esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Force Surrogate Model and Its Application in Pantograph-Catenary Parameter Optimization
## 1 Introduction
Based on the response surface method [16] or the Pseudo-monte Carlo method [17], a quadratic function of the SDCF on several pantograph parameters could be established, and then the parameters' optimal solution could be obtained by solving the minimum value of the function. By using the backpropagation algorithm [18] or the Selective Crow Search Algorithm to train radial basis function neural networks [19], surrogate models of the SDCF with respect to six pantograph dynamic parameters were established, which were then used for pantograph parameter optimization, combined with optimization algorithms. The paper is organized as follows: In Section 2, a surrogate model capable of accurately predicting the SDCF based on operating speed and 14 pantograph-catenary parameters was constructed using a combination of a feedforward neural network and a validated pantograph-catenary finite element model. This surrogate model is intended to replace pantograph-catenary dynamic finite element calculations. Next, with the surrogate model and the variance-based method, both the first-order and total sensitivities of pantograph-catenary parameters were calculated in Section 3, considering factors such as speed and parameters interaction effects. Subsequently, based on the sensitivity analysis results, the surrogate model, and the Selective Crow Search Algorithm, joint optimization of 10 pantograph-catenary parameter combinations for speeds ranging from 250 to 450 km/h was performed in Section 4, providing pantograph-catenary parameter optimization recommendations across the entire speed range. Finally, Section 5 presents the conclusions.
## 2 Materials and Methods
One and two PT100 sensors were arranged in the center and outer surface of each plate to measure its center and surface temperatures, respectively. The material physical parameters of the PCM (water) and the cold storage plates are shown in Table 3.  
The temperature-controlled container with a cold energy storage system was placed in an open and unobstructed outdoor environment for the test, and the air velocity of the fan was 4.4 m/s. Before the test, the cold storage plates were placed in a freezer (\(-20\)\({}^{\circ}\)C) to be frozen. Sixteen cold storage plates (containing a total of 80 kg of PCM) were moved to the cold storage area, and a basket (mesh structure with an outer dimension of 0.59 m \(\times\) 0.415 m \(\times\) 0.34 m) of 40 kg fresh naively oranges was placed in the fresh-keeping area. The test was started after the door of the container was closed, and the initial temperature at the center of the cold storage plates was \(-8\)\({}^{\circ}\)C. The temperature control range of the outlet temperature from the cold storage area was set at 5-10 \({}^{\circ}\)C, which was reported as the best fresh-keeping temperature for naively oranges [24], and the fan was turned off when the outlet temperature from the cold storage area dropped to 5 \({}^{\circ}\)C and was turned on when it rose to 10 \({}^{\circ}\)C. The test was completed when the value of the temperature at the center of the cold storage plates (PCM) rose to 10 \({}^{\circ}\)C.  
**The temperature at the air inlet and outlet:** 2 PT100 sensors were placed at the air inlet and outlet (one near the outlet fan and one near the inlet fan) to measure the air temperature at the air inlet and outlet. **Ambient temperature in the fresh-keeping area:** 2 PT100 sensors were placed in the fresh-keeping area to measure the ambient air temperature. **Fresh products temperature:** 2 PT100 sensors were placed at the center of the naively oranges to measure their temperature. **Surface temperature of cold storage plates:** 2 PT100 sensors were placed on the outer surface of the cold storage plates. **Center temperature of cold storage plates:** 2 PT100 sensors were placed at the center of the cold storage plates. The distribution of each temperature monitoring point is shown in Figure 4, and the average of the above sensors was taken as the final value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Phase Transition Temperature/\({}^{\circ}\)C** & **Latent Heat Value/(J\(\cdot\)kg\({}^{-1}\))** & **Densities / (kg\(\cdot\)m\({}^{-3}\))** & **Heat Conduction / (J\(\cdot\)kg\({}^{-}\)C)\({}^{-1}\)** & **Specific Heat Capacity of the Solid State/(J\(\cdot\)kg\({}^{-}\)C)\({}^{-1}\)** \\ \hline Water & 0 & 335,100 & 998 & 2.22 & 2050 & 4186 \\ Aluminum & - & - & 2700 & 237 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Material physical parameters. Figure 3: Cold storage plates.
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 8 Polymer Coated MSNs for Diabetes  
The oral delivery of insulin is always attractive for the treatment of diabetes, but, at the same time, challenging due to the degradation of insulin in the physiological environment. To overcome this issue, [PERSON] et al. prepared poly(amidoamine) (PAMAM) dendrimer coated MSNs and investigated their capability to deliver insulin via oral route. The MSNs were loaded with insulin and coated with PAMAM dendrimer through urea linkage to provide better control over drug release. Cinnamaldehyde was loaded within the PAMAM structure to provide synergistic effects, and these nanoparticles were suspended in a chitosan-gelatine scaffold to further control the release rate of drugs. In vivo studies suggested that the prepared insulin loaded MSNs were able to decrease the blood glucose level for up to 10 h in streptozotocin-induced diabetic rat [65].
## 2 System Description
The principle is as follows: assuming that the two known frames are \(F_{1}\) and \(F_{2}\), where \(P=\{p_{1},\cdots,p_{2}\}\)\(\in\)\(F_{1}\) and \(Q=\{q_{1},\cdots,q_{2}\}\)\(\in\)\(F_{2}\) are a set of well-matched three-dimensional points, and the camera's pose is calculated by SVD decomposition, the rotation matrix \(R\) and translation matrix \(t\) are made to satisfy Equation (4). \[\forall i,p_{i}=Rq_{i}+t \tag{4}\]  
The local mapping thread is responsible for optimizing keyframes through operations such as eliminating outliers, generating new map points, and removing redundant keyframes. Subsequently, the optimized keyframes are output to the loop closing thread. The loop closing thread, using the keyframe positioned at the front of the buffer queue, replaces it with the currently detected loop closure keyframe. The similarity between the current keyframe and the consensus keyframe is computed to determine the presence of a loop closure. According to the depth information and image information provided by the camera, the two-dimensional coordinate points are converted into three-dimensional point cloud data. \(X_{i}=[x,y,z]\) represents the coordinates of the points, \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) represents the set of spatial points, and the corresponding pixel coordinate of the known three-dimensional points under the camera coordinate system is expressed as \(X_{i}=[u,v,1]\). According to the camera's pinhole model imaging principle, we can obtain the point cloud. The spatial  
Figure 2: FAST feature point. position information of the point cloud can be obtained according to the pinhole model imaging principle of the camera:  
\[z \begin{bmatrix}u\\ v\\ 1\end{bmatrix} =K(R|t) \begin{bmatrix}x_{w}\\ y_{w}\\ z_{w}\\ 1\end{bmatrix}  \tag{5}\]  
where \(K\) represents the camera internal reference; \(R\) represents the rotation matrix; \(t\) represents the translation vector; and \(z\) is the scale factor between the depth value and the actual spatial distance. In this paper, the obtained point cloud data are processed by the PCL (Point Cloud Library). The \(p_{ix}\), \(p_{iy}\), and \(p_{iz}\) of the point cloud \(p_{i}\) can be calculated according to the following equation:  
\[ \begin{bmatrix}p_{iz}\\ p_{ix}\\ p_{iy}\end{bmatrix} = \begin{bmatrix}d/depthScale\\ (n-c_{x})\ *\ p_{iz}/f_{x}\\ (n-c_{y})\ *\ p_{iz}/f_{x}\end{bmatrix}  \tag{6}\]  
For every keyframe, a corresponding point cloud is created. All of the point clouds are then stitched together using the system's keyframe position data, and the RGB image's color information is utilized to give the point clouds their color, creating a globally dense point cloud map.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 1 Introduction
Meanwhile, a sparsity penalty term is introduced in the dimensionality reduction component to regularize the reconstruction loss, while improving the performance in final task on top of AutoDNet. However, the proposed AutoDNet model in [3] and SAF model in [34] are basically processing the entire input datasets together which include different types of features such as frequencies and mode shapes. This will increase the difficulty in training a robust model due to several problems. (1) As one mode shape is associated with one frequency and different mode shapes are unrelated to each other, it is better to deal with each of them specifically than put them together in dimension reduction; (2) The data for frequencies and mode shapes are in different scale magnitudes, it is improper to put them in the same network, especially in the normalization process. In this paper, we propose a new parallel auto-encoder framework (Para-AF) and our aim is to achieve parallel dimensionality reduction and feature extraction for the frequency data and mode shape data and put them together in relationship learning for detection/identification of damage in a seven-storey steel frame structure. The proposed framework is also incorporated with sparsity to enhance the performance of parallel dimensionality reduction. The modal information such as natural frequencies and their associated mode shapes will be utilized as input separately in several sparse auto-encoders while the output will be the structural elementary stiffness parameters of the structure after integration of these several models. This is the first time for deep learning networks to deal with multi-scale dataset in SHM and it is also new in machine learning. Furthermore, the measurement noise effect and uncertainties effect are considered in the training dataset motivated by [10] in ANNs. Both baseline datasets (without noise effect) and noisy datasets (with measurement noises and uncertainties) are utilised to demonstrate the robustness of the proposed approach. The efficiency and accuracy of predictions for the proposed framework are evaluated by extensive experiments in terms of the Mean Squared Error (MSE) and the defined Regression Value (R-value) [2]. Finally, we conducted experiments by using the sparse auto-encoder method with our proposed parallel structure. The results from the previous SAF is compared with those obtained by our proposed approach, which can show the significant advantages of the proposed parallel structure. The organization of this paper is as follows: Section 2 describes the parallel auto-encoder framework (Para-AF) and the sparsity feature in details; Section 3 evaluates the performances of the proposed framework with numerical studies; Finally, Section 4 presents our conclusions.
# Chemical Regeneration of Thermally Conditioned Basalt Fibres
## 3 Results and Discussion  
The detrimental effect of the thermal recycling process on basalt fibres has already been widely discussed in the literature [9; 10; 14]. The results of a previous paper [14] pointed out a large drop in strength of 50% at 400 \({}^{\circ}\)C, reaching a 75% drop at the maximum temperature of 600 \({}^{\circ}\)C. Due to this strength decrease, basalt fibres can no longer be used in a new structural composite material but they can be re-utilized as discontinuous fillers. Therefore, it is necessary to find out suitable solutions to allow the recovery of recycled fibres [27; 28]. In this regard, the regenerative effect of different chemical treatments on thermally conditioned basalt fibres was evaluated through tensile tests on single fibres. In particular, the regenerative processes were carried out using an alkaline sodium hydroxide solution (NaOH) or an acid solution of hydrofluoric acid (HF). For both chemical agents, the results showed the beneficial effect on the mechanical strength of the heat-treated basalt fibres. Different treatments were performed for the NaOH regeneration, modifying each time the concentration (1.5 and 3 M) of the solution and the duration of the test (5 and 10 min), always keeping the solution temperature constant at \(90\pm 5\)\({}^{\circ}\)C. Instead, chemical treatments with HF were carried out at room temperature, keeping the concentration of the solution constant (1 v%), but varying the duration of the immersion from 30 up to 150 s.  
Tables 1 and 2 summarize the results obtained from tensile tests, showing the different regenerability of the two chemical agents used. As shown in Table 1, the chemical treatment using NaOH alkaline solution was able to increase the tensile strength up to 2366 MPa, highlighting a significant regenerative effect. Figure 1 shows that the different regeneration treatments with the NaOH solution can almost totally or at least partially recover the basalt fibres strength lost during the thermal recycling process. This chemical regenerative capacity becomes less effective with increase of the exposure temperature used during the previous thermal treatments, as reported for glass fibres [16]. In fact, as suggested by [PERSON] et al. [16], the average fibre strength values of heat treated fibres may represent an upper limit and the actual value is likely much lower [29], experiencing a decrease with increasing conditioning temperature. At the same time, particular attention must be paid to the regenerative capacity of the different NaOH solutions. In fact, as the concentration of the chemical agent and treatment time increase, a greater recovery of the mechanical strength of the basalt fibres was recorded. Therefore, it is possible to state that the most corrosive solution is the best performing among those investigated, yielding in the case of the heat treatment at 400 \({}^{\circ}\)C a 94% recovery of the strength previously lost.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Traffic Status Dataset
#### 5.2.2 Gradient Boosting  
A similar process to that followed with the \"lane detection\" data was carried out with the \"traffic status\" data to obtain nearly ideal values for the parameters \"number of estimators\" and \"learning rate\" in the gradient boosting classifier. Both numeric and graphical results are presented in Figure 7 and Table 6.  
Promising results from precision-recall curves can be observed, with a high average precision-recall curve for all three classes combined as well. The precision-recall curve for class 1 (aggressive) is understandable given the smaller set of sample points. We also observe that the micro-average precision-recall curve score is high, giving a good indication of how the algorithm has adapted to a multi-class model well.  
Similar inferences can be made from Figure 7b. The ROC curves, except class 1 (aggressive), are high and approach the extreme top left. The comparable ROC curve scores illustrate the robustness of the algorithm across various classes.  
The model is highly consistent across all classes and on average with highly encouraging metric scores. There are different numbers of data samples in each class; despite this, the model seems to provide consistent and much improved metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Class 0 (normal)** & 0.63 & 0.62 & 0.62 & 7840 \\
**Class 1 (aggressive)** & 0.86 & 0.62 & 0.72 & 6394 \\
**Class 2 (drowsy)** & 0.62 & 0.77 & 0.69 & 7652 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Multi-class evaluation metrics with gradient boosting classifier for traffic status.  
Figure 7: Gradient boosting simulation results: (**a**) precision–recall curve; (**b**) ROC curve.
## 2 Materials and Methods
### Methodologies
#### 2.4.3 Hasse Diagram Technique  
A Hasse diagram is a visualization tool used to display partial order relationships [49]. Let X be a set containing a finite number of objects, and IB be a set of indicators qi (where i = 1, 2,..., 1 IB1): these objects and indicators can form a partially ordered set. In this study, the object set consists of GCO in the 18 counties of the HBO region during the periods 2000-2010 and 2010-2020, represented as P1 and P2, respectively. The indicator set contains 12 driving factors for grassland conversion, divided into four driving factor groups. The object sets are denoted as Counties_P1 and Counties_P2, and these four factor groups are denoted as IB_weather, IB_human, IB_economic, and IB_urbanization. Therefore, a total of 10 partial orders are formed: (Counties_P1, IB_weather), (Counties_P1, IB_human), (Counties_P1, IB_economic), (Counties_P1, IB_urban), (Counties_P2, IB_weather), (Counties_P2, IB_human), (Counties_P2, IB_economic), and (Counties_P2, IB_urban). The comparison between objects follows the following rules:  
\[\begin{array}{rcccccccc}\text{x }\geq&\text{y}&<=>&q_{i}(\text{x})&\geq&q_{i}( \text{y})&\forall&q_{i}\in\text{IB}\\ \text{x }\leq&\text{y}&<=>&q_{i}(\text{x})&\leq&q_{i}(\text{y})&\forall&q_{i}\in \text{IB}\\ &\text{x}|\text{y}\text{ else}\end{array} \tag{1}\]  
Objects can be compared when x \(\geq\) y or x \(\leq\) y; when x \(\mid\) y, the objects cannot be compared.  
For ease of comparison, all input data for the Hasse diagram need to be normalized so that the data input range is [0, 1]. The normalization formula is as follows [50]:  
\[\mathrm{q}\mathrm{n}_{\mathrm{i}}(\mathrm{x})=\frac{(\mathrm{q}_{\mathrm{i}}( \mathrm{x})-\mathrm{q}_{\mathrm{i}}\mathrm{min})}{(\mathrm{q}_{\mathrm{i}} \mathrm{max}-\mathrm{q}_{\mathrm{i}}\mathrm{min})} \tag{2}\]  
where \(\mathrm{q}\mathrm{n}_{\mathrm{i}}\) is the value of the indicator, and \(\mathrm{q}_{\mathrm{i}}\mathrm{max}\) and \(\mathrm{q}_{\mathrm{i}}\mathrm{min}\) are the maximum and minimum values, respectively.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 5 Conclusions  
This paper addresses the control accuracy issue caused by the load changes in autonomous commercial vehicles and proposes a MPC equipped with a mass estimator. By incorporating a forgetting factor into the recursive least squares method, the influence of historical data on estimation is reduced. In conjunction with M-estimation, the robustness of the estimation is enhanced, suppressing the interference from sensor noise. A model predictive controller is established based on the incremental steering angle dynamic error model, improving control accuracy and increasing the smoothness of steering. To counteract the execution delay of the steering system, a steering angle compensator is introduced to enhance the response speed of steering. In real vehicle experiments, the MFFRSL + MPC tracking performance is the best, closely following the reference path, with smooth and quick steering responses, exhibiting good robustness. The maximum lateral error is 0.13 m, and the heading error is at most 0.085 rad, both of which are less than those of the MPC and LQR controllers. Both simulation and real vehicle test results indicate that this path tracking control method, while ensuring higher control accuracy, possesses better robustness and adaptability compared to LQR and MPC control methods.  
This paper considers the design of a path tracking controller for autonomous commercial vehicles, taking into account changes in vehicle load and execution delay. Real-vehicle tests  
Figure 11: Actual vehicle mass estimation diagram.  
are conducted in low-speed scenarios for light commercial vehicles. Subsequently, tests will be carried out under high-speed conditions and for heavy commercial vehicles to further expand the research, aiming to meet the control needs of more vehicle types and scenarios.  
Writing--original draft preparation, Z.W. and J.Z.; writing--review and editing, J.Z., Z.Z. and W.Z.; supervision, J.H.; project administration, J.H.; visualization, J.Z. and Z.Z.; funding acquisition, J.H. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Research Projects of Hubei Province (grant number (JD)2023 BAA017).  
Not applicable.  
The data will be made available by the authors on request.  
The authors declare no conflicts of interest.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 6 Conclusions and Future Work  
In this paper, we proposed a novel approach, GCBAM-UNet, for sun glare segmentation using deep learning. Our model builds upon the pre-trained VGG19-UNet architecture, initialized with ImageNet weights, and incorporates the Convolutional Block Attention Module (CBAM). This integration enables the model to focus more effectively on critical features across both spatial and channel dimensions, significantly enhancing segmentation performance. The addition of CBAM ensures that the model prioritizes the most relevant regions, improving its ability to handle the challenging scenarios caused by glare and varying lighting conditions.  
Extensive experiments on the sun glare dataset demonstrate that GCBAM-UNet outperforms state-of-the-art methods in terms of both accuracy and robustness. By delivering precise segmentation results, our approach addresses a crucial challenge in Advanced Driver Assistance Systems (ADAS), directly contributing to improved visibility analysis and decision-making in real-world driving scenarios. Moreover, the proposed approach is efficient and scalable, making it suitable for real-time applications, which is essential for practical deployment in ADAS.  
In future work, we plan to integrate GCBAM-UNet into a complete ADAS pipeline to localize and segment regions affected by sun glare in real time. This information will not only alert the system to potential visibility issues but also enable corrective actions, such as adaptive adjustments to camera exposure or issuing driver warnings. Furthermore, we aim to evaluate the model's performance under diverse environmental conditions and explore its application in other related domains, such as autonomous navigation and weather-based hazard detection. By addressing these challenges, we envision GCBAM-UNet as a pivotal tool in advancing the safety and reliability of intelligent transportation systems.  
**Author Contributions:** Conceptualization, N.Z. and A.J.; methodology, N.Z.; software, N.Z. and A.J.; validation, I.E.; formal analysis, M.D.N.; investigation, M.D.N. and R.C.E.O.; resources, M.D.N.; data curation, I.E.; writing--original draft preparation, N.Z.; writing--review and editing, I.E. and M.G.; visualization, A.J.; supervision, R.C.E.O.; project administration, M.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is available publicly at [[https://github.com/maesfahani/glaredetection](https://github.com/maesfahani/glaredetection)]([https://github.com/maesfahani/glaredetection](https://github.com/maesfahani/glaredetection)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.