esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 5 Experiments with Deep Models for Defect Detection and Depth Estimation
### Model Training  
The dataset consisted of 38 measurements, with 26 allocated randomly for training, 6 for validation, and the remaining 6 for testing. To optimize the model's performance, a linear learning rate scheduler was implemented during the training process.  
All training data underwent preprocessing using the TSR, PCT, and PPT methods. Specifically, TSR was executed with a \(5^{\circ}\) decomposition. This preprocessing resulted in multiple frames of images generated by each method. The coefficients generated by TSR and PCT and the phase coefficient generated by PPT were selected for further processing. For integration with the ImageNet pretrained model, three specific frames were selected to construct RGB-like images, as is shown in Figure 9. For the PPT, PCT, and TSR coefficients, frames from the second to fourth sequences were chosen, excluding the first frame. The rationale for this exclusion was that the first frame represents only the average temperature of all thermal sequences, which is less relevant in this context, which focuses on defects with significant thermal contrast.  
The model was trained on all three types of images: PPT, PCT, and TSR. Table 2 provides an overview of the hyperparameters employed in the experiments. Data augmentation techniques, including perspective transformations like rotation, scaling, and shearing, were dynamically applied to enhance the robustness of the training images.  
The results are shown in Tables 3-5 and in Figure 10. Performance on various validation datasets was assessed using precision, recall, the mean average precision [35] at 50% overlap (mAP50), and the confusion matrix [36]. Precision quantifies the accuracy of the positive predictions, while recall measures the ability of the model to detect all the relevant instances. The mAP50 is a widely used metric for object detection. It considers a predicted bounding box as valid when it has more than 50% overlap with an actual target area. The mAP provides a comprehensive evaluation by integrating the precisions of the model at different recall levels. The confusion matrix extends the insights from recall and precision by offering detailed evaluation results for each class.  
The results indicated that the PPT image preprocessing method achieved the highest mAP, signifying its superior efficacy. This can be attributed to its advanced image enhancement capability. Therefore, it is essential to recognize the significant impact of preprocessing methods in the hyperparameter selection process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch size & 32 \\ Optimizer & Stochastic gradient descent \\ Learning rate & 0.005 \\ Learning rate scheduler & Linear \\ Training epochs & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters to train the YOLOv5 model.  
Figure 9: Comparison of training images generated by TSR, PPT, and PCT, respectively.
## 3 Results and Discussion
The obtained flood risk map is presented in Figure 11. As can be seen, the areas with higher flood risk are visually correlated to the areas with greater flood water depths in Figure 9. Furthermore, it is interesting that the total area of the identified depressions is 148,432,800 m\({}^{2}\) and it is similar to the maximum estimated flooded area (extended), which is 151,872,700 m\({}^{2}\).
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 1 Introduction
By quantifying the cost of the robot's obstacle-crossing, the robot makes a better choice between obstacle crossing and obstacle avoidance and further optimizes the path while adapting to the obstacle-crossing function.
## 3 Numerical Micromodulus
### Two-Dimensional Numerical Micromodulus  
Assume a 2D isotropic plate with unit thickness, the bond elongation is a constant \(s\) under uniform deformation. \(E\) is the elastic modulus. The plate is discretized into \(N\) material points spaced \(\Delta x\) apart and the horizon's radius is \(\delta=m\Delta x,\quad(m=2,3,4,5)\). As demonstrated in Figure 4 for \(\delta=2\Delta x\), the cross-section \(A_{i}\) of point \(x_{i}\) has several bonds in the positive direction passing through or ending at it. The numerical micromodulus denotes \(c_{2}\).  
The projection of a single bond force in the \(x\)-direction can be expressed in Equation (14)  
\[f_{x}=f\frac{\left|x_{j}-x_{i}\right|}{\left\|\xi\right\|} \tag{14}\]  
in which \(\left|x_{j}-x_{i}\right|\) is the \(x\)-distance for point \(x_{i}\), \(x_{j}\) and \(f=c_{2}s\). It is a remarkable fact that \(f\) is reduced to \(1/2\) when the horizon \(H_{x_{i}}\) contains half the \(dV_{j}\), and \(f\) is reduced to \(4/5\) if the horizon \(H_{x_{i}}\) contains four fifths \(dV_{j}\). The PD stress \(\sigma_{x}\) for the point \(x_{i}\) can be expressed as  
\[\sigma_{x}=\frac{1}{A_{i}}\sum_{j=1}^{N_{M}}\left(f_{x}V_{j}\right)V_{i} \tag{15}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Horizon \(\delta\)** & **Numerical Micromodulus \(c_{1}\)** \\ \hline
2\(\Delta\)x & \(\frac{E}{2\Delta x^{4}}\) \\
3\(\Delta\)x & \(\frac{2E}{3\Delta x^{4}}\) \\
4\(\Delta\)x & \(\frac{E}{8\Delta x^{4}}\) \\
5\(\Delta\)x & \(\frac{2E}{25\Delta x^{4}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Numerical micromodulus \(c_{1}\).  
Figure 4: Bonds acting on the cross-section \(A_{i}\left(\delta=2\Delta x\right)\).  
where \(N_{M}\) is the number of bonds.  
The definition of bond elongation is similar to the concept of strain \(\epsilon_{k\quad(k=x,y)}\) in CCM. Thus, \(s=\epsilon_{k\quad(k=x,y)}\) and the PD stress \(\sigma_{x}=\sigma_{y}\) due to the isotropic expansion. The relationship between the PD stress and the bond elongation, \(s\), can be rewritten as Equation (16) by using [PERSON]'s law of linear elasticity.  
\[E=\frac{\sigma_{x}}{s}-\frac{\
u\sigma_{y}}{s} \tag{16}\]  
It is worth noting that the BBPD restricted to a material with a fixed Poisson's ratio (\(\mathbf{v}=1/3\) for the plane stress problem). Thus, the numerical micromodulus \(c_{2}\) can be calculated by substituting \(\mathbf{v}=1/3\), Equations (14)-(16), which are illustrated in Table 2 for different horizon radii.  
In summary, a preliminary exploration of numerical micromoduli \(c_{2}\) is carried out based on the isotropic deformation. The great significance lies in addressing the existing research gap and providing a new insight for the numerical method application of PD. Further research, such as in the nonuniform deformation condition, will be needed in future.
# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
### Current Challenges and Research
Another research direction for lane and road edge detection has addressed the specific combination of encoder, residual network, and densely connected network architectures [25]. The method itself consists of using three identical neural networks that process RGB images of road surfaces and produce binary masks for detecting road edges and markings. The method has been evaluated on a dataset of orthophotographs of Estonian highways and shows high accuracy (over 90%) for both road edge and road marking detection. In conclusion, traffic lane detection is a vital and evolving field, driven by advances in computer vision, machine learning, and sensor technologies. While significant research and progress has been made, studies must continue to address the complexity of real-world driving conditions to enhance the robustness, safety, and efficiency of future transportation systems (vehicles). This paper proposes a new method for lane detection based on the original U-Net architecture. The new architecture is called hybrid U-Net and incorporates several carefully designed components/modules: geometric skip connection, spatial attention module, multi-scale detection module, and transformer encoder. The integration of these components/modules was based on the benefits that each of them offers independently in increasing the robustness of the lane detection process. Through this innovative combination of modules, an improvement in detection accuracy was achieved, demonstrating the effectiveness and robustness of the proposed algorithm. The results obtained are promising, demonstrating on all three datasets used (TuSimple, Carla and TuSimple+Carla) that hybrid U-Net achieved superior scores on the mIoU and F1 score parameters. The structure of the article is organized as follows. Section 1 presents the general notions about ADAS systems with a focus on the process of traffic lane detection. Section 2 analyzes the previous research in the field of lane detection in light of the current challenges in the field. In Section 3, an overview of the original U-Net architecture, the newly proposed hybrid U-Net architecture, and the modules integrated in the latter is provided. Section 4 includes the presentation of the experimental results and their analysis through discussions. Finally, in Section 5, the article concludes by presenting the results of this approach and discussing potential directions for future research.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 4 Experiments
### Experimental Results
#### 4.3.6 Waste Ratio Results of the Proposed and Conventional Methods  
Figure 13 shows the waste ratio that was prefetched but not used by the client. Figure 13a,b show the results of the conventional and proposed methods, respectively. The proposed method had an average waste ratio of 50.25%, and it was lower than that of the conventional method (66.28%). The proposed method initially exhibited a low waste ratio. Prefetching did not proceed because of the lack of user data and unpredictability. The amount of prefetching content increased as the user data accumulated, and this resulted in a waste ratio that increased to approximately 50% and was maintained.
## 4 Proposed Model
### Formatting of Mathematical Components  
A residual VGG-based Siamese network model is proposed in this study. The model architecture consisted of two identical neural networks, and two samples were inputted as a combination into the model simultaneously each time. The sample pair had the same or different classes of labels. Afterward, two identical neural network models performed feature extraction and exported low-dimensional vectors. Then, the distance between the two low-dimensional vectors was calculated by using the multiple similarity distance equation, which was sent to the fully connected layer at last to obtain the probability distance, representing the similarity between two input samples. The two models had identical weights during training. An image is sent through one of the neural networks,where the contrastive loss function and optimized model parameters enhance classification accuracy, allowing the model to effectively classify even previously unseen samples.  
In Ref. [39], the contrastive loss function used by the Siamese network could effectively calculate the resulting loss of the same or different labels classified by the neural network. When the class labels were identical and the distance measure was large, the contrastive loss function value was large; when the class labels were different and the distance measure was small, the contrastive loss function value was large. This loss function effectively captures the model's matching degree during training, enabling the optimization process to facilitate learning with fewer samples and ensuring convergence. The contrastive loss function is expressed in Equation (6) [39]:  
\[C_{loss}(Y,\ MSD_{w})=0.5(1-Y)(MSD_{w})^{2}+0.5(Y)[max(0,m-MSD_{w})]^{2} \tag{6}\]  
where \(MSD_{w}\) is the multiple similarity distance. The value of \(Y\) is 0 or 1, representing the sameness or dissimilarity of two samples. When \(Y=1\) (same samples), the loss function was only \(0.5[max(0,m-MSD_{w})]^{2}\), and the \(MSD_{w}\) value was larger than \(m\) as much as possible. When \(Y=0\) (different samples), the loss function was only \(0.5(MSD_{w})^{2}\), and the \(MSD_{w}\) value was reduced as much as possible.
## 5 Results and Discussion
### Summary  
According to the previous simulations and discussions, the following points are worth noting:  
1. If all the data or the majority of data are balanced across different categories, the performance of classifier will be significantly correlated with the training data scale.
2. The SVM method can achieve a relatively high performance with small sample scales, while the KNN is not applicable in this situation. However, the KNN method has a better potential and a higher upper limit of accuracy in classification than the SVM. With increases in training sample scale, the performance of the KNN method will have a significant improvement, and the performance of SVM will stagnate after a certain degree of growth.
3. For the KNN method, the accuracy and the optimal parameter \(K\) are all mainly dependent on the size of the smallest category.
## 3 Results and Discussion
### Dynamic Pressure Components  
The dynamic pressure generated by rotational kinetic energy consists of two main components: the centrifugal and Coriolis forces. These components, governed by distinct physical principles and equations, respond differently to the operational parameters of a PRO system.  
Figure 9 presents the variations in centrifugal pressure for different draw solution concentrations as the angular velocity \(\omega\) changes. As the draw solution concentration increases, the influence of \(\omega\) on centrifugal pressure becomes more pronounced, requiring lower angular velocities to achieve a given pressure. Higher-concentration draw solutions provide a greater salinity gradient, leading to higher theoretical energy generation. However, in PRO systems, the effects of ICP are more severe with higher-concentration solutions, significantly reducing the available energy. While utilizing rotational kinetic energy to mitigate ICP consumes electrical energy, its efficient application could potentially enhance power density in PRO systems.  
Figure 8: Power density compensated for by rotational kinetic energy. The yellow area indicates half of the osmotic pressure.  
Figure 7: ICP waterflow losses compensated for by a fixed angular velocity with variations in applied _PD_. The yellow area indicates half of the osmotic pressure.  
Regarding the Coriolis pressure component, the solution-diffusion model, which incorporates ICP and rotational kinetic energy, reveals that due to the differing paths of the feed and draw solutions in the spiral-wound modules of the PRO system, the Coriolis forces act in opposite directions and do not cancel each other out. As a result, the Coriolis pressure is primarily influenced by the flow velocity \(\overrightarrow{\
u}\) and is not affected by the draw solution concentration. Figure 10 illustrates this behavior.
# Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach
## 2 Augmented Huber Non-Convex Penalty Regularization  
### Sparse Representation  
Generally, estimating a sparse component \(x\) from its noisy observation \(y\) can be expressed as  
\[y=\mathbf{A}x+w \tag{1}\]  
where \(w\) is the additive noise. It should be noted that Equation (1) belongs to a highly underdetermined equation, i.e., ill-posed or non-deterministic polynomial-time hard (N-P hard) problem [26], and there are an infinite set of solutions. Convex and non-convex optimization approaches are commonly used to estimate a sparse component from its noisy signal. A suitable optimization problem for estimating \(x\) is  
\[\hat{x}=\underset{x}{\arg\min}\{\frac{1}{2}\|y-\mathbf{A}x\|_{2}^{2}+\lambda\|\mathbf{ D}x\|_{1}\} \tag{2}\]  
where \(\lambda\) is regularization term parameter and \(\mathbf{D}=\left[\begin{array}{cccc}-1,&1&&&\\ &\cdots&&&\\ &&\cdots&\\ &&&-1,&1\end{array}\right]\),\(\mathbf{D}\in\mathbb{R}^{(N-1)\times N}\), which controls the sparsity of the approximating value of \(x\). The above problem is well-known as the L1-norm minimization, and the L1-norm is used as a convex function for sparsity. If the signal \(x\) is a sparse component, i.e., most of the amplitude values in \(x\) tend to zero, then the problem in Equation (1) can be solved by L1-norm fused lasso optimization (LFLO) algorithm, i.e.,  
\[\hat{x}=\text{argmin}\{\frac{1}{2}\|y-Ax\|_{2}^{2}+\lambda_{0}\|x\|_{1}+ \lambda_{1}\|Dx\|_{1}\} \tag{3}\]  
where \(\lambda_{0}\) and \(\lambda_{1}\) are regularization parameters. The solution of the LFLO algorithm can be given by a soft-threshold function [27], i.e.,  
\[x=\text{Soft}(\text{tvd}(y,\lambda_{1}),\lambda_{0}) \tag{4}\]  
where \(\text{tvd}(\cdot,\cdot)\) is the total variation de-noising (TVD) model [28; 29; 30] and the soft-threshold function is defined as follows:  
\[\text{Soft}(x,\lambda)=\left\{\begin{array}{cc}x+\lambda,&x<-\lambda\\ 0,&-\lambda\leq x\leq\lambda\\ x-\lambda,&x>\lambda\end{array}\right. \tag{5}\]
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithms Based on the In-Network Control Mode  
CC algorithms based on the in-network control mode are also called explicit CC algorithms, and the router informs the data sender of the congestion status in the network. They mainly include the ECN protocol, the XCP protocol, the RCP protocol, and so on.  
The explicit congestion notification (ECN) protocol is a standardized explicit control technology. The ECN mechanism informs the sender in advance whether the current link is congested, thereby avoiding packet loss caused by router buffer overflow. Its main disadvantage is that the ECN protocol can only inform the sender whether the link is congested but cannot tell the congestion degree of the link. As a result, the sender cannot accurately control the transmission speed, resulting in insufficient link bandwidth utilization [21].  
The explicit control protocol (XCP) is a rate control protocol designed by the Massachusetts Institute of Technology (MIT) in which the router directly feeds the window value to the data sender. The XCP protocol informs the data sender to reduce or increase the window by calculating the load of the link. The XCP protocol adds a 20-byte packet between the IP header and the TCP header, which means that all routers and receivers on the network must support the XCP protocol, which is obviously impossible. In addition, the control of the XCP protocol depends on the estimation of the number of flows, but the number of flows is constantly changing, and its estimation is inaccurate, which will cause network jitter and low link bandwidth utilization [22; 23; 24].  
The RCP (rate control protocol) [25] is an explicit rate control protocol designed by Stanford University. Its core idea is to assign an initial rate to the connection in the TCP handshake stage and then update the rate according to the stream data and feed it back to the data sender. The disadvantage of the RCP protocol is that RCP can only be run on customized RCP routers [26; 27] and cannot be run on general routers, so its deployment is difficult, which is also the reason why RCP is not widely used in the network. Similar to the XCP protocol, the number of current data streams cannot be accurately estimated, resulting in a decrease in bandwidth utilization.
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 3 Methods
### Determining Thermal Conductivity, Specific Heat, and Density  
Thermal conductivity is a vital factor for determining the temperature inside the Earth [28; 29]. In this study, a Nanoflash instrument (model LFA-447 of Netzsch Co., Germany) equipped with an infrared thermometer was used to measure the thermal diffusivity, by transferring heat from the bottom side to the top side of the rock sample. The LFA-447 was calibrated with a standard sample (pyroceram 9606) of thermal diffusivity of 1.926 mm\({}^{2}\)/s at 25 \({}^{\circ}\)C; the measurement error is no greater than \(\pm\)3% between the measured value and true value.  
The thermal conductivity of a dried rock sample can be estimated as follows:  
\[\kappa=\rho\cdot D\cdot c_{p} \tag{7}\]  
where \(\rho\) denotes the bulk density of the sample, \(D\) denotes the thermal diffusivity, and \(c_{p}\) denotes the specific heat. The dry sample is prepared in a vacuum dryer for 24 h.  
The thermal conductivity changes significantly according to the dried/saturated state and porosity of the sample. The weighted geometric mean of the thermal conductivity of a saturated sample can be expressed as follows [30]:  
\[\kappa_{sat}=\kappa_{water}\;^{n}\cdot\kappa\;^{1-n} \tag{8}\]  
where \(\kappa_{water}\) denotes the thermal conductivity of water (0.62 W/mK), \(\kappa\) denotes the thermal conductivity of the dry sample, and \(n\) denotes the porosity (used to determine the thermal conductivity of the saturated natural rock). The porosity was determined from the volume differences between saturated and dried samples. [PERSON] and [PERSON] [31] assumed a constant specific heat of 4.2 kJ/kgK and density of 1000 kg/m\({}^{3}\) for estimating the vertical flow velocity. In contrast, in this study, specific heat and density values were applied for different temperature intervals of the temperature profile in the boreholes, as shown in Table 1.  
Figure 2: Conceptual model of groundwater flow in fractured media (modified after Ge [27]).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Borobide** & \begin{tabular}{c} **Gradient** \\ **(C/Ams)** \\ \end{tabular} & \
\begin{tabular}{c} **Depth** \\ **Interval** **(m)** \\ \end{tabular} &
\begin{tabular}{c} **L
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 3 Kubernetes Architecture
### Node Components  
Worker nodes are the physical or virtual machines that execute application workloads. Each node is outfitted with components that interact with the control plane and guarantee the integrity of container execution.  
* Kubelet: The principal node agent that registers the node with the control plane and guarantees the operational integrity and health of the containers specified in a pod definition.  
Figure 1: Kubernetes architecture, [[https://commons.wikimedia.org/wiki/File:Kubernetes.png](https://commons.wikimedia.org/wiki/File:Kubernetes.png)]([https://commons.wikimedia.org/wiki/File:Kubernetes.png](https://commons.wikimedia.org/wiki/File:Kubernetes.png)), accessed on 23 April 2025.  
* Container Runtime: responsible for initiating and overseeing container lifecycles. Kubernetes accommodates multiple runtimes, such as containerd, CRI-O, and, previously, Docker.
* Kube-proxy: manages network routing on every node. It facilitates internal communication among services and pods across nodes and external communication when exposed through an ingress.  
Collectively, these node-level components facilitate the deployment and execution of containers in alignment with control plane directives.