_{ yx}{}^{2}\right)} \tag{8}\]
## 3 Results
### PLS Scoring of the R/NR Scale  
PLS model was built on previously selected pixels of samples from the calibration set. PLS is a process offering the possibility to consider continuous and quantitative scores. Thus, PLS was run with the TMA spots allocated to calibration as reference inputs, by considering a scale from 0 to 10, with the scores of 1 and 9 for NR and R, respectively. Results for the two representative samples (NR and R) are depicted in Figure 1D,H. Globally for all samples of the calibration set, the RMSE achieved a value of 3.68 in cross-validation that corresponded to the best results obtained with a number of latent variables equal to 12. This dimension of the vectorial space, corresponding to the first minimum of the RMSE, is considered appropriate to avoid under and over-fitting for the PLS model. More interestingly, in a further step to demonstrate the validity of our approach, the PLS model was applied to independent TMA spots samples of the external validation set as shown in Figure 2B,E. Black pixels corresponded to non-selected pixels by PLS-DA; this automated process of pixels rejection being considered as a quality test on these samples of the test set. The remaining pixels were colored according to the R/NR scale.  
In addition, PLS processing gives access for each TMA spot to a histogram that represents the distribution of the pixels as a function of their score. Such representation allows evaluating quantitatively the spectral heterogeneity of the tissues (Figure 2C,F). Histograms from the external validation set were then used to build maps of sensitivity and specificity according to the two parameters, R/NR score and percentage of pixels in the image whose score is below a given R/NR threshold score. Indeed, for each couple of these parameters values, sensitivity and specificity were calculated. All these data made it possible to draw 2D maps (Figure 3A,B) displaying the values of sensitivity and specificity obtained as a function of the two parameters.  
Figure 2: Histology (**A,D**); PLS scoring (**B,E**) and histogram (**C,F**) of test samples from one NR (up) and one R (down) patients. The histograms indicate the number of pixels as function of the R/NR PLS score.
## 2 Data and Methods
### Optimize the Inversion Results  
In this study, the fitting results are further evaluated and optimized using Goodness of Fit (GOF). GOF is generally used to characterize how well the fitted curves fit the observations [29] and is calculated using Equation (6). GOF ranges from 0 to 1, with values closer to 1 indicating a higher correlation between the fitted and the observed radial wind speed. Conversely, values nearing 0 indicate lower fitting quality. Hence, GOF serves as a reliable indicator for evaluating the accuracy of inversion results. Figure 4 shows the GOF across three fitting scenarios. Notably, when data points closely follow a sinusoidal curve and _CNR_ values exceed \(-21\) dB, the fitting results are excellent and GOF approaches 1 (Figure 4a, GOF = 0.989). Conversely, a GOF of 0.768 (Figure 4b) indicates a lower fitting quality, characterized by data points clustering near but not precisely on the fitting curve. In situations where data points are significantly scattered with considerable _CNR_ fluctuations, the GOF drops substantially (Figure 4c, GOF = 0.565). These observations imply that lower GOF values, influenced by non-uniform wind fields, are associated with decreased precision in the derived wind speed.  
Due to the absence of a standard threshold for Goodness-of-Fit (GOF) values, previous studies often rely on the proximity of GOF to 1 as an indicator of fitting quality [30]. In this study, a critical GOF value appropriate for the observational context of this research is identified by examining how GOF varies with the relative absolute error (RAE) of the fitted wind speed. RAE is primarily utilized to assess the accuracy of the fitting results [9], with better fitting results as it approaches zero. RAE is calculated using Equation (7).  
Figure 5a,b illustrate the variation of RAEs as a function of the GOF values for 10 m at S1 and S3, respectively. As the GOF value increases, the corresponding amount of usable data decreases. Specifically, at S3, when GOF is below 0.65, the RAE is above 50%, when GOF is greater than 0.65, the usable data are less than 90%. Consequently, in this study, a GOF above 0.65 is adopted to optimize the final fitting results. Figure 6 depicts the data quality control process proposed in this study, along with the tradi  
Figure 4: Fitting results with different GOF. (**a**) GOF = 0.989; (**b**) GOF = 0.768; (**c**) GOF = 0.565.  
Figure 3: Same as Figure 2b, but using \(\sigma_{CNR}\) and \(Z_{e}\) filters.  
referenced in the literature (delineated by dashed lines). The procedure initiates with the removal of rainfall-affected data, followed by the exclusion of data with anomalously high or low \(\mathit{CNR}\) values using \(\left|\mathit{CNR}_{i}-\overline{\mathit{CNR}}\right|>1.2\sigma_{\mathit{CNR}}\). An initial sinusoidal fitting is then conducted and the standardized residual (\(Z_{e,i}\)) of the results is calculated. Data points with \(\left|Z_{e}\right|>2\) are excluded, before the second sinusoidal fitting. Finally, the horizontal wind speed and direction inversion outcomes with GOF above 0.65 are retained.
## 4 Performance Evaluation
### Analysis of Low-Temperature Performance  
In order to study the low-temperature performance of different grades of asphalt mixture and the effect of the substitution ratio on the low-temperature performance of the mixture, a low-temperature bending test was carried out at \(-10\)\({}^{\circ}\)C. This experiment was conducted with reference to the \"Highway Engineering Asphalt and Asphalt Mixture Test Procedures\" (JTG E20-2011). The test results are shown in Figure 8.  
In practice, it is very difficult to achieve high strength and deformation ability at the same time, so it could not be determined if one indicator was better than the others. Thus, it was better to judge the low-temperature performance [41; 42]. It can be seen in Figure 8 that the gradation order obtained by the two evaluation indexes was not completely consistent. The main reason was that the strain reflected the deformation ability, and the cracks appearing in the mixture were produced by a plurality of factors. Therefore, it was necessary to use the indexes of bending strain and bending strength for a comprehensive evaluation.  
We considered that the index of the flexural strain energy density could simultaneously reflect the bending strain and the bending strength of the mixture. The function formula is shown as Equation (8) [43]:  
\[W_{f}=\int_{0}^{\varepsilon_{c}}\sigma(\varepsilon)d\varepsilon, \tag{8}\]  
where \(W_{f}\) = the flexural strain energy density (N-mm); \(\varepsilon_{c}\) = the strain value corresponding to peak stress; \(\sigma(\varepsilon)\) = the stress component; and \(\varepsilon\) = the strain component.  
The flexural strain energy density was calculated according to the stress-strain relationship, and this was used to evaluate the low-temperature crack resistance of the asphalt mixture. The test results are shown in Figure 9.  
It can be seen in Figure 9 that the flexural strain energy density had a good correlation with the substitution ratio, and its value became larger and then smaller as the substitution ratio increased. In addition, there was a maximum value when the substitution ratio was 0.5. When the substitution  
Figure 8: Low-temperature test results for different mixture types.  
ratio was large, the fine aggregate did not completely fill the skeleton formed by the coarse aggregate in the asphalt mixture. Thus, there was a large gap, which made the binder ineffectively wrap on the aggregate surface, resulting in poor low-temperature performance. When the substitution ratio was lowered, the coarse aggregate skeleton void was reduced, and the mixture was more uniform and denser. At this time, the low-temperature performance of the asphalt mixture also increased. However, when the substitution ratio was further reduced, the rubber dimension interference with coarse aggregates resulted in a significant reduction in the low-temperature performance of the asphalt mixture.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 6 Parametric Analysis Model of Ultrasonic P-Wave Velocity  
### Variation of Characteristic Parameters under the Influences of Single Factors  
Influences of various factors on the ultrasonic p-wave velocity of specimens were revealed through the above tests with different mix proportions. The results are displayed in Figure 19. The ultrasonic p-wave velocity tends to decrease, and then increase with the increments of factors A and B, while it increases with factors C and D.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of** & **Sum of Squared** & **Degree of Freedom** & **Variance** & **F-Value** & **F\(\alpha\)2** & **Significant Level** \\ \hline A & 8.8312 & 2 & 4.4156 & 3.884 & 1\(\ast\) \\ B & 2.5488 & 2 & 1.2744 & & F0.01(2,6) = 10.925 \\ C & 2.2027 & 2 & 1.1013 & & F0.05(2,6) = 5.143 \\ Error e & 2.0701 & 2 & 1.0351 & & F0.1(2,6) = 3.463 \\ Correction error e & 6.8216 & 6 & 1.1369 & & F0.25(2,6) = 1.762 \\ Sum & 15.653 & & & & \\ \hline \hline \end{tabular}  
* * in the table means that the factor has a certain impact on the wave speed. 2 The symbol F\(\alpha\) represents the critical values of the F-statistic for degrees of freedom of 2 in the numerator and 6 in the denominator, at significance levels of 1%, 5%, 10%, and 25%. These critical values are used in hypothesis testing in analysis of variance to help ascertain if the differences between groups are significant.  
\end{table}
Table 11: Analysis of variance of test data.  
Figure 19: Ultrasonic p-wave velocities of materials under influences of different factors. (**a**) The effects of coal slime; (**b**) The effects of high-water-content material; (**c**) The effects of cement; (**d**) The effects of fly ash.
## 4 Parametric Analysis and Parametric-Sensitivity Analysis  
The previous section shows that the creep described by the fractional-order generalised Kelvin model is the stable creep and Equations (14) and (15) show the functional relationship between instantaneous and ultimate settlements and each parameter; however, the influence of each parameter on the creep process cannot be intuitively observed from this function. Therefore, a specific example is used to analyse the influence of each parameter on the settlements at different moments.  
Here, a rectangular area of a half-space viscoelastic surface is subjected to a uniform load of 1 MPa (Figure 2), and a set of basic parameters (Table 1) are selected according to the applicable parameters in the literature [24]. One of the parameters is increased by 20%, 40% and 60%, decreased by 20%, 40% and 60%, and the other parameters are kept constant, which allows the effect of each parameter on creep displacement to be studied.  
For the given load distribution form in this example, the elastic and viscoelastic solutions of surface settlements are still calculated by Equations (8) and (12). \(F(x,y)\) in the equation is given as follows:  
\[F(x,y)=p_{0}\bigg{(}a_{1}\text{ln}\frac{a_{3}+b_{1}}{-a_{4}+b_{3}}+a_{2}\text{ ln}\frac{a_{3}+b_{2}}{-a_{4}+b_{4}}+a_{3}\text{ln}\frac{a_{1}+b_{1}}{-a_{2}+b_{3}}+a_{4} \text{ln}\frac{a_{1}+b_{3}}{-a_{2}+b_{4}}\bigg{)} \tag{16}\]  
where \(a_{1}=b/2-x\), \(a_{2}=b/2+x\), \(a_{3}=a/2-y\), \(a_{4}=a/2+y\), \(b_{1}=\sqrt{a_{1}^{2}+a_{3}^{2}}\), \(b_{2}=\sqrt{a_{2}^{2}+a_{3}^{2}}\), \(b_{3}=\sqrt{a_{1}^{2}+a_{4}^{2}}\), \(b_{4}=\sqrt{a_{2}^{2}+a_{4}^{2}}\), where \(a\) and \(b\) are the lengths of the rectangle along the x and y axes, respectively; \(p_{0}\) is the uniformly distributed load acting on the surface, which is 1 MPa for this example.  
Because the viscoelastic solutions of the settlements, \(w(x,y,t)\) and \(F(x,y)\), are linearly related, the settlements at different locations have the same variation law with time; for convenience, we only selected the settlements at the centre of the rectangle for analysis.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Shear modulus \(G_{1}\) (MPa) & 60 \\ Shear modulus \(G_{2}\) (MPa) & 60 \\ Bulk modulus \(K\) (MPa) & 80 \\ Viscosity coefficient \(\eta\) (MPa-d) & 1000 \\ Fractional differential order \(\alpha\) & 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the fractional-order generalised Kelvin model.  
Figure 2: Load distribution range.