esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and formulations
### Wavenumber Domain Approach for the Total Radiation Efficiency of the Finite LR Plate  
A finite LR plate with elastic boundary conditions is embedded in an infinite rigid baffle, as shown in Figure 2. As the acoustic medium is air in this paper, the effect of the fluid load is ignored. A harmonic motion at the arbitrary frequency \(\omega\) is assumed. The radiated pressure \(p(x,y,z)\) satisfies the Helmholtz equation:  
\[\mathbf{\left(\
abla^{2}+k^{2}\right)}p(x,y,z)\ =\ 0, \tag{26}\]  
Figure 2: Schematic of the locally resonant plate embedded in an infinite rigid baffle. where \(\
abla^{2}~{}=~{}\partial^{2}/\partial x^{2}+\partial^{2}/\partial y^{2}\), \(k~{}=~{}\omega/c_{0}\), and \(c_{0}\) is the speed of sound. The propagation of acoustic pressure in a plane, harmonic, bending wave in 3-D space can be represented as:  
\[p(x,y,z,t)~{}=~{}\widetilde{p}e^{-jk_{x}x-jk_{y}y-jk_{z}z}e^{j\omega t}, \tag{27}\]  
where \(k_{x}^{2}+k_{y}^{2}+k_{z}^{2}~{}=~{}k^{2}\). Substituting Equation (27) into the momentum equation:  
\[\frac{\partial p}{\partial z}+\rho_{0}\frac{\partial v}{\partial t}~{}=~{}0, \tag{28}\]  
Yields:  
\[p(x,y,z)~{}=~{}\frac{\rho_{0}c_{0}k}{k_{z}}v(x,y,z), \tag{29}\]  
where \(v(x,y,z)\) is the particle velocity in air, and \(\rho_{0}\) is the air density. By using the spatial Fourier transform as:  
\[P\Big{(}k_{x},k_{y},z\Big{)}~{}=~{}\int_{-\infty}^{\infty}\int_{-\infty}^{ \infty}p(x,y,z)e^{-jk_{x}x-jk_{y}y}dxdy, \tag{30}\]  
\[V\Big{(}k_{x},k_{y},z\Big{)}~{}=~{}\int_{-\infty}^{\infty}\int_{-\infty}^{ \infty}v(x,y,z)e^{-jk_{x}x-jk_{y}y}dxdy, \tag{31}\]  
Equation (29) can be written as:  
\[P\Big{(}k_{x},k_{y},z\Big{)}~{}=~{}Z_{a}\Big{(}k_{x},k_{y}\Big{)}V\Big{(}k_{x},k_{y},z\Big{)}, \tag{32}\]  
where \(Z_{a}\Big{(}k_{x},k_{y}\Big{)}\) is the acoustic impedance, expressed as:  
\[Z_{a}\Big{(}k_{x},k_{y}\Big{)}~{}=~{}\pm\Bigg{\{} \begin{array}{l}\frac{\rho _{0}c_{0}k}{\sqrt{k^{2}-k_{x}^{2}-k_{y}^{2}}},~{}k_{x}^{2}+k_{y}^{2}<k^{2}\\ j\frac{\rho_{0}c_{0}k}{\sqrt{k_{x}^{2}+k_{y}^{2}-k^{2}}},~{}k_{x}^{2}+k_{y}^{2} >k^{2}\end{array} . \tag{33}\]  
Considering the radiation of the air-plate interface, if \(V\Big{(}k_{x},k_{y},z~{}=~{}0\Big{)}\) is known, the upper surface pressure \(P\Big{(}k_{x},k_{y},z~{}=~{}0\Big{)}\) can be derived. The term \(V\Big{(}k_{x},k_{y},z~{}=~{}0\Big{)}\) can be determined by the base plate velocity \(v(x,y)\), which can be acquired by multiplying \(w(x,y)\) by \(j\omega\). For the baffled LR plate under harmonic excitation, the total acoustic power can be calculated by integrating the acoustic intensity over the plate-baffle plane, i.e., \(z~{}=~{}0\), expressed as:  
\[W~{}=~{}\frac{1}{2}\text{Re}\bigg{(}\int_{-\infty}^{\infty}\int_{-\infty}^{ \infty}p(x,y)v^{*}(x,y)dxdy\bigg{)}, \tag{34}\]  
where the superscript of \({}^{*}\) means a complex conjugate.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 4 Robust Consensus Analysis  
Equation (14) is the mathematical model of the consensus system for the particular case of three DC motors considering a cyclic topology; however, it is worth noting that the method presented in this section is valid for any topology that could be considered. In real life, it does not exist a perfect mathematical model that represents the dynamic performance of a physical system, so different areas of control theory, such as robust control, seek to compensate for modeling errors. Robust control considers the uncertainty in the mathematical model, taking it into account in the analysis stage so that the results are closer to reality than in the case when uncertainty is not considered. The state of the multi-agent system (14) represents the information exchange of the variables that must be synchronized, which, in our case, are the speeds of the DC motors. For the above, it is essential to use sensor devices frequently affected by the measurement noise. This phenomenon is modeled considering parametric uncertainty in the coefficients of the _Laplacian matrix_, as shown below, see [29]  
\[\dot{\xi} =\,\,\,\,\mathcal{L}(\mathbf{q})\xi+Bu_{1} \tag{27}\]where  
\[\mathcal{L}(\mathbf{q})=\left[\begin{array}{ccc}q_{11}&q_{12}&0\\ 0&q_{22}&q_{23}\\ q_{31}&0&q_{33}\end{array}\right]\qquad\quad B=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right]\qquad\quad\mathbf{q}=\left[\begin{array}{c}q_{11}\\ q_{12}\\ \vdots\\ q_{33}\end{array}\right]\in Q\]  
and \(Q\) is defined as  
\[Q\equiv\left\{\mathbf{q}=\left[\begin{array}{ccc}q_{11}&\cdots&q_{33}\end{array} \right]^{T}:q_{ij}^{-}\leq q_{ij}\leq q_{ij}^{+}\right\} \tag{28}\]  
the sets defined as in (28) are generally called boxes because of the geometric shape they have. On the other hand, manipulating Equation (27), the following equation can be obtained:  
\[\mathcal{L}(\mathbf{q})=\mathcal{L}(\mathbf{q^{0}})+\mathcal{L}(\mathbf{r}) \tag{29}\]  
the vectors \(\mathbf{q^{0}}\) and \(\mathbf{r}\) are the nominal and uncertain parameters of the multi-agent system, respectively. Their structure is as follows:  
\[\mathbf{q^{0}}=\left[\begin{array}{c}q_{11}^{0}\\ q_{12}^{0}\\ \vdots\\ q_{nm}^{0}\end{array}\right]\mathbf{r}=\left[\begin{array}{c}r_{11}\\ r_{12}\\ \vdots\\ r_{nn}\end{array}\right]\text{ and }r_{ij}\in[q_{ij}^{-}-q_{ij}^{0},q_{ij}^{+}-q_{ij}^{0}]\]  
the new way of representing the parametric uncertainty in (29), allows for describing the system (27) as follows:  
\[\xi = \mathcal{L}(\mathbf{q^{0}})\xi+Bu_{1}+\mathcal{L}(\mathbf{r})\xi \tag{30}\]  
As we can see in the above equation, the uncertainty was only considered in the multi-agent system because our main objective was to establish the consensus in this system and achieve synchronization in the DC motors' speed. The uncertainty in the DC motors' mathematical model was treated independently through the control law applied to each motor (11).
## 5 Simulation
### Performance Measures  
In addition to the usual performance measurements for VM consolidation, such as the reduction in energy consumption, number of migrations, and cost, SLA violation representing the quality of the DC operation environment is considered. Users want stable service from the cloud provider for a mission-critical system influenced heavily by the short delay rate of the service that the user is trying to provide. One of the most obligatory conditions for a cloud provider to provide a stable service environment to users is the stable operation of the DC. [PERSON] et al. proposed an equation for determining SLAV representing the number of SLA violations that occurred in Infra structure as a service (IaaS) environment. The SLAV uses the SLA violation time per active host (SLATAH) and performance degradation due to migrations (PDM), where SLATAH represents the percentage of overload occurring time during the PM operation, as shown in Equation (15). Overload is calculated on the basis of whether CPU utilization has exceeded the threshold. The PDM is calculated by dividing the estimated performance degradation \(C_{degry}\) by the total CPU capacity request  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dell R515** & **HP DL380 G8** & **HP DL585 G7** \\ \hline \# of Processors & 2 & 2 & 4 \\ \# of Cores & 6 & 8 & 12 \\ Memory (GB) & 16 & 32 & 64 \\ Idle Power (W) & 213 & 109 & 258 \\ Peak Power (W) & 420 & 276 & 396 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of PMs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**\# of VMs**} & \multicolumn{3}{c}{**CPU**} & \multicolumn{3}{c}{**Memory**} \\ \cline{3-5}  & & **Mean (\%)** & **Std Dev. (\%)** & **Mean (\%)** & **Std Dev. (\%)** \\ \hline
2013-7 & 500 & 4.893 & 9.076 & 10.807 & 13.505 \\
2013-8 & 500 & 6.959 & 13.23 & 9.263 & 10.065 \\
2013-9 & 500 & 5.732 & 11.234 & 9.937 & 11.553 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of Bitbrains trace (Rnd).  
for the VM \(C_{req_{j}}\), and \(C_{deg_{j_{j}}}\) represents the performance degradation (10%) caused by VM migration as shown in Equation (16).  
\[SLATAH=\frac{1}{P}\sum_{i=1}^{P}\frac{T_{over_{i}}}{T_{active_{i}}} \tag{15}\]  
\[PDM=\frac{1}{V}\sum_{j=1}^{V}\frac{C_{deg_{j}}}{C_{req_{j}}} \tag{16}\]  
\[SLAV=SLATAH\cdot PDM \tag{17}\]
# Automated Generation of Daily Evacuation Paths in 4D BIM
## 3 Objective and Scope  
The primary objective of this paper is to investigate the frameworks for generating and visualizing evacuation plans in 4D BIM and to evaluate its feasibility and practicability throughout a case study. Evacuation path planning needs to take into account various factors associated to available exits, construction activities, equipment, material stacks, and others. Since these objects cannot be easily defined in the early planning stages, these factors should be generated as user-defined parameters according to the particular project requirements and site conditions. With the assumption that these user-input factors are correctly defined, this paper presents the framework and development of the evacuation planning using 4D BIM. For evaluating the accessible and secured evacuation path, this framework adopts the A* algorithm, which is one of path finding and graph traversal methods calculating the shortest and directed path among multiple nodes. Even though there are three path planning algorithms including A*, Dijkstra, and Genetic Algorithm, the A* algorithm can generate more logical and optimal paths than the other two algorithms [1]. Therefore, this paper implements the A* algorithm on top of the 4D BIM platform to identify an evacuation path using project information and user inputs. To assess its accuracy and feasibility, the case study of the real construction project using its BIM model for evacuation path planning is included in this paper. Out of the entire construction period, four critical construction phases were selected to provide the associated project's 4D BIM and user-defined parameters for the pathfinding implementation.  
The application of the accurate pathfinding algorithm using user-defined parameters to 4D BIM is critical because daily-changing construction work and site conditions should be evaluated by a robust pathfinding method to generate the daily egress paths. The analyzed daily evacuation paths and secured space information are expected to be shared in daily pre-task tool-box meetings with work crews so they can recognize the accessible and secured pathways to quickly get out of the emergent or dangerous areas. In addition, the analyzed results can be significantly useful to dispatch a rescue team to the right place throughout a secured pathway in the case of construction accidents or emergency situations.
## 3 SEIRS-F Agent-Based Model  
The SEIRS-F (i.e., susceptible, exposed, infected, recovered, and failed) agent-based model proposed in this work is an individual, discrete, and stochastic model. This model has allowed analyzing the component failure behavior and the malware behavior from a cross-domain perspective by integrating new elements that will enable the adjustment of the characteristics of the model to more realistic interactions between component and malware. Therefore, sensors, malware, network topology, environment, and maintenance have been defined as agents. Additionally, transition rules have been adjusted with characteristics of different agents and behaviors of the WSN in the environment.  
Additionally, the behavior and characteristics of an agent can be evaluated individually in a slice of time \(t\). Finally, this model uses the advantages of agent-based models for cross-domain dependency in wireless sensor networks.  
In the proposed SEIRS-F model, the sensor nodes adopt, in each slice of time \(t\), one of the following states (see Figure 1):  
Susceptible: the sensor is working correctly without subsystem failure and has not been infected by malware, but it has the characteristics to be failed or infected.  
Exposed: the sensor is reached by malware but cannot transmit malware to the neighbor sensor due to the characteristics of the sensor. Whether the sensor works properly depends on the type of malware.  
Infected: the sensor infected by malware. The infected sensor obtains the ability to transmit malware to its neighbors. Whether the sensor works appropriately depends on the type of malware.  
Recovered: the sensor acquires temporal resistance to malware when it has successfully removed malware or installed security fixes.  
Failed: the sensor that dies because of malicious physical damage, subsystem failure, battery life out, or the quick discharge when infected by malware.  
The population of nodes is supposed to remain constant, consequently: \(S(t)+E(t)+I(t)+R(t)+F(t)=N\) at each step of time \(t\). For a given time \(t\), \(N\) is the total number of  
Figure 1: Scheme of the SEIRS-F model.  
agents, \(S(t)\) are the susceptible agents, \(E(t)\) stands for the number of exposed agents, \(I(t)\) represents the number of agents in the infected state, \(R(t)\) are the recovered agents, and finally, \(F(t)\) denotes the agents in the failed state.  
Agents concern the meaning of autonomy and interact, collaborate, coordinate, and negotiate with both each other and the environment, based on the transition rules. In the model proposed in this work, the agents and the characteristics will be defined in Section 3.1. In Section 3.2, these transition rules will be detailed according to agents' behavior, which is established by rules that define the agents' response to other agents and the environment.
###### Abstract  
Theoretically, the topological charge \(l\) in the vortex can be any integer or fraction, thus the vortex carrying different topological charges can form an infinitely orthogonal orbital angular momentum state space, which can provide new dimensional resources for optical communication. However, high-capacity optical communication requires low delay, thus real-time detection of the OAM is significant for communication. Metasurfaces have the characteristics of low loss, ultra-thin, easy integration, and flexible phase controls, which provide a meaningful way to realize integrated OAM generation and detection. Here, an optimized streamlined metasurface (OSM) is presented, which can detect high-order vortex beams in a single, simple, and rapid manner by photon momentum transformation (PMT). Since different vortices are converted into focusing modes with distinct azimuthal coordinates on a transverse plane through PMT, a single measurement can determine OAMs in an ample mode space. In addition, the OSM can detect more and higher order OAMs compared with a discrete metasurface (DM) at the same size, due to its better wavefront sampling capabilities. With the merits of an ultra-compact device size, simple optical structure, and outstanding vortex recognition ability, our approach may underpin the development of integrated optics and quantum systems.  
optimized streamlined metasurface; vortex recognition; photon momentum transformation; orbital angular momentum; large mode detection Article
## 3 Results and Discussion
### Comparison of the Numerical and Analytical Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{2}{c}{**Young’s**} & \multicolumn{1}{c}{**Tangent**} & \multirow{2}{*}{**Yield Strength**} & \multirow{2}{*}{**Factor of Safety**} & \multirow{2}{*}{**Factor of Safety**} & \multirow{2}{*}{**Factor of Safety**} \\  & \multicolumn{1}{c}{**Modulus (GPa)**} & & & & & \\ \hline
1 & 100 & 0 & 0.7 & 1.12 & 1.08 & 3.70 \\ \hline
2 & 200 & 20 & 0.7 & 1.17 & 1.12 & 4.37 \\ \hline
3 & 300 & 10 & 0.7 & 1.13 & 1.09 & 3.64 \\ \hline
4 & 100 & 10 & 1 & 1.46 & 1.40 & 3.88 \\ \hline
5 & 200 & 0 & 1 & 1.41 & 1.36 & 3.11 \\ \hline
6 & 300 & 20 & 1 & 1.45 & 1.39 & 3.81 \\ \hline
7 & 100 & 20 & 1.3 & 1.81 & 1.74 & 3.97 \\ \hline
8 & 200 & 10 & 1.3 & 1.72 & 1.68 & 2.86 \\ \hline
9 & 300 & 0 & 1.3 & 1.69 & 1.66 & 2.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the numerical and analytical results for the safety factor of the autofrettaged cylinder without machining under working pressure of 400 MPa. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{2}{c}{**Young’s**} & \multicolumn{1}{c}{**Tangent**} & \multirow{2}{*}{**Yield Strength**} & \multirow{2}{*}{**Factor of Safety**} & \multirow{2}{*}{**Factor of Safety**} & \multirow{2}{*}{**Factor of Safety**} \\  & \multicolumn{1}{c}{**Modulus (GPa)**} & & & & & \\ \hline
1 & 100 & 0 & 0.7 & 1.12 & 1.08 & 3.70 \\ \hline
2 & 200 & 20 & 0.7 & 1.17 & 1.12 & 4.37 \\ \hline
3 & 300 & 10 & 0.7 & 1.13 & 1.09 & 3.64 \\ \hline
4 & 100 & 10 & 1 & 1.46 & 1.40 & 3.88 \\ \hline
5 & 200 & 0 & 1 & 1.41 & 1.36 & 3.11 \\ \hline
6 & 300 & 20 & 1 & 1.45 & 1.39 & 3.81 \\ \hline
7 & 100 & 20 & 1.3 & 1.81 & 1.74 & 3.97 \\ \hline
8 & 200 & 10 & 1.3 & 1.72 & 1.68 & 2.86 \\ \hline
9 & 300 & 0 & 1.3 & 1.69 & 1.66 & 2.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the numerical and analytical results for the safety factor of the autofrettaged cylinder with only internal machining under working pressure of 400 MPa. Upon examination of Table 12, it is observed that the safety factor determined for the autofrettaged cylinder with only internal machining decreases by an average of 11.94%, whereas the safety factor for the autofrettaged cylinder with external machining decreases by an average of 4.97%. This indicates that the internal machining operation leads to a greater residual stress loss compared to the external machining operation. This effect can be attributed to the distribution of equivalent residual stresses obtained after the autofrettage process, which decrease from the inner surface towards the outer surface of the cylinder. Consequently, internal machining, which directly affects the region with the highest residual stresses, causes a more significant reduction in residual stresses than external machining.
## 3 Method
### Cochlea Model  
All sounds were processed using a cochlea model with post-processing of the cochlea output. The cochlea model has been used before in a study of phase synchronization of partials in the transition between mechanical motion on the basilar membrane into neural spikes [27], in a coincidence detection model using an Iszikevich neural model [27], when studying cello surface sounds [43], and as foundation of a pitch theory [18].  
The model assumes the basilar membrane (BM) to consist of 48 nodal points and uses a finite difference time-domain (FDTD) solution with a sample frequency of 96 kHz to model the BM motion. The sample frequency is needed to ensure model stability. The recorded sounds played to the subjects were used as input to the model. The sounds had a sample rate of 96 kHz, corresponding to that of the model. The BM is driven by the ear channel pressure, which is assumed to act instantaneously on the whole BM. This is justified as the sound speed in the peri- and endolymph is about 1500 m/s, while the sound speed on the BM is only 100 m/s down to 10 m/s. The pressure acting on the BM is the input sound. The output of the BM is spike activity, where a spike is released at a maximum of the positive slope of the BM and a maximum slope in the temporal movement at one point on the BM. This results in a spike train similar to measurements.  
With periodic sounds the spikes are only present at the respective Bark bands of best frequency of the periodic sound. With harmonic overtone spectra all Bark bands of the spectral components show spikes, while the other Bark bands do not. Also, the delay between the high-frequency spikes and the low frequencies at the BM apex is about 3-4 ms, consistent with experiments. The model does not add additional noise. It also has a discretization of 48 nodal points, which is two nodal points for each Bark band.  
In terms of the 3-4 ms delay, it is interesting that, although the lymph is acting instantaneously on the whole basilar membrane, the wave travelling along the membrane builds up. This is the result of the inhomogeneous stiffness and damping of the basilar membrane leading to this very special kind of wave.
# Research on Tracking Control of Urban Rail Trains Based on Improved Disturbance Observer
## 3 Main Results
### Controller Design and Stability Proof Based on Improved Disturbance Observer
: _A switching function in the observer (13) causes jittering phenomena, adversely affecting the traction and braking equipment of the UTRs. Here, we use the saturation function \(\mathsf{sat}(\mathsf{e_{d}(t)})\) instead of the switching function \(\mathsf{sgn}(\mathsf{e_{d}(t)})\) in the observer, i.e.,_  
\[\mathsf{sat}(\mathsf{e_{d}(t)})= \left\{ \begin{array}{cc}\frac{e_{d}(t)}{\delta},&if|e_{d}(t)| \leq\delta;\\ \mathsf{sgn}(\mathsf{e_{d}(t)}),&if|e_{d}(t)|>\delta.\end{array} \right. \tag{28}\]  
where \(\delta>0\).
## 5 Results Analysis
### Pure Overturning Moment  
#### 5.3.1 Bearing Contact Load Distribution before and after Optimization  
Figure 11a,b shows the bearing contact force distribution before and after optimization under the overturning moment. It can be seen from the diagram that before optimization, each rolling element of the bearing is basically in a normal contact, and the other is in a two-point contact state with normal separation. The load distribution of the rolling element is sinusoidal, and the load distribution of the rolling element is symmetrically distributed along the geometric center of the bearing. When the azimuth is \(\psi=181.8^{\circ}\), the contact force \(Q_{1}\) reaches the maximum 9209 N; when the azimuth is \(\psi=0^{\circ}\), the contact force \(Q_{2}\) reaches the maximum 9217 N. After optimization, the contact force in the rolling body has changed significantly compared with that before optimization. The maximum contact force of \(Q_{1}\) and \(Q_{2}\) is 4005 N and 4007 N, respectively, which is respectively 5205 N and 5210 N lower than that before optimization. The azimuth of the maximum contact force has not changed. It can be seen that the optimized increase in the bearing area of the rolling element, making the load distribution more uniform, effectively improves the bearing capacity.
# Research on the Robustness of Interdependent Networks under Localized Attack
## 3 Results and Analysis
### Artificial Network
#### 3.1.2 The Robustness of Network under PDLA Is Better Than That under TA, RA, or TLA  
From Figure 4, we can see that networks, including isolated and coupled ones, are vulnerable under targeted attack, random attack, or traditional localized attack. The results under TLA and RA (Figure 4b) are consistent with [21; 23], which show that the \"localized attack has stronger attack power than random attack\". However, these two papers do not consider the attenuation of destructive force, so the failure range under localized attack is larger than the actual situation.  
Actually, as shown in Figure 4, the robustness of a network under localized attack with failure probability degradation is relatively strong. For isolated networks, such as WS and BA networks, all nodes are affected by attack (the scale of the attacked area \(p\) = 1), there are still about 30% of nodes (\(S_{\text{WS}}\approx\) 0.33, \(S_{\text{BA}}\approx\) 0.28) that are functional after cascading failures stop, and the remaining functional nodes in an NN network are fewer, about 12.5%. Additionally, from Table 2 we can see that there is no critical threshold of interdependent networks under PDLA. In addition, it can be seen from Figure 4d,e that the failure process of interdependent networks under the targeted attack exhibits a first-order phase transition, while under PDLA, the process is second-order.