esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 3 Overview of the SWARMs Context-Aware Framework for Underwater Robots
### The SWARMs Ontology  
The SWARMs ontology can address context heterogeneity by providing a formal representation of information that is necessarily exchanged between vehicles and middleware/MMT components. By complying with the SWARMs ontology, a common understanding for the exchanged information can be achieved by heterogeneous vehicles. The SWARMs ontology is a networked ontology. Figure 3 shows a simplified version of the SWARMs ontology structure and the full version is accessible online [41]. As shown in Figure 3, the SWARMs ontology is composed of different domain-specific ontologies  
Figure 2: Overview of the proposed context-aware framework.  
that are linked by a core ontology. Different domain-specific ontologies model common classes, relationships, and axioms of certain domains, including mission & planning, environment recognition & sensing, robotic vehicle, and communication & networking. Specifically, the robotic vehicle ontology models the robots and vehicles used in the different SWARMs missions. The mission & planning ontology represents the whole mission composition and planning procedure of the low-level planning at a vehicle level. The environment where missions are carried out is characterized in the environment recognition & sensing ontology. The communication & networking ontology models the communication links that are available in the SWARMs missions. The core ontology defines a set of relationships to interconnect different domain-specific ontologies. For instance, as shown in Figure 3, the concept Service in the mission & planning ontology can be related to the concept Asset in the Robotic Vehicle ontology through a pair of inverse relationships, namely, _providedBy_ and _contributes_. The concept VehicleLevelTask can be interrelated to the concept RoboticVehicle by relationships _assignedTo_ and _allocatedTo_. The mission & planning ontology can also be linked to the robotic vehicle ontology through the relationship (_canPerform_ and _performedBy_) between the concept Action and the concept RoboticVehicle. The concept Sensor from the environment recognition & sensing ontology and the concept CommunicationLink from the communication & networking ontology are subsumed into the concept System in the robotic vehicle ontology. The concept Vehicle from the robotic vehicle ontology is modeled as a subclass of the concept ManmadeObject from the environment recognition & sensing ontology. With the four domain-specific ontologies interconnected by the core ontology, the SWARMs ontology could provide a general description of information exchanged in the scope of the SWARMs project. When dealing with different missions (e.g., oil spill detection, corrosion repair, seabed mapping, berm construction, and plume tracking), the SWARMs ontology can be extended with application semantics. In addition, context uncertainties in specific missions can be annotated by using the PR-OWL ontology constructs and modeled in MEBN theories.
## 3 Heat Transfer Theory of Linear Heat Sources in Soil Media
### Derivation of the Heat Transfer Formula for Linear Heat Sources
After \(\Delta t\), the following formula is used to calculate the temperature change in the linear heat source and the soil medium:  
\[\Delta T=\frac{\Delta Q}{mc} \tag{15}\]  
In the formula, \(\Delta T\) represents the change in temperature of the medium; \(m\) is the mass of the corresponding medium; \(c\) is the specific heat capacity of the corresponding medium. After changing the temperatures of the linear heat source and the soil medium, the temperature of the linear heat source at the next moment is calculated using Equation (5) by substituting the heating power into \(\Delta Q\), thus obtaining the increased temperature of the linear heat source. Then, the temperature of the linear heat source at this moment is substituted into the next moment's temperature \(T\) for calculation. At the same time, the heated soil medium will also heat the surrounding soil medium, and with each time element \(\Delta t\), the heat field will advance by a distance \(\Delta x\). The analytical solution of the non-steady-state heat transfer formula for the linear heat source is as follows:  
\[T(t)-T_{0}=\left\{ \begin{array}{l}\frac{Q}{4\pi\lambda}(\ln\frac{4\pi t}{r^ {2}}-\gamma),0\leq t<t_{1}\\ \frac{Q}{4\pi\lambda}\ln\frac{t}{t-t_{1}},t\geq t_{1}\end{array} \right. \tag{16}\]  
During the heating process, when \(T(t)=T_{0}\), the heat is exactly transferred to a position at a distance \(r\) from the linear heat source. By rearranging this equation, the relationship between \(\Delta x\) and \(\Delta t\) can be obtained:  
\[\Delta x=\sqrt{\frac{4\pi\Delta t}{e^{\gamma}}} \tag{17}\]  
Since the linear heat source is considered to be infinitely long, the physical quantities related to the length of the linear heat source in Equations (14) and (16) are taken as per unit length. The above two formulas are further transformed into the following:  
\[\Delta Q=4r\sqrt{\rho_{solid}c_{solid}\lambda\pi\Delta t}(T-T_{\infty}) \tag{18}\]  
\[\Delta T_{fe}=\frac{\Delta Q}{\rho_{fe}c_{fe}\pi{r_{0}}^{2}} \tag{19}\]  
\[\Delta T_{solid}=\frac{\Delta Q}{\rho_{solid}c_{solid}\pi((r_{1}+\Delta x)^ {2}-{r_{1}}^{2})} \tag{20}\]  
In the formula, \(r_{0}\) is the radius of the linear heat source, and \(r_{1}\) is the inner diameter of the cylindrical soil medium corresponding to each \(\Delta x\).
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN  
In P2M architectures, WAN communication characteristics should satisfy the real-time constraints required for executing the logics used in wide-area applications. With cellular communication, mobile operators do not provide meaningful Quality of Service for the end users. Therefore, it is necessary to measure the communication path characteristics (e.g., propagation delay and packet loss) in order to evaluate the feasibility of using a particular communication technology for wide-area applications, especially time-critical protection applications. The aim of this section is to evaluate the feasibility of utilizing the cellular (5G an 4G) Internet for communication in wide-area communication-based protection applications. The objective is to measure multicast R-GOOSE communication latency under different configurations and to analyze measured data statistically. The idea is to utilize real networking devices and build a test setup to experiment the architectural solutions proposed in Section 6. The test setup is used for communicating time-synchronized and secured R-GOOSE messages over commercial cellular Internet. In this communication, delay and packet loss are measured and analyzed in MATLAB.
# Thermal Stability and Entropy Generation Analysis for
## 4 Method of Solution
### Bejan Number  
Figures 23-29 demonstrate the irreversibility ratio trends in response to variations in the flow governing parameters. A striking feature of all seven graphs is that the irreversibility ratio is minimal at the core region of the channel. This shows that, in this flow, irreversibility due to the combined effect of viscous dissipation and porous-medium permeability attains its maximum value at the channel core region while it weakens at the channel walls. Figures 23, 24 and 26 show that, with the increasing variable values of the viscosity parameter, viscous heating, and Grashof number, there is a perfect dominance of irreversibility due to heat transfer over irreversibility due to the combined effect of viscous dissipation and porous-medium permeability in the bulk of the fluid and closer to the channel walls. However, we noted that, at the channel core region, this dominance is weakened by viscous dissipation and porous-medium permeability irreversibility, as can be seen from the magnitude of the Bejan number. An interesting observation in Figure 25 is that the irreversibility ratio is constant at \(y=0\) in the channel core region except around the walls, where heat transfer irreversibility is significant. Also, in Figure 25, increasing activation energy parameter values elevate the channel's Bejan number. The effects of the Grashof number on the irreversibility ratio, Figure 26, mirror those of the viscous heating parameter, albeit at a less pronounced intensity. In Figure 27, an increase in the chemical reaction parameter reveals effects that are like the impact of the activation energy parameter described in Figure 25. In Figure 28, an increase in the fluid's non-Newtonian properties has negligible effects on the Bejan number, showing only a marginal increase in the bulk of the fluid. The third-grade material parameter contributes more to the viscous dissipation and porous-medium permeability irreversibility in the flow channel. Similar behavior is seen in Figure 29 for increasing porous-medium shape parameter values.  
Figure 21: Variation of the entropy generation with the third-grade material parameter.  
Figure 22: Variation of the entropy generation with the porous-medium shape parameter.  
Figure 23: Variation of the Bejan number with a variable viscosity parameter.  
Figure 24: Variation of the Bejan number with the viscous heating parameter.  
Figure 25: Variation of the Bejan number with the activation energy.  
Figure 26: Variation of the Bejan number with the Grashof number.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 2 Results and Discussion
### In Vitro Biological Evaluation
#### 2.2.3 Cytotoxicity Activity  
Compounds **3c**, **f**, **g**, **k**, **r**, and 3z** showed the highest cytotoxicity, for which IC\({}_{50}\) or IC\({}_{50\text{abs}}\) were observed in the early micromolar range (less than 10 uM) against the tested cell lines. The dependences of the number of cells on the concentration of the compounds are shown in Figure 3. It should be noted that compounds **3b**, **3e**, and **3g** showed a preferential suppression of the growth of rapidly dividing A549 cells compared to slower growing fibroblasts of non-tumor etiology. In addition, there was a significant proportion of living cells compared to the untreated control over a wide range of high concentrations. The greatest difference was observed for compound **3e**. Although this dependence can be explained, for example, by the solubility of the compounds, it is more likely to be explained by a cytostatic rather than a cytotoxic effect. A similar pattern of cytotoxicity was observed, for example, for antitubulin compounds in the nanomolar concentration range. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Conc.,**} & \multicolumn{10}{c}{**Fluorescence Change (BACTEC MGIT 960 Growth System), Days**} \\ \cline{2-13}  & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **18** & **25** & **30** & **35** & **41** \\ \hline Control & + & + & + & + & + & + & + & + & + & + & + & + & + & + & + & + & + \\
100.0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
50.0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
25.0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
10.0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
5.0 & - & - & - & - & - & - & - & - & - & - & - & - & - & + & + & + & + \\
2.5 & - & - & + & + & + & + & + & + & + & + & + & + & + & + & + & + & + \\
1.0 & - & - & + & + & + & + & + & + & + & + & + & + & + & + & + & + & + \\ \hline \hline \end{tabular} Control—_M. tuberculosis_ H\({}_{37}\)Rv inoculum without chemical compounds; - lack of growth; + growth of mycobacteria.  
\end{table} 
Table 2: Activity of indolylquinazolinone **3k** against _Mycobacterium tuberculosis_ H\({}_{37}\)Rv. For another group of compounds (**3f** and **3k**, etc. ), there was a different pattern of cytotoxicity, when complete cell death was observed at high concentrations. Therefore, two parameters of action on the cells were calculated for the compounds: IC\({}_{50}\) (half of the maximal inhibition) and IC\({}_{50\mathrm{abs}}\) (half of the absolute quantity of cells) (see Table 3). The rest of the compounds had a low cytotoxicity or did not show noticeable toxic effects in the studied concentration range.
# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 3 Neural Networks for Graph Embedding
### Embedder  
The embedder computes an initial embedding for all nodes \(v,w\in N\) and edges \((v,w)\in E\) to be used in the propagation phase. It is assumed that all nodes and edges have a numeric feature vector that expresses the contained information of a node or an edge. The functions \(\textit{feat}_{\text{node}}:N\rightarrow\mathbb{R}^{l_{n}}\) and \(\textit{feat}_{\text{edge}}:E\rightarrow\mathbb{R}^{l_{e}}\) map nodes and edges to their vector encoding in the \(l_{n}\)-dimensional and \(l_{e}\)-dimensional real space, respectively. For instance, when dealing with NEST graphs, these functions should encode the type and the semantic description of a node or an edge. We discuss the implementation of these functions for our case in Section 4.1 and stick to the generic definition at this point. The embedding procedure can be defined as follows:  
\[\begin{array}{ll}h_{v}^{(0)}=&\text{MLP}_{\text{node}}(\textit{feat}_{ \text{node}}(v)),\quad\forall v\in N\\ h_{v,w}=&\text{MLP}_{\text{edge}}(\textit{feat}_{\text{edge}}(v,w)),\quad \forall(v,w)\in E\end{array} \tag{1}\]  
Each node \(v\) is mapped to an embedding vector \(h_{v}^{(0)}\in\mathbb{R}^{l_{\textit{feat}_{\text{node}}}}\) that is part of an \(l_{\textit{nEmb}}\)-dimensional vector space. This embedding vector has the value of transformation step 0 (i.e., the initial embedding), which is depicted by the superscript 0. The mapping is performed by a Multi-Layer Perceptron (MLP) that can be configured in terms of the number of layers, number of hidden neurons, and so on. The edges are mapped in the same fashion. The feature vector of each edge is embedded into \(h_{v,w}\in\mathbb{R}^{l_{\textit{nEmb}}}\) by a separate MLP. The resulting edge embedding is located in the \(l_{\textit{eEmb}}\)-dimensional vector space. In contrast to node embeddings, edge embeddings do not have an associated transformation step (i.e., superscript 0), because edge representations remain unchanged after the embedding by the embedder. The embeddings of node features are simultaneously used as the initial representation for nodes in the following propagation component (see Section 3.3). They are constantly updated by the neural network, which is indicated by the assigned transformation step, e.g., \(h_{v}^{(1)}\) after the first update and \(h_{v}^{(5)}\) after the fifth update. Furthermore, the values of \(l_{\textit{nEmb}}\) and \(l_{\textit{eEmb}}\) (referred to as _embedding size_) can be used to parameterize the MLPs. The larger the embedding size, the more information can be stored in the particular embedding vector but the more time it takes to compute these vectors.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Global Path Planning  
Global path planning refers to path planning where the environment is known a priori and therefore does not have to be planned by the onboard computer of the autonomous drone. When conducting an infrastructure inspection using autonomous drones, infrastructure locations are known in advance and are used to represent the flight waypoints. For safety and security reasons, the drones are kept close to the infrastructure using the electrical power transmission system as a reference for route planning. A global path planning algorithm has been developed and deployed in the cloud. It can be engaged through the web interface where a user plans the inspection missions. The algorithm solves a modified Traveling Salesman Problem (TSP) where drones start from different positions and reach the inspection targets selected by the user. The environment has been represented in a graph structure with nodes containing locations of power towers, bridges, and railways. The edges provide distances between the neighboring nodes. The algorithm builds a distance matrix for each set of sources (drones) and targets (inspection target locations) using the A* algorithm to compute the shortest distance for each combination. The data model created for near-optimal route determination includes the distance matrix accompanied by the number of vehicles, their starting positions, and their desired ending positions. Global inspection paths are determined by applying OR-Tools methods [45] to the data model to find the combination with near-optimal costs, i.e., travel distances. Visualization of the web interface shows an example solution of path planning for four drones reaching four inspection targets (Figure 7). A user chooses inspection targets (green  
Figure 6: Visualization of cable detection data. **Left:** Onboard camera view with projected 3D radar data. The green line is the direction of the cables relative to the drone. **Middle:** Rviz2 visualization of 3D data and drone frame. **Right:** Drone position in a powerline test setup. The numbers are used to identify the different cables in the figure.  
circles) on the map-based interface and runs a path calculation. Paths are calculated for each drone (orange circles) to reach a selected target and are shown in red. Afterward, the user can name the mission plan and store it. To begin the inspection mission, paths should be transferred to drones.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 6 Security Analysis
### Provision to Identity Privacy Preserving  
In practical D2D communication scenarios, the adversary, including the insider and outsider attacker, is able to perform illegal tracing on particular device. The user privacy is damaged in this way. Therefore, in our design, the original identity of certain UE will not be revealed during the whole communicating phase. Furthermore, for all the participating vehicles, the user unlinkability is also provided. Hence the multiple messages generated by the same vehicle cannot be linked together. The brief description is given as follows.  
Theorem 4.1: _The proposed authentication scheme is resistant to illegal tracing, and provides UE unlinkability. That is, particular UEs can not be traced by extracting the featured information from the delivered messages._  
Proof of Theorem 4.2.: In our assumption, the real identity \(ID_{i}\) for certain UE \(i\) is hidden all the time. As shown in the aforementioned authentication phase, the one-way hashed function \(H_{1}\) is employed. The newly constructed temporary identity \(\mathit{Tid}_{i}\) is derived as \(\mathit{Tid}_{i}=\zeta_{i}^{r_{i}}\), which contains the randomly generated value \(r_{i}\). Note that \(r_{i}\) is previous selected in the SN side. The transmitting message \(\langle\mathit{Tid}_{i},\mathcal{T}_{i},Auth_{i}\rangle\) shows no similarity with the subsequent data exchange. In this way, the tracing towards certain device is prevented.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### The Usefulness of Internet-Based Data Collection for Rockfall Hazards  
This study highlights the significant utility of using internet-based data collection to study rockfall hazards. Internet news sources can provide diverse information from various locations that might otherwise go unnoticed. By systematically collecting and automatically notifying researchers of relevant data, the study ensured a comprehensive dataset over the span of 4 years and 11 months. This approach enabled the first comprehensive rockfall hazard susceptibility analysis of Taiwan's entire highway system, rather than focusing on specific locations or slopes. This breadth of coverage is unprecedented in the literature, providing a more holistic understanding of the risks involved.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 3 Numerical Simulation
### Finite Element Modeling  
Modeling and analysis were carried out with ANSYS (15.0; ANSYS, Inc., Pittsburgh, PA, USA; 2013). Table 1 shows the pipeline parameters studied in this paper. The pipeline model uses shell63 element and both ends are fixed constraints. A total of 40 units are divided along the length and 8 units along the pipeline circumference. The pipeline is divided into 10 substructures along the axial direction and each substructure contains 32 units. The substructures are numbered as illustrated in Figure 1. Table 2 shows the first four natural frequencies of the pipe model. Figure 2a-d show the corresponding first four mode shapes. In the vibration pattern, the simple beam-type mode shapes give a low-order mode performance, whereas the torsion of the pipe gives a high-order mode of performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Length/m Outside Diameter/mm** & **Thickness/mm Elastic Modulus/Gpa** & **Poisson’s Ratio** & **Density/kg/m\({}^{3}\)** \\ \hline
**Straight pipe** & 2 & 114 & 3 & 205 & 0.3 & 7850 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pipe parameters.  
Figure 1: Pipe substructure number.  
A shell63 element has 4 nodes and 6 DOFs (degrees of freedom). The initial finite element model is established with a total of 328 nodes and 1776 DOFs. Considering the actual engineering and for easy calculation, the Guyan method [17] was adopted to reserve the DOFs in the (_x,y_) direction and reduce all remaining DOFs. Finally, the DOFs of the pipeline model was reduced to 296. Table 3 shows the frequencies calculated from the \"new model.\"
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction
### Motivation
Despite introducing quantization noise, these ADCs can maintain system performance through techniques such as larger antenna arrays and advanced signal processing. In [25], a framework for cell-free mmWave massive MIMO systems is presented. This study shows how low-resolution ADCs, combined with hybrid precoding/decoding, can improve performance, reduce power consumption, and handle limited fronthaul capacity. Additionally, ref. [26] underscores the importance of efficient fronthaul usage and advanced signal processing techniques in maintaining high performance in cell-free massive MIMO systems equipped with low-resolution ADCs. This study highlights how these techniques contribute to reducing overall power consumption. In [27], the uplink performance of cell-free massive MIMO systems using low-resolution ADCs at access points is analyzed, revealing that 5-bit ADCs strike a balance between minimizing hardware cost and power consumption while sustaining adequate system performance. Finally, ref. [15] explores the uplink performance of cell-free massive MIMO systems under conditions of limited fronthaul capacity and hardware impairments. This study emphasizes the critical role of reducing power consumption through efficient ADC solutions to maintain system performance.