esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanical Analysis Model for TSWs
### Comparative Study of Computational Results with Existing Method  
Two commonly used thin plate mechanical models are available: one is the Theory of Plates and Shells model [23], which calculates deflection and bending moment under fixed boundary conditions and uniform distributed load; the other is the theoretical model from the Handbook of Building Structural Statics [33], which calculates the bending moment under simple support boundary conditions. To compare these two commonly used methods with the results from this study, TSW structural parameters are taken from Section 4.2. The in-plane forces in the y- and z-directions are assumed to be in situ stresses, while the load in the x-direction is a uniformly distributed load over the entire TSW side. The deflection and bending moment coefficients calculated by the different models are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Calculation Method** & \(w_{mn}\) **(L/2,h/2)** & \(M_{gmn}\) **(L/2,h/2)** & \(M_{mm}\) **(L/2,h/2)** & \(M_{gmn}\) **(0,h/2)** \\ \hline This paper & 0.0013246 & 0.024199 & 0.024209 & \(-\)0.052965 \\ Theory of Plates and Shells & 0.00126 & 0.0231 & 0.0231 & \(-\)0.0513 \\ Error (\%) & \(+\)4.88\% & \(+\)4.54\% & \(+\)4.58\% & \(-\)3.14\% \\ Working Group of Handbook of & - & 0.0368 & 0.0368 & - \\ Building Structural Statics & - & \(-\)52.07\% & \(-\)52.01\% & - \\ Error (\%) & - & \(-\)52.07\% & \(-\)52.01\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with the results calculated by the two theoretical methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Term** & \(w_{mn}\) **(L/2,h/2)** &  
According to Table 4, the results from the Theory of Plates and Shells show an error of less than 5% compared to the results from this study, while the error between the results from the Working Group of the Handbook of Building Structural Statics and this study is 52%. This demonstrates that the results from this study more effectively account for the influence of in situ stresses and boundary conditions.  
It is noteworthy that when boundary stresses are not considered, the results from this study match those obtained from the Theory of Plates and Shells. When boundary stresses are not considered and simple support conditions are applied, the results from this study align with those from the Working Group of the Handbook of Building Structural Statics. This indicates that the results from this study are consistent with previous models under certain conditions.
## 4 Simulation Results
Moreover, the gap between the proposed DQN-RS scheme and the greedy algorithm became larger with the increasing switching loss. Only considering the link with the best CSI, the greedy algorithm resulted in more reps and more frequent switching between the relay nodes, which inadvertently reduced the capacity. To illustrate the effectiveness of our algorithm, we considered \(\mathcal{G}_{\mathbb{C}}=\mathbb{C}_{\text{DQN-RS}}/\mathbb{C}_{\text{greedy}}-1\), defined as the capacity gain by our proposed algorithm over the greedy algorithm. In Figure 6, with the increase of the switching loss on the horizontal axis, the capacity gain \(\mathcal{G}_{\mathbb{C}}\) could reach 18%. Figure 5: An example of relay selection in 10 TSs. Figure 6: Channel capacity versus switching loss \(\tau\). As can be seen in Figure 7, the channel capacity did not increase monotonically with the number of the relay nodes, and the capacity gain \(\mathcal{G}_{\text{C}}\) ranged from 2.59% (\(N=2\)) to 14.6% (\(N=5\)). It is different from the situation where there was no switching loss. As the number of relay nodes increased, the possibility of multiple links fading at the same time decreased, and there was potential to establish better channel links. However, the switching between the relay nodes would reduce the channel capacity. As a result, there was a tradeoff between the number of relay nodes and the switching loss. Therefore, the relay-aided FSO system should consider both the number of relay nodes and the switching time carefully in actual systems. It can also be concluded that our proposed DQN-RS algorithm performs better than the traditional greedy algorithm with an arbitrary number of relay nodes and different switching loss.
# Development of Building CFD Model Design Process Based on BIM
## 3 Design Method of Optimized CFD Model Using BIM
### Extraction of BIM Data for CFD Model  
IFC stores geometry and attribute data, such as the coordinates, shapes, and materials of each element of a building, in an object called entity. Therefore, entity analysis and data extraction are required in IFC to convert the data required to design a CFD model. To do this, the IfcOpenShell library was used to analyze the IFC data structure. The IFC data used for the design of CFD models is classified into geometric data about the location and size of the structures constituting the building, and attribute data about the physical properties of the structural materials. The geometry (volume, area, location, and layout) of the CFD model was designed using the IFC geometric data. The attribute data was used to enter the material information and thermal transmittance of the wall. To extract geometry data, data related to the spatial structure (IfcSpace), structure (IfcWall, IfcSlab), and openings (IfcDoor, IfcWindow) was analyzed. The attribute data was extracted from IfcMaterial entities, which contain information about the composition of each structure.  
The geometric data was extracted from the IFC file by a three-step process. First, data about the space and connected walls was extracted, then information about the walls and openings in the target space was extracted and converted into 3D CAD format. Finally, the data was converted into a format of OpenFOAM to complete the extraction of geometric data. IfcConverter was designed to extract geometry information, and a function to analyze the relationship between the space and walls was developed to extract information about the walls in the target space. In addition, openings (doors, windows) near the IFC entity in the target space were determined, and the data related to these openings was extracted. IfcOpenShell was used to convert the extracted wall and opening data into CAD data.  
In terms of attribute data, the name, thickness, and thermal conductivity of the materials were extracted by searching the material layer from attribute objects, such as IfcWall. The 3D CAD data was converted to input the extracted information into OpenFOAM. Wavefront.obj format data can store individual components of the model separately, making it suitable for CFD input format where conditions must be set for each boundary. Therefore, ObjConverter class was designed to convert CAD data into the OBJ format. The individual surfaces of the model were first extracted into STL format, and then a second extraction was performed in OBJ format, which combines all of the individual surfaces.
# Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks
###### Abstract  
This research presents an intelligent beam-hopping-based grant-free random access (GFRA) architecture designed for secure Internet of Things (IoT) communications in Low Earth Orbit (LEO) satellite networks. In light of the difficulties associated with facilitating extensive device connectivity while ensuring low latency and high reliability, we present a beam-hopping GFRA (BH-GFRA) scheme that enhances access efficiency and reduces resource collisions. Three distinct resource-hopping schemes, random hopping, group hopping, and orthogonal group hopping, are examined and utilized within the framework. This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. Also, a kind of activity mechanism is proposed based on the constraints of the spatio-temporal distribution of devices. We assess the system's performance through a thorough mathematical analysis. Furthermore, we ascertain the access delay and success rate to evaluate its capability to serve a substantial number of IoT devices under satellite-terrestrial delay and interference of massive connections. The suggested method demonstrably improves connection, stability, and access efficiency in 6G IoT satellite networks, meeting the rigorous demands of next-generation IoT applications.  
LEO satellite; grant-free random access; Internet of Things (IoT); beam-hopping +
Footnote †: journal: Sensors
## 4 Distributed Clustering Algorithm for the UAV Swarm
### Distributed Parallel Clustering Algorithm  
In this part, we mainly focus on the design of a distributed parallel clustering algorithm. Multiple UAVs can choose the switch operations in parallel during each decision-making cycle, thus reducing the iterations of clustering.  
In order to resolve the conflict in parallel decision-making, we assign UAV two states: idle and busy.  
1. Idle state: The UAV is not involved in any current selected switch operations.  
2. Busy state: The UAV is involved in a switch operation initiated by itself or another UAV. The state transition process of UAVs is depicted in Figure 3.  
Moreover, we adopt the CSMA mechanism for conflict avoidance to assist the state transition of UAVs. Algorithm 2 describes the details of the designed distributed parallel clustering algorithm.  
```
// UAV \(i\) belonging to group \(g_{i}\) and cluster \(C_{k}\), starts a decision-making process at time \(t=0\)
1: Initialize the state UAV \(i\) with \(state_{i}=idle\)
2: UAV \(i\) randomly chooses a waiting time \(\tau_{i}\) on \([0,\tau_{m}]\) according to the uniform distribution
3:for\(t<\tau_{i}\)do
4:if\(state_{i}=idle\)then
5:if UAV \(i\) receives the switch operation request with \(\sigma_{kJ^{*}}(\mathcal{P}_{s^{*}})\) from other UAVs then
6:\(state_{i}=busy\), \(UAV_{i}\) transfers to cluster \(C_{l^{*}}\)
7:if\(t=\tau_{i}\) and \(state_{i}=idle\)then
8: UAV \(i\) calculates the best switch operation \(\sigma_{kJ^{*}}(\mathcal{P}_{s^{*}})\) according to Algorithm 1
9:if\(\sigma_{kJ^{*}}(\mathcal{P}_{s^{*}})\
eq\mathcal{O}\)then
10:if all UAVs invloved in \(\mathcal{P}_{s^{*}}\) are in idle state then
11:\(state_{i}=busy\), \(UAV_{i}\) transfers to cluster \(C_{l^{*}}\)
12: Send a switch operation request with \(\sigma_{kJ^{*}}(\mathcal{P}_{s^{*}})\) to UAVs in \(\mathcal{P}_{s^{*}}\)
13:for\(t>\tau_{i}\)do
14:if\(state_{i}=idle\)then
15:if UAV \(i\) receives the switch operation request with \(\sigma_{kJ^{*}}(\mathcal{P}_{s^{*}})\) from other UAVs then
16:\(state_{i}=busy\), \(UAV_{i}\) transfers to cluster \(C_{l^{*}}\)
```  
**Algorithm 2** The distributed parallel clustering algorithm based on coalition game  
In Algorithm 2, each UAV is initially in an idle state and randomly chooses a waiting time on the uniform distribution \([0,\tau_{m}]\). If the UAV is idle and receives a switch operation request from another UAV, it will change to the busy state and then participate in this switch operation. When UAV \(i\) is still idle after \(\tau_{i}\) in the decision cycle, the UAV calculates the optimal switch operation according to Algorithm 1. If all UAVs involved in the optimal switch operation are idle, the switch operation is valid. UAV \(i\) changes to the busy state and sends the switch operation request to related UAVs. In this way, conflicts in parallel decision-making are effectively avoided by introducing the two states of idle and busy.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem
### Relationship between Nodes and MS
Equation (7) represents the flow constraint through the physical link. It shows that if flow from \(i\) to \(j\) exceeds the upper bound \(f_{ij}^{max}\), then it results in packet loss/packet drop.
## 3 Pseudo-Dynamic Tests and Numerical Analysis of Model Structure
### Finite Element Modeling Program
#### 3.2.3 Numerical Model Description  
Figure 7a shows the whole FE model of the test specimen. In practice, the steel diagonal braces were connected with the column through corbels in the specimen, as shown in Figure 7b. However, in order to simply the FE modeling procedure, the steel diagonal brace was connected with the column directly. Four pieces of steel plates were added on the four elements around the brace-to-column connection point to avoid the stress concentration. Figure 7c shows the simplified brace-to-column connection of the FE model. The tie constraint was used as the general interaction law for all the welded steel components, i.e., the welded connections in the A-shaped frames, steel truss, A-shaped frame-to-truss connections and truss-to-brace connections, and hence possible relative displacements and rotations of the welded connections were fully neglected. Integral modeling method was adopted to simulate the concrete and steel rebars in RC columns, where the embedded constraint was used for the steel rebars. The pin-connected joint was used to simulate the column-to-truss connection, which is between the rigid and pin connections in the test specimen where the top of column and steel truss was connected through the welded connections between the embedded steel plate in the top column and the steel truss, as shown in Figure 7d. Meanwhile, in order to transfer the actions from the steel truss to the columns uniformly, the three-dimensional truss element T3D2 was used to realize the load transfer process, as shown in Figure 7e. It has two nodes, and each node has 3-degree-of-translational freedom.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 3 Minimum Interference Channel Assignment
### Finding Channel Separation of Zero
We assume that there exists \(CS_{zero}\) between nodes A and D in the multicast tree shown in Figure 2, which means that the same channel number can be assigned to A and D. In this case, MICA allocates channel number 6 to these two nodes for their sending interface and put A and D to \(S_{D}\). After that, it keeps searching for a pair of nodes with \(CS_{zero}\). Nodes A and D have already been put in \(S_{D}\), so MICA needs to keep comparing all other nodes which are located in \(S_{N}\) with the two nodes A and D, for instance, after checking both channel separation between A and H and channel difference between D and H. If these two channel separation values (\(CS\) <A, H> and \(CS\) <D, H>) are all zero, MICA can assign the same channel number as that of A and D to H and put it into \(S_{D}\). As a result, \(S_{D}\) = {A, D, H} and \(S_{N}\) = {S, B, C, E}. This process continues until all of the nodes in \(S_{N}\) are examined. There is one more situation in the second step. Let us assume that there is no pair of nodes with \(CS_{zero}\) in the first place. In this scenario, MICA just chooses two nodes with a channel separation of a maximum value. Suppose that a channel separation of \(A\) and \(H\) has a maximum value of 5. Now, we can assign channel number 6 to A and 11 to H and put these two nodes into \(S_{D}\) based on the given algorithm. Finally, all remaining nodes--S, B, C, D, and E--are located in \(S_{N}\). Therefore, \(S_{D}\) = {A, H} and \(S_{N}\) = {S, B, C, D, E}.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Comparison with the State-of-the-Art Methods
#### 4.2.2 Scenario 2  
In this scenario, we investigate the impact of varying the mean standard deviation, \(\mu_{o}\), of the measurement errors in elevation angle. Figure 5 displays the comparison results when \(N=5\), \(T=5\), \(\mu_{m}=10\,\mathrm{deg}\), and \(\mu_{n}=6\,\mathrm{dB}\). The proposed method stands out from other algorithms, as it accounts for the measurement errors at each node and employs a weighted least squares approach with appropriate weights. This results in superior performance in the considered scenario, validating the effectiveness of the proposed approach.  
Figure 4: Comparison of RMSE as mean standard deviation, \(\mu_{m}\), of azimuth measurement error distribution.  
Figure 5: Comparison of RMSE as mean standard deviation, \(\mu_{v}\), of elevation measurement error distribution.
## 2 Methods  
### Model Electron Cross-Section Formulae  
When an imaging electron interacts with the specimen, it will be scattered elastically without losing energy or inelastically with energy loss. The chance of an electron undergoing event \(i\) (e.g., elastic scattering, inelastic scattering) within a sample thickness \(dt\) is given by  
\[P=\sigma_{i}\rho dt=K_{i}dt, \tag{1}\]  
where \(\sigma_{i}\) is the cross-section for event \(i\), and \(\rho\) is the sample density, \(K_{i}\) is the scattering coefficient. The density of amorphous ice is 0.92 g/cm\({}^{3}\), as used by [PERSON] [28] and [PERSON] et al. [29]. The angular distribution of scattering from a target atom can be described by the differential scattering cross-section. For elastic scattering, the differential cross-section follows the Rutherford formula for the screened Coulomb potential of the nuclear charge. In the Wentzel approximation, the differential cross-section for elastic scattering in first-order Born approximation becomes [10; 17]:  
\[\frac{d\sigma_{el}}{d\Omega}=\left[\frac{2 ZR^{2}\Big{(}1+\frac{E}{E_{0}}\Big{)} }{a_{H}\Big{(}1+\Big{(}\frac{\theta}{\theta_{0}}\Big{)}^{2}\Big{)}}\right]^{2 },\,\theta_{0}=\frac{\lambda}{2\pi R},R=a_{H}Z^{-1/3} \tag{2}\]  
where \(\sigma_{el}\) is the elastic scattering cross-section, \(\Omega\) is the solid angle, \(Z\) is the atomic number, \(E\) is the electron energy, \(E_{0}\) is the rest energy of the electron, \(a_{H}\) is the Bohr radius (0.0529 nm), \(\theta\) is the scattering angle, \(\theta_{0}\) is the characteristic scattering angle below which 50% of the electrons are scattered into, \(\lambda\) is the electron wavelength. The characteristic scattering angle for oxygen is 15 mrad for 200 keV electrons. Integrating Equation (2) yields the total cross-section [10; 28; 30]:  
\[\sigma_{el}\approx\frac{1.4\times 10^{-6}Z^{3/2}}{\beta^{2}}\Big{(}1-\frac{0.26Z }{137\beta}\Big{)}\,\ \beta^{2}=1-\left[\frac{E_{0}}{E+E_{0}}\right]^{2} \tag{3}\]  
The angular dependence and the cross-section of inelastic scattering can be approximated with a Bethe-model [10; 18]:  
\[\frac{d\sigma_{inel}}{d\Omega}\approx\frac{Z\lambda^{4}\Big{(}1+\frac{E}{E_{0 }}\Big{)}^{2}}{4\pi^{2}a_{H}^{2}}\left[\frac{1-\left(1+\frac{\theta^{2}+\theta _{0}^{2}}{\theta_{0}^{2}}\right)^{-2}}{\left(\theta^{2}+\theta_{E}^{2}\right) ^{2}}\right],\ \theta_{E}=\frac{\Delta E}{E}\frac{E+E_{0}}{E+2E_{0}} \tag{4}\]  
where \(\sigma_{inel}\) is the inelastic scattering cross-section, \(\theta_{E}\) is the characteristic angle that is responsible for the decay of the inelastic scattering, \(\Delta E\) is the mean energy loss from a single inelastic scattering event (e.g., 39.3 eV for amorphous ice [30]). An inelastic scattering is concentrated within much smaller angles than elastic scattering. The characteristic angle \(\theta_{E}\) is typically of the order of 0.1 mrad for 200 keV electrons.
## 6 Discussion  
### The Need for WuR  
The operation of WuR is not only simple but also energy efficient. The dual radio architecture is superior because the power consumption of the main communication radio scales up with increasing network traffic. The energy consumption could be reduced significantly when using appropriate wake-up techniques [15]. Consequently, it results in more relaxed synchronization requirements for schedule-based MAC protocols [14]. Based on our previous research, work reported in [8], the power consumption of WuR-based MAC protocol, synchronous MAC protocol and asynchronous random access protocols is compared. It is shown in Figure 5 that the power consumption of WuR is very low compared with the power consumption of scheduled access and random access mechanisms. The ultra-low-power and performance trade-off can be achieved by creating a proper Tx/Rx link-budget for the main radio. The WuR sets a lower-bound of power consumption in idle state compared with the current available technologies which could be mitigated by applying duty-cyling to the WuR.
## 3 Results
### Selecting Network Configuration
While the above analysis settles upon the usage of the dropout layer and the noisy training vectors, the question regarding the sizes of the hidden layers must still be resolved. On one side, increasing the number of neurons leads to the lowering of the MSE and MAPE scores. On the other hand, however, it escalates the risk of overfitting. Figure 9 presents the distributions of the \(\Delta\) parameter estimates against the true values in the test set. Regression analysis shows good agreement between the predicted and the actual parameter estimates. The performed \(t\)-test and its resulting large \(p\)-values indicate that one cannot reject the hypothesis of equality between the group means. However, the overfitting effect manifests itself in the case of noise-free data, and it appears deeper for the more complex MLP architectures. The network tends to predict parameter values close to some discrete levels. Although the determination coefficients \(R^{2}\) are higher in these cases, clearly the predicated outcomes do not conform to the actual data distribution. This tendency is not observed in the case of noisy data; however, changing the number of neurons in the first hidden layer from 40 to 50 did not bring significant improvement in the \(R^{2}\) score (0.85 versus 0.86), and simultaneously, as shown in Figure 8d, it slightly increased the mean absolute percentage error for the validation set (from 13.9% to 14.3%). Therefore, for the rest of the experiments we chose configuration #10, with 40 and 20 perceptrons in the first and second hidden layers, respectively, and with a dropout layer in between, with a dropout rate = 0.2. This setting ensures the lowest mean absolute percentage error, relatively high \(R^{2}\) scores for all estimated parameters (Figure 10), and that the network model's capacity is appropriate for the data set's complexity. Figure 8: (a–d): Mean absolute percentage error obtained for various network architectures and with respect to existence of noise in the training and validation data sets. Figure 9: Predicted against actual values of the \(\Delta\) parameter in the test set for three configurations of the MLP structure with the dropout layer. The number of neurons in the hidden layers: 30/15 (**a**,**b**), 40/20 (**c**,**d**), 50/25 (**e**,**f**). Plots in the left and right columns represent, correspondingly, noise-free and noisy data.