esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methods
### Tip-Vortex Regeneration Using Vortex Model
\begin{array}{ll}u_{r}\frac{\partial u_{r}}{\partial r}+\frac{u_{\theta}}{r} \frac{\partial u_{r}}{\partial\theta}&+w\frac{\partial u_{r}}{\partial z}- \frac{u_{\theta}^{2}}{r}\\ &=-\frac{1}{\rho}\frac{\partial p}{\partial r}+v\bigg{(}\frac{\partial^{2}u_{ r}}{\partial r^{2}}+\frac{1}{r^{2}}\frac{\partial^{2}u_{r}}{\partial\theta^{2}}+ \frac{\partial^{2}u_{r}}{\partial z^{2}}+\frac{1}{r}\frac{\partial u_{r}}{ \partial r}-\frac{2}{r^{2}}\frac{\partial u_{\theta}}{\partial\theta}-\frac{ u_{r}}{r^{2}}\bigg{)}\end{array}  \tag{4}\]  
Typically, the radial velocity, \(u_{r}\), is negligible when compared to the tangential velocity \(u_{\theta}\) of vortex core [36]. Therefore, by ignoring the radial direction component and assuming no velocity variation in the tangential direction, i.e., \(\frac{\partial u_{\theta}}{\partial\theta}=0\), a more straightforward relationship between the tangential velocity and pressure [37; 38] is obtained from Equation (4) as follows:  
\[\frac{u_{\theta}^{2}}{r}=\frac{1}{\rho}\frac{\partial p}{\partial r} \tag{5}\]  
where, subscript \(r\), \(\theta\), and \(z\) denote the radial, tangential, and axial directions, respectively, with its origin on the center of the vortex core. From Equation (5), pressure distribution, \(p\) (\(r\)), in the Scully vortex model can be derived as follows:  
\[p(r)=p_{\infty}-\frac{\rho\Gamma^{2}}{8\pi^{2}}\frac{1}{r^{2}+a_{c}^{2}} \tag{6}\]  
The pressure field around the vortex core is reconstructed by combining Equation (6) with the background RANS field. Azimuthal velocity components in the RANS solution are replaced with that of the Scully model, while the axial velocity components in the RANS solutions are retained. The vortex structure generated on the wing-tip is physically smeared with the thickening of the core-radius via viscous diffusion, as it is convected downstream. [PERSON] and [PERSON] [39] derived the formula for predicting the variation in the tangential and axial velocities of the tip-vortex during its convection in the downstream direction using the lifting-line theory under the assumption of inviscid flow. The singularity that occurs on the vortex center in the inviscid flow field is treated by considering the influence of viscous stress inside the vortex core. The vortex core-radius also increases due to the viscous effects, as the vortex moves toward the downstream direction. The variation of the vortex core-radius due to the viscous effects is considered using the following equation [39]. \[\frac{\Delta a_{c}}{c}=2.92\Big{(}\frac{x}{c}\Big{)}^{0.5}\frac{1}{R_{c}^{0.5}} \tag{7}\]  
where, \(\Delta a_{c}\) denotes the variation in vortex core-radius, \(c\) denotes the chord length, \(x\) denotes the distance between wing-tip and the concerned position downstream, and \(Re_{c}\) denotes Reynolds number dependent on the chord length.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 7 Conclusions  
This paper proposes a bow tie FSS design to block lower-band 5G frequencies (FR1). Multiple simulation results proved that this is a decent attempt, with wide and deep attenuation valleys around the desired center frequencies of 700 MHz and 3.5 GHz. Through the equations derived in this work, other researchers could adapt the design to be used for  
Figure 14: Measurements with vertical polarization, compared with simulations.  
Figure 13: Measurement setup with FSS under test, set for vertical polarization.  
their purposes. For instance, adjusting the design to new frequencies in the specified low and high frequency bands would only require solving a 2 \(\times\) 2 linear system determined by Equations (1) and (2) to obtain \(t\) and \(w\). The other important parameters were shown to be less reliable in adjusting, but they can be used for minor tweaking of the center frequencies. Different angles of incidence show that the design is limited in real-life applications due to its poor performance in \(\phi\) changes. It does not mean that it will not be usable, just that this fact needs to be considered before going through with its implementation. As an overall conclusion, this design, supported by measurements, is versatile and provides clear FSS behavior that could make it useful for specific applications.  
Conceptualization, I.C., M.G.S. and J.V.; methodology, M.G.S. and I.C.; software, B.D. and S.M.; validation, M.G.S. and R.C.; formal analysis, I.C., M.G.S. and J.V.; investigation, B.D. and S.M.; writing--original draft preparation, B.D. and S.M.; writing--review and editing, I.C., R.C. and J.V.; project administration, M.G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Government, Ministerio de Ciencia e Innovacion, Secretaria General de Investigacion, grant number PID2020-112545 RB-C52; Xunta de Galicia, project ED431C 2019/26; atlantTlic Research Center and the European Regional Development Fund (ERDF); FCT/MCTES through national funds and when applicable co-funded EU funds under the projects UIDB/EEA/50008/2021.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### Enhancement of Hydrophilicity by Polydopamine Modification
It is important to note that the total BET SSA of the AC was reduced by approximately 10% after PDA modification (851 m\({}^{2}\) g\({}^{-1}\) for P60 and 864 m\({}^{2}\) g\({}^{-1}\) for YS-2) in consideration, the increase in electrode mass was about 20%. Therefore, the higher SAC, regardless of the reduced SSA, means that a more hydrophilic electrode promotes the access of both ions and electrolytes into the electrode pores [38]. However, the SAC decreased by approximately 8% when the IEM was modified with PDA in both the types of electrodes. This decrease in SAC with PDA-modified IEM may be due to the unchanged hydrophilicity (Supplementary Figure S2) and the hindered ion transportation through the PDA, which covered the IEM and possibly acted as an insulator. Despite the negative effects of the PDA-modified IEM, the SAC slightly increased when the IEM was simultaneously used with a PDA-modified electrode in the MCDI. Although there is no evidence for an increased SAC when using both PDA-modified electrode and IEM, there appears to be a synergetic effect where the dissolved salt ions are transported faster and/or in higher quantity between similar hydrophilic materials. Figure 4: Relative salt adsorption capacity (SAC) in MCDI with three combinations of electrode and ion-exchange membrane (IEM); PDA-modified electrode and pristine IEM (EPDA + IEMP), pristine electrode and PDA-modified IEM (EP + IEMPDA), and PDA-modified electrode and IEM (EPDA + IEMPDA). Relative SAC was calculated by SAC at each combination divided from SAC at MCDI with pristine electrode and IEM. To evaluate the effects of hydrophilic electrodes on desalination performance from the thermodynamic and kinetic points of view, a CDI Ragone plot was drawn, as shown in Figure 5. A shift in to the right means an improvement in the SAC and in the upward direction means a fast rate of salt adsorption (i.e., ASAR) [35]. As seen in Figure 5, YS-2 values (blue points) were positioned in a higher region, indicating better kinetics than P60 (black points) both before and after the PDA modification. After PDA modification, each plot shifted higher and to the right, indicating a higher and faster desalination performance. The improvement in SAC with the P60 electrode was larger than that with the YS-2 electrode, which is consistent with the larger increase in capacity (Figure 4). The results in Figures 4 and 5 support the theory that the use of hydrophilic electrodes could improve the desalination performance of MCDI.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 4 Discussion
### Reproducibility of Measurements  
It is very interesting to compare two measurements along the same trajectory performed in different frequency bands. The behavior of the recorded SNR as a function of distance is almost equal at 434 and 868 MHz. This confirms that distance and terrain elevation are the main parameters influencing the propagation. Tropospheric propagation is not expected to have a significant impact, since this propagation mode is occurring mainly at low and middle-latitudes. Therefore, this phenomenon is very unlikely to occur in the Antarctic. In addition, the trajectories followed by the snowmobile are of course only approximately identical. As there are no roads, the vehicle is driving purely on GPS. A slight difference in trajectory did not cause a measurable difference in propagation characteristics, indicating an insignificant influence of multipath propagation.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 5 Route Maintenance
### Improvement of the Routing Table  
To determine the route repair method at the break, the G-AODV protocol needs to improve the routing table. In the routing table, add four fields: hop count, number of hops required to reach the destination node, next two-hop nodes and upper two-hop node. The improved routing table is shown in Table 5. Compared with the original routing table, 16 bytes of data are added to the improved routing table. The data packets transmitted per second increase the bandwidth by up to \(16*R\), where \(R\) is the packet transmission rate (packets/s).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**G-AODV Route Discovery Process:**} \\ \hline Step 6: & The downstream node determines whether it has received the same RREQ packet. If the same RREQ packet has been received, the packet will be discarded. Otherwise, go to Step 7. \\ Step 7: & Determine whether the local node is the destination node. If satisfied, an RREP packet is returned to the source node along the reverse request path, and Step 10 is executed. Otherwise, execute Step 2. \\ Step 8: & Extract the source node and destination node information, construct the RCS signal, and send the RCS signal to the upstream node. \\ Step 9: & Set the routing status in the downstream node routing table to TAG\_CONGEST and execute Step 2. \\ Step 10: & Data transmission is completed, and an effective route from the source node to the destination node is obtained. \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c l} \hline \hline \multicolumn{2}{c}{**The Fields of the Improved Routing Table**} \\ \hline \hline \multicolumn{2}{c}{The IP address of the destination node} \\ \hline \multicolumn{2}{c}{The destination node serial number} \\ \hline \multicolumn{2}{c}{Next-hop node} \\ \hline \multicolumn{2}{c}{Route status flags} \\ \hline \multicolumn{2}{c}{List of pioneers} \\ \hline \multicolumn{2}{c}{Life cycle} \\ \hline \multicolumn{2}{c}{Hop count (4 bytes)} \\ \hline \multicolumn{2}{c}{The number of hops required to reach the destination node (4 bytes)} \\ \hline \multicolumn{2}{c}{Next two-hop node (4 bytes)} \\ \hline \multicolumn{2}{c}{Upper two-hop node (4 bytes)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Improving the routing table.
## 3 Results and Discussion
### Root Cause Analysis  
In Natural Language Processing (NLP) tasks, Convolutional Neural Networks (CNNs) can effectively extract local features of text vectors. However, due to the affinity dependence of natural language on structure, text classification based on CNN will ignore the contextual meaning of words, which limits the accuracy of text classification [36]. A Recurrent Neural Network is an ideal choice for processing dynamic input sequences that are common in NLP. For serialized input, Recurrent Neural Networks (RNNs) can effectively integrate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1(\%)** \\ \hline CNN & 75.26 & 76.11 & 79.62 & 77.83 \\ BiLSTM & 79.23 & 78.24 & 85.77 & 81.83 \\ GRU & 79.14 & 78.77 & 84.54 & 81.56 \\ ET\_p & 83.70 & 82.05 & **89.74** & 85.72 \\ ET\_s & 83.25 & 82.05 & 88.70 & 85.25 \\ ET\_s BiGRU & 83.94 & **84.48** & 86.42 & 85.44 \\ ET\_s\_BG+p & **84.30** & 83.95 & 88.35 & **85.98** \\ \hline \hline \end{tabular} Note: Bold indicates the optimal value.  
\end{table}
Table 3: Comparison of experimental results.  
Figure 6: Model performance comparison line chart.  
adjacent position information [20; 37] and process various tasks in NLP. Both Bi-directional Long Short-Term Memory (BiLSTM) and the Gate Recurrent Unit (GRU) are variants of RNN, so BiLSTM and GRU perform better than the CNN model in the experiment.  
At present, pre-trained models have achieved the most advanced results in various NLP tasks. In general, pre-trained language models are trained on a large amount of text data in a self-supervised manner, and then fine-turned during downstream tasks or directly deployed through zero/few-shot learning without task-specific fine-tuning. Such pre-trained language models have become the new paradigm for Natural Language Processing tasks. The pre-trained language model Ernie [27] has proved to be effective for improving the performance of sentiment classification tasks [38]. Therefore, the performance of each evaluation metric of the models using Ernie-Tiny is better than other basic models.
## 1 Introduction
[22] compared the three methods by using the GPS satellite, and the results showed that the differences between the repeat shift time from the three methods are less than 1.2 s on average, the broadcast ephemeris method has the lowest computation, and the aspect repeat time method is optimal for real-time applications. As discussed above, most of the related research has focused on the global GPS and BDS systems based on the broadcast ephemeris strategy. In addition, they have mainly used the characteristic of the satellite repeat shift time to mitigate multipath error or observation noise, but they have not carried out an analysis and systematic study itself. Meanwhile, few efforts have been made to analyze the characteristics of the Galileo system, nor to compare the performance between the three global systems. Therefore, this paper aims to gain more insights into the characteristics of the satellite repeat shift time itself. Furthermore, this characteristic between the global GPS, BDS, and Galileo systems is also compared and analyzed. Since the GLONASS system broadcast ephemeris is different from the other systems, it is not discussed in this article. Finally, the results are discussed and concluded.
###### Abstract  
Despite the fact that a large number of research studies have been conducted in the field of search and rescue robotics, significantly little attention has been given to the development of rescue robots capable of performing physical rescue interventions, including loading and transporting victims to a safe zone--i.e., casualty extraction tasks. The aim of this study is to develop a mobile rescue robot that could assist first responders when saving casualties from a dangerous area by performing a casualty extraction procedure whilst ensuring that no additional injury is caused by the operation and no additional lives are put at risk. In this paper, we present a novel design of ResQbot 2.0--a mobile rescue robot designed for performing the casualty extraction task. This robot is a stretcher-type casualty extraction robot, which is a significantly improved version of the initial proof-of-concept prototype, ResQbot (retrospectively referred to as ResQbot 1.0), that has been developed in our previous work. The proposed designs and development of the mechanical system of ResQbot 2.0, as well as the method for safely loading a full-body casualty onto the robot's'stretcher bed', are described in detail based on the conducted literature review, evaluation of our previous work, and feedback provided by medical professionals. We perform simulation experiments in the Gazebo physics engine simulator to verify the proposed design and the casualty extraction procedure. The simulation results demonstrate the capability of ResQbot 2.0 to carry out safe casualty extractions successfully.  
rescue robotics; search and rescue; robot design; mobile robot; patient transfer; casualty extraction +
Footnote †: journal: Applied Sciences
## 1 Introduction
[17] investigated the heat transfer properties between Li\({}_{4}\)SiO\({}_{4}\) pebbles and the helium gas. The results show that the temperature of purge gas will rapidly rise from 20 \({}^{\circ}\)C to 500 \({}^{\circ}\)C as the purge gas flows into the pebble bed. For the packed pebble beds with different pebble sizes and pebble properties, [PERSON] et al. [18] and [PERSON] et al. [19] examined the influence of particle size distribution and packing factor on helium flow behavior in pebble beds. The results show that the pressure drop increases not only in proportion to packing factor, but also in inverse proportion to the difference in pebble size. [PERSON] et al. [20] analyzed the friction coefficient of the packing structures and gas flow behaviors of ceramic pebble beds. Their finding indicates that the pressure drop gradually increases with the decrease in the friction coefficient between pebbles. [PERSON] et al. [21] modeled the purge behaviors of helium gas in breeder pebble beds and analyzed the distribution of tritium within the beds. The results show that the velocity has the same damped oscillating profile with radial porosity distribution. [PERSON] et al. [22] conducted numerical and experimental investigations on the flow behaviors of nitrogen and helium gasses in pebble beds. The results demonstrate that localized packing structures can significantly affect the flow characteristics of helium in pebble beds. The above numerical investigation explored the effects of particle size, pebble bed size, temperature distribution, and friction coefficient on the packing behaviors and helium flow characteristics of the pebble bed. However, there is a significant lack of research on the purge gas flow characteristics inside the pebble bed under different wall conditions in the solid tritium-breeding blanket of a fusion reactor. In this study, the effects of fixed walls on the packing structures and flow characteristics of purge gas helium in pebble beds are investigated numerically by combining DEM and CFD methods, which results in an effective tool for simulating the heat and mass transfer in particle packed bed [15; 18]. The pebble packing method based on the DEM simulation as well as the methodology of building a model, mesh-independent analysis and simulation setups based on CFD simulation are introduced in Section 2. In Section 3, the detailed packing structures and flow characteristics, such as packing factor and porosity, pressure distribution, and velocity distribution are obtained. These results are discussed in detail in Section 3. Finally, some conclusions are summarized in Section 4.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics
### Normalized Metrics  
All fairness indexes mentioned above assume maximum fairness, e.g., yield a value of \(1\), only if the same capacity is assigned to all end-to-end demands. Dependent on the fairness method being used, e.g., BSF where fair distribution of resources is focused on a link and not end-to-end, the fairness index may yield a value smaller than 1 even if the maximum possible fairness is achieved. For this reason, the fairness index results which are computed from the LP solutions are normalized by those from the algorithmic solution. In case the normalized index is \(\gamma_{\text{norm}}=1\), this proves that the LP determined an optimum solution.  
The normalized versions of the aggregated allocation, Jain's fairness index and Vardalis's fairness index are defined in Equations (31) to (33):  
\[Alloc_{\text{norm}} =\frac{Alloc_{\text{LP}}}{Alloc_{\text{Algorithm}}}, \tag{31}\] \[\gamma_{\text{Jain,norm}} =\frac{\gamma_{\text{Jain,LP}}}{\gamma_{\text{Jain,Algorithm}}},\] (32) \[\gamma_{\text{Vardalis,norm}} =\frac{\gamma_{\text{Vardalis,LP}}}{\gamma_{\text{Vardalis,Algorithm}}}. \tag{33}\]
## 4 The Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection
### The CBA-CLSVE Framework for Intrusion Detection  
The CBA-CLSVE framework for intrusion detection is divided into three stages. In the first stage, the chi-square index is used for feature selection, that is, to find the optimal subset and delete irrelevant and redundant features. In the second stage, the dataset is divided into training dataset and testing dataset based on the feature subset. The SVM, KNN and DT methods are selected as the base classifiers for the ensemble model, and the Class-Level Soft-Voting strategy is used to integrate the above base classifiers. The class-level weight of each classifier is determined by the Chaos Bat Algorithm. In the third stage, the class-level weights are then used in the soft-voting ensemble to obtain the final evaluation result of the ensemble for each output class.  
The flowchart of the CBA-CLSVE for intrusion detection is shown in Figure 1.
## 3 Numerical Modelling
### Loading and Boundary Conditions
#### 3.3.2 Boundary Conditions  
The boundary conditions between components were defined as shown in Figure 6. A fixed condition was applied to the bottom of the concrete pad. The connection between the concrete pad and the steel beam frame was assumed to be fixed. Between the PDF panel and the steel frame, contact conditions that allowed sliding were applied. It was assumed that the main members of the steel beam frame were completely fixed.  
To quantify the dead load, \(D\), gravitational acceleration was set for the whole structure. Seismic acceleration, \(E\), was applied to the centre of gravity in the water tank. The hydrostatic pressure, \(H\), inside the water tank was modelled according to the coordinates on the \(z\)_-axis_ from the bottom of the tank. Furthermore, a no-separation contact condition was applied between the steel beam frame and the PDF panel. This condition linked the target and contact surface, thereby allowing the contacting nodes to move in a tangential direction. Figure 6 shows the definitions of the boundary conditions used in the FEM analysis.  
In the quasi-static structural analysis, zero period acceleration was applied in three directions, with the maximum derived zero period acceleration being \(0.236\times g\) on the \(y\)_-axis_. According to the design criteria, the factors in the horizontal and vertical directions were defined as 1.5 and 0.8. Figure 7 gives the schematic definitions of the seismic loads, based on the magnitude, \(E\), and the angle, \(\alpha\), from the \(x\)_-axis_. Table 3 summarises the detail of seismic load cases shown in Figure 8.  
Figure 6: Definition of boundary conditions in the quasi-static the transient structural analysis. (**a**) Fixed boundary condition applied at the bottom of the concrete pad; (**b**) Dead load (yellow arrow), \(D\), applied to the firefighting water tank; (**c**) Applied seismic load (yellow arrow), \(E\), at the centre of the tank; (**d**) An example of the hydrostatic pressure (red arrow) applied to the inside of the tank.  
Figure 7: Schematic definition of the seismic loading in the quasi-static structural analysis.  
In the transient structural and one-way FSI analyses, the seismic acceleration time histories shown in Figure 5 were directly applied at the centre of gravity for 30 s. The total simulation time was 45 s, as this allowed the post behaviour of the whole structure to be captured. The size of time step in FEM was 0.05 s.  
In the quasi-static and transient structural analyses, the fluid in the tank was assumed to be a solid body. In contrast, in the one-way FSI analysis, the calculated pressure--which was caused by the sloshing in the tank--was applied to the tank wall.