esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Construction and Parameter Analysis of Space-Based Information Network
### Model Mapping Based on Complex Network
#### 3.2.2 Parameter Analysis of SBIN  
1. Relationship between nodes and joint edges in the space-based information network  
Based on the abstraction of the nodes and connected edges of the SBIN, we established a complex network model \(G=\langle V,E\rangle\) to describe the mapping relationship between its nodes and the connected edges, and \(V=\{v_{i}|i=1,2,\cdots n\}\), \(E=\left\{e_{ij}|e_{ij}=(v_{i},v_{j}),v_{i},v_{j}\in V\right\}\). \(\forall e_{ij}\in E\), there exists a pair of nodes \((v_{i},v_{j})\) to it, and when there is a connection relationship between the two nodes, \(e_{ij}=(v_{i},v_{j})=1\), otherwise \(e_{ij}=0\).  
This paper introduces the adjacency matrix \(A\) to describe the mapping result between nodes and connected edges, which can be expressed as:  
\[A=\left[\begin{array}{cccc}e_{11}&e_{12}&e_{13}&\cdots&e_{1n}\\ e_{21}&e_{22}&e_{23}&\cdots&e_{2n}\\ e_{31}&e_{32}&e_{33}&\cdots&e_{3n}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ e_{n1}&e_{n2}&e_{n3}&\cdots&e_{nn}\end{array}\right] \tag{1}\]  
2. Parameter Analysis of Space-based Information Network Based on Metric Index Parameters  
The space-based information network studied in this paper consists of 107 nodes and 324 connected edges, including 5 GEO satellites, 2 HEO satellites, 20 MEO satellites, 35 LEO satellites, 15 space vehicles and 30 ground accesses terminal, and the network topology is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Complex-Network Characteristics** & **SBIN Features** \\ \hline \hline
**Multi-layer features** & Information transmission process involves the resource layer, service layer, function layer and mission layer \\
**Multi-stage characteristics** & Mainly composed of the bottom distribution sensor, convergence network and terminal application \\
**Multi-dimensional traffic** & Transmitted information includes data, images, voice, and video \\
**Multiple attributes/guidelines** & Different attributes and guidelines for abstract entities and interactions \\
**Congestion** & Network communication capabilities and user information transmission \\  & requirements lead to data transmission congestion control problems \\
**Coordinating optimization issues** & Global optimization results of data applications under limited resources \\  & may not satisfy the optimal data service requirements of individual nodes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics analysis of the space-based information network  
Figure 3: Space topology diagram of the space-based information network.  
According to the node composition of SBIN, we statistically calculated the nodes and joint edges participating in the space task. The average degree, average clustering coefficient, average path length and network diameter are calculated to reveal the basic properties of the topology model of SBIN. The parameter analysis of SBIN is shown in Table 2.
# Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath
## 2 Methodology for Smoothing Toolpaths
### Building Smooth Curves from Temperature Field
#### 2.3.2 From Interpolation to Smooth Curve Decomposition  
This model of interpolation, between two points, makes it possible to fit and decompose a discrete curve, in this case, an isotherm, into a small number of splines connected in tangency and curvature (named **patches**). Figure 5 explains the procedure followed.  
\(D_{max}\) is the maximum acceptable distance between the curve and patch points; \(C_{0}\) represents the algebraic initial curvature of the first patch. \(D_{max}\) and \(C_{0}\) are user-fixed parameters. \(D_{opt}\) is the optimized (i.e., minimized) distance between the patch points and HQ spline. **GA** is for the Genetic Algorithm method, and **GM** is for the gradient descent method. \(C_{1}\) is the calculated final curvature of the patch that becomes the initial curvature of the new patch.  
By using this procedure, a discrete isotherm may be split into a minimum number of Hermite quartic splines, continuous, connected in tangency and curvature (called **isoHQ**), and respecting a maximum chordal error.
# CarTwin--Development of a Digital Twin for a Real-World In-Vehicle CAN Network
## 6 Experimental Evaluation of the Digital Twin  
In the first part of this section, we provide details related to the Matlab/Simulink model integration in the CarTwin experimental setup and compare data extracted from a real-world vehicle trace with the output from our CarTwin models. Then, we discuss possible applications and future improvements for CarTwin. Finally, we compare CarTwin with related approaches for digital twins in the automotive domain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CAN Signal** & **CAN ID** & **Transmitter** & **Data Size (bits)** \\ \hline Vehicle speed target & 0 \(\times\) 7 FA & CAN tool & 16 \\ Vehicle direction & 0 \(\times\) 7 FB & CAN tool & 2 \\ Brake status & 0 \(\times\) 7 FB & CAN tool & 1 \\ Steering wheel angle & 0 \(\times\) 7 FD & CAN tool & 16 \\ Buckle status & 0 \(\times\) 7 FE & CAN tool & 1 \\ \hline Engine speed & 0 \(\times\) 11 & PCM & 32 \\ Gear & 0 \(\times\) 13 & PCM & 4 \\ \hline Vehicle speed & 0 \(\times\) 24 & ABS & 32 \\ \hline Vehicle steering offset & 0 \(\times\) 30 & PSCM & 32 \\ Vehicle position X & 0 \(\times\) 31 & PSCM & 32 \\ Vehicle position Y & 0 \(\times\) 31 & PSCM & 32 \\ \hline Airbag status & 0 \(\times\) 40 & RCM & 1 \\ \hline Vehicle average speed & 0 \(\times\) 21 & IPC & 32 \\ Trip distance & 0 \(\times\) 21 & IPC & 32 \\ Buckle alert & 0 \(\times\) 22 & IPC & 1 \\ \hline Door lock status & 0 \(\times\) 40 & RFA & 1 \\ \hline Rear camera video status & 0 \(\times\) 12 & APIM & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of signals transmitted by CAN bus nodes.  
First of all, in order to verify the correctness of the ECU model integration on the CAN bus, we provided the same input signals to each of the models in Matlab/Simulink and from the CarTwin setup using the C# tool, logged the outputs from the experiments and verified that the output value arrays are the same in Matlab/Simulink and on the CAN bus. For evaluation purposes, the signals of interest that we analyzed from the vehicle trace and the CarTwin model are: (a) vehicle speed, (b) engine speed and (c) trip distance.
## 4 Simulation
### Network Life  
The network life is an important assessment criterion of a protocol from the aspect of total energy consumption. The experiments show the network lifetime of the four protocols, and the results are  
Figure 6: Impact of node move speed. **(a)** Average delivery ratio; **(b)** Average delay.  
described in Table 2. We assume that the energy of the sink node is unlimited, and the initial energy of each sensor is 10 J. The energy needed in each transmission and receiving action is as specified in paper [27]. We consider the network dead when over a half of all sensor nodes deplete their energy.  
We can see from Table 2 that the direct transmission protocol achieves the longest network lifetime, since sensors does not receive or transmit any messages except those generated by the sensor itself, and thus much energy can be saved. Sending and receiving too many messages copies expends too much energy in epidemic routing, so its network lifetime is the shortest among the four protocols. Moreover, we also see that MEDR has much longer network life than FAD. The reason is that, different from the multiple-copy feature of FAD, MEDR is a single-copy transmission scheme, thus it can efficiently reduce communication overhead. In a word, the total energy consumption of MEDR is much less than FAD and epidemic routing, which demonstrates the advantage of our proposed protocol in the aspect of economizing energy.  
What deserves to be mentioned is it is almost equal that the energy consumed for sending and receiving hello messages in the MEDR, FAD and epidemic protocols, respectively. However, it is obviously different that the proportion of the energy expended on hello messages accounts for the total energy consumption in each one of the above three protocols. Table 3 shows the network life without considering the energy consumed by hello messages. From it we see that the network lifetime of MEDR reaches 2,884.7 hours, while the network lifetimes of FAD and epidemic are 196.18 and 27.16 hours respectively. Therefore, without considering the energy consumed by hello messages, MEDR clearly outperforms FAD and epidemic from the view of energy savings.
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 2 System Model  
The image signal transmission scenario considered in this paper is shown in Figure 1. All participators, including the transmitter [PERSON], the legitimate user [PERSON], and the eavesdropper [PERSON], were equipped with multiple dual-polarized antennas. The information was broadcast from [PERSON] to [PERSON], which could then be recovered by the eavesdropper [PERSON].  
If image signals were sent without any protection, both [PERSON] and [PERSON] could recover the same information, which resulted in information leakage. To overcome the problem, the  
Figure 1: System model.  
PPF-OM scheme was proposed to enhance information security and eliminate the PDL at the same time, and this is detailed in the following sections.
## 5 Optical Power Distribution  
Optical power distribution is monitored at different layers, between the silicon substrate and back oxide, layer 1; at the top of Si substrate, layer 2; in the middle of the SiNWs, layer 3; and at the top of NWs, layer 4. Figure 10 shows the locations of the four layers.  
Figure 11 illustrates the optical power distribution on the four different monitoring layers for all proposed solar cells, traditional SiNWs, SiNWs with C layer, SiNWs with AuNPs, and SINWs with C and Au NPs.  
Figure 11a, monitoring layer 1, shows the effect of the graphene layer on the top of NWs; it behaves like a nanoantenna, which retransmits the optical power into the active layer. Au NPs absorb the optical power on the substrate surface, as clearly shown in monitoring 2 with Au NPs; however, monitoring layers 3 and 4 have the same effect.  
Finally, the proposed solar cell structure is compared to other structures in the literature, fabricated structures as in [1; 5; 6; 13] and analytical analysis work as in [14], as illustrated in Table 8. The proposed structure does not give the maximum open-circuit voltage, 0.63 V, or _FF_, 0.73; however, it produces a high short-circuit current density, 41.7 mA/cm\({}^{2}\), and consequently a high value of the power conversion efficiency, 19.0%.  
In brief, the main advantage of this study is that the proposed structure enhances the absorption of the SiNWs in the NIR region in addition to the visible region, which leads to improvement in the overall solar cell performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**NW height (\(\mu\)m)** & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **PCE (\%)** \\ \hline
**3** & 30.5 & 13.9 \\
**3.5** & 32.5 & 14.9 \\
**4** & 33.4 & 15.2 \\
**4.5** & 37.6 & 17.1 \\
**5** & 41.7 & 19.0 \\
**5.5** & 40.2 & 18.3 \\
**6** & 39.0 & 17.8 \\ \hline \end{tabular}
\end{table}
Table 7: Short-circuit current density and overall efficiency for different NW lengths.  
Figure 10: Different positions of electric field monitoring layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Different** & **V\({}_{\text{oc}}\) (V)** & **FF** & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **PCE (\%)** \\ \hline
[5] & 0.56 & 0.61 & 17.3 & 5.31 \\
[6] & 0.47 & 0.59 & 13.7 & 3.80 \\
[1] & 0.82 & 0.73 & 15.2 & 9.30 \\
[13] & 0.51 & 0.61 & 33.6 & 11.61 \\
[14] & 0.76 & 0.86 & 38.8 & - \\
**Proposed** & 0.63 & 0.73 & 41.7 & 19.00 \\
**structure** & & & & \\ \hline \end{tabular}
\end{table}
Table 8: Comparison between other solar cells and the proposed structure.  
Figure 11: Optical power distribution at different monitoring layers for four different proposed structures.
## 4 Results and Discussion
### Transmission Coefficient (dB)  
The simulated and measured transmission coefficient (Sij/Sji) results are illustrated in Figure 6. It is easily seen that the mutual coupling between port 1 and port 3 (S\({}_{13}\)) exhibits the worst-case mutual coupling of \(-17.5\) dB as they are spaced only 48 mm apart. Further, the antenna elements (port 1 and port 2 (S\({}_{12}\))) show the minimum coupling of \(-18\) dB as they are spaced at 145 mm apart. Moreover, all the antenna elements exhibit the worst-case mutual coupling of \(-17.5\) dB, which is acceptable for the independent operation of each antenna element in the desired operating band of interest [12; 25; 26].  
Figure 4: Fabricated antenna geometry of 4-element mobile MIMO antenna: (**a**) Top view; (**b**) Top view with 50 \(\Omega\) termination; (**c**) Inset view of antenna element.  
Figure 5: Simulated and measured reflection coefficient of 4-element mobile MIMO antenna.
## 2 System Model
### Scenario Description  
When the RBs are reused by D2D pairs in the downlink cellular network, the interference from CUs to D2D pairs can be inhibited by cascade precoding and beamforming. Meanwhile, it is difficult to analyze the interference caused by D2D pairs, as it affects all CUs in downlink cellular network. Additionally, the efficiency of spectrum resources in downlink is higher than that in the uplink. Consequently, the case that D2D pairs reuse the RBs of cellular uplink users is investigated in this research. As shown in Figure 1, D2D transmitters cause interference to cellular uplink users at BS, while cellular uplink users cause interference to D2D receivers mutually.  
After the RBs are scheduled to cellular uplink users by BS, each CU can only occupy one RB in a scheduling period to transmit signals. That is, each CU corresponds to one RB, so the available RBs mentioned henceforth in the paper are those that have occupied by CUs. Then D2D communication is set up under this situation, as shown in Figure 2.  
Figure 1: Scenario of D2D communication with uplink RBs’ reusing.  
The steps of session setup for D2D communication are shown as:  
1. User \(i\) sends the request of D2D communication to BS.
2. BS selects user \(j\) by traffic detection and then requires both users to feedback the Channel State Information (CSI).
3. Feasibility tests for D2D communication are implemented by BS.
4. If both users satisfy the conditions of session setup, BS send the invitation of user \(i\) to user \(j\).
5. D2D communication is set up between user \(i\) and user \(j\), and a D2D pair is formed.  
As shown in Figure 2, BS is vigorous to determine the session setup of D2D communication, as D2D pairs communicate while remaining detected by BS. That is, if two users are not proper to communicate in underlaid mode, both of them would be back to work in the overlaid mode which can refer to CUs communicating in cellular mode, users communicate through the usage of the orthogonal RBs appropriately allocated. In the underlaid mode, BS allocates the RBs of CUs to each D2D pair, whereas the transmit power is determined differently according to the diverse power control methods. The centralized power control is that the transmit power of D2D pairs are managed by BS, while D2D pairs determine the transmit power by themselves in a distributed power control method. In this paper, the latter is adopted.
## 3 Research Findings
### Practical Implications  
The results demonstrate the effectiveness of the proposed model in optimizing BTS hub locations. By incorporating environmental disruptions and dynamic demand patterns, the model provides a robust solution for telecommunication network operators. Future research can focus on integrating real-time data and machine learning techniques to enhance predictive capabilities further.  
The proposed model has significant applications in optimizing the location of BTS hubs in urban and rural telecommunication networks. For instance, it can be used to improve coverage in underserved areas while minimizing costs. Additionally, the integration of green supply chain principles ensures that the environmental impact of new installations is reduced. A real-world application could involve deploying this model to enhance emergency response systems by optimizing communication infrastructure in disaster-prone areas.  
The optimization model presented in this study offers practical applications in telecommunications, emergency services, and beyond. For telecommunication networks, it directly addresses the challenge of optimal Base Transceiver Station (BTS) site selection, ensuring better coverage, lower costs, and resilience to demand fluctuations. The model can also  
Figure 8: Depicts the Pareto front solutions generated by the NSGAII and MOPSO algorithms.  
be extended to green infrastructure planning, providing a sustainable solution to telecom companies aiming to minimize environmental impact while optimizing their networks. Additionally, the model's flexibility allows for applications in dynamic environments, where demand can vary significantly, such as in disaster response or urban growth scenarios.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 2 Numerical Study
### Effect of the Curvature on the Propagation Characteristics of \(Qes_{0}\)
#### 2.2.2 Mode Shapes
To study the scattering characteristics of \(QES_{0}\) at partially through-thickness edge defects, two types of partially through-thickness edge defects with a depth of 8 mm were investigated with the developed FE model. Types I and II of partially through-thickness edge defects were located in the inner- and outer-side surfaces of the edge, respectively, as illustrated in Figure 8c,d. The width of the partially through-thickness edge ranged from 1 to 4 mm with increments of 1 mm. The reflected and transmitted waves were obtained from MP1 and MP2, respectively, whose locations are also illustrated in Figure 8a. In addition, to investigate the amplitude of scattered waves in different directions, nine measurement points, centred at the base of the edge defect, were assigned on the side surface of the edge ranging from 0 to \(-180^{\circ}\) in steps of \(-22.5^{\circ}\), as shown in Figure 9a,b. It should be noted that the distance between these measurement points and the base of the edge defects was fixed at 50 mm. To allow for a direct comparison between the amplitudes of scattered waves collected in different measurement points, the \(U_{x}\) displacement of each measurement point was converted to the corresponding normal displacement (\(u_{normal}\)) of the side surface, as illustrated in Figure 9c. Figure 10a-c and Figure 10d-f present the typical contour plots of FE simulation results for \(QES_{0}\) before and after encountering through-thickness edge defects with depths of 2, 8, and 14 mm, respectively. The central frequency of the input signal was 200 kHz. Figure 10d-f illustrate that the magnitudes of scattered waves are highly correlated to the depth of the edge defect, which also reveals the mode conversion between \(QES_{0}\) and scattered waves. Figure 7: Normal displacement (\(U_{2}\)) of line 2 at FTV = (**a**) 2.0, (**b**) 2.5 and (**c**) 3.0. Figure 8: (**a**) Schematic of edge defect location in the 3D FE model. Snapshot of (**b**) through-thickness (**c**) type I and (**d**) type II of partially through-thickness edge defect. Figure 9: (**a**) Schematic of the location of measurement points for measuring scattered waves. (**b**) Configuration details of measurement points for measuring scattered waves. (**c**) Schematic diagram of conversion between \(u_{x}\) and \(u_{normal}\) of the measurement point for the scattering study.
# Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training
###### Abstract  
Simulation for surgical training is increasingly being considered a valuable addition to traditional teaching methods. 3D-printed physical simulators can be used for preoperative planning and rehearsal in spine surgery to improve surgical workflows and postoperative patient outcomes. This paper proposes an innovative strategy to build a hybrid simulation platform for training of pedicle screws fixation: the proposed method combines 3D-printed patient-specific spine models with augmented reality functionalities and virtual X-ray visualization, thus avoiding any exposure to harmful radiation during the simulation. Software functionalities are implemented by using a low-cost tracking strategy based on fiducial marker detection. Quantitative tests demonstrate the accuracy of the method to track the vertebral model and surgical tools, and to coherently visualize them in either the augmented reality or virtual fluoroscopic modalities. The obtained results encourage further research and clinical validation towards the use of the simulator as an effective tool for training in pedicle screws insertion in lumbar vertebrae.  
surgical simulation; augmented reality; spine surgery; hybrid simulator; pedicle screws fixation training; unity game engine Article
## 5 Discussion  
Comparing Figures 2-4, the slopes of left panel are reasonably steeper than the respective right ones, as the former curves correspond to weaker turbulence than the latter ones. Additionally, the red curves, i.e., stronger SJ effect, have a smoother slope than the black ones, meaning that when link misalignments get significant, more SNR is required in order to achieve the same ABER level. What is more, faster links face stronger TJ effects than slower ones and when TJ is quite intense, the inevitably generated ABER thresholds get higher, particularly delimiting the performance of high-data-rate links. Finally, among the three studied modulation schemes, the OOK-RZ format presents moderate slopes and mediocre influence on TJ; the 4-PPM format has steeper slopes, but is heavily affected by the TJ effect; while the 4-PAM format is only slightly affected by TJ, but it presents smoother slopes.  
Next, comparing Figures 4 and 5, it can be seen that faster transmission links induce increased ABER thresholds in any case, with some of them tending to a completely flat performance. In addition, the individual link's performance is significantly improved when the receiver's diversity is higher. Furthermore, OOK-NRZ curves seem to be inferior to the corresponding OOK-RZ curves; however, both share the same ABER thresholds for any given TJ and bit rate values. For its part, the 16-PPM scheme curves are also inferior to the respective 4-PPM ones, but the expected steeper slope of the 16-PPM cases, allowing small BER to be achieved with less SNR requirements, is abolished, due to the large ABER thresholds. On the contrary, the 16-PAM scheme deepens the ABER performance with the respective 4-PAM. However, the improved curves are shifted to larger SNR values. Last but not least, expanding the total link's length from one to five identical hops, all curves are slightly shifted to worse ABER performance; however, this degradation is inferior to the aggravated performance of a single hop link of the same total link length, as referred to in [24].