esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Transport Protocol Strategies for Rail Applications
## 5 ACS Emulator  
### Testbed  
The testbed of Figure 8 is based on a network of four Linux boxes, two of which are used to simulate the ACS GWs. As a relevant example of a virtual circuit and its implication on routes and multi-path tunnels, the scheme reports a configured virtual circuit used to carry out the performance test where different tunnel configurations were tested.  
Each gateway of such a simulated network can be configured to assume the role of the OG or the NG. As shown in Figure 8, the OG is simulated by a Linux box named host1 and a network gateway by a Linux box named host2. Each gateway (i.e., Linux box) has three network Fast Ethernet interfaces, where each Ethernet link is a 100 Mbit/s crossover link:  
* One is used to allow the agents (running on host3/host4) to access the ACS network (interface named fe3). This interface is configured to provide access to any on-board/network User Agent which is registered to provide or use a service (e.g., file storage, transmitting a video, etc.). Such an interface is controlled by a Dynamic Host Configuration Protocol (DHCP) server which assigns private IP addresses to the  
Figure 8: ACS simulator: testbed scheme.  
Figure 7: SIP INVITE method used to create virtual circuit in ACS.  
host3/host4 eth0 interface via a DHCP client running on such hosts. So, each pair eth0/fe3 represents an LAN between a gateway and the host where the agents run.
* Two Ethernet network interfaces (fe1 and fe2) are used to represent the bearers. From a topological perspective, a bearer is a physical device which can be identified by an IP address. For our emulations, static IP addresses were assigned to such interfaces.  
Linux boxes named host3 and host4 are used to run the User Agents, which can be thought as server/client applications along with the logic to be registered and connected each other via virtual circuits created by SIP sessions. A User Agent can be registered either as service provider or as service client. Each service endpoint can be identified by a unique identifier, which is conveniently represented by an assigned IP address along with a symbolic name. The IP address is assigned dynamically once the registration of an agent is completed. An agent running on host3 can directly communicate with the gateway running on host1 (which simulates an OG). While an agent on host4 can directly communicate with the gateway running on host2 (which simulates the NG).
# Sand and Dust Storms: Impact Mitigation
## 2 SDS Definitions  
There are numerous sources of small particulate matter in the atmosphere, including volcanic dust, sea salt, cosmic dust and various pollutants derived from combustion and industrial processes, but this paper refers to minerals that originate from land surfaces. These particles are commonly graded according to their size, consisting of clay-sized (<4 microns), silt-sized (4-62.5 microns) and sand-sized (62.5 microns-2 mm) material. There is no strict distinction in the definitions of sand and dust storms, since there is a continuum of particle sizes in any storm. Generally, larger particles tend to return to the land surface soon after being entrained and atmospheric concentrations naturally diminish with distance from source areas as material in suspension is deposited downwind by wet and dry processes. Most of the particles transported more than 100 km from their source are <20 microns in diameter [16].  
A dust storm is formally defined by the World Meteorological Organization (WMO) as the result of surface winds raising large quantities of dust into the air and reducing visibility at eye level (1.8 m) to less than 1000 m [17], although severe events may result in zero visibility. A less severe wind erosion event is often referred to as blowing dust. There is no equivalent formal definition of sand storms, but storms dominated by sand tend to have limited areal extent and hence localized impacts, including sand dune encroachment. Dust storms also have local impacts but their smaller particles can be transported much farther--over thousands of kilometers from source, often across international boundaries--which can bring hazardous dust haze to distant locations.  
The duration of SDS events varies from a few hours to several days. Their intensity is commonly expressed in terms of the atmospheric concentration of particles and a distinction is typically highlighted between particles with diameters <10 microns (PM\({}_{10}\)) and those with diameter <2.5 microns (PM\({}_{2.5}\)). Atmospheric PM\({}_{10}\) dust concentrations exceed 15,000 \(\upmu\)g/m\({}^{3}\) in severe events [18]. Hourly maximum PM\({}_{2.5}\) concentrations can exceed 1000 \(\upmu\)g/m\({}^{3}\) during intense dust storms [19].  
In chemical terms, the main component of the particles that make up SDS is silica, typically in the form of quartz (SiO\({}_{2}\)). Other material commonly found in desert dust includes aluminum, iron, calcium, magnesium and potassium as well as organic matter and a range of salts, pathogenic microorganisms--including fungi, bacteria and viruses--and anthropogenic pollutants.
## 3 Methodology
### Computing Platform  
The computing platform used to conduct the experimental evaluation is a workstation equipped with an Intel I9-990 KF Central Processing Unit (CPU) (Intel is based in Santa Clara, CA, USA) with a clock speed of 3.6 GHz, 32 Gbytes of Random Access Memory (RAM), and the Graphic Processing Unit (GPU) NVIDIA RTX4000. NVIDIA is based in Santa Clara, CA, USA. MATLAB R2023a from MathWorks was used to perform the scientific computations with the Signal Processing toolbox and the Machine Learning and Deep Learning toolbox. MathWorks is based in Natick, MA, USA.
## 4 Engineering Applications and Verification
### Risk Evaluation of Water Intrusion
#### 4.2.2 Constant Weights Analysis  
The importance of evaluation indicators will be expressed by the weights. Lots of methods were used to obtain the weights, the most commonly used of which was the AHP method [37]. The constant weights of each indicator are obtained using the MFIM method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Section** & \(\mathbf{I_{1}}\) & \(\mathbf{I_{2}}\) & \(\mathbf{I_{3}}\) & \(\mathbf{I_{4}}\) & \(\mathbf{I_{5}}\) & \(\mathbf{I_{6}}\) & \(\mathbf{I_{7}}\) \\ \hline
1 & 65 & 50 & 22 & 40 & 35 & 60 & 28 \\
2 & 65 & 45 & 20 & 35 & 30 & 55 & 20 \\
3 & 75 & 45 & 25 & 45 & 60 & 60 & 26 \\
4 & 70 & 60 & 27 & 60 & 80 & 80 & 30 \\
5 & 55 & 35 & 35 & 20 & 30 & 30 & 45 \\
6 & 45 & 75 & 30 & 17 & 25 & 25 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation values of six sections.  
Figure 9: Location of Qiyueshan Tunnel.  
Additionally, at the same time, the C-OWA operator is used for the code of the multi-factor interaction matrix.  
For example, the active effort of I\({}_{1}\) to I\({}_{2}\) is 3 in Figure 10. The calculation is as follows. Firstly, the five experts score the active effort of I\({}_{1}\) to I\({}_{2}\) from 0 to 4, respectively. Additionally, the initial matrix A is [2, 3, 3, 3, 4]. In addition, arrange the matrices in descending order as matrix B [4, 3, 3, 3, 2]. Then, the w is calculated using Equation (5); they are [1/16, 4/16, 6/16, 4/16, 1/16]. Finally, the P\({}_{12}\) is calculated using Equation (6), P\({}_{12}\) = 3. All the other interaction values on the non-principal diagonal can be calculated from the indicator riskiness scores using the C-OWA operator to derive the interaction matrix. The results are shown in Figure 10.  
The relation between the active and passive effect of seven factors is shown in Figure 11. The constant weight of each indicator can be obtained by using Equations (1)-(6) and the interaction matrix and C-OWA coding values, \(\omega\) = [0.169, 0.149, 0.126, 0.158, 0.116, 0.122, and 0.160] T. It is shown in Figure 12.  
Figure 11: Relation of active and passive effect of seven factors.  
Figure 10: Construction of multi-factor interaction matrix.  
The variable weight is obtained based on the actual score or measured value, so the variable weight of each section is different. For example, the variable weight of Section 1 is [0.292, 0.109, 0.117, 0.115, 0.084, 0.167, and 0.117] T. The variable weight is shown in Table 9.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 4 Experimental Design  
This study adopts a multi-objective optimization approach for path planning by combining the A* algorithm with a genetic algorithm and applying weighted objectives to achieve optimal solutions. This section provides a detailed explanation of this experimental scheme for multi-objective path planning. The specific experimental scheme is shown in Figure 1.  
This study uses open-source road network data obtained from OpenStreetMap [26]. The road network data are converted into a graph structure, and graph search algorithms are applied for path planning. Each intersection is treated as a node, and the segments between adjacent intersections serve as edges. The node information includes geographic coordinates and other details, while edges represent road segments and contain attributes such as length and road type. Additionally, to better simulate real traffic conditions, this study introduces randomly generated traffic lights and congestion attributes in the experimental design. Traffic light attributes include red and green signals, simulating the signal control characteristics of real-world traffic intersections. Furthermore, each edge is randomly assigned a congestion attribute to mimic the impact of varying vehicle densities on traffic speed, making path planning more aligned with actual requirements.  
In this experiment, an initial population is generated using Algorithm 1, consisting of paths with the same start and end points but different routes. To evaluate the quality of each path individual, a comprehensive fitness function is designed. The fitness function is based on the weighted sum of the following five optimization objectives: total path length, length of congested segments, total time, energy consumption, and safety. To  
Figure 1: Multi-objective path-planning scheme.  
avoid the influence of scale differences between the various objectives on optimization and to make each objective comparable in fitness calculations, each objective is normalized. This normalization ensures that indicators with different units have a consistent basis for comparison. Let \(x\) be the raw value of a certain objective, and let \(x_{\text{min}}\) and \(x_{\text{max}}\) be the minimum and maximum values of this objective within the current population, respectively. The normalized value \(x_{\text{norm}}\) is calculated as follows:  
\[x_{\text{norm}}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{16}\]  
Through this normalization, the range of each objective is unified within the interval [0, 1], ensuring a balanced influence of different objectives on fitness during the optimization process. The normalized initial population data are then processed by Algorithm 2, where continuous crossover and mutation operations are applied to find the optimal path under multi-objective constraints.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 2 Elastic Global and Local Shear Buckling Stress of CSWs  
### Physical Equivalent Parameters of CSWs  
For trapezoidal CSWs that are commonly used in actual girder bridges, when treated as an orthotropic plate or shell, the equivalent poisson's ratios \(v_{x}\), \(v_{y}\)[28], the equivalent elastic moduli \(E_{x}\), \(E_{y}\), the equivalent shear modulus \(G_{xy}\)[29], the equivalent flexural stiffnesses \(D_{x}\), \(D_{y}\) and the torsional stiffness \(D_{xy}\) per unit length of a CSW [7] can be expressed as Equations (1)-(8).  
\[v_{x}=v\frac{E_{x}}{E} \tag{1}\]  
\[v_{y}=v \tag{2}\]  
\[E_{x}=\frac{\dot{P}^{2}(a+b)}{d^{2}(3a+c)}E \tag{3}\]  
\[E_{y}=\frac{s}{4}E \tag{4}\]  
\[G_{xy}=\frac{q}{s}G \tag{5}\]  
\[D_{x}=\frac{q}{s}\frac{E_{y}}{12}^{3} \tag{6}\]\[D_{y}=\frac{E(3a+c)d^{2}}{6q} \tag{7}\]  
\[D_{xy}=\frac{s}{q}\frac{E^{3}}{6(1+v)} \tag{8}\]  
where \(v\), \(E\), \(G\) are the poisson's ratio, the elastic modulus and the shear modulus of original steel plate respectively, and \(t\) is the web thickness. As shown in Figure 1b, \(a\) is the flat panel width; \(b\) is the horizontal projection width of the inclined panel; \(c\) is the inclined panel width; \(d\) is the corrugation depth; \(\partial\) is the corrugation angle; \(q\) is the horizontal projection length of one periodic corrugation; \(s\) is the total folded panel length of one periodic corrugation.
## 3 Theoretical Analysis and Numerical Model
### Mathematical Models  
The air flow velocity in the tunnel is not large and the pressure change is small, so the compressibility of the air flow can be ignored. Therefore, the air flow in the tunnel is considered as a three-dimensional incompressible and stable viscous turbulence. The model of turbulent flow is a high Reynolds number \(k\)-\(\epsilon\) model. Mathematical models include continuity equations, momentum equations, and \(k\)-\(\epsilon\) model equations [26; 27].  
Incompressible Continuity Equation:  
\[\frac{\partial\rho\overline{u}_{i}}{\partial x_{i}}=0 \tag{11}\]  
Incompressible Momentum Equation:  
\[\frac{\partial(\rho\overline{u}_{i})}{\partial t}+\frac{\partial(\rho \overline{u}_{i}\overline{u}_{j})}{\partial x_{i}}=\rho f_{i}-\frac{\partial p }{\partial x_{i}}+\frac{\partial}{x_{i}}(\mu\frac{\partial\overline{u}_{i}}{ \partial x_{i}})-\frac{\partial(\rho\overline{u}_{i}^{\prime}\overline{u}_{j}^ {\prime})}{\partial x_{j}} \tag{12}\]  
Realizable \(k\)-\(\epsilon\) Turbulence model:  
\[u_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon} \tag{13}\]  
\(k\) equation:  
\[\frac{\partial k}{\partial t}+\overline{u}_{j}\frac{\partial k}{\partial j}=v _{t}\left(\frac{\partial\overline{u}_{i}}{\partial x_{i}}+\frac{\partial u_{ j}}{\partial x_{i}}\right)\frac{\partial\overline{u}_{i}}{\partial x_{j}}- \frac{\partial}{\partial x_{j}}\left[\left(\frac{v_{t}}{\sigma_{k}}-v\right) \frac{\partial k}{\partial x_{j}}\right]-\epsilon \tag{14}\]  
\(\epsilon\) equation:  
\[\frac{\partial\epsilon}{\partial t}+\overline{u}_{j}\frac{\partial\epsilon}{ x_{j}}=-C_{\epsilon_{1}}\left(\partial\overline{u}_{i}\overline{u}_{j}\right)\frac{ \partial\overline{u}_{i}}{\partial x_{j}}-\frac{\partial}{\partial x}\left[ \left(\frac{v_{t}}{\sigma_{\epsilon}}-v\right)\frac{\partial\epsilon}{ \partial x_{j}}\right]-C_{\epsilon_{2}}\frac{\epsilon^{2}}{K} \tag{15}\]  
In Equations (11)-(15), \(\rho\) is the fluid density, kg/m\({}^{3}\); \(u_{i}\) and \(u_{j}\) are the velocity components of the fluid, m/s, respectively; \(p\) is the pressure on the fluid microelement, Pa; \(\mu\) is the dynamic viscosity, Pa-s; \(\mu_{t}\) is the turbulent viscosity Pa-s; \(k\) is the turbulent flow energy, m\({}^{2}\)/s\({}^{2}\); \(\epsilon\) is the dissipation rate, m\({}^{3}\)/s; \(\sigma_{k}\) and \(\sigma_{\epsilon}\) are the Prandtl numbers corresponding to \(k\) and \(\epsilon\) equations, respectively. According to the relevant experiments, the model constants were \(C_{\mu}=0.09\), \(C_{\epsilon 1}=1.44\), \(C_{\epsilon 2}=1.92\), \(\sigma_{k}=1.0\), and \(\sigma_{\epsilon}=1.3\).
# Wireless Home Assistive System for Severely Disabled People
## 2 Materials and Methods  
The architecture of WHAS shown in Figure 1 is mainly to introduce the complete concept of the whole assistive communication system, including three important parts. (1) The assistive input accessories include three kinds of digital switches such as the mechanical switch, sensing switch, and bio-signal switch. (2) MCT is an important core device embedded with fuzzy algorithms for translating Morse codes into American standard code for information interchange (ASCII) and playing the roles of keyboard and mouse. Meanwhile, a Bluetooth component is integrated with MCT for the wireless communication function. (3) In addition to the two kinds of hardware mentioned above, the related application software including the PC-based AAC and home appliance control interface were developed to be compatible with the assistive input accessories and MCT. The WHAS consists of three important parts mentioned above in Figure 1 and is described in detail as follows.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 3 Methodology
### Fisher Discriminant Analysis (FDA)  
FDA is a linear discriminant method proposed in 1936. It is a classic supervised learning method often used for dimensionality reduction and feature extraction. It projects the data points that are highly dimensional onto a low-dimensional space (such as a one-dimensional straight line) to make the data points denser and then determines the discriminant analysis function according to the principle of maximizing the distance between classes and minimizing the distance within classes, and finally classifies the new samples [47]. The primary target of FDA is to find the optimal linear approximation of the object feature vectors, which can be used effectively and reasonably in various classification tasks [48]. The discriminant performance of the FDA model is closely related to the information provided by the labeled dataset. It can separate the labeled training data into different categories by using the best projection vectors found by utilizing the label information [49]. FDA has been widely used in many areas including text classification [48], bioprocess monitoring [50], and predicting pillar stability for underground mine [51].
## 6 Scheduling Algorithms
### Aco  
ACO is a bio-inspired meta-heuristic iteration-based optimization technique. Using pheromone trails, chemicals as signals to other ants left on the ground known as the Stigmergy principle, starting from their nest, in search of food, ants find the shortest routes between their origins to the destination. If an ant wants, with a certain probability, to follow a particular path, it follows the pheromone trail. It reinforces the other ants by laying more pheromone on the same trail. As the movement of ants increases on a route, the amount of pheromone increases. Since pheromones' nature is volatile, so the shortest path has more pheromone as compared to the longer one. As a result, ants' movement increases on the shortest route.  
Using this principle, ACO is used for the solution of discrete combinatorial search optimized-solution problems. Self-organization and self-healing are the distinguishing properties of ACO. ACO is used for the residential consumer's energy optimization, which is a novel scheme for such energy management problems [24]. The ACO parameters are given in Table 5.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**S. No.** & **Parameter** & **Value** & **S. No.** & **Parameter** & **Value** \\ \hline
1 & No. of Ants & 12 & 4 & Evaporation rate & 5 \\
2 & Pheromone intensity factor \(\alpha_{1}\) & 2 & 5 & Trail decay factor & 0.5 \\
3 & Visibility intensity factor \(\beta_{1}\) & 6 & 6 & Max. iterations & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ACO parameters.  
Figure 4: Flowchart for the proposed algorithm Time-constrained Genetic (TG)-MFO.  
```
1 Initialization: GA parameters, MFO parameters, the maximum size of the population, No. of iterations. Input: RTP \(\zeta_{n}\), \(AP,\rho,X,\alpha,\beta,V_{T},X_{bat},\lambda\).
3 MFO phase: Random generation of the initial population of moths \(Q_{i,j}\) matrix using Equation (16)
4 Random generation of the initial population of flames \(U_{i,j}\) matrix using Equation (17)
5 Fitness function \(O_{M}\) is evaluated by objective function \(f(M)=(U_{b}(i)-l_{b}(i))*rand()+l_{b}(i)\)
6 Position of individual moths \(Q_{i,j}\) is updated as per the position of flame \(U_{i,j}\)
7 whileNo. of iterations \(<\) population sizedo
8 fori =1:Mdo
9 forj =1:Ndo
10 end for
11 New solution is evaluated
12 Present \(P_{best}\) is assigned to the old \(P_{best}\)
13
14 GA phase: On the basis of \(P_{best}\), generate chromosomes \(x_{i}\) for i = 1,2,...,n
15 Two parent values are selected as 1:2
16 if(The crossover is donethen
17 One offspring is selected
18 Mutation is done
19 else
20 Two parent values are selected again till the crossover is finished
21 end if
22
23 end for
24 if(Mutation is finishedthen
25 The new population is generated
26 else
27 One offspring is again selected until the mutation is finished
28 end if
29 Present \(P_{best}\) is assigned to old \(P_{best}\)
30 end for
31 Output:\(E_{T},E_{RES},\tau_{\varpi},\mu\)
```  
**Algorithm 1**Pseudocode of the proposed TG-MFO algorithm.
## 5 System Performance Analysis
### Relationship Between Component Parameters and EVM  
A sweeping RF carrier frequency with a variable symbol rate can give an insight into the effects of various component design parameters on the EVM [56]. The methodology presented here can be scaled to extract EVM contributions with various modulation sources in conjunction with the performance metrics of mm wave filters, diplexers and other passive components [57]. To assess the effect of filter characteristics on EVM, we employ the following methodology:  
* Sweep a 64-QAM-modulated RF carrier with 1.5, 2 and 3 GHz symbol rates over the passband of two filters (A and B) for band n257 (Figure 20).
* The filters have different bandwidths, roll offs and ripple characteristics.
* As the symbol rate increases, the EVM rapidly degrades, especially near the band edge.
* Filter A shows low EVM variation (\(\Delta\)EVM \(<\) 2.3% at 1.5 GHz) with a minimum EVM of 5.6%.
* Filter B has a minimum EVM of 5.2% and \(\Delta\)EVM \(<\) 1.9%, indicating the impact of a lower ripple compared to Filter A.
* The rejection roll-off of filters influences the asymmetric EVM response.  
Highlighting the sensitivity of EVM to ripple, bandwidth and roll-off factors underscores the utility of this methodology in linking filter parameters to system performance and defining component specifications.  
Figure 19: Constellation diagram of 64–QAM signal with 3 GHz symbol rate modulated on 28 GHz RF carrier: (**a**) ideal and (**b**) distorted.  
Figure 18: Constellation diagram of \(\pi\)/4 DQPSK signal with 3 GHz symbol rate modulated on 28 GHz RF carrier: (**a**) ideal and (**b**) distorted.