esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 AI in Research and Development Processes
### Intelligent Tire Design  
AI is also revolutionizing tire design processes. Continental tires [20] use AI algorithms to develop innovative tire tread designs that optimize performance characteristics such as grip, noise reduction, and aquaplaning resistance. By simulating and analyzing the tire's interaction with the road, AI-based design methods enable tire manufacturers to create cutting-edge tire designs that deliver improved safety and performance in a variety of road conditions.  
Integrating AI into research and development processes enables tire manufacturers to accelerate innovation, optimize designs, and develop superior products. By using AI-based simulations, analyzing huge datasets, and optimizing material selection and design, tire companies can deliver high-performance tires that meet the evolving needs of consumers.  
Pirelli's Digital Solutions Centre (DSC) in Bari, Italy, is a joint effort between the public and private sectors (Pirelli R&D with 13 research centers employing more than 2000 people worldwide) and the academic world (including the University of Bari and Bari Polytechnic University) to develop software and algorithms dedicated to advanced planning processes. These include a big data platform for collecting, transforming, and analyzing data from different ecosystems of this multinational company in order to reduce product development times, improve product quality and machine performance, optimize commercial efficiency, and develop new digital services. The aforementioned research will be carried out on the tire's 'digital twin', enabling the design and development of new performance analyses on virtual prototypes [21].  
Nexen Tire has developed an artificial intelligence-based tire performance prediction system using machine learning (ML) at the conceptual design stage to provide a faster and more accurate prediction of performance (handling, noise, fuel economy, etc.). During pre-processing of the collected data, it can detect and replace anomalies in the data, and finite element analysis (FEA) is used to predict tire performance with a 3D virtual tire to model tire shapes and material properties. The aforementioned capability effectively increases the number of prototypes and reduces development time [22].  
Goodyear's ReCharge concept tire uses AI to monitor and learn from driver behavior. Throughout the life of the tire, it dispenses a synthetic material inspired by the carbon structure of spider silk, changing the composition of the tire and adapting it to the driver's driving behavior. This leads to a stronger surface and longer tire life. Goodyear has applied the digital twin concept to design, prototyping, and testing, resulting in a reduction in product development costs of at least 50% [23].  
AI is playing a key role in improving Bridgestone's manufacturing efficiency by enabling intelligent maintenance of smart factory facilities. AI-based data analysis is used to predict potential machine faults and optimize maintenance cycles [24].
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Isotropic Eavesdropper  
We first conduct the first group of simulations with consideration of an isotropic eavesdropper in IUSNs and AUSNs, respectively.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 6 Conclusions  
This study presents an enhanced DeepLabV3+ framework for underwater crack detection that addresses critical challenges of optical distortion, scale variance, and boundary ambiguity. Our three core innovations--physics-grounded light propagation correction, hierarchical multi-scale feature fusion, and curvature flow-guided optimization--collectively achieve state-of-the-art performance:  
1. A total of 82.5% IoU and 79.8% Crack Detection Accuracy, outperforming DeepLabV3+ by 10.6% _IoU_.
2. A total of 78.1% small crack IoU for sub-0.5 mm defects, resolving prior scale limitations.
3. Less than 9% performance degradation under low-light or high-turbidity conditions (>12 NTU).  
The integration of interval fusion with preference aggregation further enhanced boundary precision, reducing false positives by 49.7% in high turbidity. The current limitations persist in extreme turbidity (>20 NTU) and highly irregular crack morphologies. Future work will integrate multi-modal sensing for near-zero visibility scenarios, develop hardware-optimized deployment for real-time ROV inspection, and extend adaptive thresholding to multi-spectral compensation. This framework provides a robust solution for automated underwater infrastructure assessment, significantly advancing structural health monitoring capabilities.  
Conceptualization, W.A., J.Z. and S.T.; methodology, W.A., J.Z. and Z.L.; software, S.W. and S.T.; validation, J.Z. and Z.L.; formal analysis, W.A. and S.W.; data curation, S.T.; writing--original draft preparation, W.A., J.Z. and Z.L.; writing--review and editing, S.W. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangzhou Railway Polytechnic, grant number: GTXYR2431.  
The data is available at [[https://data.mendeley.com/drafts/g962](https://data.mendeley.com/drafts/g962) rbm7b]([https://data.mendeley.com/drafts/g962](https://data.mendeley.com/drafts/g962) rbm7b) (accessed on 29 May 2025).  
The authors declare no conflicts of interest.
## Results  
### Insertion and Removal Torque  
The IT and RT means of the study groups were presented in Tables 1-3. The group D implant showed a significantly higher IT mean of 20.75 \(\pm\) 3.5 Ncm compared to group A (16.83 \(\pm\) 1.9 Ncm), group B (19.1 \(\pm\) 1.2 Ncm) and group C (12.65 \(\pm\) 3.0 Ncm). The group D implant showed a significantly higher RT mean of 15.85 \(\pm\) 3.2 Ncm compared to group A (5.85 \(\pm\) 1.09 Ncm), group B (8.45 \(\pm\) 1.28 Ncm) and group C (10.2 \(\pm\) 3.2 Ncm).  
An IT and RT significant difference was reported between the study groups as being in favour of the wide diameter implant design (Group B-D), compared to the narrow diameter implant (Groups A-C) (Figure 4A,B) (Tables 2 and 4) (\(p\) < 0.01).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Insertion Torque [N/cm]** & **Mean** & **SD** \\ \hline CRYSTAL 3.8 & 16.83 & 1.947 \\ \hline CRYSTAL 4.8 & 19.1 & 1.236 \\ \hline ELISIR 3.8 & 12.65 & 3.034 \\ \hline ELISIR 4.8 & 20.75 & 3.514 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Insertion Torque for the study groups.  
The study group comparison showed a significant increase in IT and RT related to the dental implant diameter when compared to smaller screws (Tables 2 and 4).  
All implant fixtures showed no evidence of loss of stability during positioning in the preparation site.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Removal** & **Mean Diff.** & **95.00\% CI of Diff.** & **Adjusted \(p\) Value** \\ \hline CRYSTAL 3.8 vs. CRYSTAL 4.8 & \(-2.600\) & \(-4.014\) to \(-1.186\) & \(<\)0.0001 \\ \hline CRYSTAL 3.8 vs. ELISIR 3.8 & \(-4.350\) & \(-5.764\) to \(-2.936\) & \(<\)0.0001 \\ \hline CRYSTAL 3.8 vs. ELISIR 4.8 & \(-10.00\) & \(-11.41\) to \(-8.586\) & \(<\)0.0001 \\ \hline CRYSTAL 4.8 vs. ELISIR 3.8 & \(-1.750\) & \(-3.164\) to \(-0.3359\) & \(0.0072\) \\ \hline CRYSTAL 4.8 vs. ELISIR 4.8 & \(-7.400\) & \(-8.814\) to \(-5.986\) & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA post-hoc of removal mean comparisons of the study groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Insertion Torque** & **Mean Diff.** & **95.00\% CI of Diff.** & **Adjusted \(p\) Value** \\ \hline CRYSTAL 3.8 vs. CRYSTAL 4.8 & \(-2.275\) & \(-3.819\) to \(-0.7306\) & 0.0008 \\ \hline CRYSTAL 3.8 vs. ELISIR 3.8 & \(4.175\) & \(2.631\) to \(5.719\) & \(<\)0.0001 \\ \hline CRYSTAL 3.8 vs. ELISIR 4.8 & \(-3.925\) & \(-5.469\) to \(-2.381\) & \(<\)0.0001 \\ \hline CRYSTAL 4.8 vs. ELISIR 3.8 & \(6.450\) & \(4.906\) to \(7.994\) & \(<\)0.0001 \\ \hline CRYSTAL 4.8 vs. ELISIR 4.8 & \(-1.650\) & \(-3.194\) to \(-0.1056\) & 0.0297 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA post-hoc of insertion torque comparisons of the study groups.  
Figure 4: Insertion torque (**A**), removal (**B**) and Periotest analysis (**C**) study outcome. Means and standard deviations are presented in tables (_p_\(<\) 0.05).
# Accusations and Law Articles Prediction in the Field of Environmental Protection
## 5 Experiment
### Experiment Results
#### 5.3.2 Analysis of Charge and Article Prediction  
Table 4 presents a performance comparison of various neural network models on legal charge and article predictions, focusing on key metrics such as Macro Precision, Macro Recall, Macro F1-Score, and Accuracy.  
The EKICAN model stands out, achieving the highest F1 score of 90.1% for charges, illustrating its effectiveness in integrating external knowledge to enhance contextual interpretation. Conversely, traditional models like CNN and GRU, while effective, do not match the contextual processing capabilities of EKICAN, with GRU reaching a Ma-F1 of 85.1% for charges. Similarly, LSTM and HAN show moderate success, demonstrating the utility of sequential and hierarchical processing in legal text analysis. Advanced models like Transformer and RoBERTa validate the effectiveness of self-attention mechanisms, with RoBERTa achieving a Ma-F1 of 88.1% for charges, highlighting its ability to extract relevant features. This comparative analysis emphasizes the need to tailor model selection to the specific complexities of legal text analysis, with EKICAN's superior performance underscoring the benefits of sophisticated attention mechanisms and external knowledge integration.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Charge**} & \multicolumn{4}{c}{**Article**} \\  & **Ma-P** & **Ma-R** & **Ma-F1** & **Acc** & **Ma-P** & **Ma-R** & **Ma-F1** & **Acc** \\ \hline CNN & 81.3 & 83.1 & 82.1 & 77.3 & 65.6 & 64.02 & 64.8 & 61.7 \\ GRU & 83.5 & 86.8 & 85.1 & 79.5 & 67.1 & 65.52 & 66.3 & 63.5 \\ LSTM & 84.7 & 87.3 & 86.0 & 81.4 & 67.7 & 67.3 & 67.5 & 66.8 \\ HAN & 85.4 & 87.9 & 86.6 & 83.6 & 68.1 & 67.7 & 67.9 & 68.4 \\ Transformer & 87.0 & 88.6 & 87.7 & 84.2 & 70.8 & 67.9 & 69.9 & 69.9 \\ RoBERTa & 87.6 & 88.7 & 88.1 & 84.6 & 71.2 & 68.3 & 69.7 & 70.2 \\ LADAN & 88.5 & 90.2 & 89.3 & 87.1 & 73.0 & 71.5 & 71.9 & 71.9 \\ TopJudge & 87.8 & 89.1 & 88.4 & 86.9 & 73.7 & 70.9 & 72.7 & 72.7 \\ RLJP & 88.1 & 90.5 & 89.3 & 87.3 & 75.3 & 68.8 & 71.9 & 72.3 \\ NeurJudge & 89.2 & 91.2 & 90.1 & 89.9 & 74.4 & 69.9 & 72.1 & **73.2** \\ EKICAN & **92.3** & **91.5** & **91.9** & **90.1** & **76.3** & **72.4** & **74.3** & 72.1 \\ \hline \hline \end{tabular}  
* **Bold numbers** indicate the best performance for each metric.  
\end{table}
Table 4: Performance of different models on charge and article prediction tasks.  
Figure 6: Performance trends of various architectures across training epochs.
# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
###### Abstract
[23] demonstrate progress in contextual interaction and structural awareness. However, many models still struggle to address high-resolution, multi-scale, and complex crack geometries encountered in practical deployments. Two major challenges remain unresolved:  
1. **Accurate identification of intersecting cracks. ** In real scenarios, cracks often branch or intersect. Without sufficient receptive field or contextual awareness, models tend to miss or misclassify these areas [24]. 2. **Continuous modeling of long-range cracks. ** Cracks are typically thin and extended. In the absence of strong global context modeling, segmentation results become fragmented, particularly under high-resolution or multi-scale settings [7]. To address these issues, we propose a novel segmentation framework--ETAFHrNet (Efficient Transformer-Enhanced and Adaptive Fusion Attention Network)--which integrates convolutional and Transformer paradigms to balance accuracy and efficiency. 1. **Global-local collaborative feature modeling:** We introduce an Efficient Hybrid Attention Transformer (EHAT) module into HRNet's high-resolution branches, combining axial positional encoding and window attention to capture long-range dependencies while controlling computation [25; 26]. 2. **Adaptive multi-scale fusion:** A novel Cross-Scale Hybrid Attention Module (CSHAM) adaptively weights spatial and directional features through cascaded axial and cross-scale attention, enhancing the detection of intersecting or subtle crack patterns [27; 28]. The overall workflow of the proposed method is illustrated in Figure 1, which outlines the entire pipeline from data acquisition to output segmentation. This structured design ensures reproducibility and operational scalability in pavement crack detection tasks.
## 3 Engineering Background and Main Parameters Affecting Support Structure Design
### Major Parameters
#### 3.2.2 Water Pressure  
The reduction coefficient method is commonly used to estimate external water pressure for tunnels with relatively simple hydrogeological and engineering geological conditions, as described in the references [35; 36]. The formula for external water pressure is as follows:  
\[P_{\rm e}=\beta_{\rm e}\gamma_{\rm w}H_{\rm e} \tag{1}\]  
where \(P_{\rm e}\) is the external water pressure (\(\rm kN/m^{2}\)), \(\beta_{\rm e}\) is the external water pressure reduction coefficient, \(\gamma_{\rm w}\) is the specific weight of water (\(\rm kN/m^{3}\)), and \(H_{\rm e}\) is the effective water head from the groundwater level to the center of the tunnel (m).  
For concrete-lined tunnels, the external water pressure reduction coefficient is typically selected based on the groundwater activity conditions and the influence of groundwater on the stability of the surrounding rock. The groundwater occurrence environment and permeability characteristics of the rock mass suggest that the groundwater  
Figure 2: Relationship between \(\alpha_{\rm H}\) and \(D_{\rm e}\).  
in the mountain-crossing section is not abundant. Therefore, the external water pressure reduction coefficient is estimated to range from 0.1 to 0.4, as indicated in Table 1. Given the unfavorable influencing factors, the preliminary determination for the external water pressure reduction coefficient in the mountain-crossing section is set at 0.4. Under the most unfavorable working conditions, the maximum internal water pressure is calculated to be 1.48 MPa.
## 2 Materials and Methods
### Unmanned Helicopter Airborne FTIR Remote Sensing System  
The unmanned helicopter airborne FTIR remote sensing system comprises a Fourier transform infrared spectrometer, an electric control box, a data processing system with a lower computer, a GPS module, a Wi-Fi module, a visible light camera and a ground personal computer (Figure 6). The FTIR consists of a parallel oscillating mirror interferometer, a cooled infrared MCT detector and a helium-neon laser at 640 nm. Figure 6 shows the internal structure and optical path design. The housing material of the spectrometer is aluminum alloy and the bottom optical platform is designed in a cellular style to reduce the weight. The spectrometer is suspended from the internal beam of the unmanned helicopter using shock absorbers. Figure 6b,c show the effectiveness of the system vibration isolation, and it can be seen that the quality of the interference fringes collected in the unmanned helicopter after takeoff is as high as before takeoff.  
The optical axes of the camera and the spectrometer are parallel to each other, which is used to observe the area pointed at by the spectrometer. The GPS measures the position of the system. The data processing system converts the interference data into spectral data and identifies the gas components. The visible light images, GPS information, restored spectrum and gas identification results are transmitted to the computer on the ground through the Wi-Fi module. Images, spectra data and identification results can be displayed on the ground computer in real time.  
Table 1 shows the FTIR parameters and Table 2 shows the unmanned helicopter parameters. The total weight of the spectrometer and electric control box is 12.5 kg, and the maximum power consumption is 50 W. The detection system is powered by a 28-volt 2500 mAh lithium battery pack, enabling continuous detection for 1 h. The maximum load of the unmanned helicopter is 40 kg and the endurance time is 1.5 h when the load is 30 kg.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Spectral resolution/cm\({}^{-1}\) & 4 \\ Spectral range/\(\upmu\)m & 8–14 \\ Field of view/mrad & 30 \\ Scan rate/(scan/s) & 8 \\ Detector & Stirling MCT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of FTIR.  
Figure 5: Image of parallel oscillating mirror interferometer module.  
Figure 6: Schematic of unmanned helicopter airborne FTIR system and laser interference fringes. **(a)** The unmanned helicopter airborne FTIR remote sensing system composition: Fourier transform infrared spectrometer, an electric control box, a data processing system with a lower computer, a GPS module, a Wi-Fi module, a visible light camera and a ground personal computer. **(b)** laser interference fringes before takeoff (blue). **(c)** laser interference fringes in flight (red).
## 4 Experiments and Analysis
### Experimental Results
#### 4.2.2 The Generalization Ability of the Proposed Method for Indoor Polarized Images  
Furthermore, we used a publicly available dataset to obtain the images shown in Figure 14, which feature a bus model captured at close range under indoor natural light conditions. We used this dataset to validate the method proposed in this paper [28].  
Figure 12: A comparison of the reconstructed depth maps of the target. (**a**) The depth map obtained by the proposed method and (**b**) the depth map obtained from the diffuse reflection model.  
Figure 13: Reconstruction results of the target in Figure 9. The reconstruction results obtained using (**a**) the proposed method and (**b**) the diffuse reflection theoretical model.  
Figure 15a is relatively clear, particularly with the distinct outline of the double-decker bus. The front and rear outlines, roof, and the layered structure of the bus are clearly visible. The 3D appearance of the bus is well represented, especially between the upper and lower decks, where the contours are more pronounced.  
In contrast, Figure 15b displays a blurred outline. Although the basic outline of the double-decker bus can still be discerned, the boundaries are not as distinct as in Figure 15a. The front of the bus appears blurred, and the sense of layering is weaker. The surface depth difference between the upper and lower decks is not as apparent as in Figure 15a, and the gradient of the contours is smoother and lacks depth of clarity.  
The overall structure of the double-decker bus is illustrated in Figure 16. Figure 16a exhibits a strong sense of depth, with the outline of the bus well represented, particularly in  
Figure 14: Close-range polarized imaging of the bus model. The polarization component of light (**a**) in the horizontal direction, (**b**) along the 45\({}^{\circ}\) direction, (**c**) in the vertical direction, and (**d**) along the 135\({}^{\circ}\) direction.  
Figure 15: A comparison of the reconstructed depth maps of the target. The depth map obtained using (**a**) the proposed method and (**b**) the diffuse reflection model.  
the overall contour, side of the body, windows, and lettered areas, where the boundary lines are very clear, and the overall structure is well preserved. The depth difference between the two buses, front and back, is distinct, and the sense of depth is prominent, highlighting the depth difference between the bus body and the ground.  
In comparison, although the basic outline of the bus is still visible in Figure 16b, the depth difference between the front and back buses is less pronounced, and the layering between the buses is not as clear as in the left image, lacking support from finer details. The 3D appearance and sense of layering of the bus are moderately weakened. The lettered area in the upper part appears blurry, and the outlines of the letters are not distinct, especially toward the edges of the image, where the shapes of the letters become indistinct. The right image loses some detail, with the edges of the letters appearing less defined.
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Reference** & **Network Type** & **Classification Techniques** & **Feature Selection** & **Accuracy (\%)** & **Remarks** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. [48]. \\ \end{tabular} } & \multirow{3}{*}{SD-VANET} & Hyperparameter- & \multirow{3}{*}{Yes} & \multirow{3}{*}{98.7} & Focused on SD-VANET \\  & & optimized model with & & & with optimized \\  & & feature selection & & & parameters \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON] et al. [18]. \\ \end{tabular} } & \multirow{3}{*}{IoT Networks} & Decision Trees, MLP, & \multirow{3}{*}{Yes} & \multirow{3}{*}{Above 99} & IoT-specific but relevant \\  & & Random Forest, SVM, FLD, & & & methodologies for DoS \\  & & Naive Bayes + PCA & & & detection \\ \hline \multirow{3}{*}{
\begin{tabular}{c} This Study \\ \end{tabular} } & \multirow{3}{*}{SD-VANET} & Hybrid 1D-CNN and & \multirow{3}{*}{No} & \multirow{3}{*}{99.6} & Real-time detection \\  & & Decision Tree (1 DCNN-DT) & & & tailored for dynamic \\ \cline{1-1}  & & & & SD-VANETs \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of DDoS Detection Techniques and Performance Metrics Across Related Studies. The authors also acknowledge Horizon Europe for the support of our research group. The authors declare no conflict of interest.
# Name-Based Security for Information-Centric Networking Architectures
## 2 Background and Related Work  
### Identity-Based Encryption  
An Identity-Based Encryption (IBE) scheme is a public key encryption scheme in which an arbitrary string can be used as a public key. An IBE scheme is specified by the four algorithms [3], Setup, Extract, Encrypt, and Decrypt, summarized as follows:  
* Setup: Executed once by a _Secret Key Generator_ (SKG). It takes as input a security parameter and returns a master-secret key (_master_) and some generator specific _public parameters_ (_PP_). _Master_ is kept secret by the SKG, whereas _PP_ are made publicly available. _Master_ is only known to the SKG and it is used only for _extracting_ other secret keys (see next algorithm).
* Extract: Executed by a SKG. It takes as input _PP_, _master_, and an arbitrary string _ID_, and returns a secret key \(SK_{ID}\).
* Encrypt: Executed by users. It takes as input an arbitrary string _ID_, a message \(M\), and _PP_, and returns a ciphertext \(C_{ID}\).
* Decrypt: Executed by users. It takes as input \(C_{ID}\) the corresponding private decryption key \(SK_{ID}\), and returns the message \(M\).