esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Analysis
### Telemetry Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**S-Band Downlink (Eb/No) Method**} \\ \hline \multicolumn{10}{c}{Calculating path distance} \\ \hline Earth radius (Km) & 6371 & 6371 & 6371 & 6371 & 6371 & 6371 & 6371 \\ \hline Orbit (Km) & 400 & 400 & 400 & 400 & 400 & 400 & 400 \\ \hline Elevation angle (\({}^{\circ}\)) & **0** & **5** & **10** & **20** & **30** & **40** & **50** & **60** \\ \hline Distance (Km) & 2292.8 & 1803.8 & 1439.4 & 984.03 & 739.31 & 598.14 & 511.72 & 457.41 \\ \hline \multicolumn{10}{c}{S-band transmitter specifications} \\ \hline Downlink Frequency (MHz) & 2300 & 2300 & 2300 & 2300 & 2300 & 2300 & 2300 & 2300 \\ \hline Data rate (bit/s) & 128,000 & 128,000 & 128,000 & 128,000 & 128,000 & 128,000 & 128,000 \\ \hline Bandwidth (Hz) & 650,000 & 650,000 & 650,000 & 650,000 & 650,000 & 650,000 & 650,000 \\ \hline S-band Transmitter (dBW) & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline S-band antenna gain (dBi) & 4 & 4 & 4 & 4 & 4 & 4 & 4 \\ \hline \multicolumn{10}{c}{Ground station specifications} \\ \hline S-band receiver gain & 50 & 50 & 50 & 50 & 50 & 50 & 50 \\ \hline S-band antenna gain (dBi) & 31 & 31 & 31 & 31 & 31 & 31 & 31 \\ \hline Line Losses & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 \\ \hline System Noise Temperature & 230 & 170 & 161 & 156 & 154 & 154 & 154 & 153 \\ \hline \multicolumn{10}{c}{Losses} \\ \hline Loss of polarization & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline Atmospheric losses & 0.7 & 0.7 & 0.7 & 0.7 & 0.7 & 0.7 & 0.7 \\ \hline Satellite Cable Losses & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 \\ \hline Free Space Losses (dBi) & 166.88 & 164.79 & 162.83 & 159.53 & 157.05 & 155.21 & 153.85 & 152.88 \\ \hline \end{tabular}
\end{table} 
Table 7: Light-1 \(S-\) band link budget at the Vilnius ground station.
# Low-Frequency Sound Absorption in Air Pipelines and Water-Filled Pipelines Based on Metamaterials
## 2 Design Paradigm and Theoretical Method  
This section introduces the design paradigm of HR sound absorbers on the side branch of pipelines. Under this paradigm, we design low-frequency asymmetric absorption metamaterials for air pipelines and low-frequency and broadband composite absorbing metamaterial for water-filled pipelines. The flowchart of the methodology implemented is shown in Figure 1.  
First, we propose an asymmetric absorption metamaterial model for low-frequency sound elimination in air pipelines based on the Helmholtz cavity paradigm and provide theoretical solutions for the sound absorption coefficient and impedance. We then construct  
Figure 1: Methodological flowchart of metamaterial for low-frequency asymmetric sound absorption of air pipeline and composite metamaterial for low-frequency sound absorption of water-filled pipeline based on Helmholtz basic paradigm.  
a model using the finite element method and provide analytical solutions for the sound absorption coefficient. Based on the theoretical solutions, we verify the validity of the finite element model. Continuing with the finite element method strategy, we solve the physical fields like pressure and velocity to study the low-frequency sound absorption mechanism. To match the structural impedance with water while maintaining a similar structure thickness, we introduce rubber materials based on the Helmholtz cavity paradigm and propose a composite metamaterial model for low-frequency sound absorption in water-filled pipelines. Using the finite element method, we calculate the sound absorption coefficients and physical field parameters of the materials, conduct studies on the absorption mechanism, and finally optimize the sound absorption coefficient using the Nelder-Mead optimization algorithm in COMSOL software.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 3 Proposed Two-Stage Localization Method  
In this section, we propose a two-stage LWLS method for solving the RSS-AOA time series localization problem with multiple types of anchor nodes. The first stage is to estimate the target node location roughly using the time-averaged RSS-AOA values. The second stage is to estimate the variance of each residual of the LWLS equation. Based on these variances, a weight matrix is calculated and used to reapply the LWLS method to obtain a more accurate solution for the target node location.
# Two-Stage Configuration of User-Side Hybrid Energy Storage Based on Fuzzy Optimization
## 5 Conclusions  
In this application scenario, supercapacitors are used to suppress the fluctuation of photovoltaic output, and batteries are used to reduce the user's maximum load demand. The characteristics of this model are as follows.  
1. This paper uses fuzzy optimization theory to optimize capacitor configuration from two aspects of power grid and users. Considering the benefits of users and the stability of the power grid, it can be seen from the calculation examples that both sides reach a more satisfactory state.
2. The whole multi-objective optimization process is divided into two stages to make the optimization process clear. In the second stage, the multi-objective optimization uses the method of dividing objective functions to ensure the user's benefit and reduce the number of charging and discharging cycles in each CC, to improve the service life of energy storage. It can be seen from the calculation results that ES life is extended to 10.61 years after the number of CCs is  
Figure 8: Energy storage configuration for maximum load.  
Figure 7: Energy storage configuration for medium load.  
determined, which successfully compensates for the investment and operation cost of ES systems and enables users to make profits.  
Conceptualization, Y.X.; methodology, Y.X. and Q.X.; software, Y.X.; validation,Y.X.; formal analysis, J.Z. and C.W.; writing--original draft preparation, Y.X.; writing--review and editing; supervision, Q.X.; project administration, Q.X.; funding acquisition, Q.X.  
This research was funded by Chinese National Natural Science Foundation, grant number 51936003 and Chinese National Natural Science Foundation, grant number 51877044.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript.  
\begin{tabular}{l l} SC & supercapacitor \\ cha & charge \\ dis & discharge \\ CNY & Chinese Yuan \\ CC & Charge Cycle \\ PV & Photovoltaic \\ ES & Energy Storage \\ MF & Median Filter \\ WT & Wavelet Transform \\ MAF & Moving Average Filter \\ SOC & State of Charge \\ \end{tabular}
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 4 Experimental Results
of \(\pm\)0.4 dB to the NPR test results [15], it can be considered that the change in the noise signal bandwidth from 100 MHz to 500 MHz had no impact on the NPR index when the output power of LTWTA was reduced to 3 dB, which means that the LTWTA has good broadband characteristics.
# A Study on the Optimisation of Tennis Players' Match Strategies from the Perspective of Momentum
## 4 Construction of a Momentum-Based Player Match Strategy Optimisation Model
### Prediction of Match Trends
#### 4.2.1 Key Indicators Influencing Match Trends  
In order to capture fluctuations in match trends, the difference in momentum scores between Player P1 and Player P2 is used as the dependent variable. A positive difference indicates that Player P1 is in a dominant position, while a negative difference suggests that Player P2 holds the advantage. The point at which the difference switches from positive to negative, or vice versa, represents the transition of dominance between Player P1 and Player P2. The magnitude of this transition is quantified by the absolute value of the difference between the momentum scores of the two players.  
This study proposes a multiple linear regression equation, predicated on the differences between Player P1 and Player P2 in both quantitative indicators (rally_count, distance_run, speed_mph, and delta_point) and qualitative indicators (ace, winner, double_fault, unforced_error, net_pt, net_pt_won, serve_width, serve_depth, return_depth,  
Figure 3: Line chart of comprehensive indicator scores.  
break_pt, break_pt_won, break_pt_missed, and winner_shot_type), with the difference in momentum scores between Player P1 and Player P2 as the dependent variable, to investigate the impact of each indicator on the match trends. The reference expression for the multiple linear regression equation is shown in Equation (11).  
\[\hat{y}=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{m}x_{m}+\varepsilon, \tag{11}\]  
where y represents the dependent variable, \(\beta_{0}\) is the constant term, \(\beta_{k}\) is the regression coefficient of the k-th independent variable, and \(\varepsilon\) is the random error term. By substituting the data from this study into the analysis, the regression equation is derived, and a significance test is conducted on the regression equation [18]. The test formula is shown in Equation (12).  
\[F=\frac{SSR/m}{SSE/(n-m-1)}\ \sim F(m,n-m-1), \tag{12}\]  
where \(SSR=y_{i}\)2 represents the sum of squares due to regression and \(SSE=\sum(y_{i}-\hat{y}_{i})^{2}\) represents the sum of squares due to residuals. By substituting the match data without outliers, which were selected from each round of the match, into the analysis, it is found that under a 95% confidence interval, the \(p\)-value is 0.000 \(<\) 0.05. This outcome consequently leads to the rejection of the null hypothesis, thereby indicating that the regression equation is statistically significant. In order to further confirm the feasibility of this regression model, it is also necessary to test for heteroscedasticity and multicollinearity within the model.
## 7 Summary and Discussion
All authors have read and agreed to the published version of the manuscript. **Funding:** This work has been partially funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Forderkennzeichen 165 GU054B, IndustrieStadtpark: 5G-Anwendungen im Projektgebiet IndustrieStadtpark Troisdorf) and by the German Federal Office for Information Security of the Federal Republic of Germany (BSI) under project (Forderkennzeichen 01 MO23003B, PlusMoSmart) funding reference number. The authors alone are responsible for the content of this paper. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
**3 GPP**: 3 rd Generation Partnership Project
**BS**:  base station
**COTS**:  Commercial Off-the-Shelf
**CapEx**:  Capital Expenditure
**DoS**:  Denial of Service
**DNS**:  Domain Name System
**EPC**:  Evolved Packet Core
**HSS**:  Home Subscriber Service
**eMBB**:  Enhanced Mobile Broadband
**ETWS**:  earthquake and tsunami warning system
**GUTI**:  Globally Unique Temporary Identifier
**IMSI**:  International Mobile Subscriber Identity
**TMSI**:  Temporary Mobile Subscriber Identity
**LTE**:  Long-Term Evolution
**MAC**:  Message Authentication Code
**MNO**:  Mobile Network Operator
**MCC**:  Mobile Country Code
**MNC**:  Mobile Number Code
**NSA**:  Non-Stand-Alone
**NAS**:  Non-Access Stratum
**NFV**:  Network Functions Virtualization
**NR**:  New Radio
**MITM**:  man-in-the-middle
**MN**:  Master Node
**MME**:  Mobility Management Entity
**mMTC**:  Massive Machine Type Communications
**PC**:  personal computer
**P-GW**:  PDN Gateway
**RAN**:  Radio Access Network
**SDN**:  Software-Defined Networking
**SDR**:  Software-Defined Radio
**SN**:  Secondary Node
**SA**:  Stand-Alone
**SIB**:  System Information Block
**SIM**:  Subscriber Identity Module
**SIB**:  System Information Block
**S-TMSI**:  Serving Temporary Mobile Subscriber Identity
**S-GW**:  Serving Gateway
**PRACH**:  Physical Random Access Channel
**PBCH**:  Physical Broadcast Channel
**TAC**:  Tracking Area Code
**TAU**:  Tracking Area Update
**TMSI**:  Temporary Mobile Subscriber Identity
**URLLC**:  ultra-reliable low-latency communication
**UE**:  User Equipment
## 2 Numerical Modeling of Ropes and Large Floating Structures Based on the ANCF
### Mass Matrix and External Load Matrix  
#### 2.3.1 Mass Matrix  
Deformation in flexible structures is often complex, and coordinate transformations undeniably lead to a significant computational workload. In the ANCF method, the mass matrix for cable elements does not involve coordinate transformations, resulting in a constant matrix composition [39]. Consequently, the constant mass matrix in the ANCF method significantly enhances computational efficiency. Differentiating Equation (1) with respect to time yields the absolute velocity vector while integrating the element's kinetic energy over its volume allows expression in quadratic form:  
\[\begin{array}{ll}T_{e}&=\frac{1}{2}\int_{V}\rho\dot{\mathbf{r}}^{T}\dot{ \mathbf{r}}dV=\frac{1}{2}\int_{V}\rho\dot{\mathbf{q}}^{T}\mathbf{N}^{T} \mathbf{N}\dot{\mathbf{q}}dV\\ &=\frac{1}{2}\dot{\mathbf{q}}^{T}\cdot\left(\int_{V}\rho\mathbf{N}^{T} \mathbf{N}dV\right)\cdot\dot{\mathbf{q}}=\frac{1}{2}\dot{\mathbf{q}}^{T} \mathbf{M}^{j}\dot{\mathbf{q}}\end{array} \tag{34}\]  
where \(\rho\) and \(V\) are the structural density and volume of the cable element, respectively, and \(\mathbf{M}\) is the constant mass matrix defined in the global inertial frame. Coriolis and centrifugal forces are considered zero in the equations of motion. Here, the mass matrix remains constant, with its derivation based on the variation principle and interpolation functions:  
\[\mathbf{M}=\int_{V}\rho\mathbf{N}^{T}\mathbf{N}dV=\int_{0}^{l_{0}}\rho_{l} \mathbf{N}^{T}\mathbf{N}dx=(\rho_{l}l_{0}+C_{\alpha}\rho_{w}V)\mathbf{M}_{a} \tag{35}\]  
\[\begin{array}{ll}\mathbf{M}_{a}=\int_{0}^{1}\mathbf{N}^{
## 2 Model Description
### Braking Condition Analysis  
Under braking conditions, the variation in wheel speed over time is shown in Figure 2, and the corresponding parameter values are listed in Table 3.  
As shown in Table 3, the aircraft begins emergency braking at an initial speed of \(v_{0}=36.7\) m/s, relying solely on the disc braking system. With a constant deceleration of \(\alpha=-1.468\) m/s\({}^{2}\), the braking duration is 25 s. The relationship between the aircraft's speed \(v_{\rm t}\) and time \(t\) during braking is as follows:  
\[v_{t}=v_{0}-at=(36.7-1.468t)\ \text{m/s} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Parameters** & **Value** \\ \hline Outer diameter of the rotor disc (mm) & 341.25 \\ Inner diameter of the rotor disc (mm) & 273.34 \\ Thickness of the rotor disc (mm) & 20 \\ Outer diameter of the stator disc (mm) & 316.78 \\ Inner diameter of the stator disc (mm) & 245.01 \\ Thickness of the stator disc (mm) & 23.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of brake discs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thermophysical Parameters** & **Value** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 2000 \\ Elastic modulus \(E\) (GPa) & 82 \\ Poisson’s ratio & 0.3 \\ Specific heat capacity \(c\) (J/(kg \({}^{\circ}\)C)) & 1000 \\ Thermal conductivity \(k\) (W/(m\({}^{\circ}\)C)) & 40 \\ Linear expansion coefficient \(\alpha\) (10\({}^{-6}\)/\({}^{\circ}\)C) & 5 \\ Tensile strength \(\sigma\) (MPa) & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical parameters of brake discs.  
Figure 2: The curve of wheel speed variation with time during the braking process.  
During the braking the process of an aircraft, the brake disc decelerates along with the aircraft, and the law of the angular velocity of the brake disc changing with time is as follows:  
\[\omega=v_{t}/R_{w}=(36.7-1.468t)/0.35=(104.86-4.2t)\text{ rad}/\text{s} \tag{2}\]  
where \(\omega\) is the angular velocity; \(R_{w}\) is the radius of the aircraft wheel. The change law of angular velocity will be used as the boundary condition to define the brake disc rotation speed in the brake disc coupling model.  
During the braking process, the heat flux density generated is calculated using the following formula. The heat flux density changing with time is shown in Table 4.  
\[q=(80090209-26696736.633t)\text{ W}/\text{m}^{2} \tag{3}\]
## 3 Case Study Results
Finally, the relation between practical alternatives and given criteria is graded, and the correlation matrix is obtained as shown in Table 7.  
Table 7 is derived using Equations (15)-(17), and it shows correlations between technologies and three criteria. For example, the correlation between the ROV with the cost (C\({}_{1}\)) criteria is 0.8, which indicates a high correlation. Also, the ROV has an ideal correlation with urgency (C\({}_{2}\)) criteria. Finally, it can be observed, the ROV technology has a weak correlation (0.4) with the accuracy (C\({}_{3}\)) criteria. It has to be pointed out that a high correlation between technology and criteria indicated suitability between the criterion and technology. Next, the final step in WSM includes the optimum solution of each alternative is obtained using the Equation (18). Therefore, ranked technologies for the survey area are represented in Table 8.  
From Table 8, it can be observed that according to the WSM method, the optimal technology following the given criteria for the proposed Kastela Bay study case is UAV technology. Further, the second-best alternative with a score of 0.90 is SDB technology. It can be observed that there is a small difference between the two best options candidates for the Kastela Bay hydrographic survey. These numerical results suggest that both alternatives can be considered because they meet the given criteria almost equally. On the other hand, the MBES technology has the lowest score (0.53). Therefore, it can be concluded that the technology is least desirable to use for the proposed hydrographic survey area. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{\Sigma}\) & **Weights Vector** & **CV** \\ \hline \(C_{1}\) & 1 & 3 & 9 & 2.0795 & 0.6932 & 2.9998 \\ \(C_{2}\) & 0.33 & 1 & 3 & 0.6907 & 0.2302 & 3.0004 \\ \(C_{3}\) & 0.11 & 0.33 & 1 & 0.2294 & 0.0764 & 3.0026 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized pair-wise comparison matrix, weights, and consistency vector. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) \\ \hline ROV & 0.8 & 1 & 0.4 \\ UAV & 1 & 0.8 & 0.6 \\ LIDAR & 0.6 & 1 & 0.8 \\ AUV & 0.8 & 1 & 0.6 \\ SDB & 1 & 0.6 & 1 \\ MBES & 0.4 & 1 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The normalized decision matrix. \begin{table}
\begin{tabular}{c c} \hline \hline
**Technology** & **Value** \\ \hline UAV & 0.92 \\ SDB & 0.90 \\ AUV & 0.83 \\ ROV & 0.81 \\ LIDAR & 0.70 \\ MBES & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ranked technologies for the survey area.
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
## 5 Simulation Results
### DL Scenario
In this case, the uniformity of the spreading of all propagation paths lowers the range of SIR variation about 10 dB and reduces the differentiation of the side lobes' influence. For LOS conditions, we can also observe that, despite the larger separation angle for \(\Delta\alpha=20^{\circ}\), we obtain a lower useful beam resistance to interference compared to \(\Delta\alpha=15^{\circ}\). This effect is the result of the concentration of the received power on the side-lobe direction and the first minimum of the useful Rx beam, respectively. Figure 10 shows that this phenomenon does not occur under NLOS propagation conditions. The scattering phenomenon of electromagnetic waves under these conditions makes it impossible to concentrate the received power in the Tx-Rx direction.
## 5 Experimental Results and Analysis
### Experimental Platform  
To run the experimental verifications, the middleware cloud is deployed on the Linux operating system by Docker, and the IoT LoRaWAN system architecture is used to realize service discovery and management. Here, we use the local host as the requester to simulate user requests. Figure 18 shows the successful startup of the middleware software. After the network service is started, the actual server address is configured in the LoRaWAN gateway according to the cloud middleware. The gateway hardware is shown in Figure 19 and the hardware description is listed in Table 6. The terminal module is connected to the LoRaWAN network via the gateway. The cloud platform is used to periodically send out data to the terminal nodes. the LBOP-ARIMA model proposed in this paper is applied to predict the downlink bandwidth occupancy and thereby perform the MPSRCS for the optimal downlink route selection. The optimal route to reach the best performed gateway is selected so that the downlink data packet joins the gateway data sending queue. When the terminal node receives the data sent out by the gateway, the MCU side responds in time and parses the data packet. The node data is uploaded to the platform side according to the downlink data request. Figure 20 is a prototype of the terminal node device.  
Figure 17: Comparison of average bandwidth occupancy.  
Here, the strategy performance of the HCARS and MPSRCS are compared. The link bandwidth occupancy is compared under different load rates and the results are shown in Figure 21. It is seen that when the link load rate is small, there is no significant difference. With the continuous increase of the downlink load rate, the link bandwidth occupancy rate of the MPSRCS demonstrates better performance. It is calculated that the overall average  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Name** & **Label** & **Name** \\ \hline
1 & Power indicator & 11 & SX1301 board power indicator \\
2 & WI-FI indicator & 12 & 4G module main antenna IPEX interface \\
3 & USB indicator & 13 & 48 V power interface \\
4 & WAN indicator & 14 & Power supply 12 V ground interface \\
5 & LAN indicator & 15 & 12 V power input interface \\
6 & 3G/4G indicator & 16 & WAN interface \\
7 & WiFi antenna SMA & 17 & LAN interface \\
8 & & InRa antenna SMA & 18 & Hardware reset button \\  & & interface & \\
9 & GPS antenna SMA & 19 & Factory reset button \\  & & LTE antenna SMA & \\
10 & & interface & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The LoRaWAN gateway hardware description table.  
Figure 19: The LoRaWAN gateway hardware.  
Figure 18: The successful startup of the middleware software.  
link bandwidth occupancy rate of the MPSRCS strategy is increased by 12% compared with the HCARS, which effectively improves the overall performance and reliability of the LoRaWAN network.