esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Nomenclature  
#### Abbreviations  
DG: distributed generation  
GA: genetic algorithm  
HVDC: high-voltage direct current  
OPF: optimal power flow  
p.u.: per unit  
PV: photovoltaic  
RPG: reactive power generation  
SVC: static Var compensator  
WTG: wind turbine generator  
**Variables**  
_A:_ [ \(a_{i}\) ] \(a_{i}\) is a bus, excluding a slack bus ]  
_B:_ the number of branches  
\(C_{Loss}\): cost function for branch losses  
\(C_{RPG}\): cost function for reactive power generation installation  
\(C_{V}\): cost function for voltage variation  
\(C_{Loss,max}\), \(C_{RPG,max}\), and \(C_{V,max}\): worst case costs for losses, reactive power generator installation, and voltage variation  
\(C_{gen,i}\): generation cost of unit \(i\) in \(\$\)/kW  
\(\delta_{V(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text{\texttext{\text  
\(S_{\text{min},i}\): minimum nameplate capacity of a reactive power generation unit \(i\)  
\(S_{\text{max},i}\): maximum nameplate capacity of a reactive power generation unit \(i\)  
\(S_{\text{Loss},i,h}\): losses of transmission line (or branch) \(i\) at period \(h\)  
\(\mathbf{S_{load}}\): complex power of loads connected to each node: \(P+j\mathcal{Q}\):\(|S|\angle\delta_{\text{s}}\)  
\(T\): the number of tap-changing transformers  
\(Tap_{i}\): tap position of transformer \(i\)  
\(Tap_{min}\) and \(Tap_{max}\): minimum and maximum tap positions  
\(\mathbf{V^{(k)}}\): voltages induced in each node at iteration \(k\)  
\(V^{(k)}_{,h}\): voltage (magnitude) of bus \(i\) at period \(h\) and iteration \(k\)  
\(V_{m}\) and \(V_{n}\): voltages of buses \(m\) and \(n\)  
\(\mathbf{V_{nom}}\): magnitude of the nominal (or rated) voltage  
\(V_{\text{set}}\): set voltage magnitude of a reactive power generation unit  
\(x_{i}\): location to which a reactive power generation unit can be connected  
\(W_{Loss}\), \(W_{RPG}\), and \(W_{V}\): weighting factors for losses, reactive power generator installation cost, and voltage variation, respectively  
\(\mathbf{Y_{bus}}\): bus admittance matrix  
\(Y_{eq}\): series admittance of a tap-changing transformer  
\(Y_{ex}\): excitation admittance of a tap-changing transformer  
\(y_{i}\): capacity of a reactive power generation unit  
\(Y_{m0}\) and \(Y_{n0}\): admittances of buses \(m\) and \(n\) connected the ground  
\(\mathbf{Y_{parallel}}\): admittance matrix of parallel elements connected to the ground  
\(Z_{bus}\) or \(\mathbf{Z_{bus}}\): bus impedance matrix
## 1 Introduction
### Motivation and Contributions  
Based on the abovementioned, secure and multicast routing in FANETs have been studied considerably. However, there is still a gap in the literature regarding secure multicast routing against wormhole attacks in FANETs with CF mMIMO networks. To fill this gap, we proposed a DL-based secure multicast routing (DLSMR) protocol against wormhole attacks that allows the protocol to predict the next secure node within a short time. Furthermore, the combination with TD-PSO-based clustering allows the protocol to find the optimal cluster header. The goal is to improve the security, scalability and stability performance, and achieve an optimal secure route that meets the requirements of 5G and beyond 5G wireless networks. The main contributions of this article can be summarized as follows:  
* We propose a deep learning-based secure multicast routing (DLSMR) protocol to establish a high-stability multicast tree to avoid wormhole attacks in FANETs with CF-mMIMO. Specifically, we utilize a deep learning model to predict whether the next node is a wormhole or not. Additionally, we use various network parameters to establish routes that support more strong connectivity.
* We design a novel top-down particle swarm optimization (TD-PSO)-based clustering protocol in FANETs to reduce control overhead and improve route connectivity. The proposed TD-PSO, considering the node position, velocity, direction, and remaining energy, forms a cluster to optimize the cost function by combining the remaining energy weight, cosine similarity, cosine distance, and node degree. This strategic approach leads to electing cluster heads. Furthermore, to ensure communication continuity between cluster heads when the subsequent ones fall outside of the transmission range, our TD-PSO protocol also designates gateway nodes.
* The performance evaluations show that the proposed DLSMR with TD-PSO protocol can establish a more robust route against wormhole attacks than the benchmark protocol. Additionally, the proposed TD-PSO clustering supports stronger connectivity as clustering changes the network topology hierarchically. In addition to that, we also compare the proposed protocol under two different mobility models (reference point group mobility and random waypoint) to show the effectiveness of the proposed protocol.  
The remainder of the paper is organized as follows. Section 2 introduces the overview of wormhole attack. Section 3 introduces the particle swarm optimization theory. Section 4 introduces the proposed routing protocol that consists of the basic concept of the proposed routing protocol, the proposed TD-PSO-based clustering protocol, the proposed DLSMR, and proposed deep-learning design. Section 5 presents the performance evaluation that consists of simulation environments and parameters, performance metrics, and numerical results. Section 6 concludes the paper. For ease of presentation, Abbreviations summarizes the main abbreviations used in this paper.
## 4 Enabling Technologies for FSO Communication
### Adaptive Transmission  
This section presents adaptive transmission methods that dynamically adjust transmission parameters including, code rate, transmit power, and modulation size, to optimize communication performance. By adapting these parameters in real-time, these methods aim to maintain reliable and efficient data transmission even under varying channel conditions and interference levels. Also, by employing these adaptive transmission techniques, wireless communication systems can significantly enhance their performance, achieving a balance between reliability, efficiency, and speed. This dynamic adjustment capability is crucial for modern communication systems, including cellular networks, satellite communications, and emerging technologies like UAV-to-ground FSO systems, where varying conditions are the norm.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 2 Literature Review
### PDIV Modelling  
With the purpose of ensuring that PD-free service lasts as long as possible, it is crucial to consider the PDIV during the preliminary design stage of the insulation system. As previously mentioned, the turn-to-turn insulation is the weakest point of the insulation system and has a higher probability of developing PD activity. Therefore, to design an insulation system adapted to the application preventing PD, it is necessary to develop accurate models for the estimation of turn-to-turn PDIV.  
Several models are developed in the literature and can be separated into three categories: analytical equations, FEM (Finite Element Method) simulations, and probability models. Among them, several models are based on [PERSON]'s law, which defines the breakdown voltage of a gas between two electrodes. Hence, first, [PERSON]'s law is introduced and some notes on its use in twisted pair PDIV estimation are gathered.
# Embracing the Future Internet of Things +
## 2 Future Hyper-Connected IoT Framework
### IoT Framework for Autonomous Driving on a Large Scale  
As an application scenario of the future hyper-connected IoT, we analyse an IoT framework for autonomous driving in large scale environments such as cities. We first describe a layered view of IoT for autonomous driving functionalities and then provide a federation view for scalability.  
Figure 2(a) shows a functional view of a high-level IoT architecture for the considered use cases. This view can be seen as an extension to the IoT Architecture Reference Model (IoT ARM) [5]. There exist four layers, starting from the _things and external services layer_ and continues with _network layer_, _IoT layer_, and _application layer_.  
The things layer includes any possible actively or passively connected (e.g., observer, actuator, or an object being observed) things. The vehicles include autonomous cars, drones, public transportation vehicles, non-autonomous vehicles, and so on. External services can provide information related to traffic situations or other related information such as weather conditions. The network layer provides the communication capabilities to provide connectivity through advanced 5G technologies and ITS-G5 (Intelligent Transport Systems operating in the 5 GHz frequency band) for vehicle to vehicle or vehicle to road-side communication. The IoT layer has functional building blocks such as device management, context management, semantic interoperability, process and service management, security, and data analytics. Applications of autonomous driving systems are built on top of the IoT infrastructure and they may include services such as optimization of autonomous transportation using cars or taxis.  
Figure 2: Future Hyper-connected IoT.  
Figure 3b shows the federated architecture of multiple pilot sites (regions) where each pilot has its own cloud infrastructure served by IoT platforms such as FIWARE [6,7] and oneM2M [8]. The pilot sites are connected to each other through the federated IoT platform. The interfacing between the different pilot sites (or different clouds of pilot sites) are provided by the IoT Broker components which implement Next Generation Service Interface (NSSI) protocol [9].
# Effect of Thermal Boundary Condition on Tilting Pad Journal Bearing Behavior
## 2 Numerical Methods  
### Varying Viscosity Reynolds Equation  
Viscous shearing in the thin film is the main heat source in the bearing system. Since the iso-viscosity Reynolds equation does not account for the change in viscosity in the thickness direction of the oil film, a modified equation should be used to consider heat transfer and generation in the thin film. In this study, the generalized Reynolds equation was used to consider the temperature distribution in the film thickness direction and the resulting viscosity change. An FE model was created to calculate the hydraulic pressure by discretizing the Reynolds equation. Equation (1) represents the generalized Reynolds equation derived using [PERSON]'s second law and mass conservation law. The Reynolds equation calculates the fluid film pressure between two surfaces in relative motion.  
\[\
abla\cdot(D_{1}\
abla p)+\ (\
abla D_{2})\cdot(U_{2}-U_{1})\ +\ (\
abla h) \cdot U_{1}\ +\frac{\partial h}{\partial t}=0 \tag{1}\]  
From Equation (1), assuming that the bearing pad does not produce sliding motion results in a more generic form as in Equation (2),  
\[\
abla\cdot(D_{1}\
abla p)+\ (\
abla D_{2})\cdot U\ +\ \frac{\partial h}{ \partial t}=0 \tag{2}\]  
where \(D_{1}\) and \(D_{2}\) are defined as  
\[D_{1}=\int\limits_{0}^{h}\int\limits_{0}^{z}\frac{\xi}{\mu}d\xi dz\ -\frac{\int \limits_{0}^{h}\frac{\xi}{\mu}d\xi}{\int\limits_{0}^{h}\frac{1}{\mu}d\xi}\int \limits_{0}^{h}\int\limits_{0}^{z}\frac{1}{\mu}d\xi\ dz \tag{3}\]  
\[D_{2}=\ \frac{\int\limits_{0}^{h}\int\limits_{0}^{z}\frac{1}{\mu}d\xi\ dz}{ \int\limits_{0}^{h}\frac{1}{\mu}d\xi} \tag{4}\]The time derivatives of the oil film thickness (\(h\)) presented in Equations (1) and (2) were used to calculate the bearing damping coefficient, and was also used to calculate the bearing equilibrium state using the time transient approach. Lubricant viscosity is assumed to be a function of only temperature and the following Equation (5) presents the relationship between the viscosity and the temperature, where \(\mu_{0}\) is a viscosity at a reference temperature \(T_{0}\) and \(\beta\) is a viscosity coefficient.  
\[\mu=\mu_{0}e^{-\beta(T-T_{0})} \tag{5}\]  
For the use of the generalized Reynolds equation, the following assumptions were made.  
1. Full laminar flow
2. Constant pressure in the radial (film thickness) direction
3. Negligible shaft curvature effect
4. Negligible fluid inertia
5. Constant fluid density
6. Temperature-affected viscosity as depicted in Equation (4)
7. Incompressible Newtonian fluid
8. Non-slip at the solid and fluid interface
9. Reynolds cavitation boundary condition with a back substitution presented by [PERSON] and [PERSON] [37].
## 2 Methodology
As noted above, and to mimic the quantum-INS experiments of reference [23], we were interested in the structure of the D\({}_{2}\) guest molecules within the cages. A natural way to analyse the data is to generate a three-dimensional density of the centre of mass with respect to the large (or small) cages, averaged over all large (small) cages in the simulation cell and over all time steps in our (path-integral) molecular dynamics simulation. The resulting density can then be displayed through examination of isosurfaces of the spatial-density data (vide infra). Turning to the matter of investigating the so-defined and resultant tetrahedral sites in terms of their hopping dynamics and distribution of dwell times therein, we identified every doubly occupied large cage in our simulation cell, recording the labels of the molecules forming the vertices of each cage. Using this information, we could then find the geometric centre of each large cage's four hexagonal faces on each time-step of a molecular-dynamics simulation. Then, for each of the two guest molecules, we recorded which of the four hexagonal mid-points that the guest molecule was closest to on each step of the simulation. As the simulation progresses, the guest molecules gradually switched between the vicinities of different mid-points, which we characterised by taking a histogram of the hopping lifetimes. Each lifetime was an unbroken sequence of (classical) molecular-dynamics time steps in which a particular guest molecule was closest to a particular midpoint. Unfortunately, PIMD has well-known (in-principle) pathologies with respect to theoretically rigorous time-series representation of dynamical/vibrational properties, so we conducted this moderate towards ambient-temperature analysis (80, 100 and 125 K) for classical propagation only in a manner consistent with QENS studies [26], where quantum phenomena do not dominate [23]. Finally, we characterised the resulting histogram by fitting the long-time data points (\(t>5\) ps) to a double exponential decay profile for both guest types (a pair of D\({}_{2}\) in the large cages of a D\({}_{2}\)O lattice, and a H\({}_{2}\) duo in large cavities of H\({}_{2}\)O-lattice type), i.e.,:  
\[h(t)=A_{1}\exp(-t/\tau_{1})+A_{2}\exp(-t/\tau_{2}) \tag{1}\]  
where \(\tau\) is the characteristic relaxation time for the guest molecules' site-residence times, with \(\tau_{2}\) giving the longer-time characteristic decay. From assessing a single to a double exponential fitting strategy [28], we found that the latter gave better-fidelity fits. This is reflective of the 'bifurcated' nature of caged/confined dynamics of guest molecules [28; 29; 30], which undergo local site-specific vibrational motion, and more diffusive inter-site translations [23; 26]. Therefore, the initial decay, \(\tau_{1}\), is more reflective of local site-specific rattling motion.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 5 Types of Methodology
### Keyframe Selection  
The primary versions of the video summarization systems fundamentally employed frame categorization methods that relied on the labeled data inputted into them. Features drawn from video frames are used as input for an algorithm that categorizes the frames as being of interest or not being of interest. Only frames of interest are incorporated into the summary. The description and detection of important criteria is a challenge and is used to select the appropriate keyframes. The techniques for keyframe selection are presented below.  
* _Object-based keyframe selection:_ Summaries that are based on objects specifically concentrate on certain objects within a video. These are scrutinized and used as benchmarks to identify the video keyframes. The techniques in the following studies [13; 16; 26; 46; 48; 52; 58] are object-based keyframe selection.
* _Event-based keyframe selection:_ Event-oriented summaries pay special attention to particular events in a video, which are examined and used as benchmarks for the summarization process. The keyframes of the video are identified based on the selected event. The personalized video summary technique in [71] is the selection of event-based keyframes.
* _Perception-based keyframe selection:_ The detection of the important criterion of user perception is a factor in keyframe selection. In studies [22; 32], the keyframes are detected based on the user's perception.
## 4 Analysis and Discussion
### Field Monitoring Results of Foundation Pit
#### 4.1.3 Monitoring Results of Surface Settlement  
Figure 6 illustrates the surface settlements around the foundation pit during the excavation of sub-pit 3A10-1. The surface settlement in the vicinity of the Yuantai Building is presented in Figure 6a. Monitoring points DBC 28-31 were located at the surface between the foundation pit and the building. Due to the limited distance, only two monitoring points were set up at DBC 28-30 and only one monitoring point was set up at DBC 31. It was observed that the surface settlements at monitoring points DBC 28-31 were all smaller than those at the other three monitoring points, and that the soil in some areas produced a small uplift due to the combined effect of the foundation pit excavation and the Yuantai Building. This justified the interpretation of the deep displacement of the soil at this location. The shallow soil formed a \"silo\" shape between the rigid foundation pit enclosure and the reinforced foundation of the Yuantai building, and the compression of the loads and supports caused this part of the soil to rise towards the surface. As the Yuantai building was subjected to large loads, the soil settled under the loads, and the surrounding soil may experience some uplifts under the action of the sliding surface. This was also a possible cause of the surface uplift. Monitoring points DBC 26, 27, and 32 all produced a relatively large settlement. However, the settlements at the fourth measurement point of all three monitoring locations were smaller than those at the other points, which also supported the idea that the loads of the Yuantai Building caused the upward displacement of the surrounding soil.  
Figure 6b illustrates the surface settlement behind the diaphragm wall on the north side of sub-pit 3A10-1. Consistent with that reported by [PERSON] and [PERSON] (1998), the displacement at this location essentially showed a notch-type distribution pattern. The surface settlements behind the diaphragm wall on the west side of sub-pit 3A10-1 are illustrated in Figure 6c, with a \"triangular\" pattern at the three monitoring points DBC 19, 20 and 22. This may be due to the large horizontal lateral displacement at the top of the wall at this location and the uniform settlement at monitoring points DBC 23 and 25. The dense stacking of materials at this location at the construction site may contribute to this phenomenon.  
Figure 6: Monitoring results of surface settlement. (**a**) The side of Yuantai Building. (**b**) The north side of sub-pit 3A10-1. (**c**) West side of sub-pit 3A10-1.
## 2 Materials and Methods
### Evaluation  
The current SIF verification methods include two main categories: site-measured data and existing SIF products. Since the experimental area was covered by dense forests, it was difficult to measure the variables directly. Furthermore, most current SIF datasets have limited temporal and spatial resolutions to meet the needs of accurate evaluation, and thus there remains the problem of obtaining instantaneous, high-resolution SIF maps [3]. Thus, to verify our results, the SFM algorithm was also used to retrieve SIF in this study. The core of the SFM algorithm involves using broader spectral regions to fit the least squares formula and then obtaining the SIF and reflectance curves simultaneously. The least squares function is shown in Equation (7):  
\[min\sum(L_{\text{TOC}}(\lambda)-\rho(\lambda)L_{\text{WLR}}(\lambda)-F_{\text {s}}(\lambda))^{2} \tag{7}\]  
where \(\lambda\) is the corresponding wavelength in the fitting windows, \(L_{\text{TOC}}\) is the radiance signals, \(L_{\text{WLR}}\) is the radiance of the standard plate, \(\rho\) is the surface reflectance, and \(F_{\text{s}}\) is the SIF signal function from the top of the canopy (TOC). The SIF and reflectance signal curves could be obtained by least squares fitting through the radiance and irradiance signals. In all, this research jointly compared and certificated the inversion results of the SFM and PCA algorithms.
## 3 Test Results
### Polarisation Curve Test Results
It can be seen from Figure 5 that, from 0 to 2160 days, the Tafel slope of the anodic polarisation curve is less than that of the cathodic polarisation curve; that is, the solution continuously invaded the coating, causing corrosion of the steel bars. Figure 6 shows that \(i_{\mathrm{corr}}\) of group A is 0.11 \(\upmu\)A/cm\({}^{2}\) at 0 days, and that of group B is 0.03 \(\upmu\)A/cm\({}^{2}\) at 0 days. According to Table 4, group A specimens are in a low-corrosion state, and group B specimens are in a no-corrosion state. That is, when reinforcements are not protected by the coating, they will reach a state of corrosion after curing in MOCC. \(i_{\mathrm{corr}}\) for group A is 7.88 \(\upmu\)A/cm\({}^{2}\) at 2160 days, and that of group B is 0.08 \(\upmu\)A/cm\({}^{2}\) at 2160 days. According to Table 4, the specimens in group A are in a serious corrosive state, whereas those in group B are still in a no-corrosion state. These results demonstrate that the YP coating can protect the steel bars from corrosion in MOCC. Figure 5: Polarisation curves for group B specimens.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### Feature Modeling-Based Approach  
This category of approaches focuses on feature modeling concepts that support the representation of components within a Software Product Line (SPL) [97]. The proposed technique in the context of cybersecurity addresses security variability to identify potential vulnerabilities and detect cyber risks. Feature Modeling (FM) handles system configuration to represent pertinent attributes of each element, capture relationships between those attributes, synthesize all dependencies and constraints, discover potential variabilities among software systems, and then reason about the compactly represented systems' possible configurations [98]. This approach ensures comprehensive coverage of the target system and the dependency management tasks required, as shown in Figure 19. This helps to identify indirect vulnerabilities by assessing how suspicious changes in one system element may impact other components. In order to improve cyber detection accuracy, the FM makes use of its knowledge of syntactic and semantic properties of the code to identify hidden and context-specific vulnerabilities. Next subsections provide more details on the previously provided features.