esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Detection Model for Apparent Defects in Metro Tunnel Liniings
#### 3.1.4 The Proposed Defect Detection Model for Subway Tunnel Linings  
The proposed model framework for detecting apparent defects in metro tunnel linings is shown in Figure 7. The model consists of three parts: the feature extraction network (backbone), the feature fusion network (neck), and the prediction network (head). The CA attention mechanism is embedded in the 6 th, 9 th, and 12 th layers of the feature extraction network, allowing the model to focus on and highlight defect-prone areas in feature maps of different scales, thereby improving defect detection accuracy. Additionally, the BoT3 global feature extraction module is added after the SPPF module to increase the connections between defect-prone areas and other target areas in the global features of the high-dimensional feature layers. This approach helps to uncover the correlations between apparent defects in the linings and other targets, obtaining more feature information. In actual metro tunnel scenarios, the target sizes of auxiliary facilities (e.g., cables, pipes, and fixtures) are relatively large, while the sizes and positions of bolt holes and grouting holes are relatively fixed and moderate in scale. Among the categories of apparent defects, leakage areas are relatively large, but exfoliation and cracks vary in size. In particular, for small-scale fine cracks, the detection effectiveness is limited, indicating that the detection images contain complex targets of different scales. The original YOLOv8 model includes three output detection layers. When the input detection image size is 640 \(\times\) 640, the corresponding detection layer feature map sizes are 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20. Given the small size of fine defects, detecting small target defects requires higher resolution and more accurate positioning information, with a greater emphasis on the ability to express image details. In model calculations, shallow feature maps have relatively high resolution and contain clearer defect locations and structural information. Therefore, fully integrating  
Figure 6: The calculation process of the MHSA [35].  
and utilizing shallow, high-resolution feature maps can improve the detection of small target defects.  
The loss function calculation of the model includes class loss and position loss. Class loss measures the difference between the predicted class of the bounding box and the true class, calculated using binary cross entropy (BCE). Position loss measures the distance between the predicted box and the true box, which consists of bounding box regression loss (bbox) and confidence loss (dfl). In this experiment, bounding box regression loss uses CIoU loss, while confidence loss uses distribution focal loss (DFL). The final loss function calculation is the weighted sum of these three components, as follows:  
\[Total\ Loss=\alpha Loss(cls)+\beta Loss(\text{bbox})+\gamma Loss(dfl) \tag{7}\]
# Compassionate Embodied Virtual Experience Increases the Adherence to Meditation Practice
## 3 Results
### Efficacy of the In-Person Session Practices
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**VR (\(n\) = 21) a**} & \multicolumn{2}{c}{**Audio (\(n\) = 20) a**} & \multicolumn{2}{c}{**_t-Student_**} \\ \cline{2-7}  & **Pre** & **Post** & **Pre** & **Post** & \(t\) & \(p\) & \(\eta^{2}\) \\ \hline SMS (Total Score) & 74.65 (14.28) & 85.29 (11.73) & 67.30 (13.34) & 82.68 (11.29) & \(t\) (38) = \(-\)1.68 & 0.101 & 0.07 \\ SMS (Body subscale) & 20.86 (4.70) & 22.43 (4.14) & 18.05 (4.65) & 21.30 (5.30) & \(t\) (39) = \(-\)1.92 & 0.062 & 0.09 \\ SMS (Mind subscale) & 53.80 (10.22) & 62.86 (9.10) & 49.25 (10.17) & 61.16 (7.48) & \(t\) (38) = \(-\)1.41 & 0.166 & 0.05 \\ VAS-SC & & & & & & \\ (Self-compassion) & & & & & & \\ VAS-SC (Positive & & & & & \\ affiliiative affect) & 23.44 (3.14) & 23.33 (3.74) & 21.85 (5.72) & 20.90 (5.21) & \(t\) (39) = \(-\)1.00 & 0.324 & 0.03 \\ VAS-SC (Self-criticism) & 7.40 (1.76) & 7.05 (1.95) & 7.53 (1.73) & 7.08 (1.76) & \(t\) (39) = 0.18 & 0.859 & 0 \\ VAS-SC & & & & & & \\ (Energized level) & & & & & & \\ PANAS & & & & & & \\ (Positive Affect) & & 32.14 (5.58) & 32.53 (7.33) & 30.20 (7.54) & 32.56 (5.60) & \(t\) (39) = \(-\)0.94 & 0.353 & 0.02 \\ PANAS & & & & & & \\ (Negative Affect) & & & & & & \\ (CLS & 118.52 (16.54) & 117.63 (16.87) & 122.80 (9.75) & 121.47 (14.82) & \(t\) (39) = 1.00 & 0.322 & 0.03 \\ \hline \hline \end{tabular} Note. PANAS = Positive and Negative Affect Scale; CLS = Compassion Love Scale; SMS = State Mindfulness Scale; VAS-SC = Visual  
Analogic States for State Changes. \({}^{\text{a}}\) All values represent the means (and standard deviations in parenthesis).  
\end{table} 
Table 2: Descriptive statistics and independent-samples \(t\)-test results of baseline data.
## 4 Conclusions  
The results presented in this paper are intended to understand the potential of shear-critical HPFRC-RC members subjected to impacts. According to the results, the following conclusions can be drawn:  
1. RC beams presented brittle shear failure with significant fragmentation. Strengthening with HPFRC reduced fragmentation but types I and II cracks also formed. The beams strengthened with HPFRC layer presented an additional resisting mechanism with the HPFRC working similarly to a stress ribbon. This additional contribution was activated in the second stage of tests characterized by global behavior. The stress ribbon mechanism provided an energy absorption in the second stage (\(E_{2}\)) 2.1 and 2.7 times that of the unstrengthened beams, in the case of specimens with 35 mm and 55 mm strengthening layers, respectively.
2. Interface debonding can occur when the tensile reflected wave induced by the impact crosses the interface between concretes, as observed in the last stage of impact tests of RC-U55 series. In such series, the interface was placed at the level of the longitudinal reinforcement, which reduced the contact surface between concretes and weakened the impact behavior. Accordingly, it is recommended to avoid such design configuration.
3. In comparison with results from previous research of R-HPFRC and R-SFRC beams failing by flexure under impact, HPFRC-RC presents an intermediate behavior between them and conventional RC in terms of strength and deformation capacity.
4. Strengthening with HPFRC tensile layer presents interesting potential for existing RC structures as a new resisting mechanism provided by the HPFRC working similarly as a stress ribbon can reduce impact debris, thereby protecting humans and goods below the structure. Future research is still needed in order to quantify the contribution of the new resisting mechanism.
5. The research presented in this paper has shown that a structural solution for existing shear-critical RC members susceptible to suffer impacts can be based on the application of a thin HPFRC layer on the tensile side. In order to achieve the stress ribbon mechanism found for this structural solution, structural designers are recommended to consider the following guides:  
1. A fiber amount of at least 2% for the HPFRC to achieve strain hardening.
2. The interface between the old concrete and the HPFRC should not be placed at the level of the longitudinal reinforcement.
3. A surface preparation of the old concrete with hydro-jetting or sand-blasting before application of the HPFRC is recommended. In case the tensile side is at the bottom, a prefabricated HPFRC laminate can be alternatively bonded with epoxy adhesive.
## 5 Experiments and Discussion  
### Experimental System Design  
This paper constructs a forest point cloud real-time reconstruction system, as shown in Figure 4. The system includes a monocular camera, IMU, a hand-held bracket, a single-line lidar, and a computing platform. The monocular camera and the IMU use a realsense D435i device. The single-line lidar uses Rplidar A2, which has a 360\({}^{\circ}\) field of view and a 0.9\({}^{\circ}\) angular resolution. Each sensor is time-synchronized in advance, and the transformations among sensors are regarded as constant values, which are calibrated offline. The computing device adopts a PC with a quad-core 2.5 GHz frequency and an i7 processor, which uses the Ubuntu18.04 operating system and the ROS robot operating system.  
We collect data from the forest of Dongsheng Bajia Country Park in Haidian District of Beijing with the designed real-time reconstruction system of the forest point cloud. The experiments regarding point cloud densification and loopback correction are carried out separately. In addition, we conduct studies at different scenes, such as a single tree, a row of trees, and some forest areas. In the experiment, the monocular camera outputs forest image information at a frequency of 20 Hz, the IMU outputs acceleration and angular velocity information at a frequency of 200 Hz, and the single-line lidar outputs forest laser point data at a frequency of 12 Hz.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.3 Constraints Definition  
The establishment of a number of scenario-specific restrictions should precede the launch of the mapping process. One universal undisputed constraint is to ensure that the residual substrate resources are going to suffice for the mapping of the computational and networking requirements of the candidate VNR as implemented in [18]. Prior to the deployment of the link mapping phase, the respected algorithm attempts to allocate all the possible substrate paths from a source node to a destination node that is fit for the embedding of a virtual link \(l^{i}\in L^{i}\), based on the following constraint:  
\[\forall l_{S}\in L_{S},\sum_{i=1}^{r}BW(l^{i})_{l_{S}}\leq BW(l_{S}) \tag{1}\]  
As (1) explicitly suggests, the total BW units spent in order to accommodate the demands of \(r\) virtual links, or parts of them, cannot surpass the total BW capacity of the specified \(l_{S}\in L_{S}\).  
In situations where tailored solutions are expected, algorithms may incorporate additional constraints. Such is the case of the distance constraint that thresholds the maximum permitted hops between a node of the processed VNR and the respective physical one, introduced in [22]. The authors suggest that the function implementing the mappings of virtual nodes upon substrate ones, should comply with the constraint as expressed by the following inequality:  
\[dis(loc(m),loc(n))\leq D^{V} \tag{2}\]  
Let \(m\in N^{i}\) be a virtual node of the processed VNR, and \(n\in N_{S}\) its candidate physical one. After using \(dis()\) to measure the distance between their location, which is calculated by \(loc()\), the authors then delimit its value by the non-negative constraint \(D^{V}\).  
A more combinatorial approach can be adopted, as in [18]. Here, the authors take into consideration not only the residual available computational resources of a physical node \(n\in N_{S}\) but also the remaining BW capacity of its neighboring substrate links, creating the parameter \(H(n)\), defined in (3), thus, avoiding falling into the trap of classifying the mapping candidate nodes unidimensionally.  
\[H(n)=\text{CPU}(n)\sum_{l\in L_{S}(n)}BW(l) \tag{3}\]  
While taking into account elements that could attribute a suggested solution with more real life characteristics, the hidden hop (HH) property should also be considered. It establishes the proportional consumption of CPU cycles of an intermediate substrate nodedue to its involvement in the forwarding of network packets by being part of an embedded path that aggregates more than one physical link [24, 25].
# Global IoT Mobility: A Path Based Forwarding Approach
###### Abstract
Solutions such as Line Speed Publish/Subscribe Inter-Networking (LIPSIN) [14], Stateless Multicast Switching in Software Defined Networks [15], Bit Indexed Explicit Replication (BIER) [16], and IP-over-ICN [13] rely purely on path information for end-to-end forwarding of packets instead of relying on host address-based communication with routing information distributed over various network elements. In these path-based approaches, path information can be stored in the forwarded packet (using Bloom filters) and forwarding is done by performing a simple set membership test to deliver a packet traversing the network to the final destination. Therefore, these architectures allow for efficient mobility management, specifically macro mobility support, which is becoming increasingly important for many IoT devices that need to roam across different domains. In this paper, evaluation results using an IP-over-ICN embodiment have shown promising results in terms of the packet delivery cost and signalling costs required to support macro mobility across different IoT domains. The rest of the paper is structured as follows: Section 2 provides an overview of mobility management solutions in different access technologies, while Section 3 presents the related work. Section 4 introduces mobility over path forwarding architectures, and presents the proposed macro mobility handover solution. The simulation and cost evaluation results are presented in Section 5, and, finally, the paper is concluded in Section 6.
# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 3 Measuring and Characterizing Perception Workloads on the Edge
### Features and Formulation  
To assess how incoming AV workloads will affect available edge nodes, we consider two sets of variables: the inputs from AVs and the resources available from edge nodes. We define the set of AV inputs as \(V=\{v_{1},v_{2},\dots\}\) and the set of edge nodes as \(E=\{\eta_{1},\eta_{2},\dots\}\). Each element \(v_{i}=\{v^{i}_{\text{data}},v^{i}_{\text{task}}\}\) and \(\eta_{i}=\{\eta^{i}_{\text{conf}},\eta^{i}_{\text{queue}}\}\) is monitored and managed by the Prediction Module.  
As depicted in Figure 1, upon receiving input requests from vehicles, the edge nodes within the service range can process these tasks. Each edge node evaluates the incoming workload requests and determines its capacity to handle them. The nodes then communicate their task requirements and resource availability to the edge manager, which efficiently allocates the workload to an edge node capable of completing the task within an acceptable timeframe.  
To formulate potential edge configurations, we define the following key variables: processor resource, RAM as the memory resource, workload size (derived from the requested service and input size), and time (as the quantifying metric).
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 3 Construction of EB-YOLOv8
### Constructing Weighted Fusion Splicing Module to Realize Multi-Scale Feature Fusion
Compared to standard additive fusion, its special advantage lies in its channel dimension protection mechanism. Additive operations require the forced unification of channel dimensions, leading to the loss of critical features of small targets during convolution compression; in contrast, weighted concatenation preserves the original channel information, improving the contour integrity of sub-millimeter defects by nearly 40%.
## Results
### Orange TSS Detection by Fusion Data of VIS/NIR Spectrometer and E-nose
#### 3.3.2 PCA-BPNN Detection  
The BPNN was applied to further improve the detection accuracy of orange TSS based on fusion data. However, there were 786 features in total for the fusion data, which results in an input layer of 786 neurons, which in turn makes the computation burden beyond most of today's computer. Thus, before BPNN operation, PCA was applied for dimensionality reduction of input data. As shown in Figure 9a, the total contribution of the first 6 principle components (PCs) almost reaches 100%. This means that the first 6 PCs contain the majority of the information from all 786 features which were used for the next BPNN modeling. After repeated training, the BPNN model parameters were as follows: the nerve cell number of the hidden layer was 21, the treatments utilized the BPNN \"trainlm\" training algorithm, the treatments had a BPNN hidden layer number of four, the BPNN output layers used the \"tansig\" function; and the BPNN iteration number and sampling frequency was 1000 and 25, respectively. The correlation coefficient (R\({}^{2}\)) also was applied to evaluate the correlation between the predicted value and actual value of BPNN detection. The PCA-BPNN detection results are shown in Figure 9b, the R\({}^{2}\), RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively. Thus, PCA-BPNN has good orange TSS detection ability based on fusion data.  
Figure 8: PLSR detection results of TSS based on fusion data.  
Figure 9: Principal component analysis (PCA)-BPNN detection results of TSS based on E-nose with combined feature extraction method. (**a**): the contribution rate distribution of principal component (PC); (**b**) BPNN detection results.
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 2 Methods
### Color Restoration Network  
The low-light image enhancement algorithms based on Retinex theory often overlook color correction and noise removal. Therefore, this paper proposes a color restoration network module based on the 3D LUT theory. Figure 4 illustrates the network architecture of the color restoration module. The input image on the left side of the network is the preliminary experimental result enhanced by the preceding two modules. The CNN weight predictor generates the corresponding weight values. The input image is then processed through weighted fusion and 3D LUT to achieve color restoration. The output image is compared with the reference image to compute the loss function and optimize the network parameters, continuously improving the color restoration performance of the network.  
The low-light images, after being processed by the image decomposition network, are decomposed into illumination maps and reflectance maps. However, the reflectance maps often contain complex noise. To address this issue, a denoising network is incorporated into the module. By learning the noise patterns in the input images, the network aims to preserve the details while effectively removing the noise. Figure 5 illustrates the structure of the denoising network.  
Figure 3: Structure of illumination map enhancement network.  
After the enhancement by the color restoration module, the input image is compared with the reference image to calculate the loss, aiming to improve the module's enhancement effect. The color restoration network module adopts a loss function denoted as \(J_{\text{Rec}}\) and given by Equation (9):  
\[J_{\text{Rec}}=\frac{1}{M}\underset{i=1}{\overset{M}{\sum}}|Q[S(x,y)]-GT(x,y)| \tag{9}\]  
In the equation, \(M\) represents the number of pixels, \(S(x,y)\) represents the preliminary enhanced result after the illumination enhancement module, \(Q[S(x,y)]\) represents the output image after the color restoration module, and \(GT(x,y)\) represents the reference image under normal lighting conditions.
## 3 Remote Sensing with CM-CLaDS
### System Characterization
The received laser power has changed by more than 5% during the measurement (mainly due to line-locking feedback that adjusted the laser current to compensate for laser frequency drift caused by ambient temperature fluctuations). This drift is clearly visible in the Allan Variance calculated for the beat-note power in Figure 6. However, due to the CLaDS signal immunity to power variations, those drifts did not affect stability of the CM-CLaDS system. Based on the Allan plot the CM-CLaDS signal can be effectively integrated for up to one thousand seconds and it does not show a drift that is clearly observed in the Allan plot for the RF power (power drift starts dominating at integration times longer than 20 s). The ultimate SNR of 1,000 that is obtained for the maximum integration time tested (1,000 s) yields a minimum detection limit of 15 ppbv\(\times\)m. It should be noted that the system performance in its current configuration is far from optimal because the frequency split achievable with commercially available AOMs (\(<\)100 MHz) is not sufficient to match the observed transition line width (\(\sim\)3 GHz at atmospheric pressure). The signal loss due to non-optimal frequency splitting (100 MHz in this work) reduces the signal by \(\sim\)7 times with respect to an optimized signal at 300 Torr and by \(\sim\)16 times with respect to an optimized signal at atmospheric pressure. In addition to this signal loss, the 5% data acquisition duty cycle results in \(\sim\)\(\sqrt{20}\) times higher noise. Based on this stability test a bandwidth- and optical-path-normalized detection limit to N\({}_{2}\)O interpolated for conditions relevant for remote open-path atmospheric sensing is \(\sim\)1.2 ppmv\(\times\)m/Hz\({}^{1/2}\) (current system specifications have been used). This can be improved to below 50 ppbv\(\times\)m/Hz\({}^{1/2}\) with an optimum frequency shifter and a 100% duty cycle data acquisition. However, despite the performance limitations, the current system can still achieve the single-ppbv N\({}_{2}\)O detection limits required for atmospheric studies when implemented with longer (\(>\)50 m) optical paths. Figure 6: The Allan variance analysis (**A**) was performed for: (**B**) a time series of CM CLaDS signal (normalized to the first data point) acquired for 151 ppmv N\({}_{2}\)O in N\({}_{2}\) mixture in a 10 cm gas cell at 300 Torr, and (**C**) a received RF power series that was acquired simultaneously with data in (**B**). Active locking of the laser frequency to the peak of the transition at 2,207.6 cm\({}^{-1}\) was enabled during this measurement. A dashed magenta line in (**A**) shows a theoretical performance of a random noise-limited system.
## 5 Challenges and Future Directions
### Generalization  
One of the key challenges in deploying GraphNDTs for cost-effective trials in networks is ensuring their generalizability across various scenarios and configurations, including disruptive events that may lead to service disruptions. In zero-tolerance systems, exploring these disruptive scenarios is impossible. As a data-driven approach, GraphNDTs rely on training data to make accurate predictions, but without access to data from such scenarios, they may struggle to accurately predict and simulate disruptions in networking systems.  
To address this challenge, two potential solutions can enhance the generalizability of GraphNDTs. On the one hand, we can train GraphNDTs on additional data generated from simulations or testbeds that simulate a wide range of network scenarios and configurations [109; 110; 140]. By being exposed to diverse simulated disruptive events,GraphNDTs can learn to recognize patterns and dynamics associated with service disruptions. Training on additional data helps GraphNDTs capture the complexities of disruptive scenarios and improves their ability to generalize to unseen disruptions in zero-tolerance systems. After that, we can leverage transfer learning techniques to finetune the pre-trained GraphNDTs to the actual network systems being twinned. This fine-tuning process allows GraphNDT ability to adapt its predictions to the specific dynamics and characteristics of the target system, leveraging the prior knowledge acquired during training on the additional data. On the other hand, the aggregation of different GraphNDTs can also contribute to generalization by capturing the knowledge of the same application (e.g., routing, network slicing) across different networking systems. However, personalization becomes a key challenge due to the varying characteristics of different networking systems, which can be addressed by employing FL techniques. FL enables collaborative learning and model aggregation, preserving the privacy of individual network systems [129; 130; 131] while allowing GraphNDTs benefit from a broader understanding of various configurations.