esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the impact of flower tube grouting reinforcement on surface settlement in shield tunnels under complex geological conditions, using the Wuhan Metro Line 12 project as a case example. A simulation model was constructed using PLAXIS 3D software to analyze surface settlement under unreinforced conditions and compare it with reinforced conditions. The effects of grouting pressure, soil pressure, and tunnel depth on surface settlement were also examined. Results indicate that surface settlement decreases with increasing tunnel depth, decreasing by approximately 0.15 mm per 1 m increase in depth. Soil silo pressure positively correlates with surface settlement, increasing by about 0.08 mm for every 10 kPa rise in pressure. A critical grouting pressure of approximately 400 kPa was identified, beyond which surface settlement increases with higher grouting pressure. Flower tube grouting significantly reduced surface settlement, by 92% on average, limiting maximum settlement to 3 mm. Numerical simulation results closely matched field monitoring data, with errors within \(\pm\)0.5 mm, validating the model's reliability. The study demonstrates that flower tube grouting reinforcement effectively mitigates the effects of tunnel depth, grouting pressure, and soil bin pressure on surface settlement, ensuring project safety when shield tunnels pass through sensitive areas such as long-distance railroads.  
shield tunnel; surface settlement; flower tube grouting; ground settlement Article
# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 5 Conclusions  
This paper presents a lightweight vehicle target detection model, YOLO-AL, which is an enhanced version of YOLOv8n. Specifically, the ADown module was introduced in the backbone and neck, which improved the convolution of the original CBS module in the network, improved the ability to express characteristics through effective down-sampling, reduced the complexity and computational complexity of the model, and built a lighter feature extraction aggregation network, thus improving the accuracy of small target recognition and overall detection performance in vehicle target detection. The LSC module is used to optimize the head and reconstruct the network structure of the original  
Figure 14: **(a)** YOLOv8n. **(b)** YOLO-AL. Comparison of F1 scores of YOLO-AL on UADETRAC dataset.  
Figure 15: **(a)** Original. **(b)** YOLOv8n. **(c)** YOLO-AL. Heatmap comparison of YOLO-AL on UADETRAC dataset.  
model detection head. It adopts group normalization to design shared convolutional layers so that classification, regression, and confidence prediction tasks share the same features. This improves feature expression capabilities and category distinction, and significantly reduces redundant calculations and parameters. In the KITTI 2D dataset, the YOLO-AL mAP improved by 1.1%, and the FPS increased by 12%. Moving on to the UA-DETRAC dataset, mAP showed a growth of 2.0% with a simultaneous 11% increase in FPS. These results highlight the generalizability of the model and its ability to adapt to various traffic situations. The novel model introduced in this study managed to decrease the number of parameters by 33% and FLOPs by 28%. This reduction is of significant importance in addressing the challenges posed by limited computational resources and the complexities associated with deploying vehicle detection algorithms effectively in real-world traffic settings. This design can balance detection accuracy and inference speed and is very suitable for efficient and accurate target detection in complex traffic environments.  
Conceptualization, M.Z. and Z.Z.; methodology, M.Z. and Z.Z.; software, M.Z.; validation, M.Z.; formal analysis, M.Z. and Z.Z.; investigation, M.Z. and Z.Z.; resources, Z.Z.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z. and Z.Z.; visualization, M.Z.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 7 Perturbation Step Size Converge Analysis  
To demonstrate the capacity of our proposed method to obtain highly accurate sensitivities, we performed a convergence study of the perturbation step size for the case of distinct eigenvalues and compared it with FD. Figure 10a-c shows the variability in the relative error measurement for the derivatives of the eigenvalues corresponding to the first mode of vibration. Notably, the error for HYPAD remains constant regardless of the chosen step size for analysis. In contrast, the FD results exhibit high variability concerning the step size. Initially, the error decreases with the value of the step size up to an optimal point; then, as the step size keeps decreasing, the error starts to become dominated by the subtractive cancellation error, increasing its nominal value. Furthermore, the error with FD amplifies with the order of the derivative, resulting in errors with magnitudes of \(10^{10}\). In the method proposed here, the error in any arbitrary-order sensitivity is in the same order as that of the real solution. The Supplementary Material presents the same analysis for the higher vibration modes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mode i** & \(\lambda_{i,d}\times\mathbf{10^{4}}\) & \(\lambda_{i,dd}\times\mathbf{10^{6}}\) \\  & **(\%Error \(\times\mathbf{10^{-8}}\))** & **(\%Error \(\times\mathbf{10^{-8}}\))** \\ \hline
1 & 0.0819 (11.746) & 0.0819 (23.098) \\
2 & 0.000 & 0.000 \\
3 & 3.220 (0.856) & 3.220 (0.383) \\
4 & 0.000 & 0.000 \\
5 & 25.249 (5.260) & 25.249 (5.334) \\
6 & 0.000 & 0.000 \\ \hline \end{tabular}
\end{table}
Table 8: Eigenvalue sensitivity to the width of the cross-sectional area for a system with repeated eigenvalues. The subscripts imply derivation.  
Figure 9: Eigenvector sensitivities with respect to directional geometric parameters. Solid black lines represent the analytical solution, red represents the first and second mode, magenta the third and fourth mode, and green the fifth and sixth mode.
## 4 Experimental Results
### One-Load Experiment on CWRU Dataset  
We repeated the experiment from [4], in which models were trained and tested on each specific dataset (from Table 2) using our new technique. The results are provided in Table 5. The last outcome of the table corresponds to the case when the model was trained on the entire dataset, which consists of data with different load levels. This case is the most important in practice since this model is able to recognize the defect type for any load.  
In most cases, the accuracy was 100%. Given the stochastic nature of the proposed technique, all experiments were repeated 10 times, and the average results are reported below. Misclassifications only occurred in the case of sub-dataset #2.  
Figure 14 shows examples of classification results for two runs of sub-dataset #2 in the form of confusion matrices. The numbers in green on the diagonal correspond to the correct answers of the model, and the non-zero off-diagonal elements to its errors.  
The ball defect class is the most challenging for the model trained on sub-dataset #2, and there may be confusion between objects of classes 1 (Ball 0.007\({}^{\prime\prime}\)), 2 (Ball 0.014\({}^{\prime\prime}\)), and 3 (Ball 0.021\({}^{\prime\prime}\)) (Figure 14). In the subfigure on the left, the model predicts one sample of  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of trials & 10 \\ Number of LPC coefficients & 50 \\ Number of neurons in hidden layer & 500 \\ Number of epochs & 100 \\ Patience Length of signal frame, s & 0.208 \\ \hline Sampling rate, Hz & CWRU \\ SUSU & 31,175 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters values in experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sub-Dataset** & **Average Accuracy, \%** \\ \hline
1 & 100 \\
2 & 99.71 \\
3 & 100 \\
4 & 100 \\ Entire dataset & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Testing LPC-NN on all sub-datasets and on the entire dataset.  
class 2 as a sample of class 1, in the subfigure on the right, the model assigns a sample of class 2 to class 3. Figure 14 shows the confusion matrices for only two program runs, but our detailed analysis indicates that confusion between the ball defect classes 1, 2, and 3 remains likely. This confirms the findings in [4] and [31] that the ball defect class is the most difficult to be accurately identified.  
The paper [4] considered only vibration signals with loads of 1, 2, and 3 HP and used 900 samples (600 for training, 300 for testing). They also investigated the dataset as a whole. Table 6 compares our results with those reported in [4].  
The LPC-NN method is superior in accuracy to the very computationally complex method described in [4], which uses the continuous wavelet transform to convert bearing vibration data into images, and employs a convolutional NN to extract fault features, which are then scanned by gcForest and classified via a cascade forest strategy.
# Effects of Magnetic Stimulation on Dental Implant Osseointegration: A Scoping Review
## 3 Results
### Clinical Studies
#### 3.3.2 Pulsed ElectroMagnetic Fields  
Four clinical studies focused on the effects of PEMF on implant osseointegration by using emitting healing caps and other PEMF emitting devices. Two studies are RCTs, one is a CCT and one is a retrospective study. The first RTC study [62], pioneered the evaluation of PEMF advantages in clinical settings, showing that implants exposed to PEMF (2 h/day for 12 days with a frequency of 2 Hz applied in the first hour, 4 Hz in the second hour) had a higher radiodensity immediately postoperatively and until 1, 3, 6, and 12 months compared to unexposed ones. Also, a significantly lower bone loss was measured up to 1 year follow-up, but no statistical differences were observed regarding the stability of the implants, measured by RFA, probably due to the small size of the analyzed groups. In 2020, [PERSON] et al. [60] in their RCT found that implants receiving a healing abutment able to provide a PEMF stimulation (0.05-0.5 mT), showed an increased stability, measured by RFA, and is bone loss compared to implants not receiving PEMF up to 6 months of follow-up. An increased implant stability due to PEMF exposure was also demonstrated in the retrospective CCT by [PERSON] et al. [61], in which the authors also analyzed some differences regarding the maxillary and mandibular location of tested implants. However, [PERSON] et al. [59] did not find statistically significant differences in terms of bone radiodensity measured by CBCT at 60 days between implants receiving PEMF from healing caps and implants with conventional healing caps.
## 4 Results
### Effusivity and Decrement Factor  
The analysis consisted in calculating the effusivity and decrement values of the different materials used to build the proposed roofs. Subsequently, the decrement factor for each proposed roof was obtained through expression (5). Table 10 illustrates the effusivity of the materials and the decrement of the thermal wave characteristic of each of them.  
Worthy of note, not all materials with high effusivity have a high decrement factor. This is the case of terrazzo paving and mortars. Materials with high effusivity--and more thermal inertia--are reinforced concrete, the slab steel sheeting and water, followed by mortars and terrazzo paving. We can observe that these are the materials with the highest percentage of decrement factor, between 76.61% and 90.88%. Thermal effusivity allows attenuating thermal amplitudes--the difference between maximum and minimum \(T_{i}\) air temperatures--in a space. That is why materials with high effusivity should be located on the inner side of the section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Material** & **Effusivity, s\({}^{12}\)W/m\({}^{2}\)\({}^{\circ}\)C** & **Decrement Factor \%** \\ \hline \multirow{2}{*}{04} & Thermal insulation: XPS & & \\  & [0.032W/[mK]] & 34.641 & 17.82 \\  & Membrane: Non-woven geoteatile & & \\  & Thermosol & & \\
06,08 & Mortar bed & 1286.782 & 12.41 \\
09 & Aerated concrete & 219.089 & 53.80 \\  & Water proofing membrane: & & 502.991 & 7.62 \\  & LBM-30-FV, LBM-40-FP & & \\
16 & Paving file & 1486.607 & 9.85 \\
18 & Marble & 3074.085 & 12.42 \\
21 & Water & 1585.560 & 90.88 \\
22 & Trim and plaster & 809.630 & 12.08 \\
23 & Plasterboard & 454.148 & 15.18 \\
25 & Air gaps. No ventilated R=0.28 & 5.00 & 6.93 \\
27 & Reinforced concrete (reticular) & 1603.122 & 76.61 \\
42 & Reinforced concrete & 2349.468 & 46.05 \\
43 & GAlvanized steel sheet & 13,247.641 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Effusivity and decrement factor of the materials involved in the roof sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Loft Flat Roofs. Housing Block**} & \multicolumn{3}{c}{**Exhibition Hall Roofs. MUA**} \\ \hline \hline \multicolumn{4}{c}{**Thermal Capacity in Transient State x\({}_{1}\), x\({}_{2}\)**} \\ \hline \multirow{3}{*}{Sections} & Per outside & Per indoor & \multirow{3}{*}{Sections} & Per outside & Per indoor \\  & surface unit, x\({}_{2}\), & surface unit, x\({}_{1}\), & & \\  & KJ/m\({}^{2}\)K & KJ/m\({}^{2}\)K & & KJ/m\({}^{2}\)K & KJ/m\({}^{2}\)K \\ \hline Q\({}_{\text{R}}\_1\) & 54.461 & 80.004 & Q\({}_{\text{M}}\_1\) & 82.050 & 34.226 \\ Q\({}_{\text{R}}\_2\) & 59.840 & 80.074 & Q\({}_{\text{M}}\_2\) & 89.089 & 32.803 \\ Q\({}_{\text{R}}\_21\_0.15\) & 129.001 & 78.983 & Q\({}_{\text{M}}\_21\_0.15\) & 129.105 & 32.222 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Internal and external thermal capacity values \(\kappa_{1}\), x\({}_{2}\).  
The decrement factor of the water-sheet roofs was between 8.3% to 9.8% higher than the solid paved roof sections (Table 11).
# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## 6 Discussion
Thus, future work should focus on implementing the presented strategy to validate its operation in real-world conditions. However, we anticipate several discrepancies impacting the proposed method's performance in a real-world setting. On the one hand, SNR variations are similar from one instant to the next in the simulations. However, this may not happen in real-world conditions where, due to channel conditions, SNR might experience significant changes in a very short time. On the other hand, the simulation assumes symmetric channels. This condition will not always happen in real-world situations, thus introducing errors in the number of events the AP counts for each of the three estimators. Then, more experiments must be conducted in heterogeneous networks where devices have different hardware features, such as antennas or transceiver capabilities.
# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 5 Results and Discussion
### Ablation Experiment
In contrast, when only the CSHAM module is incorporated, the performance improves further, with an mIoU of 72.69%, mPrecision of 79.95%, and mRecall of 80.96%, yielding an F1-score of 81.45%. The inference speed remains comparably high at 22.64 FPS. As illustrated in the corresponding visualizations in Figure 10, the cross-scale attention and adaptive weighting mechanisms in CSHAM facilitate more effective integration of multi-resolution features. This enables improved detection of fine-grained crack structures, while preserving smooth and coherent segmentation boundaries. When both the CSHAM and EHAT modules are enabled, the model achieves its best overall performance: an mIoU of 74.41%, mPrecision of 83.84%, mRecall of 84.51%, an F1-score of 83.11%, and an inference speed of 28.56 FPS. As shown in the rightmost column of Figure 10, crack patterns in examples (a)-(d) are accurately detected across multiple scales and orientations, with improved line continuity and significantly reduced background interference. In the regions highlighted by red boxes, the baseline and single-module variants exhibit noticeable segmentation gaps and discontinuities. In contrast, the combined use of CSHAM and EHAT yields contours that closely match the ground truth, demonstrating superior recognition of multi-directional and multi-scale cracks. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **EHAT** & **CSHAM** & **mIoU (\%)** & **mPrecision (\%)** & **mRecall (\%)** & **F1-Score (\%)** & **FPS** & **Params (M)** \\ \hline HRNet & No & No & 63.49 & 72.98 & 70.34 & 71.58 & 14.51 & 45.0 \\  & Yes & No & 70.18 & 81.08 & 77.05 & 78.93 & 22.83 & 47.5 \\  & No & Yes & 72.69 & 79.95 & 80.96 & 81.45 & 22.64 & 48.2 \\  & Yes & Yes & 74.41 & 83.84 & 84.51 & 83.11 & 28.56 & 50.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment performance comparison. In conclusion, the collective findings in Table 4 and Figure 10 underscore the synergistic contributions of the two proposed modules. The EHAT module primarily enhances the model's sensitivity to directional crack features, mitigating fragmentation and misclassification, while the CSHAM module improves feature expressiveness and background  
Figure 10: Ablation experiment comparison. (**a–d**) Randomly sampled images from the dataset. The red boxes indicate regions where differences occur. Figure 9: Confusion matrix visualization of ablation experiments. suppression through adaptive multi-scale fusion. Their integration leads to substantial gains in segmentation accuracy, structural consistency, and robustness under complex conditions. Additionally, the model achieves fast inference, rendering it highly applicable to real-time pavement crack detection scenarios.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 4 Results
### Turbulence Intensity  
Figure 13 shows the profiles of wake turbulence intensity from 1-10 D downstream of WT1. The turbulence intensity (\(I\)) is calculated by:  
\[I=\sqrt{\frac{1}{3U_{hub}^{2}\big{(}\big{<}u^{\prime 2}\big{>}+\big{<}v^{\prime 2} \big{>}+\big{<}w^{\prime 2}\big{>}\big{)}}} \tag{9}\]  
where \(u^{\prime}\), \(v^{\prime}\) and \(w^{\prime}\) are the fluctuating velocity components in the x, y and z axis, respectively. \(U_{hub}\) is the speed at the hub height.  
The turbulence intensity of far field wake is related to the spacing between the turbines; the smaller the spacing is, the greater the intensity will be. The increase in turbulence intensity is only about 10% behind WT1, while the turbulence intensity increases to 25% after passing through WT2. When the spacing is small, the turbulence intensity can even reach 30%. The yaw control also has a great influence on the turbulence intensity of the far wake.  
Figure 14 is the turbulence intensity contours. It can be seen from Figure 14 that the spacing of turbines has an influence on the turbulence intensity. The closer two turbines are, the greater the turbulence intensity of WT2's wake and the wider the turbulence influence range. When the yaw angle of WT1 is 0\({}^{\circ}\), the turbulence intensity and turbulence diffusion range of WT2 wake are the largest. With the increase in the yaw angle of WT1, the turbulence intensity after WT2 wake can be greatly reduced.  
Figure 13: Time\(-\)averaged turbulence intensity profiles. The turbulence intensity scale for each profile ranges from 0 to 0.5. Each consecutive profile is taken 1D downwind: (**a**) yaw = 0\({}^{\circ}\); (**b**) yaw = 10\({}^{\circ}\); (**c**) yaw = 20\({}^{\circ}\); (**d**) yaw = 30\({}^{\circ}\).  
Figure 15 shows turbulence intensity in yz slices at different locations downstream of two wind turbines when the yaw angle is 30\({}^{\circ}\) and spacing is 7D, similar to Figure 12. Figure 15a-c depict turbulence intensity contours at positions 1D, 4D and 6D downstream of WT1. It can be seen that the turbulence intensity is smaller than those at the positions downstream of WT2, and the areas with large turbulence intensity are caused by the wake curls and deflection, and the changes of turbulence intensity also present a three-dimensional characteristic. Figure 15d-f are turbulence intensity contours at 1D, 2D and 4D downstream of WT2, respectively. The distributions of turbulence intensity at the distances downstream of WT2 are asymmetric and higher due to the wake superposition of two turbines, and tend to be uniform in the far wake.  
Figure 14: Contours of instantaneous turbulence intensity in a horizontal plane at hub height: (**a**) spacing = 5D; (**b**) spacing = 6D. (**c**) spacing = 7D; (**d**) spacing = 8D.  
Figure 15: Contours of instantaneous turbulence intensity in a vertical plane at hub height: (**a**) 1D downstream of WT1; (**b**) 4D downstream of WT1; (**c**) 6D downstream of WT1; (**d**) 1D downstream of WT2; (**e**) 2D downstream of WT2; (**f**) 4D downstream of WT2.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection
### Cooperative Data Collection with Distributed LDPC Codes  
When multi-source cooperation graphs are formed, low complex distributed processing schemes using distributed LDPC codes are proposed to match the directed graphs \(D\). Overall, each source node in the set \(S\) jointly encodes its own data and the data successfully received from one or two selected partners according to the cooperation graphs and then forwards the encoded bits to the mobile sink. The detailed procedures for two cooperation models are introduced as follows.