esa-sceva/satcom-chunk-collection huggingface dataset:
# Internal Damage Analysis of Braided Composites Embedded in Carbon Nanotube Yarn
## 4 Conclusions  
CNT yarn has an excellent electromechanical performance and was applied to the damage detection of 3D braided composite specimens. CNT yarn was embedded into a composite specimen via a 3D, four-step, six-direction braiding process. The CNT yarn of five-direction and six-direction yarn was kept in a straight state. Estimating the location of internal damage based on the change in the resistance value of CNT yarn was determined to be a feasible means of monitorization.  
The signal of the CNT yarn embedded into the specimen structure included information on strain (stress) and the size and location of the damage. The quadratic matrix SVD algorithm was used to simplify the data and improve the efficiency of the algorithm. In accordance with the key characteristics of the matrix, the existence of damage was determined by calculating the eigenvalue of the healthy specimen, and the damage position was determined.  
The experimental results showed that the 2D wavelet threshold denoising method is useful for the damage recognition effect and the damage recognition rate. The quadrature matrix SVD algorithm exhibited high positioning accuracy, and the measured coordinate error value was less than 1 mm.  
Conceptualization, Z.W.; methodology, M.J. and Z.W.; investigation, M.J. and Z.W.; writing-original draft preparation, M.J. and Z.W.; writing-review and editing, M.J.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Damage** & **Damage Calculation** & **Damage Actual** & **Coordinate** \\
**Number** & **Source** & **Coordinates** & **Coordinates** & **Deviation (mm)** \\
**Number** & **Number** & \((x,y)\) & \((x^{\prime},y^{\prime})\) & \(\sqrt{(x-x^{\prime})^{2}+(y-y^{\prime})^{2}}\) \\ \hline
1 & 1-1 & (13.1, 23.5) & (12.4, 23.6) & 0.71 \\
2 & 2-1 & (10.3, 49.7) & (10.5, 49.5) & 0.28 \\
2 & 2-2 & (12.5, 32.8) & (12.3, 33.2) & 0.45 \\
3 & 3-1 & (12.9, 44.5) & (13.6, 44.8) & 0.76 \\
3 & 3-2 & (9.9, 29.4) & (10.0, 29.1) & 0.32 \\
4 & 4-1 & (13.1, 57.9) & No Detected & – \\
4 & 4-2 & (9.6, 34.3) & (9.4, 34.6) & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the measured damage location coordinate and the actual one.  
**Funding:** A research project funded by the Doctoral Program Fund of the Ministry of Education of China (200800580004).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to lack of adequate repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 2 Related Studies
### UAV-Assisted Routing Protocols  
Research on various applications using drones has been rapidly increasing, and that on UAV-assisted VANETs is also actively underway. Load carry and deliver routing (LCAD) [3] was proposed for static single-hop routing for UAVs to assist ground nodes. LCAD provides a load-carry-delivery mechanism for enhancing connectivity during the data delivery process of a sparsely connected network by applying the disruption tolerant network (DTN) concept. As a drawback, LCAD does not consider the traffic characteristics of a URN bottleneck. UAV-assisted VANET routing (UVAR) [4] and its extension (U2 RV, UAV-assisted reactive routing protocol for VANETs) [5] utilize reactive multipath routing for UAV-assisted routing. UVAR and U2 RV are based on four main processes: discovery, selection, data delivery and maintenance. These protocols calculate a multi-criteria score for considering the highest degree of connectivity and the shortest distance, as well as the minimum delay to the target destination. In terms of the selection process, the score is calculated for every discovered path by combining several metrics. As a result, the best scored path will be selected, and it is difficult to guarantee the service Quality of service (QoS) when the traffic is concentrated on the corresponding path. Therefore, similar to LCAD, these protocols do not consider the traffic characteristics or URN bottlenecks. A multi-UAV-aided MEC architecture has been proposed [6] as a joint multi-UAV deployment and task scheduling optimization for IoT networks. This paper proposed the task scheduling method using deep reinforcement learning in terms of the role of the Multi-access Edge Computing (MEC) node. However, in terms of the relay node of the URN, routing and load balancing for the traffic priority and characteristics were not considered.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Qualitative Analysis
As shown in Figures 13 and 14, similar trends are observed. YOLO-MEDet not only outperformed the baseline and ICAFusion models in terms of its [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 scores across epochs, but it also demonstrated superior convergence and lower training losses in all components. The consistent advantage on both datasets further validates the robustness and scalability of our proposed architecture under varying environmental and spectral conditions. These qualitative results demonstrate that the proposed method offers stronger feature discrimination, more complete object contours, and higher robustness against environmental interference. The improvements are primarily attributed to two architectural enhancements: the Attention-Enhanced Feature Fusion (AEFF) module, which enables effective cross-modal interaction between visible and thermal features, and the C3-PSA module, which enriches multiscale spatial awareness while preserving key structural details. Functionally, these modules improve detection precision under challenging conditions and enhance model reliability in both sparse and crowded scenes. Although they introduce moderate computational overhead, the model remains deployable on modern GPU-equipped platforms, and further optimization is possible for edge-oriented applications. Figure 12: Training loss comparison on the FLIR dataset. Figure 13: mAP progression curves of YOLO-MEDet, ICAFusion, and the baseline on the M\({}^{3}\)FD dataset.
## 3 Results and Discussion
### Cost and Environmental Impact Analyses
#### 3.2.1 Economic Evaluations  
The study considered the market prices for 92# gasoline (7.12 CNY/L) and mineral powder (425 CNY/t) in China according to the Numbeo database on 8 February 2020 [46]. Based on Equation (1), the study estimated the savings connected to the application of mineral powder to recycle gasoline in the laboratory. The cost of the gasoline necessary to dissolve 1 kg of bituminous binder is CNY 71.20. For the same purpose, the cost of the added mineral powder is 14.10 CNY; as a result, the difference is 57.10 CNY/L.  
\[C=p_{g}*v_{g}-p_{m}*m_{m} \tag{1}\]  
where \(C\) represents the saving cost(CNY), \(p_{g}\) is the reference unit price for gasoline (CNY/L), \(p_{m}\) is the unit price for mineral powder (CNY/kg), \(v_{g}\) is the volume of gasoline (l) and \(m_{m}\) is the mass of mineral powder (kg).
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
###### Abstract  
In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.  
Keywords:convolutional neural networks; medical image application; prostate segmentation; magnetic resonance imaging; MRI +
Footnote †: journal: Medical Image
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 1 Introduction
The necessity of this reconstruction lies in the following aspects: (1) In areas with complex geological conditions (e.g., landslides, high groundwater levels, or uneven soft soil distribution), it is crucial to accurately understand soil layer distribution for construction risk analysis and optimization of ground treatment plans [29]. (2) While small-scale soil interlayers may be overlooked in large-scale geological studies, local anomalies can cause support structure deformation or even collapse during deep excavation. (3) A complete 3D soil layer reconstruction of a deep foundation pit is not only essential for pre-excavation risk assessment but also valuable for tracking project progress. Using 3D models for simulation, real-time monitoring, and early warnings during construction can further enhance project safety [30]. (4) Deep foundation pit projects may commence after the demolition of existing structures, meaning that the original soil layers might have been artificially altered. This increases the likelihood of localized geological mutations. In response to the aforementioned problems, we designed a three-dimensional soil layer reconstruction method based on machine learning. The overall technical roadmap is shown in Figure 1. It is divided into five main technical links, including the following:  
(1) Soil Layer Sample Data Enhancement Algorithm. In view of the scarcity of borehole data and soil samples, we designed soil layer data enhancement algorithms and borehole data enhancement algorithms to obtain sufficient training sample datasets;  
(2) Borehole Data Preprocessing. An encoding algorithm is designed to encode borehole data with non-uniform dimensions into a feature map of a consistent form as an input to the network model;  
(3) Deep Convolutional Neural Network Model Design and Training. We design and build a deep convolutional neural network model using the preprocessed borehole features as the input dataset. The model is trained and then saved for subsequent use;  
(4) Model Adaptation Algorithm. A model adaptation algorithm is developed to fine-tune the model using a small amount of real borehole data provided by the user. The model is selected based on the number of soil layers to ensure it can be applied to modeling multiple soil layers;(5) Visualization of Soil Layer Data. The voxel values are obtained from the soil layer labels output by the model. Voxels with the same label are clustered using a three-dimensional voxel aggregation algorithm, and the three-dimensional soil layer is generated through rendering.
## 4 Results
### Classification Using Hold-Out Validation
A summary of the best classification results using the stratified 10-fold cross-validation strategy is shown in Table 13. When considering that the F1 score value is the best for selecting the best result (in this case, 0.7611 \(\pm\) 0.0133), the conclusion is that the D2V output vector dimension equal to 50 and the classifier input dimension equal to 100 _without PCA_ is the most suitable size and methodology for this experiment. However, it is interesting that the result obtained with the same D2V output dimension (50) _that used PCA_ and kept 100 features performs almost equally to the best-aforementioned result (F1 score of 0.7609 \(\pm\) 0.0137). Note that the same number of features are kept when no PCA was implemented for dimensionality reduction. Yet, this does not represent the best overall result obtained using the same parameters _without PCA_.
## 3 Governing Equations
### Potential Equation of Membrane and Solid  
Transmission of electrons through the solid phase and the transport of protons through the membrane phase are expressed in terms of charge conservation equations. The transfer of protons through the membrane causes ionic flow, and transfer of electrons from the solid parts of the electrodes leads to electrical current. These equations are obtained by [PERSON]'s law:  
\[\text{Membrane potential}:\ \
abla.(\sigma_{m}\
abla\phi_{m})=S_{pot} \tag{9}\]  
\[\text{Solid potential}:\ \
abla.(\sigma_{s}\
abla\phi_{s})=-S_{pot} \tag{10}\]where \(\sigma_{s}\) and \(\sigma_{m}\), respectively, are the electrical conductivity coefficient in the solid phase and the ionic conductivity coefficient in the polymer membrane. \(\sigma_{s}\) and \(\varphi_{m}\) also indicate the potential of the solid phase and the potential of the polymer membrane phase. The source term \(S_{pot}\) is defined as:  
\[S_{pot}=\left\{\begin{array}{ll}-J_{c}&(\textit{cathode}\ \ \textit{cl})\\ \ J_{a}&(\textit{anode}\ \ \textit{cl})\\ 0&(\textit{elsewhere})\end{array}\right. \tag{11}\]  
In theory, the activation losses, \(\eta\), which are the difference between the potentials of the solid phase and membrane, are the driving force of the volumetric exchange current density. The source terms for the solid phase and membrane phase represent the production of electrons and protons, which are calculated by the Butler-Volmer equations.  
\[j_{a}=\textit{j}_{a}^{ref}\left(\frac{C_{H_{2}}}{C_{H_{2}}^{ref}}\right)^{ \gamma_{em}}\left(exp\left(\frac{\alpha_{a}F\eta_{a}}{RT}\right)-exp\left(- \frac{\alpha_{c}F\eta_{a}}{RT}\right)\right) \tag{12}\]  
\[j_{c}=\textit{j}_{c}^{ref}\left(\frac{C_{O_{2}}}{C_{O_{2}}^{ref}}\right)^{ \gamma_{em}}\left(exp\left(-\frac{\alpha_{c}F\eta_{c}}{RT}\right)-exp\left( \frac{\alpha_{a}F\eta_{c}}{RT}\right)\right) \tag{13}\]  
The source terms of the conservation of electrical load are named as the transitional electrical current density: \(\textit{j}_{a}^{ref}\) and \(\textit{j}_{c}^{ref}\), respectively, are the exchange current density in in the anode and cathode at reference conditions. \(F\) is [PERSON]'s constant, and \(C_{i}\) and \(C_{i}^{ref}\), are the concentration of chemical species and the concentration of chemical species at reference conditions, respectively. \(\gamma\) is a factor dependent on the concentration and \(\alpha\) is the transfer coefficient.  
The activation losses, \(\eta\), in the anode and cathode are calculated as follows:  
\[\eta_{a}=\phi_{s}-\phi_{m} \tag{14}\]  
\[\eta_{c}=\phi_{s}-\phi_{m}-V_{\alpha} \tag{15}\]  
\(V_{\alpha c}\) is open circuit voltage on the cathode side. Based on the charge conservation, the total electrons flowing out of the anode catalyst layer should be equivalent to the total current entered into the cathode catalyst layer, and also for the protons.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 2 Materials and Methods
### Metrics  
The present article has provided % accuracies (Correctly Classified Instances--CCI (%)). Some other performance measures, which are F1 scores and MCC values, have also been calculated for all the results in this study. However, CCI values (%) have been used in most of the figures in this study because CCI is the most used performance metric in similar studies in the literature.  
If the elements of the confusion matrix are Tp (True Positives), Tn (True Negatives), Fp (False Positives), and Fn (False Negatives), accuracy is defined as [47]:  
\[Accuracy=\frac{Tp+Tn}{n^{+}+n^{-}}=\frac{Tp+Tn}{Tp+Tn+Fp+Fn} \tag{9}\]  
The F1 value is the harmonic mean of precision and sensitivity. Precision (Positive Predictive Value) is the ratio of true positives to all positives. Sensitivity (recall/true positive rate) is the ratio of positives to all true positives [47; 48].  
\[F1\ Score=\frac{2.Tp}{2.Tp+Fp+Fn}=2\frac{Precision.Sensitivity}{Precision+ Sensitivity} \tag{10}\]  
[PERSON]'s correlation coefficient (MCC) is a method of contingency matrix that calculates the Pearson product-moment correlation coefficient between actual and predicted values [47]:  
\[MCC=\frac{Tp.Tn-Fp.Fn}{\sqrt{(Tp+Fp).(Tp+Fn).(Tn+Fp).(Tn+Fn)}} \tag{11}\]  
The F1 score differs from MCC, and accuracy performance measures for it are independent of True Negatives; it is also not symmetric for class swapping [47].
# Deep Learning-Based Efficient Analysis for Encrypted Traffic
## 3 System Framework of Encrypted Traffic Identification
### Anonymization of Encrypted Traffic  
Since the traffic monitoring tools used in this dataset are Wireshark and TCP dump, both of which collect data packets on the data link layer, the output PCAP file contains all the packet headers above the data link layer, including the MAC address and IP address of the network adapters in the computers of both communicating parties. Considering that this information is only related to the actual network devices, such as the network adapter and router configurations, specific network applications usually do not configure these properties, and the dataset authors used the same devices when collecting the same traffic type. As a result, these attributes directly determine the type of data packets and the neural network tends to fit these features, resulting in the problem of high recognition accuracy but poor generalization ability. Therefore, to prevent the neural network from fitting these features, it is necessary to anonymize the data link layer and the network layer.  
For the transport layer, since many network applications develop their own communication modules based on the socket protocol, information related to specific types of applications will be in the transport layer header, such as the TCP source port number and the destination port number, which should be reserved. In traffic anonymization, for performance reasons, the data link layer and network layer headers of the data packets are directly deleted, and only the transport layer header and the upper layer payload are retained. The traffic anonymization algorithm is as Algorithm 2.  
```
Input:Packet Object, packet Output:Anonymized Packet Object, packet
```
1 functionPACKET-ANONYMIZATION(packet)
2 packet.remove_head(L2, L3) return packet ```
**Algorithm 2**Traffic Anonymization Algorithm
## 2 Related Work
### Dilated Convolution and Skip-Connection Methods  
Reconstructing the corrupted pixel point in a complex background is a common problem in the field of image denoising. The solution to this problem is to enlarge the receptive field size so that the CNN can capture more contextual information, and the feature-learning ability of the network is enhanced. In fully convolutional networks (FCN), the receptive field is increased by pooling to reduce the image size, and then the image size is restored by upsampling [30]. Unfortunately, this process inevitably loses precision. Dilated convolutions eliminate pooling operations by enlarging the receptive field so that each convolution output contains a more extensive range of information.  
Studies have shown that dilated convolutions contribute to multi-scale context aggregation. Therefore, adding a certain number of dilated convolutions into a limited number of network layers improves image-processing performance. An enhanced convolutional neural denoising network (ECNDNet) [31] finds a balance between increasing network depth and expanding network width using dilated convolution. [PERSON] et al. [32] developed an expanded residual CNN for Gaussian image denoising, which extracted more image information through the extended receiving domain. Therefore, dilated convolution has excellent potential for the image-denoising task.  
Skip connections operate on the principle that feature information is passed across hierarchies. The skip connection is realized by merging feature channels, equivalent to increasing the network width and creating feature reuse [33; 34]. The skip connection mode of feature channel merging provides four advantages: it reduces the vanishing gradient problem, enhances the feature propagation of feature depth extension, encourages feature reuse, and essentially reduces the number of parameters.  
U-net [35] uses the method of skip connections to form an encoder-decoder neural network. The first half extracts rich features through the downsampling module. The feature map information will be connected with the upsampling of the second half of the model to carry more practical knowledge. From this extension, we developed a combination strategy of concatenated dilated convolution and skip connection in the RDB module, which uses long skip connections to merge the characteristics of different perceptual dimensions. An interval transmission strategy that uses short skip connections in the DEB module strengthens the learning ability of noise features in the shallow convolution layer.
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 4 Results and Discussion  
### Multi-Classification  
We investigated the model performance prediction using the evaluation metrics of accuracy, precision, recall, F1-score, and AUC. It is worth mentioning that non-resampling models were employed, and their performance was poor and biased toward the majority classes, as predicted earlier. The combined classifiers could not achieve more than 0.57 accuracy. Even if the accuracy was ignored, the F1 the score was also very poor. Therefore, that eliminated the need to check the recall and precision scores.  
The importance of balancing the data is illustrated in Figure 5. Clearly, the ensemble models were capable of learning and predicting, not only in terms of accuracy but also in terms of sensitivity and precision (Table 3). It should be noted that the results demonstrated in Table 3 are the optimal models via a grid search process and by taking their macro average. Apart from LR, the other two models performed spectacularly in Figure 5. RF and XGBoost were able to distinguish between the classes by seeing the AUC reach 0.87. Furthermore, the XGBoost precision, recall, and F1-score were 0.7, 0.71, and 0.71, respectively. XGBoost gained an edge over RF in all evaluation metrics. In addition, RF had a low observation to be predicted to belong to a class when it actually did not (i.e., FP). These finding supports the claim of the ability of RF to generate desirable outcomes with accuracies of 0.69 and 0.87 AUC.  
The confusion matrix of both RF and XGBoost in Figure 5a,b were analyzed along with their ROC curves. Previously, the ensembled algorithm was not able to identity the minority classes, while in Figure 5a,b, it is vice versa. From their confusion matrices in Figure 5a, XGBoost was better able to correctly classify the minority classes than RF. The ROC curves complete the story by showing the class curves, and that the minority class area is much greater than the majority class area. Moreover, the outcomes are within other researchers' range of outcomes [10; 43; 62; 68; 69].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Multi-Classes**} & \\ \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline XGBoost & 0.71 & 0.7 & 0.71 & 0.7 & 0.87 \\ Random Forest & 0.69 & 0.68 & 0.69 & 0.68 & 0.87 \\ Logistic Regression & 0.43 & 0.43 & 0.43 & 0.43 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Models’ multi-class performances.