esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Technical Architecture and Solution
### Data Sources  
The data sources could vary based on user- or domain-specific area preferences. In the current application, focused on the public sector and e-governance, the primary sources used are Press Corner, the European Commission's official publicly available informative site, the OECD library, and selected openly available PDFs focused on research in a variety of domains and authors by the European Commission. Additionally, we incorporated web search, for which live documents are retrieved from Google with Serper, so that the user can retrieve the latest information under his preferences, using search features such as normal googling (e.g., for retrieving information from a specific site \(\rightarrow\) \"what is the green deal? site: scholar.google.com\"). In our Web search configuration, the search engine initially retrieves the top 10 most relevant results based on the user query. These results are subsequently processed and divided into smaller document segments. From these segmented documents, the retrieved then identifies and selects the top 50 most relevant sections for further processing. Lastly, we enable live document processing from various sources (e.g., PDF, CSV, and TXT) so the user can upload and use his document for Questions and Answers (Q&A). All of the above can be incorporated into a single search by using the agent's conversational pipeline, asking for the inclusion of all of the above sources.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 4 Implementation of the Proposed System
### Graphical Simulation Environment for Model-Equipment Interaction
#### 4.1.1 Data Collection and Processing  
First, the construction site was scanned using a DJI Phantom 4 Real-time Kinematic (RTK) (DJI, Shenzhen, China) drone according to the earthwork management plan. First, ground control points (GCPs) were selected at sites that provided ground coordinates using Leica RTK-Geographic Positioning System (RTK-GPS) (Leica, Wetzlar, Germany) at different reference points. Next, the drone was flown at an altitude of 100 m, and site images were captured. The accuracy of the data was verified by comparing the coordinates of unknown points selected on the construction site from the total station and drone survey. The Leica Total Station (TS), TS-16, was used to measure the coordinates of the reference points for accuracy evaluation. Errors of 2, 2, and 2.4 cm were found for this specific site in the x-, y-, and z-directions, respectively, which are considered within the allowable range for earthwork. Next, subsurface information was collected through boreholes. The borehole coordinates were determined manually using a total station. Geotechnical information was obtained through laboratory tests.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 6 MOT Algorithm Based on Transformer
### MOT Algorithm Based on Transformer Architecture
The advantage of the approach is that the features are learned from the matching, and, finally, the tracking module is used for matching to achieve object tracking. Zhu et al. [92] introduced the ViT model to the field of multi-object tracking and proposed ViTT. It uses a transformer encoder as a backbone to extract features directly with the image as input. Compared to convolutional networks, it can model the global context at each encoder from the beginning and performs well in the challenges of occlusion and complex scenes. Yang et al. [93] consider the object position prediction and propose a transformer-based multi-object motion model, which takes the object's historical position difference and the offset vector between consecutive frames as input, and considers the object at the same time. The motion of itself and the camera improves the prediction accuracy of the motion model in the multi-object tracking method, thereby improving the tracking performance. The transformer architecture in the neural network is intended to replace the traditional neural network that relies on global search, and to speed up and improve the efficiency of model training by instructing the model to focus more computing power or the gradient update of the parameters where we want it to, while ignoring irrelevant information in other areas. Most of the benchmark datasets have performed better than the conventional CNN architecture tracking technique. This demonstrates that research into algorithms with this architecture should continue [91].
## 6 Proposal of a Block Tearing Failure Mechanism
### Model for Inclined Reinforcement  
In the case of inclined reinforcement, see Figure 16b, the only forces that are supposed to act on the jacketed block are:  
* F Vertical force applied on the half joint;
* T Hanging force in the stirrups;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Specimen** & **e [m]** & **z [m]** &
\begin{tabular}{c} _F/A\({}_{sw}\)_ \\ **[MPa]** \\ \end{tabular} & **V [kN]** & **F [kN]** \\ \hline Orthogonal & Sound & 0.33 & 0.50 & 355 & 147 & **223** \\ \cline{2-6} reinforcement & Damaged & 0.38 & 0.50 & 374 & 121 & **160** \\ \hline Inclined & Sound & 0.38 & 0.50 & 346 & 115 & **222** \\ \cline{2-6} reinforcement & Damaged & 0.41 & 0.50 & 410 & 100 & **148** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Block tearing resistances.  
\(\bullet\)\(F_{\rm{ih}}\) Horizontal component of the force in the inclined reinforcements;  
\(\bullet\)\(F_{\rm{iv}}\) Vertical component of the force in the inclined reinforcements;  
\(\bullet\)\(C\). Force in the compressed chord;  
\(\bullet\)\(V\). Shear friction resistance.  
As a first approximation, in the studied beams the inclined bars pass by the lower corner of the jacketing plate and are inclined at 45\({}^{\circ}\). Thanks to these conditions the equilibriums can be written in a simplified form as follows.  
Horizontal equilibrium needs \(V+F_{ih}=C\) and the vertical one needs \(F=T+F_{iv}\).  
Vertical stirrups should at least be able to carry the vertical force F, but if their section is bigger than the needed one, they can provide additional resistance in the shear friction model presented in Equation (2). The cross-section of the joint \(A_{ej}\) is the same seen for orthogonal reinforcement.  
Therefore, Equation (2) can be rewritten as:  
\[V=c\cdot f_{ct}\cdot A_{ej}+\mu\cdot\left(A_{sw}\cdot f_{y}-F+F_{iv}\right) \tag{6}\]  
where \(A_{sw}\), the cross-section area of the vertical stirrups crossing the joint, is 302 mm\({}^{2}\) for the sound specimen and 258 mm\({}^{2}\) for the damaged one, whereas the area of the inclined reinforcement is 314 mm\({}^{2}\) for the sound specimen and 113 mm\({}^{2}\) for the damaged one.  
The equilibrium to rotation about the lower corner of the plate, where the forces \(V,~{}F_{ih},~{}F_{iv}\) have nil lever arm, can be written as follows:  
\[F\cdot e_{F}-T\cdot e_{T}-C\cdot z=0 \tag{7}\]  
that can be solved to find \(F\)  
\[F=\frac{\mu\cdot A_{sw}\cdot f_{y}\cdot z+F_{ih}\cdot z-F_{iv}\cdot\left(e_{T} -\mu\cdot z\right)+c\cdot f_{ct}\cdot A_{ej}\cdot z}{\mu\cdot z+e_{F}-e_{T}} \tag{8}\]  
The results obtained following this verification for the sound side and the damaged side are presented in Table 3.
## 2 Antenna Geometry  
Figure 1 presents the configuration of the proposed geometry of the circularly polarized antenna excited by a uniquely shaped conformal strip placed with a feed point at the central position. The design was modeled in CST(r) Microwave Studio using its time domain-based finite integration technique [23]. Hexahedral meshing was used to design a rectangular DRA with DR permittivity, \(\varepsilon_{\mathrm{r}}=10\)[11]. In settings, the cell per wavelength was set at a value of 40 and the cells per max model box edge at 20 for design mashing. The fraction of maximum cell near to model was also set at a value of 20 and total unknowns of 559,980 were obtained.  
An iterative design procedure was followed to determine the optimum dimensions of the feeding metallic strips that were needed to excite the degenerate TE\({}_{813}^{\mathrm{x}}\) and TE\({}_{183}^{\mathrm{y}}\) necessary for CP wave generation [24]. The DR profile dimensions of H = 26.1 mm, W = 25.4 mm, and D = 14.3 mm were used. The optimization of the lengths and the widths of the strip were performed by running different simulations using many parameter sweeps. The results of the design procedure are summarized in Table 1, which shows several selected dimensions of \(l_{1}\), \(l_{2}\), \(l_{3}\), \(l_{4}\), and \(l_{5}\) that could generate circular polarization in conjunction with sufficient impedance matching bandwidth.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(l_{1}\) (mm) & \(l_{2}\) (mm) & \(l_{3}\) (mm) & \(l_{4}\) (mm) & \(l_{5}\) (mm) & \multicolumn{2}{c}{**3 dB AR Bandwidth (\%)**} & \multicolumn{2}{c}{**10 dB S11**} & \multicolumn{2}{c}{**Overlapping AR \& \multicolumn{1}{c}{**Bandwidth (\%)**} & \multicolumn{1}{c}{**Bandwidth (\%)**} \\ \hline \multirow{8}{*}{3.5} & \multirow{2}{*}{7} & 1 & 8 & 1 & 6.24 & 12.43 & 100 \\ \cline{3-8}  & & 2 & 8 & 0.5 & 5.65 & 12.18 & 100 \\ \cline{3-8}  & & & & 0.5 & 6.23 & 11.92 & 100 \\ \cline{3-8}  & & 1 & 12 & 1 & 6.64 & 10.65 & 100 \\ \cline{3-8}  & & & & 1 & 7.87 & 10.4 & 100 \\ \cline{3-8}  & & & & 2.5 & 6.17 & 10.14 & 93.1 \\ \cline{3-8}  & & & & 3 & 7.08 & 11.67 & 76.2 \\ \cline{3-8}  & & & & 1.5 & 9.07 & 11.42 & 66.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AR and S\({}_{11}\) bandwidths for different feed dimensions of the proposed CP 5G RDRA.  
Figure 1: The geometry of the proposed CP 5G RDRA.
## 4 Exploitation Risk Assessment for Vulnerability
### Evaluation of Exploitation Risk Metrics
#### 4.2.3 Exploit Usage Probability  
Exploit usage probability (EUP) signifies the likelihood that an attacker will exploit a vulnerability in an actual attack. In this study, we use the results of the exploit prediction scoring system (EPSS), a scoring system that calculates the likelihood of a vulnerability being exploited within a 30-day window. EPSS maintains real-time updates with exploit information and incident reports related to vulnerabilities, resulting in a daily exploitability score. This score is considered to be one of the considerations within a risk-based vulnerability management approach. Effective vulnerability management requires a comprehensive severity assessment that incorporates insights from different perspectives. This study uses the EPSS score, in conjunction with the ECA weight assigned to each vulnerability, to calculate the ease of exploitation (EoE), which provides information on how easily a vulnerability can be exploited. The concept of EoE and its role in calculating the exploit risk of a vulnerability will be discussed further in Section 4.3.
## 5 Effects of ABH Parameters and Damping Layer
### Effect of the Damping Layer
The modal loss factor of the ABH circular plate attached damping layers of different thicknesses was calculated in this paper, which is shown in Figure 20. As can be seen from Figure 20, in the range of 0-10 kHz, the increase of the thickness of the damping layer can significantly improve the modal loss factor of the ABH circular plate. The increase of the modal loss factor has a significant contribution to the vibration and noise attenuation of the structure in the full frequency domain under a mechanical force excitation [31]. Combined with the sound transmission loss data of Figure 20, it can be seen that although the modal loss factor of the ABH circular plate is greatly improved with the increase of the thickness of the damping layer in the whole frequency domain, the sound transmission loss of the ABH plate can be significantly affected only in the damping-controlled region. It can be concluded that although the damping loss factor of the ABH circular plate when the thickness of the damping layer is 1.5 mm is lower than that of the ABH circular plate with the thickness of the damping layer of 2 mm, the ABH circular plate with a damping layer thickness of 1.5 mm exhibits better sound insulation performance. The above phenomenon is different, as the larger the damping loss factor, the larger the vibration suppression or radiation sound attenuation caused by the modal loss factor under the excitation of a mechanical driving force. Therefore, when the ABH circular plate is applied to sound insulation, the sound insulation performance cannot be improved simply by increasing the thickness of the damping layer. Figure 19: Effect of damping layer thickness on TL of a circular plate embedded with single ABH (compared with uniform circular plate).
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 3 Method of This Paper  
In response to the traditional image defogging algorithm model based on a convolutional neural network with poor ability to capture global features and incomplete defogging, this paper proposes a parallel end-to-end defogging network model with a deep coding and decoding structure. The encoder of this model consists of two branches: a CNN and a vision transformer, in parallel, which continuously downsamples the features of the image to extract local and global features of haze images captured at different scales, respectively.  
Figure 1: The physical model of atmospheric scattering.  
The decoder part of the model consists of the vision transformer, which restores the image to its original size by upsampling. To better fuse local and global features obtained through the encoder and decoder, enhanced feature extraction was finally performed using feature pyramid networks (FPN).
# Cognitive Software Defined Networking and Network Function Virtualization and Applications
## 1 Introduction  
Future networks face numerous challenges due to the growing traffic demand for emerging internet applications and the diversification of Quality of Service (QoS) requirements. Towards meeting the growing traffic demand and ensuring a dynamic QoS of applications, it is expected that future networks will be built on virtualized infrastructures. Network functions, such as network address translation, firewalls, and intrusion detection, will be implemented on cloud infrastructures in virtual machines and containers. Currently, technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are capable of making networks flexible, programmable, and vendor-agnostic, as well as cost-effective.  
Currently, several management and orchestration platforms [1; 2] have been built using SDN and NFV for automating the provisioning, configuration, and monitoring of networks in real-time using Network Functions as a service on a virtualized infrastructure. SDN and NFV bring tremendous possibilities for increasing the management flexibility and efficiency of telecom services. The use of Artificial Intelligence (AI), Machine Learning (ML), and optimization techniques in SDN and NFV for effective decision-making is prevalent, as these technologies aid in gathering a large amount of data from networks and offer many possibilities for placing and deploying network functions in networks. This Special Issue reports the use of AI, ML, and optimization techniques to solve various problems in SDN/NFV, including the following:  
1. Solving the Virtual Network Function embedding problem under failure conditions using Deep Reinforcement Learning (DRL) techniques.
2. Solving the scheduling problem of media function virtualization by using greedy algorithms, such as Breadth-First Search and Depth-First Search, and Integer Linear Programming (ILP).  
Solving the problem of performance degradation in satellite systems with the use of Q-Learning in SDN and NFV.
* 4. Solving the resource allocation problem in NFV using AI techniques, such as the Long Short-Term Memory (LSTM) algorithm.  
This editorial presents a summary of the above-reported problems and solutions from the papers in this Special Issue.
## 1 Introduction
Experimental results demonstrate that our method outperforms traditional ensemble strategies (e.g., random forest, AdaBoost) and individual supervised learning models across diverse binary datasets, achieving higher accuracy and F1 scores. This study refines the BTS framework by introducing classification confidence and integrating surprise popularity principles. First, we construct diverse base classifiers and compute their confidence levels for each sample. Using these confidences, we derive prior and posterior probabilities, comparing their discrepancies to determine whether to prioritize correct minority predictions over majority errors. Additionally, we propose a single regression model strategy with reduced spatial overhead. The key contributions of this work include the following:  
1. Designing an ensemble strategy that integrates classification confidence with surprise popularity;
2. Proposing a prior regression method based on classification confidence and defining posteriors using confidence metrics;
3. Validating the algorithm on public datasets, with experimental results demonstrating superior performance over conventional ensemble methods. Our research highlightsthe potential of this approach and provides new directions for future studies and applications in ensemble learning. The remainder of this paper is organized as follows. Section 2 reviews related work. Section 3 details the proposed ensemble strategy combining classification confidence and surprise popularity. Section 4 presents experimental evaluations. Finally, we conclude the paper.
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.2 Modal Damping  
Modal damping has been investigated as a possible damage sensitive feature due to the premise that cracking in a cross-section will increase internal friction, which in turn raises the value of the section's damping [39]. Similarly, [PERSON] et al. [40] recently observed that damping ratios are particularly sensitive to the corrosion-induced damage of reinforced concrete beams. The drawback of this feature, however, has been highlighted by [PERSON] [41] and [PERSON] et al. [42], who both concluded that measuring modal damping from vibration data produced large standard deviations that significantly impair their effectiveness as a reliable damage indicator. Damping levels in bridges are also nonlinearly influenced by vibration amplitude and operational effects [43]. Furthermore, damping levels may rise or fall depending on the damage type, for instance; cracking in reinforced concrete may increase damping due to increased internal friction and nonlinear behaviour, while the failure/removal of a truss element may reduce damping. Therefore, it is important to consider the expected deterioration when evaluating modal damping-based damage detection results.
###### Abstract  
A novel non-precious multiwalled carbon nanotubes (CNTs)--supported metal oxide electrocataly was developed for methanol electrooxidation in alkaline medium. The catalyst was fabricated by simultaneous electrodeposition of copper-cobalt-nickel ternary nanostructures (CuCoNi) on a glassy carbon electrode (GCE) modified with CNTs. The proposed electrode was characterized using X-ray diffraction (XRD), energy dispersive X-ray (EDX), and scanning electron microscopy (SEM). The electrochemical behavior and the electrocatalytic performance of the suggested electrode towards the oxidation of methanol were evaluated by cyclic voltammetry (CV), linear sweep voltammetry (LSV), and chronoamperometry (CA) in alkaline medium. Several parameters were investigated, e.g., deposition time, potential scan rate, etc. Compared to Cu, Co, or Ni mono electrocatalysts, the electrode based on ternary-metals exhibited superior electrocatalytic activity and stability towards methanol electrooxidation. For instance, CuCoNi@CNTs/GCE has shown at least 2.5 times electrocatalytic activity and stability compared to the mono electrocatalysts. Moreover, the present study found that the optimized loading level is 1500 s of simultaneous electrodeposition. At this loading level, it was found that the relation between the \(I_{\mathrm{P}}/\mathrm{v}^{1/2}\) function and scan rate gives the characteristic features of a catalytic process. The enhanced activity and stability of CuCoNi@CNTs/GCE was attributed to (i) a synergism between three metal oxides coexisting in the same structure; (ii) the presence of CNTs as a support for the metal oxides, that offers high surface area for the deposited tertiary alloy and suppresses the aggregation and sintering of the metals oxide with time; as well as (iii) the increase of the conductivity of the deposited semiconducting metal oxides.  
methanol; oxidation; CNTs; Cu-Co-Ni nanostructure; electrocatalysis +
Footnote †: journal: Applied Physics Letters  
Article
## 4 Evaluation and Performance Results
### Vehicle Counting and Tracking Result Analysis  
To enhance the capabilities of the system, virtual lines and hot zones are incorporated on both the left and right sides of the road. These additional features help improve the accuracy of vehicle detection and tracking by providing reference boundaries and focal areas for analysis. The vehicle is tracked accurately when it enters hot zones. By using virtual lines, vehicles can be counted to estimate the flow rate. The traffic flow rate refers to the number of vehicles passing through a specific point within a given unit of time and during any interval. The vehicle count, which corresponds to the number of vehicles passing through a designated line, is determined by utilizing hot zones and virtual lines. These elements aid in accurately detecting and tracking vehicles as they cross the specified line, enabling the accurate calculation of the vehicle count. When a vehicle passes through a line, it counts once. Figure 7 shows the vehicle detection results with the bounding box regression. Table 5 presents a comprehensive comparison of the proposed method with other state-of-the-art techniques, evaluating their performance. Figure 8 illustrates a comparative analysis of the proposed methods with existing deep learning-based approaches.  
Figure 6: (**a,b**) Assigning the IDs to each vehicle for tracking.  
Figure 8: Performance analysis of the proposed work for vehicle detection and tracking.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline YOLOv4-3 SPP [6] & 88.6\% & 82.4\% & 86.5\% \\ YOLOv5s [18] & 80.3\% & 89\% & 90.5\% \\ YOLOv5 [36] & 83.83\% & 91.48\% & 86.75\% \\ YOLOv3 + Deep SORT [59] & 91\% & 90\% & 84.76\% \\ YOLOv5 + Deep SORT & 91.25\% & 93.52\% & 92.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the performance parameters used for vehicle detection and tracking.  
Figure 7: (**a,b**) Vehicle tracking and counting in the proposed system.