esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 6 Conclusions  
The characteristics of EM waves emitted by surface discharge and cavity discharge were studied using FDTD simulations. Laboratory measurements using test cells designed to simulate these two types of discharge showed that the frequencies over which these discharges excite EM waves lie within the UHF range. The FDTD simulations were then used as computational tool to investigate the use of a UHF sensor for measuring EM waves emitted by such discharges. This allowed various parameters related to both the discharge characteristics and the sensor characteristics to be varied and their influence on the measured signals clarified. The results showed the limits of using a monopole UHF sensor for this type of measurement, highlighting the importance of characterizing the PD pulse on one hand, and the choice of the sensor size and location on the other. These type of studies are of great help in understanding the propagation characteristics of EM waves for PD, and for designing UHF sensors destined for these types of measurements.  
Conceptualization, N.H., H.G. and B.B.; methodology, N.H. and B.B.; software, A.L.; validation, N.H., H.G. and B.B. formal analysis, N.H. and H.G.; investigation, A.L.; data curation, A.L.; writing--A.L. and N.H.; writing--review and editing, N.H., H.G.; supervision, N.H. and H.G. and B.B.; project administration, N.H.  
This research received no external funding.  
The authors declare no conflict of interest.
# Water on Actinide Dioxide Surfaces: A Review of Recent Progress
## 3 Water on AnO\({}_{2}\) Surfaces
### Experiments  
The interaction of H\({}_{2}\)O with AnO\({}_{2}\) surfaces initiates with its adsorption and dissociation on the surfaces. It has been shown that the adsorption of H\({}_{2}\)O molecules on UO\({}_{2}\) (111) surface is reversible (80%) at 300 K, indicating that H\({}_{2}\)O is weakly adsorbed on perfect UO\({}_{2}\) (111) surface [58]. On the contrary, H\({}_{2}\)O is strongly adsorbed on surfaces with oxygen vacancies. Figure 6 shows the XPS spectra of reduced surface after exposure to heavy water (D\({}_{2}\)O), then followed by annealing at different temperatures [45]. It is clearly seen that the shoulders for the U4\({}^{\prime}\)\({}^{7/2}\) peaks disappear after exposure to D\({}_{2}\)O and subsequent annealing. This implies the recovery of uranium atoms to the formal oxidation state of +4. More interestingly, temperature programmed desorption (TPD) experiments for H\({}_{2}\) release upon exposing water to an Ar\({}^{+}\) sputtered UO\({}_{2}\) surface show that H\({}_{2}\) production can only occur on reduced (111) surface with oxygen vacancies (Figure 7). The amount of H\({}_{2}\) desorption increases with the Ar\({}^{+}\) sputtering time, namely the concentration of oxygen vacancies on the surfaces [58]. These experiments demonstrate that the interaction of H\({}_{2}\)O with AnO\({}_{2}\) surfaces is dependent on the surface oxygen vacancies, and oxygen vacancies prompt the dissociation of H\({}_{2}\)O and the production of H\({}_{2}\) on AnO\({}_{2}\) surfaces. The interaction of H\({}_{2}\)O with PuO\({}_{2}\) films has been studied using XPS and ultra-violet photoelectron spectroscopies (UPS) [60; 61]. It was found that H\({}_{2}\)O dissociates and forms a thin hydroxyl (OH\({}^{+}\)) layer with small amounts of molecularly adsorbed water at 298 K. While under 80-120 K, H\({}_{2}\)O is adsorbed as thick ice multilayers and no significant OH\({}^{+}\) is detected. The top surface layer can be reduced to Pu\({}_{2}\)O\({}_{3}\) when the ice covered PuO\({}_{2}\) is warmed up under ultraviolet light, while the mechanism of the reduction is still unclear [61]. Another study has reported that H\({}_{2}\)O can catalyze the oxidation of PuO\({}_{2}\) surfaces and the formation of stable compounds PuO\({}_{2+x}\) (x \(<=\) 0.27) containing Pu\({}^{6+}\)[1].
## 2 Materials and Methods
### Mathematical Formulation
To ascertain if the flow appears to have turbulent or laminar characteristics, the critical value of Reynolds number is taken into account through Equation (14). \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]3  
The flow is considered laminar when the Reynolds number ratio between the critical and the normal values is lower than unit \(\left(\frac{Re_{c}}{Re}<1\right)\). The factor of friction in the flow (\(f\)) is determined through the following equation. The subsequent formula utilizes the pressure drop (\(\Delta P\)), which is obtained from the computational fluid dynamics analysis conducted for each case. \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]4  
The flow regime was found to be laminar in all the examined operating points. Thus, Equation (16), which gives the theoretical values of the friction factor considering laminar flow conditions, was utilized for verifying the results of Equation (15) (numerical model) [18]. \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]5  
Except for thermal efficiency, it is important to calculate the exergy efficiency of the collector, as exergy efficiency provides a comprehensive measure of the quality and effectiveness of energy conversion within the system. The expression for the gain of useful exergy from the collector, accounting for the drop of the pressure, can be represented by Equation (17), where \(T_{0}=25\)\({}^{\circ}\)C [30]. \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]6The solar radiation exergy can be determined using Equation (18), as this is provided in References [24; 31] with the assumption that the temperature of the sun is equal to 5770 K [28]. \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]7  
Assuming that the electrical production is useful exergy [24], the collector exergy performance is calculated using Equation (19) [24]. \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]8  
Finally, the optical efficiency is calculated using Equation (20). \[\eta_{el}=\frac{\mathrm{Q}_{el}}{G_{pv}\cdot A_{pv}} \tag{2}\]9  
Parameter \(G_{eff,n}\) refers to the amount of solar radiation received on a surface that is perpendicular to the direction of the rays of the sun.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 4 Performance Comparison
### Comparison and Analysis
[57], 2022 & 0.121 & 0.971 & 5.206 & 0.214 & 0.843 & 0.944 & 0.975 \\  & VADepth [58], 2022 & 0.109 & 0.785 & 4.624 & 0.190 & 0.875 & 0.960 & 0.982 \\ \hline \multirow{10}{*}{Transformer-based} & DEST [68], 2022 & 0.095 & 0.752 & 4.207 & 0.165 & - & - \\  & PPVIT [69], 2022 & 0.120 & 0.997 & 5.033 & 0.217 & 0.859 & 0.948 & 0.977 \\ \cline{1-1}  & TransDSSL [70], 2022 & 0.095 & 0.711 & 4.321 & 0.172 & 0.906 & 0.967 & 0.984 \\ \cline{1-1}  & MT-SfMLearner [71], 2022 & 0.104 & 0.799 & 4.547 & 0.181 & 0.893 & 0.963 & 0.982 \\ \hline \multirow{10}{*}{Hybrid} & [PERSON] et al. [10], 2022 & 0.112 & 0.750 & 4.528 & 0.187 & 0.881 & 0.963 & 0.983 \\ \cline{1-1}  & MonoFormer [20], 2022 & 0.108 & 0.806 & 4.594 &pixels lost at the encoder side, which is irreversible. As a result of this limitation of CNN, considerable image features would be missed, and thus the performance would be greatly degraded. Secondly, CNN lacks the ability to capture long-distance dependencies in the image owing to its limited receptive field (3 \(\times\) 3 or 5 \(\times\) 5). Although the atrous convolution enlarged the receptive field [82], it is still limited compared to the Transformer. Therefore, Transformer-based models perform superior at handling intensive prediction tasks such as MDE. Nevertheless, there are still individual Transformer-based methods [18; 20; 21; 80; 81] in Table 3 that perform worse than some CNN-based ones. This happens due to the fact that there are some models utilizing lightweight Transformers, which pursue a dramatic reduction in parameters and computation, inevitably affecting the final performance. It is not in conflict with our conclusion. The comparisons suggest some aspects: (1) for the supervised methods, the Transformer-based method (PixelFormer [11]) achieves the best performance, while the hybrid method (DepthFormer [14]) can yield comparable results; (2) for the self-supervised methods, the hybrid method (MonoVit [21]) outperforms other methods by a large margin; (3) compared with the supervised methods, the self-supervised methods exhibit great potential since the performance gap is closer.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 5 Simulations and Results
### Redundant Link Configuration Simulation  
The objective of the redundant link configuration simulation is to improve the reliability of the decentralized controller configuration. For the redundant path simulation, AnyLogic system modeling software was used as it allows the simulation of event-based conditions such as redundancy and random link failures. To simulate the TE process, this simulation uses a source block and sink block to simulate the information flow of packets between the hosts. The relationship between sent and received packets determines the link availability between the hosts.  
The figure below shows the diagram of the simulation.  
Figure 13 shows that each SDN node is configured with a redundant link. Calculations indicate that the 99% availability per link provides 99.99% redundancy availability per data path.  
* Redundancy availability = \(1-\mathrm{e}^{-1\times(1-0.99)\times(1-0.99)}\) = 99.99%
* Data Pata path availability = \(1-\mathrm{e}^{-8\times(1-0.9999)}\) = 99.92%
* Link Failure rate = \(1-\) Data Pata path availability = 0.08%  
Packets were sent from Host 1 to Host 2, simulating a ping test per the decentralized simulation. The simulation shows that the packet loss is 0.1%. An event-based simulation was setup and initiated on AnyLogic System modelling software.  
Figure 14 shows that the improved result is a significant improvement compared to the single link simulation results of 7.9%, confirming that a redundant link configuration can significantly improve the SDN architecture's reliability. For the TE process, this improvement represents an improvement of 7.9% in successful transactions between the various hosts.
# Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
## 2 Preliminaries
### Differential Evolution (DE)  
DE is a population-based metaheuristic search method which has a population with N floating point solutions represented as \(X_{1}\), \(X_{2,G}\),..., \(X_{\text{N},G}\), (N is the population size). \(G\) is the generation counter, and each individual is a nominated solution, initialized randomly in the search space. It begins with a first random population \(X_{1,0}\), \(X_{2,0}\),..., \(X_{\text{N},0}\). Later, the DE operators, i.e., crossover mutation, and selection operators are applied to refine each generation after the other. This process continues until the stopping criteria become true. For instance, a specified number of generations has been reached or the goal accuracy error level has been minimized under a specific value (\(\varepsilon\)).  
Iteratively, in each generation, \(G\), the optimization is performed by first executing the mutation operator. The solution vector produced by the mutation is \(V_{i_{i}}\) for each single solution \(X_{i}\) (or, namely, the target vector) and would be generated by the mutation strategy.  
The famous and largely used mutation strategy is the \"DE/rand/1\", which is represented as represented in Equation (1).  
\[v_{i,G}=x_{r1,G}+F.(x_{r2,G}-x_{r3,G}) \tag{1}\]  
where \(i\) = 1, 2,..., N, and \(r_{1}\), \(r_{2}\), and \(r_{3}\) are three randomly chosen solutions from the population [1, 2,..., N], and these solutions should not be duplicated. \(F\) is the DE mutation parameter. Then, DE commonly performs binomial crossover for recombining the target vector \(X_{i}\), and the vector resulted from the mutation \(V_{i}\), and the trial vector \(U_{i,j,k}\) which is produced as follows:  
\[u_{i,j,G}=\left\{\begin{array}{ll}v_{i,j,G}&if\;rand_{j}\leq Cr\;or\;j=j_{ rand}\\ x_{i,j,G}&otherwise,\end{array}\right. \tag{2}\]  
where \(j\) = 1, 2,..., \(D\), \(j_{rand}\) \(\in\) {1, 2,..., \(D\)} is an integer that is randomly chosen, rand \({}_{j}\) \(\in\) [0,1] is a random number that is uniformly distributed in the \(j\)th dimension, while \(Cr\) is a control parameter selected between [0,1] that represents the crossover probability. Finally, in order to select the better solution among the trail and target vectors, \(u_{i,G}\) and \(x_{i,j,G}\), respectively, the selection operator is applied as shown in Equation (3).  
\[x_{i,G+1}=\left\{\begin{array}{ll}u_{i,G}&if\;f(u_{i,G})>f(x_{i,G})\\ x_{i,G}&otherwise\end{array}\right. \tag{3}\]  
In order to make the standard DE work with binary-based optimization problems such as the feature selection problem, a large number of modified versions of DE were proposed, in particular the use of logical operators such as those provided by [20] In this version, the classical mutation of the DE has been replaced by the binary mutation represented in Equation (4).  
\[v_{i,G}=x_{r1,G}\odot F\otimes(x_{r2,G}\oplus x_{r3,G}) \tag{4}\]  
where \(\otimes\), \(\odot\), and \(\oplus\) denote AND, OR, and XOR operators.
## 5 Results and Discussion
### Field Monitoring Results
The variation of calculated results is small when the values of \(m\) and \(n\) are greater than 10. It is shown that the values of \(m\) and \(n\) are 11, which is sufficient in terms of computational accuracy. Deformation monitoring points DB-1, DB-2, DB-3, DB-4, and DB-5, and horizontal deformation monitoring point CX-1 in monitoring section A-A\({}^{\prime}\) are selected to verify the accuracy of the proposed method. Figure 13 shows the comparisons of the deformation of the diaphragm wall and the ground measured by the monitoring points and calculated by the proposed method. The calculated horizontal deformation of the top of the diaphragm wall is 0.6 mm, which is a small difference from the measured value. The calculated deformation values for DB-1, DB-2, DB-3, DB-4, and DB-5 are 3.74 mm, 5.95 mm, 7.44 mm, 7.64 mm, and 6.31 mm, respectively. The maximum difference between the measured and calculated deformation is 14.6%, which is acceptable for engineering analysis. Additionally, the measured ground heave is greater than the calculated value, as plotted in Figure 13. The reason is that the vertical deformation of the diaphragm wall is not considered in the proposed model. Actually, the deformation of the wall would affect the surrounding ground heave. However, the measured deformation of the diaphragm wall is 0.2 mm, which is too small to obviously affect the accuracy of the proposed model. Hence, it is indicated that the proposed method can be used to analyze the ground heave induced by shield tunnels passing through the building envelope. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Modulus of Elasticity** & **Poisson Ratio** & **Friction Angle** \\  & \(E\) (GPa) & \(\mu\) & \(\varphi\) (\({}^{\circ}\)) \\ \hline Diaphrag wall & 20 & 0.1 & 50 \\ Equivalent soil & 12.2 & 0.3 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated parameters used in proposed model. Figure 11: The monitoring results of wall top horizontal deformation of ZL1–ZL2. Figure 12: Calculated deformation of underground diaphragm wall acting of additional thrust P.  
Figure 13: Deformation of the diaphragm wall and ground. The solid line is ground heave calculated by the proposed method; dashed line is horizontal deformation of diaphragm wall, solid square is measured deformation and open square is measure horizontal deformation of the diaphragm wall.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 2 Related Work and Motivation
### Edge Node Placement  
Extensive research has been carried out in subjects closely related to the EN placement problem (ENPP) within mobile network planning, facility location and EC implementations studies [6; 7; 8; 9].  
[PERSON] et al. [7], propose to deploy a set of macro-cells whose coverage area is estimated based on a simplification of the underlying demand. In [10], the main target is to find the optimal locations to deploy temporal BSs during emergency situations. In both [7; 10] the customer demand assumed as input is over simplified. As a result, both studies lack the flexibility to deal with highly dynamic scenarios as those found under 5G, where the demand continuously changes hindering the placement optimization process. Meanwhile, in [9] the optimum number of fog nodes (FN's) is found using stochastic geometry analysis, aiming at reducing the total number of F's. [PERSON] et al. article [11] consider workload efficient distribution and strong latency constraints as core parameters to choose adequate edge server locations.  
The authors of [12] present an edge server placement platform that ensures the discovery of unforeseen suitable sites, by analyzing user communication patterns. Some limitations of this study are: (a) user clustering should be done base on the different use cases and typical requirements of 5G networking, including both fixed and mobile baseline data, along with an analysis of demand geo-distribution characterization, (b) 5G ultra-dense deployments and its envisioned complex service demand interrelation (due to the broad mixture of use case scenarios) has to be considered and, (c) the infrastructure sizing should comprise at least the following elements: number of users, application-based requirements, high reliability and availability margins, ultra-high bandwidth requirements. In [13], the data center placement problem is formulated seeking to minimize the costs of the entire data center network. Powerful insight for the ENPP solution is provided in this paper. However, its applicability to solve the problem presented in Section 1 is limited due to the exponential number of edge nodes to deploy in a small to medium-sized 5G service area, the communication restrictions between users and services as a result of the latency constraints, forcing the formulation of a \"coverage\" problem and, the absence of a proper demand characterization in order to achieve real-life optimization.  
Facility Location Problems (FLPs) and server placement problems [14; 15] are closely related to the ENPP. Nevertheless, traditional formulations and solutions in these fields cannot be directly applied to the ENPP since, for instance, traditional FLP formulations usually follow a particular problem type guidelines (i.e., coverage FLP) and the cost function formulation is rather simple. Furthermore, within the ENPP site-dependent characteristic analysis is mandatory given the expected density in urban deployments.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview
### Supported Virtual Network Functions (VNFs) and Value Added Services (VAS)
#### 4.4.3 Value Added Services: Policy-Based Network Management  
In addition to monitoring/alerting/healing, the Telco PaaS supports one more VAS, namely policy-based network management. This is achieved with the integration of an SDN-based network policy framework [34] to the ONOS SDNC. By acting as a layer of abstraction between the network infrastructure and the VSP, the network policy framework allows for simplified control over the network infrastructure. Also, the network policy framework follows an entirely modular architecture, consisting of a Policy Manager and multiple separate Policy Type applications, thus allowing the VSP to add support for new policy types on demand, without affecting the runtime operation of the policy framework. Figure 12 illustrates a (simplified) example operation of the policy framework. There an administrator has deployed the policy manager with two policy type implementations (Firewall and NAT). Subsequently, the administrator issues a request to create a Firewall policy instance that will  
Figure 11: CPU Monitoring/Healing Dashboard of Telco PaaS.  
Figure 10: Network Monitoring Dashboard of Telco PaaS.  
drop traffic between hosts H1 and H2. Upon receiving the request, the policy manager will validate it for correctness, and if successful, it will then request the Firewall Policy Type implementation to enforce the policy in the network. This will result in the Firewall Policy Type to install flow rules in the switches adjacent to H1 and H2, which will block traffic between the two endpoints.
## 2 Materials and Methods
### Design via CT Images  
Figure 2 shows the process of designing the transducer based on the CT images. The CT images examined before fitting were recorded using the Digital Imaging and Communications in Medicine (DICOM) format, which is usually used for the transmission of the images in medical institutions. The data were sent to the manufacturing factory separately from the ear impression. In the manufacturing factory, the DICOM format was first converted into the SLT format, which is a 3D data format, using a 3D Slicer image computing platform [22]. Next, the described object was reversed to obtain the 3D data of the ear impression using 3D Builder (Microsoft, Redmond, WA, USA). Using these obtained data, instead of those obtained via the real ear impression, the hearing devices were designed on the CAD software to create the ear chip. The length of the wire and the type of transducer were determined based on CT images in the manufacturing factory. Both manufacturing processes were individually performed without knowledge of the other processes.
## 4 Results
### Case 3 and Case 4: Same Overriding Plate Movement Rates, Different Subduction Rates  
Figure 5 depicts the thermal structure of the subduction zone located beneath the Halmahera volcanic arc. This model was created with a constant overriding plate movement rate \(U_{0}\) (around 5 cm/a), but with diverse subduction rates \(V\). Specifically, Case 3 involves a subduction rate \(V_{1}\) of approximately 2 cm/a, whereas Case 4 has a rate \(V_{2}\) of about 4 cm/a.  
The results reveal that following the subduction of the cold oceanic plate beneath the Halmahera volcanic arc, a low-temperature geosynclinal region developed beneath the overriding plate. In Case 3, a high-temperature anomaly zone emerged in the mantle wedge above the subduction interface, driven by friction and shear heating within the subduction zone. This led to a decrease in the depth at which rocks undergo brittle-ductile transformation. A vertical temperature gradient formed between the cooler subduction zone and the hotter mantle wedge above, and this inversion could remain stable for a prolonged duration (Figure 5b1). Under these conditions, water released from the slab at depths greater than 100 km entered the mantle wedge, where temperatures were around 1000 \({}^{\circ}\)C. This condition was sufficient to induce partial melting and stimulate magmatism.  
In Case 4, where the subduction rate was higher (\(V_{2}>V_{1}\)), the friction and shear heating within the subduction zone decreased because the shallower rocks exhibited increased ductility. As a result, the vertical inversion of the high-temperature mantle wedge was dissipated along the subduction surface, leading to a reduced duration of the high-temperature anomaly (refer to Figure 5c1). This had a limited effect on the magmatism of the volcanic arc. By analyzing Case 3 and Case 4, it can be inferred that when the movement rate of the overriding plate is consistent, a lower subduction rate facilitates the generation of higher temperatures in the mantle wedge. Additionally, the duration of the temperature anomaly in the mantle wedge is prolonged, which further supports the partial melting of mantle material and the subsequent formation of arc magmatic activity.