esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Influence of the Discharge Current
### Variation of the Applied Voltage  
Setting the most advantageous value of the ballast resistor in 500 M\(\Omega\), the impact of the applied voltage was examined. At this point, we were expecting that by increasing the voltage, the discharge current would also increase, producing a higher output thrust. We modulated our plasma to work in the range of low potential differences of 900 V between electrodes to a higher applied voltage of 40 kV.  
One way that the applied voltage controls the operation of our thruster is the effect that it has on the electric potential distribution, which is a fundamental factor considering the electric field converts electrical to mechanical energy.  
Table 3 shows how the thruster parameters change with the applied voltage, and from them, we observe that the thrust reaches values of 550 nN, a 100-times increase from the previously developed thruster, reaching an efficiency of 86.24 mN/kW, the highest to date. However, we also found out that there was an optimal voltage of 20 kV, as higher voltages delivered the same thrust and reduce the thruster efficiency. For instance, at 30 kV, the thrust decreases more than 10 nN and at 40 kV, the thruster efficiency was halved.  
This result is in agreement with that obtained by [PERSON] and [PERSON] [9] who showed, using a theoretical model, that an increase in the voltage between electrodes promotes  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resistor** & **Current** & **Thrust** & **T/P Ratio** \\
**M\(\Omega\)** & **mA** & **nN** & **mN/kW** \\ \hline
2500 & 1.2 & 4.11 & 17.97 \\
2000 & 1.5 & 5.46 & 19.44 \\
1500 & 2.0 & 7.75 & 21.24 \\
1000 & 3.0 & 12.27 & 23.31 \\
750 & 4.0 & 16.75 & 24.55 \\
500 & 6.0 & 25.86 & 26.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results for the study of the influence of discharge current in the single-stage argon EHD thruster by varying the ballast resistor for a fixed applied voltage of 3 kV.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Voltage** & **Current** & **Thrust** & **T/P Ratio** \\
**kV** & \(\mu\)**A** & **nN** & **mN/kW** \\ \hline
0.9 & 1.8 & 5.45 & 179.44 \\
3 & 6.0 & 25.86 & 26.27 \\
9 & 18 & 107.77 & 38.36 \\
12 & 24 & 181.14 & 48.40 \\
20 & 40 & 549.93 & 86.24 \\
30 & 60 & 521.09 & 81.26 \\
40 & 80 & 521.04 & 40.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results for the study of the influence of discharge current in the single-stage argon EHD thruster by varying the applied voltage for a fixed ballast resistor of 500 M\(\Omega\).  
an opposite behavior in terms of thrust and propeller efficiency: there is a decrease in efficiency of the thrust while there is an increase in the thrust; or, in other words, the greater the thruster current (greater propulsion force), the lower the thruster efficiency.
## 7 Finite Element (FE) Modelling
### Effect of Different Reinforcement Spacing  
Overall, eight different reinforcement spacing including 50 mm, 100 mm, 150 mm, 200 mm, 250 mm, 300 mm, 350 mm, and 400 mm were considered for the parametric study. Due to the location of the perforations, the spacing allocation was limited to 50 mm intervals. The numerical analyses were performed for both 1.6 mm and 2.5 mm thick upright frames and the results were compared with each other. Figure 24 shows the normalized load-displacement curves of the numerical models for both thicknesses. It can be observed that, employing more reinforcement to partially close the section leads to increase in the strength of the upright, which means that reinforcement should be taken into account, especially for uprights with a shorter length.  
Figure 23: Comparison of failure modes, (**a**) 1800L-1.6T, (**b**)1800L-2.5T, (**c**)1800L-1.6T-400B.  
The normalized ultimate strength of the uprights with different reinforcement spacing is also represented in Figure 25. From this figure, it is concluded that by employing shorter reinforcement spacing, the ultimate strength of the sections increases, which may contribute to the reduction in the buckling length of the section and enhancement of load sharing between the bolts. In fact, the distortional buckling behavior is improved, and section failure is changed from distortional buckling to overall buckling mainly due to partial closing of the upright section. A similar outcome is observed for both thicknesses in which increasing the number of reinforcement improves the ultimate strength of the upright for both the type of sections.  
Figure 26 also shows the percentage of increased ultimate strength using different reinforcement spacing in respect to upright without reinforcement condition. Generally, the reinforcement method could have a reasonable effect on the ultimate capacity of the upright frame through increasing its capacity to the range 10% to 45%. As indicated in this figure, the addition of reinforcement from 400 mm spacing to 50 mm spacing can enhance the frame's strength around 35% and 40% for uprights with 1.6 and 2.5 thicknesses, respectively. It is also shown that up to 100 mm reinforcement spacing, decreasing the reinforcement spacing can noticeably increase the ultimate strength of the upright under compression load; however, less difference in increased ultimate load is observed when reinforcement spacing decreased from 100 mm to 50 mm.  
Figure 24: Load-displacement curves of uprights with different reinforcement spacing.  
Figure 25: The normalized ultimate load of uprights with different reinforcement ratio.
## 5 Development of an Evaluation System
### Packet Format  
Information elements used in the proposed protocol are laid out as shown in Figure 12.  
The data link and lower layers are omitted in the figure. The IP header contains the multicast address (IE1) and the IP address (IE2). The data part is 24 Bytes long. It consists of the message type (IE3), the sequence number (IE4), which is used to determine whether the message concerned has already been received, and the multi-hop number (IE5), which is used to count the number of hops. The protocol header information at the application layer is normally written in text. In a similar manner, the message type, sequence number, and the multi-hop number fields are declared as a character type. Data content can be changed as necessary. However, in the present research, the \"other data\" part in the format is not used.  
Figure 11: How the packet arrival rate varies with transmission power.  
Figure 12: Packet format used by the stray node detection application.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
###### Abstract  
This article adds to the emergent body of research that examines the potential of 6G as a platform that can combine wired and wireless sensing modalities. We apply vision transformers (ViTs) in a distributed fiber-optic sensing system to evaluate road traffic parameters in smart cities. Convolutional neural networks (CNNs) are also assessed for benchmarking. The experimental setup is based on a direct-detection phase-sensitive optical time-domain reflectometer (\(\phi\)-OTDR) implemented using a narrow linewidth source. The monitored fibers are buried on the university campus, creating a smart city environment. Backscattered traces are consolidated into space-time matrices, illustrating traffic patterns and enabling analysis through image processing algorithms. The ground truth is established by traffic parameters obtained by processing video camera images monitoring the same street using the YOLOv8 model. The results indicate that ViTs outperform CNNs for estimating the number of vehicles and the mean vehicle speed. While a ViT necessitates a significantly larger number of parameters, its complexity is similar to that of a CNN when considering multiply-accumulate operations and random access memory usage. The processed dataset has been made publicly available for benchmarking.  
distributed fiber-optic sensing; phase-sensitive optical time-domain reflectometer; deep learning Article
###### Abstract  
The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.  
automatic modulation classification; feature-based; likelihood-based; higher-order statistical; fast Fourier transform; continuous wavelet transform; decision tree; support vector machine; artificial neural networks; k-nearest neighbor +
Footnote †: journal: Electronics Electronics
## 3 Simulation Setup
### Grid Independence Test and Model Validation  
In consideration of the spray diffusion area, the computational domain was set as a cylinder with a radius of 30 mm and a height of 200 mm, where the injection nozzle was placed at the center of the top surface as shown in Figure 1. Because the simulation result highly relied on mesh quality and the spray droplets mainly took place in the center area of the computational domain, this paper introduced a central regular hexahedral mesh into the cylindrical grid to save calculation cost and guarantee calculation precision.  
However, unlike the usual concept that the smaller mesh can lead to a better result, for the Lagrangian-Eulerian algorithm, the mesh volume should be smaller than the parcel size but not too small to cause a bubble size problem [37; 38]. Thus the mesh size of the spray simulation domain should be carefully tested. Based on the above principles, the grid independence test was carried out among 4 different meshes (Figure 1), which contained the nodes (\(n_{point}\)) of 266124, 650461, 1365004, and 2666024, respectively. In addition, the spray tip penetration, as the most common parameter for mesh independence testing in fuel spray of internal combustion engines, was used to evaluate the grid quality. As shown in Figure 2, the time-dependent spray penetration distances tended to coincide as the mesh nodes exceed 650461. Specifically, as the mesh node number increased from 650461 (mesh 2) to 1365004 (mesh 3) and to 2666024 (mesh 4), the accuracy of the simulation compared with mesh 4 only improved a little (about 1%), while the calculation time almost doubled. Therefore, Mesh 2 with 650461 nodes was chosen for subsequent calculation because of the trade-off between the computational efficiency and accuracy.  
Figure 1: Schematic diagram of the computational domain.  
Figure 2: Spray tip penetration for various grid sizes.  
Droplet diameter distribution is a highly sensitive parameter in spray simulation. To validate the numerical model in this work, we firstly compared the simulation result of droplet diameter distribution with the experimental result measured by PDPA. Figure 3 shows the comparison of the droplet diameter distribution under the initial condition of 1.5 MPa and 70 \({}^{\circ}\)C at 5 mm and 80 mm away from the nozzle tip. The result shows that the simulated diameter distribution agrees well with the experimental data.
###### Abstract  
To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth-Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic scenario of the proposed method are given, followed by the mathematical model which, mainly includes four parts: (i) dynamical constraint equations for targets; (ii) GNSS-based interplanetary link for irradiation of targets; (iii) transmission loss equation of GNSS inter-satellite link signal in Earth-Moon space; (iv) CEI-based precision measurements of targets. On this basis, the full process link budget analysis is carried out, followed by the performance evaluation, which includes the reception performance of CEI receiving arrays and the measurement accuracy of targets. The feasibility of the proposed method is evaluated and verified in experiments, and it is illustrated that (i) for inter-satellite link visibility analysis, at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth-Moon space targets, with a single GEO satellite up to 8.5 h continuously, while the chain access can be available at up to 73,000 km, with the angle ranging from \(-80^{\circ}\) to \(360^{\circ}\); (ii) the Max Duration of Chain Access for BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is 50,998.804 s/day, with a Total Duration of 358,408.797 s in 7 days; (iii) for link budget and measurement accuracy analysis, even beyond the farthest Earth-Moon Lagrangian point, the \(C/N_{0}\) will be above 56.1 dBHZ, while even approaching the distances of \(4.5\times 10^{5}\) km, the \(\sigma_{DLL}\) and \(\sigma_{FIL}\) will be below 5.345 m and \(3.475\times 10^{-4}\) m/s, respectively, and the final measurement error will remain at 62.5 m with the proposed method. The findings of this paper could play a key role in future increasingly serious space missions, such as Earth-Moon space situational awareness, and will have a broad application prospect, if put into actual testing and operations.  
future lunar exploration; Circular Space Situational Awareness; Inter-Satellite Link; Connected Element Interferometry; BeiDou-3; Circular Restricted Three-Body Problem 2023
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 5 Results & Discussions
### Performance of Proposed Method
Although the 1D CNN method was obtaining good predictive results for the dataset of Beijing, with the Ghaziabad dataset, the results are not close to the multivariate LSTM method results; we can say that with more missing data, 1D CNN performance degrades. As mentioned, there was more than 15% of data missing in the Indian dataset; here, imputation plays an important role and can be observed in the better results of the multivariate LSTM prediction compared to using the raw data for prediction with complete rows removed, even if missing values were present. Further removal of anomalies in the imputed dataset using the autoencoder improves the prediction accuracy further and makes it a more reliable system by providing the least error in prediction. Visualization in Figures 4 and 5 makes it clearer. Figure 5 shows a random 100 out of nearly 3000 data points where the predicted and actual values are from the test dataset. It is also seen from Table 4 that for Ghaziabad, the RMSE value for the proposed method is 16.5374, which is quite high compared to the RMSE values of the Aotizhonhxin, Gucheng, and Tiantan location datasets. Moreover, from Table 2, it is clear that the percent of missing values of the Ghaziabad location is higher compared to the remaining three locations. From Table 4 and Figures 4 and 5, it is observed that 1D CNN works better amongst all three univariate models with a minimum value of MAE and RMSE. Graph of actual versus predicted concentration of PM\({}_{2.5}\) pollutant from sample of 100 points for each location is given in Figure 6. From the results obtained, we can conclude that with imputed data, the  
Figure 5: Graphs showing RMSE values for all experimentations. error value decreases using the univariate 1D CNN model, which motivates the researcher to handle missing values efficiently. Figure 6: _Cont._  
Figure 6: **(a–d)**: Graph of actual versus predicted concentration of PM\({}_{2.5}\) pollutant from sample of 100 points for each location. The results obtained show that the proposed MIA-LSTM model obtains the best result with greatly smaller values of RMSE and MAE in all four graphs. The proposed model performs better, obtaining the smallest value of MAE in all cities from two different countries. This validates that there is a need for data preprocessing prior to applying forecasting methods.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 4 Experiment
### Results of Different TD-LSTM Variants
#### 4.3.1 Comparisons of Different Ocean Depth  
As shown in Figure 3, seawater of different depths has different variations. The four figures in Figure 6 show the performance of each method at 50 m, 100 m, 200 m, and 500 m. MSE is still the average MSE of 5 points randomly selected. Figure 5(a) compares the performance at 50 m, and the performance of TD-LSTM ranks second, without the 24-LSTM. This is because the input sequence used by TD-LSTM is only 1/3 of the length of 24-LSTM. With a reduced number of input sequences, performance is close to 24-LSTM and can be tolerated. The performance of TD-LSTM is better than other variants of SVR and MLP. Figure 5(b) compares performance at 100 m depth. TD-LSTM performs better than 24-LSTM, much better than SVR and MLP variants. The performance of TD-LSTM is better than that of 24-LSTM because the variation of seawater temperature at 100 m depth is not obvious. The time-dependent parameter matrix fusion method can better grasp the characteristics of the variation law. Figure 5(c),d compare the performance of each method at 200 m and 500 m, respectively. The LSTM method is better than the variants of SVR and MLP. The performance of the TD-LSTM is close to that of the 24-LSTM and is better than the 8-LSTM.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 3 Concepts of the CPS Communication Hub
### Trust Checking with CPS Communication Spaces  
The basic structure of the trust checking with CPS communication spaces is depicted in Figure 4. The CPS devices are usually tightly coupled with the service provider cloud systems, and each of them has their own allowed users (customer) base; see the upper part of the Figure 4. Accordingly, each user/owner makes a separate authentication with each SPs' service cloud system and related resources. This is usually the state-of-the-practice in the CPS/M2M service sector today in vertical enterprise zone. However, this is not enough, because there is a need for smart, trustable, secure, and interoperable communications across CPS systems for the owners of the resources. As the solution, it is proposed here that the CPS communication spaces establish the trustworthy grid of communicating stakeholders and entities; see the lower part of Figure 4.  
Each stakeholder needs to be authenticated with separate credentials to the CPS communication hub, and have a specific virtual communication space there. In addition, each stakeholder needs to describe their own security policies in the policy based authorization framework relying on, for example, XACM [22; 23; 24; 25]. These security policies can then be applied by the SPs via authorization policy services, which enables consideration of the requirements of the owners. The authorization policy services need to be reliable and common for all the SPs, including CPS communication hub, in order to achieve smart cross domain/SP services for users/owners. The CPS communication hub takes care of the presence management, establishment and maintenance of the communication spaces with their trustworthy relationships, and information sharing with trust checking. The role of the trust checker is to ensure that the required information subscription is in line with the current relationship of the stakeholders and their authorized policies. The required information delivery can happen only if both the relationships and policies allow it.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 6 Discussion  
Based on the results obtained, it is essential to analyse the performance of each deployment, as shown in Figure 6. Despite the differences in sensor distribution (deployments 2 and 3), Figure 6d,f exhibit similar behaviour in terms of sensor position errors. In contrast, for deployment 1, Figure 6b shows lower signal degradation, which can be attributed to a sensor distribution that enables a more favourable drone positioning for the scenario defined in this model. This leads us to a need to optimise the position of the receiver according to the sensor distribution in order to achieve a higher SNR and, consequently, improved BER performance.  
Following the previous analysis, deployment 1 appears to have a higher signal power at the drone. This can be confirmed when we apply the same noise power, \(P_{N}\), to every distribution in the ideal case. As shown in Figure 7, deployment 3 exhibits better BER performance at lower \(P_{N}\), indicating that it has a higher SNR. This can be regarded as a case where the position selected by the drone to collect data from the sensors is more favourable, given the sensor deployment configuration. This leaves room for future work focused on determining the optimal drone positioning within WSNs, considering positioning errors in the sensors, with the goal of maximising communication BER performance and minimising the impairments caused by positioning inaccuracies.  
Finally, we analyse the number of sensors required to achieve satisfactory accuracy. From Figure 8, it can be seen that for different levels of SNR, we can improve the system's  
Figure 7: Comparison of BER performance across three 40\(-\)node systems, using deployment 1 as the reference.  
accuracy by increasing the number of sensors. However, after a certain point of increasing the sensors, the accuracy is not affected significantly, and an optimum number of sensors for the desired SNR can be decided from this. For instance, if we operate at 10 dB satisfying the required BER, then the feasible number of sensors required is eight. Increasing the number of sensors in the system would not change the system further, and we would just be wasting the power of the remaining utilised sensors.