esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Computational Overheads
#### 6.1.2 Average Delays under Unknown Attacks  
Although the 5 GSBA could resist several malicious attacks, as shown in the security analysis, it is possible that new unknown attacks in the 5G network could interrupt the authentication process. To evaluate the performance under unknown attacks, it was assumed that, for each step of the protocol, the network faced either known or unknown attacks. Specifically, there will be a probability that the protocol will encounter an unknown attack. If the incoming attack is known, the protocol should continue smoothly until completion. However, if the incoming attack is unknown, the protocol would be unavoidably interrupted. In this case, it must restart from the first step until it completes the last step. Based on the assumptions above, we created a simulation model on MATLAB 2020b. Using 1 million threads to run the protocol, we found the average execution time to complete the protocol under different unknown attacks, as shown in Figure 6. The results showed that although the 5 GSBA had slightly higher delays (from 1.67 to 4.17 milliseconds) than those of the 5G-AKA [4] in all scenarios, its performance was still much better than another blockchain-based protocol of the BB-AKA 5G [10]. Considering that the 5 GSBA ensured  
Figure 5: Experimental total computational delays.  
the best security among all protocols, we can still conclude that it can achieve a reasonable balance between high security and reasonable delays.
# Evaluating the Capacity Coordination in the Urban Multimodal Transport Network
## 2 Methodology
### Topology of Urban Multimodal Transport Network  
The term \"network\" is commonly used to describe a structure that can be either physical or conceptual. Each of these networks includes two types of elements: a set of points and a set of line segments connecting these points. This observation leads to the mathematical definition of a network as a set of nodes and a set of links connecting these nodes [13].  
In this paper, the urban multimodal transport network consisted of roadway network, transit networks (bus sub-transport network, urban rail transit sub-transport network), and a connection network, in which we considered five typical transport modes (car, bus, metro, bus-to-metro, and car-to-metro).  
In the case of the roadway network, there is only one link between two nodes, in which intersections are regarded as nodes. Similarly, transit nodes are composed of a different set of characteristics than highway nodes. The nodes are called stops, and the lines are called links or route segments. Links in a multimodal transit network have different characteristics from those in a road network. While a link in a road network is a physical segment that connects one node to another, a link in a multimodal transit network is part of the transit line that serves a sequence of transit stops (nodes).  
Transfer hubs are groups of nodes that are defined by the ease of transfer between transit lines or modes. Corresponding to the typical combined transport modes, bus-to-metro and car-to-metro, this research defined two types of transfer hubs (bus-metro transfer hub and park and ride (P + R) hub) as nodes in the transfer process, and the group of links among transfer hubs were regarded as the transfer virtual links. Figure 1 displays the schematic topology of the urban multimodal transport network.
## 4 Results and Discussion
### Model Stability Analysis  
During logging interpretation, the generated variable density image may be disturbed and distorted. In order to test whether the multi-scale perceptual convolution neural network can accurately identify, this paper explored the anti-interference of the model. To verify the stability of the model [23], this paper added 3%, 6%, and 9% white noise to the variable density image, as shown in Figure 13. Six different convolutional neural networks were used to identify the cementing quality. The operation results are as follows:  
It can be seen from Figure 14 That, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network established in this paper has a stronger anti-interference ability. This is because this model has convolution cores of different sizes, which can extract image features from multiple scales, and fuse the features, resulting in a better image representation and stronger anti-interference ability. Therefore, even if the variable density logging image is distorted and has noise, the multi-scale perceptual convolution neural network proposed in this paper can still accurately evaluate the cementing quality.  
Figure 12: Comparison of the complexity of different models.  
Figure 13: Adding white noise to the original image.
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 3 Methods and Principles  
### The Principle of YOLOv5  
YOLO (You Only Look Once) [11] is a target detection algorithm based on the deep neural network, which runs fast and can meet the requirements of real-time detection. The overall network structure of YOLOv5 and YOLOv4 is similar. It contains four models, namely YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The model complexity and depth increase in turn. In this paper, YOLOv5s is selected as the basic model. Its network structure mainly includes four parts: the input terminal 'Input', the backbone network 'Backbone', the neck 'Neck', and the detection head 'Head', as shown in Figure 1.  
Input: The data samples are randomly scaled, cropped, and arranged in the Mosaic method to enhance the data; the prediction frame is compared with the pre-labeled frame, and the difference is calculated, and then the network parameters are updated in reverse to independently calculate the maximum value of each training. Best anchor box: reduce redundant information and improve inference speed through adaptive image scaling.  
Backbone: This part can extract image features, including the C3 module, to speed up inference and the spatial pyramid pooling module SPPF. The C3 in the Backbone part uses the C3\(X\)1 structure.  
Neck: This is the feature fusion part, consisting of C3 and PANet [31] modules. The PANet module contains a Feature Pyramid Network (FPN [32]) and Pyramid Attention Network (PAN [33]). FPN is a top-down upsampling fusion feature pyramid, and PAN is a bottom-up downsampling fusion feature pyramid. The C3 in the Neck part uses the C3\(X\)2 structure.  
Head: This part is the prediction part of the network, using weighted non-maximum suppression (NMS [34]) to filter the target box, using CIoU Loss [35] as the bounding box loss function.  
Figure 1: Structure of YOLOv5s.
## 2 Testbed Setup and Measurement Methodology  
Experimental setup and measurement approaches description for both Timing and AvB measurements are described in this section. Experiments were performed on the 40 Gbps WAN lab of the Future Internet Lab Anhalt of Anhalt University of Applied Sciences.
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
## 2 System Description  
The basic idea of an RTG is the functionality verification of a radar sensor for different targets at several distances like in a realistic scenario. In the case of the level probing radar considered here, liquid levels of media with low reflectivity like palm oil as well as with high reflectivity like water should be emulated at distances from 20 cm to 10 m. The targets and the dielectric waveguide should be designed for a bandwidth of 20 GHz at the center frequency of 160 GHz. Thereby, losses, reflectivity, and the propagation velocity should be kept constant in this frequency range. Furthermore, the system should be compact for a flexible usage.  
The radiated signal of the radar under test is coupled into the presented RTG with a rectangular waveguide interface as shown in Figure 1. The compact design even for large distances is achieved with a meandered flexible dielectric waveguide, which is fed by the rectangular waveguide with a mode transformer (MT). The dielectric waveguide is stabilized with several holders and the targets are clamped on the dielectric waveguide. The dielectric waveguide is terminated with an absorber to avoid reflections from its end.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion  
### Discussion on European Option Pricing  
#### 7.1.1 Performance  
In this section, we present the results of our KAFIN-based approach for European option pricing, emphasizing its effectiveness in comparison to traditional baselines. As depicted in Figure 1, KAFIN's predictions demonstrate a close alignment with the analytical solutions, which serve as the benchmark for evaluating model accuracy. This strong performance suggests that KAFIN is proficient in capturing the underlying dynamics of option pricing, which is a task known for its inherent complexity. Further comparative analysis is provided in Table 3, which summarizes the residual statistics between the analytical solution and the predicted solutions across various models. Notably, KAFIN exhibits the least residual values across all metrics, including mean, standard deviation (STD), minimum, absolute minimum, maximum, absolute maximum, and mean squared error (MSE). Specifically, the mean residual of KAFIN is \(-0.2532\) with an STD of \(0.9578\), indicating that its predictions are not only close to the benchmark but also demonstrate minimal variability. This stability is particularly crucial in financial applications, where precision can significantly influence decision-making. In contrast, the baseline models, including FINN REG, FINN FFN, FINN DNN, and FINN MLP, show substantially larger residuals. For instance, FINN REG has a mean residual of \(-16.2222\), with an STD of \(23.7148\), highlighting a considerable deviation from the analytical solution. Such discrepancies suggest that these models either struggle to generalize from the training data or fail to adequately capture the nuances of option pricing dynamics. The visualization in Figure 3 reinforces these findings, illustrating the residuals of KAFIN bounded within the range of \([-3,+3]\). This range indicates a high degree of accuracy and consistency in KAFIN's predictions. Conversely, the other methods exhibit broader
## 8 Strut-and-Tie Model Development
### Vertical Tension Tie  
Lastly, the capacity of vertical tension tie includes the contribution from three parts, namely, tensile strength of web concrete, yield strength of stirrups and strength of FRP laminate. Since mortar repair did not recover the shear capacity of both small- and full-scale beam, it is conservative to ignore the contribution from mortar. Equation (4) is proposed to calculate the capacity of the vertical tension tie.  
\[F_{n\
u}\;=\;f_{tc}l_{c}b_{w}\;+\;f_{y}A_{sv}\;+\;\beta_{re}t_{FR}l_{FR}E_{FR }e_{FRP} \tag{4}\]  
where  
\(A_{sv}\) = total cross-sectional area of vertical stirrups crossing the inclined shear crack;  
\(E_{FRP}\) = Young's modulus of the FRP laminate;  
\(F_{n\
u}\) = nominal strength of vertical tension tie;  
\(b_{w}\) = width of girder's web;  
\(f_{tc}\) = tensile strength of concrete;  
\(f_{y}\) = yielding strength of vertical stirrups;  
\(l_{c}\) = projection of inclined crack along the longitudinal direction of the girder;  
\(l_{FRP}\) = width of FRP laminate crossed by the inclined shear crack;  
\(t_{FRP}\) = thickness of one FRP laminate;  
\(\varepsilon_{FRP}\) = effective strain of FRP laminate;  
\(\beta_{re}\) = reduction factor that accounts for the bond between the mortar layer and base concrete.  
To calculate the effective strain of the FRP laminate, the method proposed by ACI 440.2R [25] was adopted. Following Equations (5)-(8) illustrate the process to obtain \(\varepsilon_{FRP}\).  
\[\varepsilon_{FRP}=\;\frac{k_{1}k_{2}L_{e}}{11,900}\leq 0.004 \tag{5}\]  
\[L_{e}=\;\frac{23,300}{(n_{FRP}t_{FRP}E_{FRP})^{0.58}} \tag{6}\]  
\[k_{1}=\;\left(\frac{f^{\prime}}{27}\right)^{\frac{3}{3}} \tag{7}\]  
\[k_{2}=\;\frac{d_{fv}\;-\;2L_{e}}{d_{fv}} \tag{8}\]  
where  
\(L_{e}\) = active bond length of FRP laminates;  
\(d_{fv}\) = effective depth of the FRP laminate;  
\(k_{1}\) = modification factor applied to account for concrete strength;  
\(k_{2}\) = modification factor applied to account for wrapping scheme;  
\(n_{FRP}\) = modulus ratio of elasticity between FRP and concrete.  
It is worth noting that the reduction factor, \(\beta_{re}\) was introduced to adjust the effectiveness of FRP repair based on the type of damage and the quality of the bond between mortar and base concrete. In this study, the factor \(\beta_{re}\) was determined through the calibration process using the test results of the CFRP repaired small-scale PC beam [8] and full-scale PC girder tests.  
Eventually, the shear capacity from STM is determined using the following equation:  
\[P_{STM}\;=\min(F_{\rm ms}sin\theta,\;F_{nlt}tan\theta)\;+\;F_{n\
u} \tag{9}\]  
where  
\(P_{STM}\)= shear capacity from STM;  
\(\theta\) = inclination angle between diagonal strut and longitudinal direction.
# Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
## 4 Generalized Low-Rank Tensor Recovery
### Optimization Algorithm to GLRTR
If \(\left\{\mathbf{\mathcal{X}},\mathbf{\mathcal{M}},\mathbf{\mathcal{E}},\mathbf{\mathcal{G}}\right\}\) are fixed, we can update the Lagrangian multipliers \(\mathbf{\mathcal{Y}}\) as follows:  
\[\mathbf{\mathcal{Y}}_{n}:=\mathbf{\mathcal{Y}}_{n}+\mu\left(\mathbf{\mathcal{X}}-\mathbf{ \mathcal{M}}_{n}-\mathbf{\mathcal{G}}-\mathbf{\mathcal{E}}\right),\,n=1,2,\ldots, \mathrm{N}. \tag{16}\]  
Once \(\left\{\mathbf{\mathcal{X}},\mathbf{\mathcal{M}},\mathbf{\mathcal{E}},\mathbf{\mathcal{G}}, \mathbf{\mathcal{Y}}\right\}\) are updated, we will increase the value of \(\mu\) by multiplying it with a constant \(\rho(>\ 1)\) during the procedure of iterations. The whole iterative procedure is outlined in Algorithm 1. The stopping condition of Algorithm 1 is set as \(\underset{\mathbf{1}\in\mathbf{n}\in\mathbf{n}\in\mathbf{N}}{\max}\left|\left|\mathbf{\mathcal{X}} -\mathbf{\mathcal{M}}_{n}-\mathbf{\mathcal{G}}-\mathbf{\mathcal{E}}\right|\right|_{\mathrm{ F}}^{2}<\varepsilon\) or the maximum number of iterations is reached, where \(\varepsilon\) is a sufficiently small positive number. ```
Input: Data tensor \(\mathbf{\mathcal{D}}\), sampling index set \(\mathbf{\Omega}\), regularization parameters \(\mathbf{\lambda}\) and \(\mathbf{\tau}\). Initialize: \(\mathbf{\mathcal{M}},\mathbf{\mathcal{E}},\mathbf{\mathcal{G}},\mathbf{\mathcal{Y}},\mu,\mu_{ \max}\) and \(\rho\). Output: \(\mathbf{\mathcal{X}},\mathbf{\mathcal{E}},\mathbf{\mathcal{G}}\) and \(\mathbf{\mathcal{M}}\). While not converged do
1. Update \(\mathbf{\mathcal{X}}\) according to (12). 2. Update \(\mathbf{\mathcal{M}}_{n}\) according to (13), \(n=1,2,\ldots,\mathrm{N}\). 3. Update \(\mathbf{\mathcal{E}}\) according to (14). 4. Update \(\mathbf{\mathcal{G}}\) according to (15). 5. Update \(\mathbf{\mathcal{Y}}_{n}\) according to (16), \(n=1,2,\ldots,\mathrm{N}\). 6. Update \(\mu\) as \(\mu:=\min(\rho\mu,\mu_{\max})\). End while
```  
**Algorithm 1** Solving GLRTR by ADMM  
In our implementation, \(\mathbf{\mathcal{M}},\mathbf{\mathcal{E}},\mathbf{\mathcal{G}}\) and \(\mathbf{\mathcal{Y}}\) are initialized to zeros tensors. The other parameters are set as follows: \(w_{1}=w_{2}=\ldots=w_{\mathrm{N}}=1/\mathrm{N},\mu=10^{-4},\rho=1.1,\ \mu_{\max}=10^{10}\) and \(\varepsilon=10^{-8}\). Furthermore, the maximum number of iterations is set to 100.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 3 Industry 4.0 towards Forestry 4.0
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. (Year)**} & \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Highlighted**} & \multicolumn{6}{c}{**Technologies**} & \multirow{2}{*}{**Forestry 4.0**} & \multirow{2}{*}{**Industry 4.0**} & \multirow{2}{*}{**Optimations**} \\ \cline{5-10}  & & & & **IoT** & **WSN** & & & **IoT** & & **General** & **Monitoring** & **Fire Detection** \\ \hline
[0] (2019) & Industry 4.0 Beyond the Factory: An Application to Forestry & Connecting the Industry to & & ✓ & & & ✓ & & & \\  & An Application to Forestry & & central software with the & help of digital twins & & & & & & \\ \hline
[44] (2018) & Forest Fire Prevention, Detection, and Fighting Based on Fuzzy Logic & Forest Fire Prevention, Detection, and Fighting & ✓ & ✓ & ✓ & & ✓ & & ✓ & ✓ \\  & Wireless Sensor Networks & & & & & & & & & \\ \hline
[2018] & A fuzzy inference and big data analysis algorithm for the prediction of forest free based on wireless sensor networks & & ✓ & & & & ✓ & ✓ & ✓ \\ \hline
[2018] & The Internet of Things- & & & & & & & & & ✓ & \\  & wireless sensor networks & & & & & & & & & \\ \hline
[2017] & The Internet of Things- & & & & & & & & & & ✓ & \\  & and their application to forestry & & & & & & & & & \\ \hline
[2020] & Smart forests and data structures & Internet of these connected & ✓ & & & & & & & ✓ & \\  & From the Internet of Trees to & with government & & & & & & & ✓ & \\  & Planetary government & & & & & & & & & ✓ & \\ \hline
[2020] & Internet of Things for Sustainable & IoT and communication applications for sustainable density systems & ✓ & & ✓ & & & & ✓ & \\  & Forestry & & & & & & & & ✓ & \\ \hline
[2014] & IoT Fore Environmental Factors & Monitor the forest environmental factors & & ✓ & ✓ & ✓ & & & & ✓ & \\  & Collection Platform Based on ZIGREE & & with the new IoT technology & & & & & & & \\ \hline
[2017] & Management of Forest Fire Using & Using of IoT Technologies & & & & & & & ✓ & \\  & IoT Devices & & & & & & & & \\ \hline \multirow{2}{*}{[2018]} & IoT-Based intelligent modeling & Global System for Mobile & & & & & & & ✓ & \\  & of smart home environment for free & Communications (SSN) to & ✓ & & ✓ & & & ✓ & ✓ & \\  & prevention and safety & & avoid the alarms by & & & & & & & \\ \hline \multirow{2}{*}{[40] (2020)} & Framework industry 4.0 toward & & Forest supply chain & & & & & & & & \\  & Forestry 4.0 of the forest supply chain & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of industrial research in forest fire detection domain.
###### Abstract  
Background: The ever-growing extended reality (XR) technologies offer unique tools for the interactive visualization of images with a direct impact on many fields, from bioinformatics to medicine, as well as education and training. However, the accelerated integration of artificial intelligence (AI) into XR applications poses substantial computational processing demands. Additionally, the intricate technical challenges associated with multilocation and multiuser interactions limit the usability and expansion of XR applications. Methods: A cloud deployable framework (Holo-Cloud) as a virtual server on a public cloud platform was designed and tested. The Holo-Cloud hosts FI3D, an augmented reality (AR) platform that renders and visualizes medical 3D imaging data, e.g., MRI images, on AR head-mounted displays and handheld devices. Holo-Cloud aims to overcome challenges by providing on-demand computational resources for location-independent, synergetic, and interactive human-to-image data immersion. Results: We demonstrated that Holo-Cloud is easy to implement, platform-independent, reliable, and secure. Owing to its scalability, Holo-Cloud can immediately adapt to computational needs, delivering adequate processing power for the hosted AR platforms. Conclusion: Holo-Cloud shows the potential to become a standard platform to facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training by providing a robust platform for XR applications.  
medical imaging; extended reality; cloud computing; computational framework; interactive immersion 2020
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 6 Empirical Results and Analyses
### Data Sources  
A logistics distribution center in Guangxi carries out product distribution for 20 demand points in the urban area where it is located, where the distribution center number is 0 and the specific distribution demand point number is 1, 2, 3,... 20. The specific demand, coordinate address, and demand time are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **Coordinates** & **Demand (kg)** & **Appointment** & **Acceptable Time** & **Service Time** \\ \hline
0 & (0,0) & 258 & 7:00–9:30 & 7:00–11:00 & 15 \\
1 & (23,38) & 171.4 & 7:00–9:30 & 7:00–11:00 & 15 \\
2 & (4,33
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 1 Introduction
Despite this limitation, they are being increasingly used in natural resource management [17], including rangeland management and monitoring [18; 19], forest biodiversity assessment [20], fire [21] and ecosystem structure monitoring [22; 23], precision agriculture [24], and wildlife counts [25]. The use of drones for detecting illegal activities has been discussed previously [26; 27]. However, applications of this technology for conservation, including land use change detection, are yet to be explored [28; 29], particularly in Cote d'Ivoire, where they have been undertested. This study aims to assess tree losses in the Bossematie classified forest over an intra-annual period. The methodology developed is based on aerial images acquired by drones as well as photogrammetry and stereoscopy techniques. The goal of this study is to contribute to the fight against illegal clearing in classified forests and protected areas.
###### Abstract  
Accurately identifying human key points is crucial for various applications, including activity recognition, pose estimation, and gait analysis. This study introduces a high-resolution dataset formed via the VICON motion capture system and three diverse 2D cameras. It facilitates the training of neural networks to estimate 2D key joint positions from images and videos. The study involved 25 healthy adults (17 males, 8 females), executing normal gait for 2 to 3 s. The VICON system captured 3D ground truth data, while the three 2D cameras collected images from different perspectives (\(0^{\circ}\), \(45^{\circ}\), and \(135^{\circ}\)). The dataset was used to train the Body Pose Network (BPNET), a popular neural network model developed by NVIDIA TAO. Additionally, a comparison entails another BPNET model trained on the COCO 2017 dataset, featuring over 118,000 annotated images. Notably, the proposed dataset exhibited a higher level of accuracy (14.5%) than COCO 2017, despite comprising one-fourth of the image count (23,741 annotated image). This substantial reduction in data size translates to improvements in computational efficiency during model training. Furthermore, the unique dataset's emphasis on gait and precise prediction of key joint positions during normal gait movements distinguish it from existing alternatives. This study has implications ranging from gait-based person identification, and non-invasive concussion detection through sports temporal analysis, to pathologic gait pattern identification.  
human key point identification; high-resolution dataset; VICON motion capture system; Body Pose Net (BPNET); NVIDIA TAO; COCO2017 dataset; gait recognition; pathologic gait patterns 2024 14 4351 1