esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case  
Most of the seaports around the globe are equipped with fixed machinery and connected to the network via cables. Mostly, trade around the world is carried out via seaports, and most of the operations are heavily dependent on humans. Recently, COVID-19 has dealt a major blow globally, resulting in shipping costs reaching and an all-time high. A reliable network is required to trace and locate the containers, and to check their custom clearance reports and temperature. The existing 4G network cannot meet these requirements due to its limitations. We present the smart port use case, where network slicing is deployed to ensure safe and efficient processes. NS based 5G trial test-bed has been implemented in port of Hamburg [7]. Results showed that in order to manage the slices, all network entities needs to be involved. A major challenge in NS is to manage the resources more efficiently [7]. We propose the network slicing model based on ML techniques in order to manage the resources more efficiently. 5G network slicing can be deployed in the following smart port scenarios:  
* Intelligent automation (IA).
* Virtual reality (VR).
* Smart guided vehicle (SGV).
## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction
#### 6.2.5 ICU Mortality  
[PERSON] et al. in [85] presented an innovative application of deep federated learning in the context of predicting ICU mortality risk. This research introduced a novel framework that leverages deep learning models while preserving the privacy of sensitive patient data in intensive care unit (ICU) settings. Their approach aimed to improve the accuracy of ICU mortality risk predictions by enabling collaborative model training across multiple healthcare institutions. It highlights the critical role of federated learning in healthcare, particularly in ICU scenarios, where early risk assessments can lead to better patient outcomes and resource allocation. Furthermore, it demonstrates the potential of deep federated learning to enhance critical care by predicting mortality risk while upholding the highest data privacy and security standards, making it a significant advancement in the field of healthcare analytics.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 2 Materials and Methods
### Effect of CAP on Monospecies Biofilms  
Monospecies bacterial biofilms of MRSA (ATCC 33591), _P. aeruginosa_ (ATCC 27853) and _E. faecalis_ (ATCC 29212) were grown in 96 wells plates, as these experiments aimed to standardize the ideal parameters of plasma device in a cost-effective manner. Standardized suspension (10\({}^{6}\) cells/mL) of each strain in Trypic soy broth (200 \(\mu\)L) was added to each well. Plates were incubated for 90 min at 37 \({}^{\circ}\)C and 80 rpm for initial adherence. After this period, the culture medium was removed, and the wells were washed twice with physiologic solution (NaCl 0.9%). Then, 200 \(\mu\)L of fresh Trypic soy broth medium were added to each well and the plates were incubated for 48 h. Subsequently, the culture medium was removed, and the biofilms were washed twice with physiologic solution (NaCl 0.9%). Biofilms were exposed to CAP for 1, 3, 5 and 7 min. A non-exposed treated group was included for comparative purposes. The number of viable cells was determined after serial dilution in physiologic solution and plating on brain heart infusion agar. The results were expressed as colony forming units per mL (CFU/mL). Three independent experiments were performed in triplicate.
[11] first applied the microseismic monitoring method for the early warning of rockbursts in the diversion tunnel of the Jinping II Hydropower Station, and the early warning results have been widely recognized. [PERSON] et al. [12] studied the failure mode of a circular tunnel under unloading condition through numerical experiments. They showed that, with the increase in the ground stress, the acoustic emission (AE) and crack propagation exhibited an evident evolution trend. [PERSON] et al. [13] systematically studied the relationship between rockburst and microseismicity in the Jinping II Hydropower Station, and observed that the distribution area of high energy density in the surrounding rock is consistent with the high-risk area of rockbursts. [PERSON] et al. [14] reported the occurrence of a rockburst, recording approximately 40 microtremors in two minutes, the distribution of which was consistent with the direction of the structural plane obtained by the on the-spot investigation. In recent years, early warning methods based on microseismic monitoring have produced promising results and successful predictions, such as for the Access Tunnel of Shuangjiangkou Hydropower Station, Sichuan-Tibet Railway Tunnel, and Hanjiang-to-Weihe River Diversion Project [15; 16; 17]. In summary, the use of microseismic information as a precursory for rockbursts is fully supported by theory and verified practically. Microseismic monitoring has gradually become an important monitoring means for the early warning of rockbursts in tunnel engineering.
## 5 Modeling and Simulating Si-SC Structures  
To optimize Si-SC performance, a comprehensive modeling and simulation framework was employed. As illustrated in Figure 4, the optimization workflow of a two-step modeling approach was adopted. Initially, the optical properties of constituent materials (SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), LiF, and CdSe/ZnS NQDs) were experimentally determined, providing a foundation for subsequent simulations. These optical parameters, including refractive index and extinction coefficient, were incorporated into the Ansys\(-\)Lumerical\(-\)FDTD simulation environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**Thickness (nm)**} & \multicolumn{6}{c}{**Wavelength (nm)**} \\ \cline{3-7}  & & **350** & **450** & **550** & **650** & **750** \\ \hline Si (no ARC) & intrinsic & 56.45 & 41.96 & 36.73 & 34.73 & 33.24 \\ \hline Si (no ARC) & n-type & 68.98 & 44.18 & 37.63 & 34.93 & 33.43 \\ \hline Si with SiO\({}_{2}\) SLARC & 90 & 68.95 & 24.67 & 10.85 & 9.15 & 11.24 \\  & 120 & 66.15 & 40.70 & 22.10 & 11.34 & 7.64 \\  & 150 & 50.97 & 44.23 & 30.02 & 17.37 & 9.88 \\ \hline Si with Si\({}_{3}\)N\({}_{4}\) SLARC & 90 & 60.20 & 39.76 & 16.77 & 3.64 & 0.16 \\  & 120 & 7.62 & 42.11 & 34.35 & 20.15 & 8.36 \\  & 150 & 65.46 & 12.92 & 35.30 & 33.31 & 24.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured reflectivity of n-type Si substrates with and without SLARCs at different thicknesses and wavelengths, against reflectivity of intrinsic Si (reference data from [PERSON] and Keevers [15]).  
Figure 4: Optimization process for designing and evaluating the optical and electrical characteristics of Si-SC with SLARC and MLARC.  
Utilizing the FDTD method, the optical performance of various SLARC and MLARC configurations was meticulously analyzed. By systematically varying layer thicknesses, materials, and structural parameters, a comprehensive design space was explored. Key optical metrics, such as absorption, reflection, and transmission spectra, were assessed to identify optimal ARC designs that maximized light harvesting within the Si-SC.  
To evaluate the electrical implications of the optimized ARC designs, the Lumerical CHARGE platform was integrated. Coupling optical generation data from FDTD simulations with device-level electrical models enabled a comprehensive assessment of Si-SC performance. Key electrical parameters, including short-circuit current, open-circuit voltage, and fill factor, were determined. Iterative optimization of ARC structures was conducted to achieve a balance between optical and electrical performance, ultimately enhancing overall Si-SC power conversion efficiency.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification
### The Effect of Packet Arrival Rate  
To study the effect of packet arrival rate on platoon communication performance, we change the platoon size and packet arrival rate \(\lambda\). In these experiments, we set \(p_{b}=1\times 10^{-5}\) and \(K=20\). The simulation and analytical results for delay and packet dropping rate are depicted in Figure 7.  
As shown in Figure 7, delay and packet dropping rate calculated based on our analytical model perfectly match simulation results for different packet arrival rate. An observation from Figure 7 is that the increase of packet arrival rate causes the delay to rise. Regarding packet dropping rate, as plotted in Figure 7b, the packet dropping rate is increasing with the growth of packet arrival rate for a fixed platoon size because the probability of unsuccessful transmission increases as the traffic load rises. Meanwhile, in Figure 7, the performance metrics rapidly increase under the non-saturated condition (\(\lambda\leq 100\)). With the growth of \(\lambda\), the network will reach saturation condition finally (\(\lambda>100\)) and then the performance metrics is constant.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 4 CPS Testbed Applications
### Module 2: General Power System CPS Operator Training
#### 4.2.2 Jammer Attack  
For the distribution system using wireless sensor networks, protection schemes must be employed against the aggressive nature of the jamming of the transmitted signal [56]. The jammer acts as high-power random Gaussian noise [57]. For a given n channel with energy \(H_{\mathrm{N}}(t)\) and \(N\) number of samples, the average jamming pulse observed is given by Equation (13).  
\[J(t)=\frac{\sum_{j=t-N+1}^{t}H_{\mathrm{N}}(j)^{2}}{N} \tag{13}\]  
Figure 10: Quantity of messages: (**a**) sent; (**b**) received with DoS attack.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Value** & **Property** & **Value** \\ \hline Attack type & DOS & Attack type & Jammer \\ Victim node & Node 2 & Jammer node & Node 15 \\ Victim IP & 10.10.1.33 & Start time & 1 \\ DOS attack type & Basic attack & End time & 25 \\ Victim port & 7200 & Scanner index & 0 \\ Configure & Interval & Jamming power & Power (dBm) \\ Interval \(\beta\) & 0.1 s & Power & 100 dBm \\ Duration & 30 s & Silent jammer & Yes \\ Ramp-up time & 0 s & Ramp-up time & 0 s \\  & & Data rate & 1 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: General configuration for standalone attacks.  
The jamming pulse is compared with the threshold value \(\varphi\) to detect the presence of the jammer. The effect of the jammer also depends on the jamming rate \(r\), which is given by Equation (14).  
\[r=\frac{(\Delta H=H_{observed}-H_{network})>0}{Total\ sampling\ window\ time} \tag{14}\]  
If \(\Delta H>\varphi\), the presence of a jammer in the channel is detected. The use case in this section simulates a scenario where the network is under a jammer attack and reveals its impact on data transmission. It involves wireless and wired communication of wind farms to demonstrate the effect of the jammer attack. The individual subnet in the wind farm distribution network is connected to the remote server via a controlled wide area network (WAN). The jamming device is placed within the wireless network. Data from Table 3 were used to configure the jammer behavior, such as the physical characteristics of the jammer devices, including antenna gain, antenna height, jammer power, and data extraction rate. Figure 11 shows the result of the attack, where the total messages received at the SCADA server were observed to be fewer than the messages sent from the wind farm.
## 4 Experiments
### Face Clustering on Extended Yale B  
To test the proposed LLE-SSC and LLE-LRR methods in more complicated scenarios, we perform clustering experiments on the wildly used face dataset, the Extended Yale B, which has face images of 38 persons captured under different illuminations with each person having 64 front face images. Some samples of the dataset are shown in Figure 3. For clustering application, this dataset is considered challenging as there exists too much varsity of illumination, expression and pose. Here, we randomly select \(c=[2,3,5,8,10]\) people and each person chooses 32 face images for clustering. All images in our experiment are resized to 32 \(\times\) 32 pixels. As a result, we obtain the initial sample vectors \(\mathbf{x}_{i}\in\mathbb{R}^{1024}\). Then we reduce the dimension of these samples to (9 \(\times\) c) by the Principal Component Analysis (PCA) method according to the similar data processing procedure in [20]. Based on the dimension reduced datasets, we implement clustering on the selected \(c\) classes. To get a stable result, we repeat the clustering experiments 30 times by re-selecting \(c\) classes of face images and use the mean  
Figure 1: The affinity matrices of the clustering methods of (**a**) SSC; (**b**) LLE-SSC; (**c**) LRR; (**d**) LLE-LRR; (**e**) NSLLRR; (**f**) SMR and (**g**) LapLRR on the synthetic dataset with 20% Gaussian noise.  
Figure 2: The clustering results of the clustering methods of (**a**) SSC; (**b**) LLE-SSC; (**c**) LRR; (**d**) LLE-LRR; (**e**) NSLLRR; (**f**) SMR and (**g**) LapLRR on the synthetic dataset with 20% Gaussian noise.  
clustering error as the final results. In the experiment, the parameters \(\lambda_{1}=20,\lambda_{2}=1\) for LLE-SSC and \(\lambda_{1}=3,\lambda_{2}=0.5*c\) for LLE-LRR.  
The experimental results are reported in Table 3. It is observed that our methods significantly outperform the other methods in all cases. Both LLE-SSC and LLE-LRR have at least 10% improvements compared to the other methods in terms of the mean error of different number of classes. In addition, our methods have the advantage that the clustering performances decrease little when the number of classes increases. On the contrary, the clustering performances of other methods drop dramatically when the number of classes increases. This demonstrates that the proposed methods have the application potential for clustering large scale datasets.
## 4 Results and Discussions
### Simulation and Prediction of Magnetic Field Effect under Different Flue Gas Velocities
Under two different flue gas velocities, the PSO-SVM model outperforms the BPNN model in terms of the agreement degree between the predicted values and simulated data, as well as the proximity of the predicted curves to the simulated ones, indicating that the PSO-SVM model has a higher accuracy. Based on the data in Figure 14, a comparative chart with a 45\({}^{\circ}\) line is depicted in Figure 15 to compare the predicted values of the two models under different flue gas velocities. It is not difficult to find that no matter how the flue gas velocity changes, the predicted values of the PSO-SVM model are closer to the 45\({}^{\circ}\) line compared with those of the BPNN model, especially at 1.0 m/s. These findings further emphasize the higher accuracy of the PSO-SVM model in predicting the magnetic field effect at different flue gas velocities. By analyzing Figures 14 and 15, it can be seen that the PSO-SVM model demonstrates a higher prediction accuracy for the grade efficiency under different flue gas velocities at 473 K. Based on this, using the same training data as in Figure 14, the PSO-SVM model is used to predict the grade efficiency under different flue gas velocities (1.0 m/s, 3.0 m/s) at 673 K with a magnetic field strength of 0.25 Tesla. Figure 16 shows the comparison and relative error between the predicted results and the simulated data. The following can be seen:  
Figure 14: Comparison between predicted values and simulated data of grade efficiency. Figure 15: Comparison of predicted and simulated values with a 45\({}^{\circ}\) line for different models. 1. Under two different flue gas flow rates, the PSO-SVM prediction curve effectively describes the changes in grade efficiency and exhibits a high degree of consistency with the simulation data, as shown in Figure 16. This confirms the strong applicability of the PSO-SVM model in predicting the magnetic field effects under different flue gas flow velocities. 2. The relative errors of the predicted values of the two flue gas velocities are all less than 3%, and they are even below 2% when the flue gas velocity is 3 m/s, as shown in Figure 16, proving that the PSO-SVM model has high accuracy in predicting the magnetic field effect at different flue gas velocities.
###### Abstract  
The paper describes a method for the accurate calibration of multi-channel SAR instruments, such as those required to support SAR polarimetry, single-pass interferometry and digital beam-forming (DBF), on the basis of dedicated SAR acquisitions containing reference targets with known properties. Unlike conventional approaches, the method is based entirely on the analysis of range-compressed raw data. It leverages the pulse-by-pulse analysis of amplitude, phase and delay variations observed within the range histories of reference targets to fully characterize and correct propagation direction dependent calibration issues such as those related to antenna pointing or antenna phase center positions. The fact that the approach does not require SAR image focusing in azimuth is especially relevant in the context of DBF, where individual channels need to be calibrated but are, by themselves, under-sampled. The calibration techniques presented are illustrated and validated using multi-channel polarimetric and single-pass interferometric SAR data acquired by DLR's airborne F-SAR and DBFSAR instruments.  
Synthetic Aperture Radar; external calibration; SAR polarimetry; SAR interferometry; digital beam-forming; baseline calibration; geometric calibration; antenna pointing +
Footnote †: journal: Remote Sensing  
12 November 2019; 15 November 2019
## 4 Simulations and Results
### Results with Balanced Technology Distribution
This is due to the fact that there are cases where the V\({}_{\rm 11p}\) do not measure the energy signals or do not receive the CTS-To-Self messages and thus transmit during S\({}_{\rm NR}\). dM\({}_{\rm C}\)-preamble and M\({}_{\rm C}\)-preamble-no-SF perform the same as the No-method and Only-1-tech method, since they do not alter the access mechanism of IEEE 802.11p. It is worth noting that the increased EED should be acceptable for Day-1 type applications that share vehicle status, such as CAMs. The common feature of Day-1 applications is in fact that they are relatively simple, human driver-based and free of driving control [33]. In this perspective, the Day-1 applications would only show warning messages and make the drivers aware of the environment. Considering the reaction time of drivers, the slight increase in EED (no more than 0.01 s) should be acceptable for the Day-1 applications [34]. The additional delay might instead become a problem for Day-2 delay-sensitive applications that share the vehicle sensor data, such as collective perception messages (CPMs) or maneuver coordination messages (MCMs). Figure 11: Average End-to-End Delay within 300 m vs. density applying different mitigation methods with balanced distribution scenario; (**a**) shows the average EED of NR-V2X packet, and (**b**) shows the average EED of IEEE 802.11p packet. Data age:Figure 12 shows the data age for links within 500 m. As shown in both subfigures, the DA slightly increases with a higher density. The preferable method is marginally eM\({}_{\text{A}}\)-time-split from the viewpoint of NR-V2X, and dM\({}_{\text{C}}\)-preamble and M\({}_{\text{C}}\)-preamble-no-SF from that of IEEE 802.11p. Only dM\({}_{\text{C}}\)-preamble and M\({}_{\text{C}}\)-preamble-no-SF do not perform worse than the No-method (dotted gray line) for both IEEE 802.11p and NR-V2X technologies. The other mitigation methods reduce the DA of NR-V2X but cause a DA of IEEE 802.11p larger than with the No-method scenario, with M\({}_{\text{B}}\)-E-signals performing worst.
## 4 Results and Discussion
### Verification of Subsidence Prediction Model for Ground Surface  
The surface subsidence prediction model proposed in Section 2.2 is utilized to forecast the control effect of surface subsidence for the grouting into overburden separation project at 8006 working face of the Wuyang Coal Mine in Changzhi, Shanxi. The model's accuracy is validated through on-site measurement data.  
According to the equivalent mining height theory method described in Section 2.3.2, the subsidence space of the key strata was segmented into ten rectangular spaces, each characterized by different mining heights. In addition, considering the geological mining  
Figure 14: Surface tilt along the midpoint line of the working face.  
conditions of the mining area, a subsidence coefficient \(q_{0}\) of 0.73 and a main influencing tangent value tan\(\beta\) of 2.711 were employed to derive the predicted subsidence values of 8006 working face. Figure 15 illustrates the average subsidence height for each of these spaces, alongside the corresponding subsidence curve for the key strata of the 8006 working face, where an injection-extraction ratio of 33% was implemented.  
The predicted values were then compared with the on-site measured values, as shown in Figure 16.  
According to Figure 16, the maximum measured surface subsidence at the grouting into overburden separation working face is 546 mm, while the corresponding theoretical prediction is 557 mm. The minimal relative error of 2% between these measurements confirms the accuracy of the prediction model, demonstrating a high degree of concordance between the predicted and measured subsidence curves on the ground surface. This concordance validates the effectiveness of the proposed prediction method in realistically reflecting conditions encountered in engineering practice. Further, combining this with the data from Figure 13b, where the theoretical predicted value of maximum surface subsidence without grouting measures 3485 mm, it is evident that the grouting into overburden separation reduces the maximum ground subsidence by 2939 mm. This represents a  
Figure 16: Comparison between measured and predicted values of ground surface subsidence in the grouting into overburden separation.  
Figure 15: Subsidence curve of key strata and average height of equivalent subsidence space.  
substantial reduction rate of approximately 84.3%, underscoring the significant impact of the grouting process in mitigating surface subsidence.  
The ground surface tilt values of the monitoring station are plotted as a curve, as shown in Figure 17. The figure illustrates that the implementation of a grouting into overburden separation project with an injection-extraction ratio of 33% at the working face achieved a maximum surface tilt of less than 3 mm/m. This outcome successfully meets the specified stability requirements for surface structures.