esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Model, References** & **Description** & **Advantages** & **Disadvantages** \\ \hline \hline \multirow{4}{*}{Advanced Missions Cost Model (AMCM)} & AMCM is developed by NASA’s Jet & \multirow{4}{*}{Tailored to space mission costs, covering a wide range of mission types.} & It is heavily based on historical mission data, which may not fully account for advances in technology or unique mission parameters. \\  & Propulsion Laboratory. It focuses on estimating costs for planetary science missions but can be adjusted for other mission types. It uses parametric cost estimating relationships based on historical mission data. & \multirow{4}{*}{Tailored to space mission costs, covering a wide range of mission types.} & It is heavily based on historical mission data, which may not fully account for advances in technology or unique mission parameters. \\ \cline{2-3}  & NASA-Air Force Cost Model (NAFCOM) & NAFCOM is a cost estimation model used for predicting the cost of space hardware. It uses regression analysis of historical data to develop cost estimating relationships. & \multirow{4}{*}{Tailored to space mission costs, covering a wide range of mission types.} & Relics on historical data, which may not reflect current trends or advancements. Also, it may not account for new, unique, or highly innovative designs. \\ \cline{2-3}  & SVLCM is designed to estimate the costs Level Cost Model (SVLCM) & & \\ \cline{2-3}  & Gusel’s Cost Model (SVLCM) & & \\ \cline{2-3}  & GR-50 & & \\ \cline{2-3}  & GR-50 & & \\ \cline{2-3}  & FECC is a parametric cost estimating tool developed by NASA for human and robotic space exploration missions. It uses cost estimating relationships based on historical NASA and commercial space & \multirow{4}{*}{It includes a risk analysis capability and can produce estimates in a short time frame.} & As with other historical-data-based models, it may not fully account for new technologies, novel mission designs, or rapidly changing industry trends. \\ \cline{2-3}  & Parametric Estimating Relationships (PERs) between system characteristics (parameters) and historical costs. & & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{2-3}  & ECN uses engineering calculations to estimate the costs of individual system components. & & \\ \cline{2-3}  & Abit estimates costs by looking at the resources consumed by activities in each process. & & \\ \cline{2-3}  & System Dynamics Cost Modeling (SDCM) & SDCM is a method for understanding the behavior of complex systems over time. & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } \\ \cline{2-3}  & & \\ \cline{2-3}  & DTC is a management strategy and methodology to determine and manage the optimal balance between operational capabilities and life cycle costs. & & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of life cycle cost models.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 4 Hybrid Training
### Decentralized Learning  
In the initial step, Q networks serve as the learning mechanisms for individual agents and are randomly initialized. Each V2V agent has a dedicated DQN that takes the current observation as input and outputs the value functions corresponding to all actions. The training process unfolds within a simulated V2V communication scenario shown in each training episode. During an episode, dynamic adjustments are made to vehicle locations and channel characteristics. Agents interact with the environment, receive observation, make decisions based on their policies, and participate in decentralized learning. We train Q networks through multiple episodes and at each episode, and all V2V agents explore state-action space with some policies. This decentralized learning is the key aspect of the training phase, thus enabling agents to adapt to dynamic conditions.
# Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT
## 5 Analysis of Vibration Signals of the Motor Bearing Rolling Element
### Comparative Analysis of Different Methods  
In order to verify the effectiveness of the improved VMD algorithm, the improved VMD algorithm was compared with the EMD and EEMD methods. The vibration signals of the motor bearing rolling element were decomposed by the improved VMD method, EMD method, and EEMD method, respectively, and the power spectrum of the IMF component was obtained. The effectiveness of the improved VMD method was proven by analyzing the effective frequency components of the power spectrum. The EMD method and the EEMD method were used, and when \(k=4\), the IMF components by the MTCFVMD method for the motor bearing rolling element fault data were obtained and the envelope signal of each IMF component was obtained by the Hilbert transform. The frequency value of the power spectrum of the envelope signal is shown in Table 4.  
As can be seen from Table 4, 29.2969 Hz represents the rotating frequency, 58.5938 Hz represents the frequency multiplication, 117.2 Hz represents the four times rotating frequency, 146.4844 Hz represents the rotating frequency of five times, 140.625 Hz represents the rolling element fault characteristic frequency, 70.3125 Hz represents the rolling element fault characteristic frequency of 1/2, 41.0156 Hz represents the difference between the rolling element fault characteristic frequency of 1/2 and the rotating frequency, and 234.375 Hz represents the difference between the rolling element fault characteristic frequency of 5/4 and the frequency multiplication. It can be seen from Table 4 that the number of effective frequencies obtained by solving the power spectrum of the IMF component decomposed by the EEMD method is more than that of the EMD method. Therefore, the decomposition effect of the EEMD method is better than the EMD method. Compared with the MTCFVMD method, the number of IMF components obtained by the MTCFVMD method is less than the EMD method and the EEMD method, and each modal can extract the effective frequency value. There were some invalid frequency values in the modes of the EMD method and the EEMD decomposition, so the decomposition effect of the MTCFVMD method is better than the EMD method and the EEMD method.  
By comparing and analyzing the improved VMD method, it can be seen that the number of decomposition modals of the MTCFVMD method is less than that of the EMD and EEMD methods, which greatly reduces the running time. According to the power spectrum analysis, each IMF component obtained by the MTCFVMD method can extract effective frequency values, and some IMF components obtained by the EMD method and EEMD method cannot extract effective frequency values. It shows that the MTCFVMD method can effectively decompose the original signal, which proves the effectiveness and superiority of the MTCFVMD method.
# Review of Power Electronics Technologies in the Integration of Renewable Energy Systems
## 3 Literature Survey
These power systems also enable the integration of ESS technologies. Batteries, which store DC power, can be connected to the system through inverters, thereby ensuring the continuous flow of power into the utility grid. A grid-connected solar PV system is depicted in Figure 7. In this network, efficient power control and transfer can only be achieved with the help of a boost converter and inverter arrangement, which is a type of PE technology. The boost converter circuit arrangement is shown in Figure 8.  
Furthermore, power systems connected with DC sources and converters offer benefits in terms of grid resilience and stability. They can also assist the utility grid with proper voltage regulation and frequency control, thereby intensifying the solidity of the electric grid. A boost converter is depicted in Figure 8, composed of various components such as an inductor, switch (IGBT/MOSFET), diode, capacitor, and load resistance unit. It is used to boost the input voltage at the output side, making it highly suitable for power systems with low-voltage sources such as solar panels. Figure 8: Boost converter circuit. Figure 7: Integration of solar PV system into an electric utility grid. The boost converter can be modelled using the below equations. \[\text{Output voltage, }V_{0}=\frac{V_{i}}{1-D} \tag{1}\]  
\[\text{Average value of inductor current, }I_{L}=\frac{I_{0}}{1-D} \tag{2}\]  
\[\text{Critical inductance, }L_{C}=\frac{D(1-D)^{2}\cdot R}{2f} \tag{3}\]  
\[\text{Critical capacitance, }C_{C}=\frac{D}{2 fR} \tag{4}\]  
where \(D\) is the duty cycle, which refers to the ratio of the time period to the total time period; \(R\) is the resistive load [\(\Omega\)]; \(L\) is the inductor in [H]; \(C\) represents the capacitor in [F]; \(f\) represents the switching frequency [Hz]. The use of DC-based power systems is not limited to renewable energy applications. They are also employed in various other sectors, such as electric vehicles and data centers. The DC power distribution within these systems reduces energy losses and improves the overall efficiency.
# Secured Real-Time Machine Communication Protocol
## 4 Vulnerability Analysis  
This section analyzes the security vulnerabilities addressed by the proposed SRMCP protocol and evaluates its robustness against widely recognized attack models. It further highlights how SRMCP mitigates these threats and compares its security features to PROFINET Security and OPC UA.
## 1 Introduction
Compared to previous individual models, the combined Prophet + LSTM model better captured the composite features of the time-series data and achieved roof pressure prediction during the advancement of the mining face. Working resistance monitoring data for the supports of a fully mechanized mining face provide a data basis for realizing the intelligent sensing of the performance of the supports and the stability of the roof [16]. The working resistance of the supports is dynamic and is affected by many factors, which leads to great challenges in pressure prediction for a mining face. A novel idea is to improve the pressure-prediction model accuracy by mining the characteristics of the massive monitoring data themselves for a mining face and applying non-linear prediction methods. The graph neural network has great advantages in extracting and analyzing the associations between nodes and in reflecting the topological relationships between objects [17; 18; 19]. The supports in the fully mechanizedmining face were regarded as the logic nodes on a topological structure, and the time-series resistance data for the supports were regarded as data nodes on a graph. The adaptive graph convolutional recurrent network is utilized to extract the spatiotemporal correlation of the resistance data for adjacent supports, which effectively improves the accuracies of pressure-prediction models. Therefore, in this study, considering the influence of the working resistance of adjacent hydraulic supports in the ultra-long mining face of a medium-thick coal seam, an adaptive graph convolutional recurrent network (AGCRN) was constructed to determine the spatiotemporal relationship between the working resistance of adjacent hydraulic supports in the mining face, thereby establishing an ultra-long mining face pressure prediction model. On this basis, a roof-weighting early warning model was constructed for pressure analysis and early warning. Engineering verification revealed that the early warning system has good performance. The results of this study provide a data reference for the status monitoring, pressure monitoring, and intelligent control of the hydraulic supports in the ultra-long mining faces of the medium-thick coal seams in northern Shaanxi.
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
###### Abstract  
During operation, the shear friction between the silicone oil and the plates of a viscous coupling (VC) will generate heat and increase the temperature of the silicone oil, inflate the volume, increase the internal pressure, and eventually deliver more torque, in what is called hump operation mode. Temperature is the root cause of the change of the operation characteristics in VCs. In this paper, the heat-transfer model for a VC is established based on the thermodynamical theory. The capacity of the heat transmission of each part of the VC are calculated to obtain the temperature of silicone oil. The real-time shear torque of the VC is finally obtained. Then, the theoretical analysis on hump phenomenon was done. The internal pressure was obtained by analyzing the characteristics of ideal gas, and the maximum torque during the hump phenomenon was calculated. The simulation of the key parameters and the entire working process for the VC were carried out based on the proposed calculation model. A prototype of a viscous limited-slip differential (VLSD) was developed to test the output torque characteristics. The test results were quite consistent with the simulation results, and the accuracy of the calculation model was verified.  
viscous coupling (VC); viscous limited-slip differential (VLSD); torque characteristic; thermodynamics +
Footnote †: journal: Applied  
doi: 10.3390/app11031110
## 6 Numerical Results
### Illustration of Optimized Network Slicing in Urban Network  
Finally, we applied the PBA method for an optimized placement of DU/CU/MEC and routing of FH/MH flows for eMBB and URLLC slices in a 17-node urban network (WRO-17) shown in Figure 15[20]. WRO-17 is a synthetic network connecting a subset of real antenna locations (79 RUs) in the Wroclaw city center (Poland). The switches are located near RUs, and the network links are routed along the streets, with the lengths reflecting physical distances. To accommodate high aggregated flow bit rates in this scenario, the link capacities have been assumed to be 50 Gbit/s, 400 Gbit/s, and 1 Tbit/s, respectively, for RU-switch, switch-switch, and switch-PP links.  
Figure 16 illustrates the optimized placement of processing resources and flow routing (in the uplink direction) in the network. Whereas the initial heuristic solution required 11 active PPs, the optimized solution found by the PBA and shown in the Figure requires just 7 active PPs. In the optimized solution, the FH flows are routed over the same paths for eMBB and URRLC, which is related to the fact that the DUs assigned for particular RUs are placed in the same PP nodes for both network slices. Still, the routing of MH flows differs in the network slices since the CUs are placed in different nodes, namely at a distant hub node for eMBB and a PP node, with a colocated MEC, within the access network for URRLC.  
Figure 15: WRO-17 network connecting RUs (triangles), PPs (squares), switches (circles), and the hub (hexagon).
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 6 Conclusions  
This paper proposes a navigation constellation-level high-precision time scale generation method, presents the navigation constellation time scale generation framework, and designs an improved KPW time scale algorithm and time-frequency control algorithm that integrates genetic algorithms. Finally, the algorithm simulation of the establishment of the constellation time scale is carried out using the GPS precision clock difference data provided by CODE. The results show that the phase deviation of time-frequency steer is kept within \(\pm\)0.2 ns, and when the sampling interval is 300 s, 1 day, and 1 \(\times\) 10\({}^{6}\) s, its frequency stability is 5.73 \(\times\) 10\({}^{-14}\), 9.17 \(\times\) 10\({}^{-15}\), and 8.87 \(\times\) 10\({}^{-16}\), respectively. Compared with the traditional KPW time scale algorithm, the short-term (300 s) and long-term (1 \(\times\) 10\({}^{6}\) s) stability have increased by 71% and 94%, respectively, which have been greatly improved. The constellation-level high-precision time scale generation method proposed in this paper can significantly improve the stability performance of long-term autonomous timekeeping of navigation constellations, which is of great significance to the research of long-term autonomous timekeeping of navigation constellations based on inter-satellite links.  
[PERSON], [PERSON] and [PERSON] conceived the study idea. [PERSON] and [PERSON] developed the simulation and analysis. [PERSON] drafted the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] provided guidance and suggestions on the research process and the problems encountered. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the sailing plan project of Shanghai \"scientific and technological innovation action plan\" with grant number 19 YF1446300. Additionally, it has received funding support from the National Natural Science Foundation of the China Youth Project (Grant No. 12104485).  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
Figure 17: The forecast error of TA(Steered) and 24 atomic clocks.  
Data Availability Statement: The datasets analyzed during the current study are available in the data repositories of The Crustal Dynamics Data Information System of NASA ([[https://cddis.nasa.gov/archive/gnss/products/](https://cddis.nasa.gov/archive/gnss/products/)]([https://cddis.nasa.gov/archive/gnss/products/](https://cddis.nasa.gov/archive/gnss/products/)) (accessed on 12 October 2022)). The datasets generated and analyzed during the current study are also available from the corresponding author upon reasonable request.
## Appendix A Mathematical Model of a Sinusoidal and Non-Sinusoidal Signal  
An electrical signal, with variable amplitude \(A_{k}\), angular frequency \(\omega_{k}\), and phase \(\theta_{k}\), can be defined as indicated in Equation (A1) [35]:  
\[S_{k}=A_{k}sin(\omega_{k}t_{k}+\theta_{k})\] (A1)  
where \"\(k\)\" represents the values at the current instant and \(k+1\) represents the value at the instant (\(t+T_{s}\)). Here, \(T_{s}\) denotes the sampling time.  
The state-space representation of the electrical signal \(S_{k}\) is [30]:  
\[\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k+1}=\begin{bmatrix}cos(\omega_{k}T_{s})&sin(\omega_{k}T_{ s})\\ -sin(\omega_{k}T_{s})&cos(\omega_{k}T_{s})\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k}+\begin{bmatrix}\gamma_{1}\\ \gamma_{2}\end{bmatrix}_{k}\] (A2)  
\[y_{k}=\begin{bmatrix}1&0\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k}+v_{k}\] (A3)Equation (A2) corresponds to the state equation of the system, while Equation (A3)) is the output equation of the system. The vector \(\left[\gamma_{1}\quad\gamma_{2}\right]^{T}\) (A2) represents the system disturbance vector, where the variations in amplitude, frequency, and phase are included, and \(v_{k}\) (A3) represents the noise introduced in the measurement by the measurement system.  
In the case of a non-sinusoidal signal containing \"\(n\)\" components (harmonics or interharmonics), the model of the electrical signal using the Fourier series decomposition is [34]:  
\[S_{k\_ns}=\sum_{i=1}^{n}A_{ik}sin(\omega_{ik}t_{k}+\theta_{ik})\] (A4)  
In Equation (A4), \"\(n\)\" represents the number of spectral components composing the signal \(S_{k}\), while \"\(i\)\" denotes the order of the spectral component. \(\omega_{ik}=(2\cdot\pi\cdot f_{i})_{k}\); \(f_{i}\) is the frequency value of spectral component; and \"\(i\)\" and \(\theta_{ik}\) is the phase angle value of the spectral component \"\(i\)\" at \(t=0\).  
For each component \"\(i\)\" of (A4), a state-space representation can be obtained. It follows that by the matrix combination, the signal \(S_{k\_ns}\) has a state-space representation as shown in (A5) and (A6).  
\[\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{1n}\\ x_{2n}\end{bmatrix}_{k+1}=\begin{bmatrix}M_{1}&\dots&0\\ \vdots&\ddots&\vdots\\ 0&\dots&M_{n}\end{bmatrix}_{k}\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{1n}\\ x_{2n}\end{bmatrix}_{k}+\begin{bmatrix}\gamma_{1}\\ \gamma_{2}\\ \vdots\\ \gamma_{1n}\\ \gamma_{2n}\end{bmatrix}_{k}\] (A5)  
\[y_{k}=\begin{bmatrix}1&0&\dots&1&0\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{1n}\\ x_{2n}\end{bmatrix}_{k}+v_{k},\] (A6)  
with  
\[M_{i}=\begin{bmatrix}\cos(\omega_{ik}T_{s})&\sin(\omega_{ik}T_{s})\\ -\sin(\omega_{ik}T_{s})&\cos(\omega_{ik}T_{s})\end{bmatrix}\] (A7)  
being the state matrix corresponding to the harmonic component \"\(i\)\".  
The equations determining the state-space model of an electrical non-sinusoidal signal (A5)-(A7) have the same structure as the equations defining the Kalman filter.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 3 Structural Parameters Determination and Model Verification
### The Main Parameters of the Structural System
#### 3.1.2 Determination of Support Stiffness
Combine all the direction cosines of the \(x\), \(y\), and \(z\) axes as the following matrix:  
\[\mathbf{T}=\left( \begin{array}{ccc}n_{x_{\rm A}x}&n_{x_{\rm A}y}&n_{x_{\rm A }z}\\ n_{y_{\rm A}x}&n_{y_{\rm A}y}&n_{y_{\rm A}z}\\ n_{z_{\rm A}x}&n_{z_{\rm A}y}&n_{z_{\rm A}z}\end{array} \right) \tag{14}\]  
where \(\mathbf{T}\) is the coordinate transformation matrix. Figure 4: Local coordinate system and deformation cloud diagram of top-left support A: (**a**) local coordinate system of top-left support A; (**b**) deformation cloud diagram of top-left support A. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stiffness (N/m)**} & \multicolumn{4}{c}{**Mount \#**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline \(k_{x^{\prime}}\) & 1.20 \(\times\) 10\({}^{7}\) & 1.00 \(\times\) 10\({}^{7}\) & 5.40 \(\times\) 10\({}^{6}\) & 1.30 \(\times\) 10\({}^{7}\) \\ \(k_{y^{\prime}}\) & 7.80 \(\times\) 10\({}^{8}\) & 2.60 \(\times\) 10\({}^{8}\) & 3.50 \(\times\) 10\({}^{8}\) & 3.60 \(\times\) 10\({}^{8}\) \\ \(k_{z^{\prime}}\) & 7.50 \(\times\) 10\({}^{7}\) & 3.70 \(\times\) 10\({}^{8}\) & 1.50 \(\times\) 10\({}^{8}\) & 2.50 \(\times\) 10\({}^{8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The stiffness of each support in the local coordinate system. The transformation relationship between the local coordinate system and the global coordinate system of the top-left support A can be written as:  
\[\mathbf{T}_{A}=\left( \begin{array}{ccc}\cos\theta_{x_{A}x}&\cos\theta_{x_{A}y} &\cos\theta_{x_{A}z}\\ \cos\theta_{y_{A}x}&\cos\theta_{y_{A}y}&\cos\theta_{y_{A}z}\\ \cos\theta_{z_{A}x}&\cos\theta_{z_{A}y}&\cos\theta_{z_{A}z}\\ \end{array} \right)=\left( \begin{array}{ccc}|\cos 4^{\circ}|&|\cos 93^{\circ}|&| \cos 88^{\circ}|\\ |\cos 87^{\circ}|&|\cos 8^{\circ}|&|\cos 97^{\circ}|\\ |\cos 92^{\circ}|&|\cos 83^{\circ}|&|\cos 8^{\circ}|\\ \end{array} \right) \tag{15}\]  
The stiffness of the top-left support A in the global coordinate system can be obtained as:  
\[\mathbf{K_{A}}=\mathbf{T_{A}K^{{}^{\prime}}}_{\mathbf{A}}\mathbf{T_{A}^{T}} \tag{16}\]  
Calculations of the stiffness of supports B, C, and D in the global coordinate system along the directions of the \(x\), \(y\), and \(z\) axes, as well as the stiffness of each support in the global coordinate system are listed in Table 4.