esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Studies
### Delivery Ratio  
In brief, delivery rate is the main network metric used to evaluate network performance. It is defined as the ratio of the number of packets successfully delivered to the destination to the number of packets sent by the source node. The equation for the Number of Delivered Bundles (\(NDB\)) against the Number of Generated Bundles (\(NGB\)) is as follows [25]:  
\[Delivery\ Ratio=\frac{NDB}{NGB} \tag{10}\]
# Performance-Constraint Fault Tolerant Control to Aircraft in Presence of Actuator Deviation
## 2 A Class of Aircraft Dynamic Model Considering Actuator Deviation
After \(t\geqslant T_{r}\), tracking error and extended state will enter in bounded range \(\mathcal{B}\). \[\mathcal{B}:\left\{(\tilde{x}_{i},\boldsymbol{\Psi}(\tilde{x}_{n}))\ |\ V\leqslant\min \biggl{[}\frac{\varepsilon}{(1-\theta_{0})\sigma},\biggl{(}\frac{\varepsilon} {(1-\theta_{0})\varrho}\biggr{)}^{\frac{1}{\beta}}\biggr{]}\right\} \tag{25}\]  
This means that the output tracking error \(|\boldsymbol{y}-\boldsymbol{y}_{d}|\) will remain a sufficiently small neighborhood near the origin after \(t\geqslant T_{r}\), and the states errors satisfy condition of \(|\tilde{x}_{ij}|<k_{ji}\), \(j=1,2,\ldots,m\) in the whole process. Further, it indicates the virtual control functions (15) and controller (19) signals are bounded, \(\|\boldsymbol{a}_{i}\|\leqslant\rho_{i},\mathrm{i}=1,2,\ldots,\mathrm{n}-1, \|\boldsymbol{u}\|\leqslant\rho_{u}\), so the system state always lies in the set of  
\[\Theta:\{\boldsymbol{x}_{i}=(x_{i1},x_{i2},\ldots,x_{im})\ |\ \|\boldsymbol{x}_{i}\|<\| \boldsymbol{k}_{i}\|+\rho_{\mathrm{i}}\leqslant k_{io}\} \tag{26}\]  
The above analysis can be summarized as the following theorem, and the FTC-FT controller framework is as shown in Figure 1.  
**Theorem 1**. : _Consider a class of strict-feedback MIMO system with actuator failure (1), design a finite-time-convergence fault-tolerant (FTC-FT) controller incorporated with adaptive mechanism (19), and the virtual functions (10) and (15), then the closed-loop system is practical finite time stability, and tracking error will enter the origin neighborhood (25), the system state always lies in the predefined range in whole convergence process._
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 1 Introduction
To overcome these problems, an effective method is to use the preconditioned method with multiplying the preconditioning matrix to the time-derivative term, which can reduce the disparity between the convective and acoustic wave velocities through the modification of the eigensystem of the governing equations. The preconditioned method has already been applied to the flow analysis [7; 8; 9], but it hasn't been coupled with CSD for FSI application yet. In the current study, a complete integrated CFD-CSD computational method is implemented for aeroelasticity analysis in the time domain. CFD solver adopts the preconditioned Navier-Stokes governing equations, which can provide accurate flow solutions for both compressible and incompressible flows, are solved to evaluate aerodynamic loads, especially the nonlinear loads in the transonic regime. The structural model is tightly coupled with the fluid model by a pseudo time-stepping temporal discretization method. A modified mesh deformation method based on reduced control points RBF is utilized to update the fluid volume mesh, and an RBF based mapping algorithm is employed for data exchange on the fluid-structure interface. To evaluate the validity of the developed methodology, the flutter boundary and the limit cycle oscillation of Isogai wing and the flutter boundary of AGARD 445.6 wing, both of which have typical nonlinear characteristics, are analyzed and validated by experimental data.
## 4 Origin-Destination Matrices Estimation
### Implemented Solution
#### 4.1.1 Destination Estimation Algorithm  
The origin of trips is identified by combining smart card and GPS location data, since the location of the bus is recorded whenever a passenger pays for a ticket using a smart card. However, since passengers are only required to validate their smart cards when boarding and not when alighting the bus, the destination of each trip is unknown and must be estimated in order to generate OD matrices.  
A destination estimation algorithm was developed based on the assumptions of the trip chaining method: (i) the origin of a new trip is near the destination of the previous one; and (ii) at the end of the day, users return to the origin of their first trip of the day. Figure 13 shows an example of the proposed method: the passenger performs three smart card transactions throughout the day. The boarding bus stops associated to each transaction are marked in green, and the estimated destinations of trips and trip legs are marked in orange.  
In the example, the first transaction of the day occurs at 07:30, when the passenger boards bus line \(A\) at bus stop \(A_{19}\). Later, at 08:15, the passenger boards bus line \(B\) at bus stop \(B_{9}\) without paying for a new ticket. Since the boarding occurred within the validity of the previous ticket, the trip is assumed to be a transfer between buses. The closest stop from line \(A\) to bus stop \(B_{9}\) is \(A_{23}\), which is assumed to be the destination of the leg trip starting at 07:30. The last transaction of the day occurs at 17:20, when the passenger boards line \(C\) at bus stop \(C_{4}\) and pays for a new ticket. Bus stop \(B_{12}\) is identified as the destination of the leg trip starting at 08:15, since it is the closest stop from line \(B\) to bus stop \(C_{4}\)  
Figure 13: Example of the trip chaining algorithm to estimate destinations.  
Since a new ticket was payed for, no further transfers are considered. Thus, an OD pair is identified between bus stops \(A_{19}\) and \(B_{12}\). Finally, the destination of the last trip of the day is assumed to be bus stop \(C_{8}\), since it is the closest bus stop of line \(C\) to the origin of the first transaction of the day (\(A_{19}\)). As a result, two OD pairs are identified, one consisting of two leg trips with a bus transfer and the other being a direct trip.
## 2 Related Work
### System Modelling
#### 3.3.1 Adapted Whale Optimizer
* Position update based on exploration and the exploitation mechanism. In the hybrid framework, two optimizers are integrated, namely, the adapted grey wolf optimizer and the adapted whale optimizer. During each iteration, the best optimal solutions are determined simultaneously from both optimizers, and these solutions are further compared to select the best solution. This best solution is again given to both the optimizers to produce new better solutions as compared to previous solutions obtained. The cycle is repeated again in the same way. Figure 3: Integrated view of the proposed optimization framework, consisting of Phase 2. * The best search agent update. During each iteration \(t\), the solution vector gets updated. Next, the fitness of each solution is determined by using the fitness function (35). The solution that has a fine fitness value (maximum best suitable route index (BSRI)) is assumed as the best search agent, which is going to be used in the next iteration. * Termination When the maximum iterations are achieved (\(t_{max}\)), the best optimal reliable node-disjoint route from each \(SN_{i}\) to BS is discovered in the EH-WSN. If the maximum iterations are not achieved, then the cycle is again repeated for finding the better solutions. From the solution vector, the best solution in terms of the most-reliable node-disjoint route is selected after several iterations, simultaneously comparing solutions from both optimization algorithms, namely, the whale optimization and grey wolf optimization algorithms. The best solution is one of the solutions from the available solutions in the solution vector, fulfilling all criteria as per the fitness function.
## 2 Methods
### Material Properties and Heat Transfer Model  
The heat transfer model is set up in Comsol by simplified 3D geometry of the cells and the surrounding passive cooling system (heat transfer material) as depicted in Figure 2. The geometry of the cooling system is inspired by the design of a power tool cell pack; however, it is reduced and simplified to increase computational efficiency. Heat transfer in the cells and the cooling material is computed by Comsol heat transfer in solid materials. Heat loss to the environment is taken into account by a convective heat flux surface boundary condition for all surfaces in contact with surrounding air. Ambient temperature was set to 25 \({}^{\circ}\)C and convection coefficient was set to 5 \(W/\)m\({}^{2}\) K (moderate air movement). Event interfaces were used to control charge stop at \"stop temperature\" of 50 \({}^{\circ}\)C and charge restart at \"start temperature\" of 46 \({}^{\circ}\)C. Non-convective air (as reference) and three solid passive cooling materials were investigated in this study. Table 1 sums up the key parameters of the materials. Phase change in the latent heat storage material was implemented by adapting the heat capacity of the material at melting/solidifying temperature. Further documentation of the heat transfer in solids as well as the convective heat flux surface boundary condition and the equations used can be found in Appendix B as well as in the Comsol documentation [48].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Material** & **Cooling** & **Cooling** & **Cooling** & **Cooling** & **Cooling** \\ \hline Reference (non-conv. air) & & & Comsol database “air” [48] & & - & - \\ Heat-conductive polymer I & 1540 & 1470 & 2,263,800 & 2.2 & - & - \\ Heat-conductive polymer II & 980 & 3500 & 3,430,000 & 0.74 & - & - \\ Latent heat storage material & 1220 & 2100 & 2,562,000 & 0.4 & 39 \({}^{1}\) & 70,000 \({}^{1}\) \\ \hline \hline \end{tabular}  
* Latent heat storage: Phase change at 39 \({}^{\circ}\)C.  
\end{table}
Table 1: Investigated cooling materials and their key properties.  
Figure 2: 3D geometry of the investigated power tool battery pack. Ten Samsung INR2170040T cells are enclosed in a matrix of passive cooling material.
It has been known for a long time that SRB prefer to grow at pH range 6-8 [7]. However, SRB occur in acidic environments [8; 9] and alkaline environments [10; 11; 12; 13]. SRB can be divided into three groups based on their optimum environmental pH for growth: acidophilic, alkaliophilic and neutroph. Acidophilic SRB have been found to be able to grow at pH 2.9 to 6.5 [14; 15; 16]. Alkialophilic SRB grow optimally in extremely alkaline environments from pH of 6.9 to 9.9 [13]. Neutrophil SRB species are better known than acidophilic and alkaliophilic SRB and thought to be only able to grow at pH range 6-8 [7]. However, later studies found that neutroph SRB could also survive and grow in extreme pH conditions [17; 18] and current bioremediation treatment of acid mines mainly use neutroph SRB which is highly sensitive to acidic environment [19].  
Extreme pH environments can be human-made, e.g., acid mine tailings, or natural, such as alkaline ground waters and there are many places that equipment have been working under such conditions. After time of exposure to these conditions, the life expectancy of equipment materials might be reduced due to the deterioration of materials caused by corrosion. The existence of bacteria in these environments also causes MIC, especially with SRB, the dominant species accounting for MIC. Thus, corrosion under these conditions is caused by the combined effect of environmental pH on corrosion, bacteria metabolism for their survival and growth which have effect on microbial corrosion. Understanding survival ability of SRB in extreme pH environment can propose a suitable mechanism of MIC caused by SRB in such conditions and possible mitigation methods for corrosion.  
MIC of metals is influenced by a number of conditions including the type of metal, temperature, pH, flow conditions, applied voltage, coatings, biocides, and hydrophobicity. However, pH of the environments presents some issues that have not been discussed widely in MIC literature. The goal of this present work is to elucidate and summarise existing knowledge on how neutrophil SRB survive and grow under extreme pH conditions, including sulphate reduction activities, and its effect on material corrosion.
## 4 Proposed Scheme
### Message Signing and Verification Phase
#### 4.5.1 Message Signing  
When vehicle \(V_{i}\) wants to assign a message, it has to execute the steps below, where \(t_{i}\) is the latest timestamp and \(M_{i}\) is infotainment information or safety-related messages.  
* Vehicle \(V_{i}\) obtains an updated domain key \(s_{d}\) via a one modulo division operation \(\gamma_{d}\) mod \(sk_{i}=s_{d}\).
* Vehicle \(V_{i}\) calculates \(\alpha_{i}=H_{2}\left(AID_{i}||R_{i}||M_{i}||T_{i}\right)\) and then computes \(\beta_{i}=H_{3}(AID_{i}||R_{i}||T_{i})\), where \(M_{i}\) is safety-related-message and \(T_{i}\) is the current timestamp.
* By using multiplication inverse \(\frac{1}{p}\), vehicle \(V_{i}\) sets the message signature \(\sigma_{i}=s_{d}.\alpha_{i}+\frac{1}{p}\beta_{i}\) mod q, which \(\frac{1}{p}\) is utilized to mitigate the receiver's verification cost.
* Vehicle \(V_{i}\) sends the message-signature tuple \(\left\{AID_{i},R_{i},M_{i},T_{i},\sigma_{i}\right\}\) to the neighboring vehicles.
# Numerical Evaluation of Hot Air Recirculation in Server Rack
## 4 Results and Discussion  
### Pressure in the System  
As shown in Figure 11, the server fans were reconfigured to a series arrangement with four additional server fans added behind the original server fans, but the overall server configuration remained unchanged. All the openings, internal compartments, boundary conditions, and fan-set speed remained unchanged. As shown in Figure 11, the results showed an increased system available pressure when the system was running against a 10 Pa hot aisle back pressure. Based on the proportionality between the fan static pressure and the airflow rate through the system, the supply inlet temperature is expected to decrease. The noticeable working point of the two systems is that after 22 s the fans in parallel provide a reduced pressure in the system. The increase in system pressure is expected to lower hot air recirculation at a back pressure of 10 Pa in comparison to the parallel-configuration fan set.  
Figure 12 shows the system's available pressure at free delivery pressure (0 Pa back pressure). The series configuration consumes significant power with no significant improvement in air distribution. It provides no further noticeable contribution to the system after 41 s. These results also reveal that the series fan configuration is not ideal for  
Figure 11: Available system head-pressure comparison between parallel and series fan configuration at 6800 rpm speed and 10 Pa backpressure.  
servers that run on a low IT load (computation workload), which is prevalent in cloud computation servers.  
Figure 13a,b shows the velocity streamlines at the 0 Pa back pressure. As shown in Figure 13a, the parallel fan configuration possesses a uniform intake flow resulting from a low system impedance and there is also poor airflow uniformity in the discharge region of the fans, which signifies hot recirculation and insufficient static fan pressure. A similar phenomenon is evident from the series fan configuration velocity streamline results shown in Figure 13b. Although the series fan arrangement improves the system's available head pressure, it does not improve the cold air intake distribution. Air is predominately drawn through the far-right corner of the server intake section, which leaves a dead zone next to the hard drive (SAS). Both configurations showed airflow recirculation under free-delivery pressure conditions.  
Figure 14a,b show the system pressure against time for the simulated series and parallel fan configurations operating at \(-\)10 Pa to 20 Pa back pressure and a constant fan speed of 6800 rpm. A significant system pressure improvement was evident for the series configuration under 20 Pa and 10 Pa back-pressure conditions. In both configurations, the \(-\)10 Pa to 5 Pa back-pressure conditions exhibited similar system trends. However, the results from the parallel fan configuration server show different behaviors. The available pressure head of the system increased with the back pressure. Contrary to common knowledge, high back pressure decreases the available system pressure.
# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
###### Abstract  
The emerging applications of deep learning in solving geophysical problems have attracted increasing attention. In particular, it is of significance to enhance the computational efficiency of the computationally intensive geophysical algorithms. In this paper, we accelerate deabsorption prestack time migration (QPSTM), which can yield higher-resolution seismic imaging by compensating absorption and correcting dispersion through deep learning. This is implemented by training a neural network with pairs of small-sized patches of the stacked migrated results obtained by conventional PSTM and deabsorption QPSTM and then yielding the high-resolution imaging volume by prediction with the migrated results of conventional PSTM. We use an encoder-decoder network to highlight the features related to high-resolution migrated results in a high-order dimension space. The training data set of small-sized patches not only reduces the required high-resolution migrated result (for instance, only several inline is required) but leads to a fast convergence in training. The proposed deep-learning approach accelerates the high-resolution imaging by more than 100 times. Field data is used to demonstrate the effectiveness of the proposed method.  
seismic imaging; high-resolution; deep learning; acceleration +
Footnote †: journal: Applied Sciences
## 4 Antennas and Antenna Related Technologies for Future Internet
### Physics-Assisted Distributed Solutions  
A number of review papers focus on electrically large antennas (ELAs) for near-field communications [282; 283; 284]. The near field of an antenna extends to a distance equal to \(2D/\lambda^{2}\), known as the Rayleigh distance, which is used to distinguish between near- and far-field regions. The extension of the near field is defined by both the wavelength (ranging from a few millimeters down to a fraction of a millimeter) and the maximum dimension \(D\) of the antenna (or the array of a RIS). Thus, structures with relatively small physical dimensions (e.g., a window supporting a RIS) may behave as an electrically extremely large antenna. In the near-field region, electromagnetic waves exhibit spherical wavefronts rather than planar ones. For mmWave and THz communications, the near-field distance varies from hundreds of meters to a few centimeters. Figure 17 depicts different configurations of ELAs, which are categorized according to antenna array distribution, antenna spacing, antenna element size modeling, and array geometry. Many studies demonstrate the importance of ELAs in improving communication reliability, security, and efficiency in near-field wireless systems.  
Figure 17: Large antenna array configurations for near-field communications [283].  
In [285], a switched-beam system serving a high-speed train is studied, enhancing connectivity through dynamic beam design. In [286; 287], near-field beam focusing is explored, aiming to enhance spatial resolution, reduce interference, and improve physical layer security in near-field communications. Improved accuracy in complex environments through range estimation is investigated in [288] with regard to near-field multistatic radar. In [289], sparse arrays are used to harness the benefits of near-field multiuser communication, achieving better spatial selectivity and improved user separation, while in [290], a novel theoretical model for received power estimation in phased-array-based wireless power transfer systems is studied, optimizing energy delivery in the near-field region. A true time delay system is utilized in [291] to improve wideband MIMO system performance in near-field environments. In [292], the study focuses on extremely large cylindrical antenna array systems, optimizing beamforming patterns for improved coverage and capacity in near-field systems. Fluid and movable near-field antenna systems are discussed in [293; 294], aiming to enhance adaptability, flexibility, and spatial diversity for multiuser near-field communication systems.
# Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock
###### Abstract  
This study investigates the effectiveness of vegetation reinforcement on the stability of a slope with red-bed soft rock in a slope along the Xining-Chengdu railway, China. Four kinds of vegetation were considered to reinforce the soil and the slope. The rooted soil parameters were determined based on the laboratory tests. A numerical model was developed based on the actual geometry and soil layer distributions. The soils were modeled as elastic perfectly plastic materials and the vegetation reinforcement was represented as addition cohesion of a series of subsoil layers within a given depth. The effectiveness of vegetation on slope reinforcement under both dry and rainfall conditions was investigated regarding this case. The potential failure surface and corresponding factor of safety of the red-bed soft rock slope for those different conditions were analyzed and compared. It has been found that the addition of vegetation increased the safety of slope stability whether the slope is under a dry condition or a rainfall condition, while the increasing proportion of factor of safety due to vegetation reinforcement for this case is very limited. The results and findings in this study are still significant for the practitioner to evaluate the reasonability of vegetation reinforcement.  
soil reinforcement; vegetation; red-bed soft rock; rainfall; finite element method +
Footnote †: journal: Applied Sciences