esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Results  
### Descriptive Statistical Analysis  
According to the results from the questionnaire, the percentage of female participants (55.8% of the total) was higher than that of male participants (44.2% of the total). The age range of all the samples was 19-38, with the most significant proportion of participants (48.6%) falling between the 21-25 age range. A bachelor's degree, the most frequently reported level of educational achievement, was held by 47.3% of the population. About 34.4% of respondents, or the majority, reported having three to five usage experiences. Figure 4 provides specific details regarding the sample's descriptive statistics.
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Rollback in Case of Suspicious Behavior  
If during the verification process \(ZKP.Verify=False\) the server performs a rollback to a previously verified state of the model \(\theta_{s}\). This happens if a client maliciously modifies the parameters, such as inserting a backdoor attack. The server maintains several previous versions of the models \(\theta_{S}^{t}\), and if an issue is detected with \(\theta_{S}^{t+1}\) it reverts to \(\theta_{S}^{t}\). This mathematical proposal combines Homomorphic Encryption and Zero-Knowledge Proofs to ensure privacy and security in Split Learning (Appendix A). This approach guarantees that neither clients nor the server can introduce harmful behavior into the trained model without being detected, ensuring that all parties comply with security protocols without exposing sensitive data or parameters.
## 3 Modeling Methodology
### Influence of Semi-coke Blending Ratio
Furthermore, with more CO produced in the main combustion zone, the atmosphere is more reductive and more already-formed NO\({}_{x}\) could be reduced, leading to the decline of the overall NO\({}_{x}\) emission. The present simulation and the results obtained by [PERSON] et al. (2019) that the NO\({}_{x}\) generation could be reduced by blending semi-coke when the content of nitrogen in semi-coke was lower than that in other type of coal are basically identical. When blending semi-coke reaches 70%, the NO\({}_{x}\) concentration was nearly half lower than that with individual lean coal. The carbon content in fly ash shows a generally increasing tendency with the share of semi-coke, which is firstly almost linearly raised (the proportion of semi-coke \(\leq\) 33%) and then establishes only a slight increase with the further rise of semi-coke content. The semi-coke is difficult to burn out from the view of low volatile content. The burnout ratio establishes a slight decrease tendency with the proportion of semi-coke blended rises. The temperature at the furnace outlet yields a reduction tendency with the semi-coke blending ratio increased. Although the calorific value of semi-coke is a little higher than that of lean coal, less heat can be released when the same amount of oxygen is consumed on account of lower H/C ratio of semi-coke. Fortunately, the temperature variation at the furnace outlet was defined within approximately 10 K as the proportion of semi-coke changes from 0% to 70%. Hence, blending semi-coke with lean coal has limited influences on the performance of the heat transfer in the downstream convection zone, but generates significant impact on NO\({}_{x}\) emission and burnout behavior in full-scale power plants. Figure 6: The average temperature and species concentrations along the height of the furnace at various semi-coke blending ratios.
## 3 Results
### Coatings by Polymer Particles
#### 3.2.3 Comparison of Polymer Particles on Water Repellency  
The water repellency depended on the surface roughness to some extent [19]. From Figure 4, the surface roughness resulted from the coating of polymer particles with different size. Accordingly, the water repellency was compared with the size of the polymer particles. Any contact angle surpassed that of bare WTBs, regardless of the \(\phi\) value (Figure 6). The water contact angle was negatively correlated with the size of the polymer particles at \(\phi\) = 0.020. The contact angle at \(\phi\) = 0.020 was fitted to an exponential relaxation: \(\theta\) = \(\theta_{\rm limit}\) + (\(\theta_{\rm limit}\) - \(\theta_{\rm bare}\))e\({}^{d/D}\), where \(d\), \(D\), \(\theta_{\rm bare}\), and \(\theta_{\rm limit}\) are the mean size of the polymer particles, a constant, the contact angle for bare WTBs, and the contact angle estimated at \(d\) = 0, respectively. The \(\theta_{\rm limit}\) value is also termed the contact angle limit. The \(\theta_{\rm limit}\) value was proportional to the logP value for each monomer (Figure 6e), suggesting water repellency depended on the hydrophobicity of the polymers. In the case of PS, PMMA, and PVA, the improved effect of water repellency using polymer particles was higher than those using polymer membranes (dipping). The same was true for the plastic surface of each polymer. In contrast, in the case of PLLA, its polymer particles had water repellency comparable to its polymer membranes. From the rough surface of the PLLA membrane, these results suggested that the improved effect of water repellency resulted not only from the hydrophobicity of polymers but also from the surface roughness.  
Figure 5: Box plots of contact angles for WTBs coated by polymer particles at two \(\phi\) values. “Bare” means the WTB without surface modification of polymer particles.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 4 CFD Analysis of Ramjet Nozzles  
### CFD Setup  
For a fair comparison of the ramjet nozzle geometrical configurations analysed previously, thrust production was the primary criterion. Thrust values were obtained using Equation 4, and comparisons were conducted based on the pressure thrust (\(((p_{9}-p_{0})A_{9})\) and momentum thrust (\((m_{9}V_{9}-m_{0}V_{0}))\) generated:  
\[F=m_{9}V_{9}-m_{0}V_{0}+(p_{9}-p_{0})A_{9} \tag{4}\]  
where thrust value S was obtained in N. From previously available data by one of the authors [5], the design point was set for a mass flow rate of \(m_{9}=m_{0}=560\) kg\(\cdot\)s\({}^{-1}\) at an altitude of 50,000 ft and at Mach 2.5. The parametric optimisation study was conducted using ANSYS Fluent in ANSYS Workbench 17, for the three nozzle types, and consisted of a data mining approach through a parametric analysis where a combination of multiple geometrical variables, i.e., length of the nozzle, curvature of the nozzle, and the height of the nozzle exit, were studied and the performance parameters compared to extract the best possible result for gross thrust production. This study focussed only on geometric constraints; details of the combustion system and flow conditions upstream of the nozzles are detailed in [5]. The secondary objective of the study was to test the effectiveness of the altitude compensating nozzle. For the purpose of simplifying the problem, a cold-flow study was employed where the extreme conditions at the nozzle inlet replicated the combustion exit conditions. A RANS (Reynolds averaged) approach to the expansion modelling was adopted due to the relatively steady state conditions and reduced computational expense [13]. The CFD study included a Weighted Area average function which noted the average pressure and velocity over the radius of the nozzle exit. The velocity and pressure results were utilised to obtain the thrust performance. Hence the performance for the best nozzle was weighed against equivalent geometrical variables, across the range of geometries modelled.  
Figure 10: **(left)** Operational mode of a dual bell nozzle: separated under sea level mode (**top**) and attached in the extension under high altitude mode (**bottom**), (**right**) the dual bell nozzle in three dimensions (3-D).
## 1 Introduction
The main contributions of the proposed method are: (1) The AMC-KF is proposed as a new robust nonlinear filter to improve the precision and robustness of multi-GNSS DF RTK [38]. (2) The DF data-aided AR method is proposed to fix ambiguities with the wide-lane and ionosphere-free combinations. The wide-lane pseudo-range is introduced for medium and long baseline RTK. (3) A nonlinear filter strategy is designed by integrating the DF data-aided AR method into the proposed AMC-KF. The test results show the significant superiority of the proposed strategy with various baselines. The remainder of this paper is organized as follows: In Section 2, the double difference (DD) RTK model is introduced, followed by the derivation of the proposed DF data-aided AR method. The loosely coupled model for multi-GNSS is also presented. In Section 3, correntropy is introduced and the AMC-KF is elaborated in detail. Moreover, the derivation of the adaptive KBW is outlined. In Section 4, the performance of the proposed filter strategy is demonstrated. Datasets collected from the continuously operating reference stations (CORS) network of Australia are used for the short baseline test, adaptive strategy test, and the long baseline test. Finally, some conclusions are given in Section 5.
## 3 System Design  
The Figure 3 depicts the UML (Unified Modeling Language) component diagram for the system in Figure 1. This figure proposes a general software design for accomplishing self-adaptive systems based on MAPE-K [16]. It is also based on the one fully described in [17] customized for the SunSPOT platform, the deployment platform.  
The component _ORAMediatorForSunSPOT_ enables the interaction among the rest of the components. It behaves as a broker decoupling observers, triggers, reasoners and actuators. Therefore, all of the components required for the reasoning engine must be previously registered on it. It also provides the observations and actions to either the reasoner or the monitor wh  
Figure 3: Components for the controller following a MAPE-K approach.  
The components _PowerScalingMonitor_, _BatteryLevelObservation_ and _NodeDegreeObservation_ in Figure 3 shape the monitoring module in Figure 1. The components _BatteryLevelObservation_ and _NodeDegreeObservation_ measure respectively the battery level and the number of neighbors of a node. The component _PowerScalingMonitor_ monitors at a specific rate the number of neighbors and the battery level of a node. With the updated values of the sensed parameters, this component then evaluates the rules discussed previously and, if required, triggers the reasoner represented by the _PowerScalingController_ component. The activity diagram for this component is illustrated in Figure 4.  
The _PowerScalingController_ component matches the reasoner module from Figure 1, including both task _TR1_ and _TR2_. Therefore, it implements the execution of the fuzzy decision-making functions to evaluate the error, either the node degree error \(e_{ND}\) or the battery level error \(e_{E}\). The activity diagram for this component is exposed in Figure 5.  
Figure 4: Activity diagram for the component _PowerScalingMonitor_.
## 2 Methodology
### Frequency-Domain Electromagnetics  
Several different measurement configurations for EM data exist, and all of them are sensitive to the EC of the subsurface. The EM method that is discussed in this work is the small loop-loop frequency-domain electromagnetic (FDEM) induction method [16]. Applying small-loop FDEM techniques, a primary electromagnetic field is generated by an alternating current of a single low frequency, in our case 9 kHz, in a transmitter coil that is approximated by a magnetic dipole [17]. The resulting EM field propagation can be described by a diffusion equation. This field induces eddy currents in conductive material that is affected by the primary field. These eddy currents generate a secondary field. The secondary field, together with the primary field, is recorded at one or multiple receiver coils placed at a defined distance from the transmitter coil. Besides the transmitter-receiver offset, the measurement sensitivity depends largely on the respective coil geometries (see Figure 1b).  
The strength of the secondary field can be related to the subsurface EC, the measurement quantity of interest. The secondary field is usually expressed as in-phase (IP) and quadrature-phase (QP) components in relation to the primary field [16], below expressed in parts-per-million [ppm].  
In this work, we simulate FDEM data using the code that was provided by [PERSON] et al. (2019) [18], applying the physical equations, as given by [PERSON] and [PERSON] (1988) [17]. Their description, using a one-dimensional full solution of [PERSON]'s equations, denotes a non-linear relation between the measurement and electrical conductivity of the subsurface volume affected by the induction phenomena.  
It is well-known that small loop-loop FDEM field data are prone to severe systematic errors [19]. Thus, for the field data application described at the end of our text, we modify the FDEM forward model by adding a simple offset term to the QP and IP responses [20]. These offsets denote the possible systematic errors that are included in the IP and QP responses.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 3 Results
### Temporal Analysis
#### 3.2.2 One Satellite: Sentinel-2  
Figure 11 shows the classification results for the time- series experiment using only Sentinel-2 data, note that the data sets are sorted by performance and by model. All data sets, except for DS_INDICES, obtained similar results. Comparing DS_INDICES with the other data sets, for the KNN and SVM algorithms, results were significantly lower, being around 22% lower than the best for the KNN. Unlike the static analysis, the results from the SVM classifier managed to outperform ensemble methods (excluding the data set DS_INDICES). The best algorithm was the SVM with an F1-score of 0.93, nevertheless, XGBoost, and RF generated results that have less variance across data sets.  
Table 5 shows the confusion matrix for the best results, with SVM using the DS_BAND data set. The \"not treated\" sections class had very high metrics close to 1. For the \"treated\" sections class, the metrics were lower, but still good, with an F1-score of 0.88, with only 10 sections being incorrectly classified as treated.  
Using time-series there is an improvement in the classification of treated and not treated sections for FMZs along roads when compared with the static analysis, with the F1-score of the treated sections class rising from 0.82 to 0.88.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Predicted\textbackslash{}Reference** & **Treated** & **Not Treated** & **Total** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Treated** & 61 & 10 & 71 & 0.37 & 0.54 & 0.44 \\
**Not Treated** & 52 & 1170 & 1222 & 0.96 & 0.92 & 0.94 \\
**Total** & 113 & 1275 & & & & \\ \hline
**Global F1-Score (macro)** & 0.69 & & & & & \\
**Global Kappa** & 0.38 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion Matrix for the RF algorithm using the Sentinel-1 data (ASC and DESC).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Predicted\textbackslash{}Reference** & **Treated** & **Not Treated** & **Total** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Treated** & 97 & 10 & 107 & 0.91 & 0.86 & 0.88 \\
**Not Treated** & 16 & 1265 & 1281 & 0.99 & 0.99 & 0.99 \\
**Total** & 113 & 1275 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix for the SVM algorithm using the data set DS_BAND.  
Analyzing the errors in more detail, in Figure 12 there are two examples of sections that were incorrectly classified as treated. These sections are in areas of intersection between FMZs that were treated and FMZs that were not treated (Figure 12a) or in the limit between the interior and exterior of house clusters (Figure 12b).
## 6 Conclusions  
It was not possible to confirm the practical effects of carbon black and graphite as radio wave absorbers. On the other hand, in the spray paint using SWCNT, a radio wave absorption effect of 5.91 dB was obtained in the 28 GHz band and when the coating thickness was 35 \(\upmu\)m. In the 75 GHz band, a radio wave absorption effect of 6.96 dB was obtained when the coating thickness was 16 \(\upmu\)m. However, the sample material used at the beginning showed a slightly lower value in the radio wave absorption in the 28 GHz band because the coating method was not appropriate. Therefore, by preparing a new uniformly coated test material and remeasuring it, we were able to obtain a radio wave absorption effect of 8.51 dB even in the 28 GHz band with a coating thickness of 18 \(\upmu\)m.  
As described above, the CNT spray paint is effective in a very thin coating film, displaying a large amount of radio wave absorption in the millimeter wave band and at a coating thickness of 18 \(\upmu\)m or less. Even if the same paint is used, it is possible to suppress the radio wave absorption effect by changing the thickness of the coating. This shows that CNT spray paint is highly practical as a radio wave absorber in millimeter waves.  
In the future, by adjusting each parameter of CNT spray paint, we would like to develop a radio wave absorber that suits the purpose and contributes to the improvement of the worsening electromagnetic wave environment.  
[PERSON] and [PERSON] jointly made this measurement and wrote this paper together. All authors have read and agreed to the published version of the manuscript.  
No one has funded this study. [PERSON] used his own fund.  
Not applicable.  
All data have not been reported yet.  
We would like to thank ZEON Corporation for providing SWCNTs (ZEONANO\({}^{\text{\textregistered}}\)-SG101) free of charge for carrying out the experiment. We would also like to thank Fukoku Co., Ltd. for spraying various carbon-based materials on the samples.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Adsorption Capacity, Separation Efficiency and Reusability Test  
(1) To evaluate the adsorption capacity, a certain mass of the material was first immersed in an organic solvent and weighed after reaching mass absorption equilibrium. The measurement was repeated three times and the average value was taken. The adsorption capacity \(Q\) of the material was calculated according to Formula (1).  
\[Q=\frac{(M_{1}-M_{0})}{M_{0}} \tag{1}\]  
where \(Q\) (g/g) is the mass-based adsorption capacity, \(M_{1}\) (g) is the mass of the sponge after oil absorption and \(M_{0}\) (g) is the mass of the sponge before oil absorption.  
(2) Next, we built an oil-water separation device and calculated the oil-water separation efficiency, \(R\), of the sponge according to Formula (2).  
\[R=\frac{V_{C}}{V_{0}}\times 100\% \tag{2}\]  
where \(R\) (%) is the oil-water separation efficiency, \(V_{c}\) (mL) is the volume of the original solvent or oil collected after separation and \(V_{0}\) (mL) is the volume of the original solvent or oil before separation.  
(3) The repeatability of the material was initially evaluated using a simple electronic universal testing machine. First, the material was immersed in an organic solvent to achieve maximum absorption and its mass was weighed and recorded as \(M_{2}\). Afterwards, the absorbed oil was recovered by mechanical compression and the mass of the material was determined again and recorded as \(M_{3}\). The reusability of the sponge was assessed by absorption-compression cycles, each time weighing the material to calculate the adsorptionmultiplier (i.e., the weight of the material after the adsorption of an organic solvent/the weight of the material itself).
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT
### Mesh Networking Technologies  
Mesh networking technologies are indispensable for IoMT systems, as they facilitate the formation of self-configuring mesh networks among devices. In the IoMT context, mesh networks are characterized by a non-hierarchical topology in which multiple devices or nodes coexist, cooperate, and offer comprehensive coverage over a wider area. Protocols such as Zigbee and Thread are commonly employed to implement mesh networks, enabling direct communication between devices without the need for centralized infrastructure.  
Through mesh networking, devices can communicate with each other via multiple hops, leveraging dynamic routing and self-healing capabilities. This enhances network coverage, scalability, and reliability in IoMT environments [59]. Mesh networks typically adopt either full mesh or partial mesh topologies. In a full mesh topology, every node is directly connected to every other node, while in a partial mesh topology only certain nodes are directly connected. The choice between a fully or partially meshed network depends on factors such as overall network traffic. Although mesh networks offer increased stability, extended coverage range, and improved security, they also entail higher costs, complexity, scalability challenges, and potentially increased latency for the nodes.
## 4 Simulation Results  
To confirm that the proposed CFF-NFPID control approach can be practically implemented in drill string systems, extensive simulations were carried out using Matlab/Simulink. The four-DOF drill string mathematical model presented in Equation (4) was implemented in an embedded Matlab function as program lines to simulate the behavior of a drill string system. This mathematical model considered nonlinear rock-bit interactions, including the mud-drilling effect and friction torque. Furthermore, the studied controllers were implemented using components of Matlab/Simulink toolboxes. The global system parameters and the coefficients of the controllers are illustrated in Table 1. The performance of the proposed CFF-NFPID approach was tested under three conditions: (i) a stepwise and slow change in the reference velocity; (ii) variation in the WOB at a constant reference velocity; (iii) parametric uncertainties at a constant reference velocity. To evaluate the efficacy of the CFF-NFPID strategy, a quantitative comparison with several controllers (CFF-FPID, CFF-PID, SMC [15], SMC [16], cascade PID, and conventional PID) was conducted.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Parameter** & **Element(s)** & **Symbol** & **Value** & **Units** \\ \hline Inertia & rotary table & \(J_{r}\) & 930 & kg\(\cdot\)m\({}^{2}\) \\ Inertia & drill collar & \(J_{l}\) & 750 & kg\(\cdot\)m\({}^{2}\) \\ Inertia & drill pipes & \(J_{p}\) & 2782.25 & kg\(\cdot\)m\({}^{2}\) \\ Inertia & drill bit & \(J_{b}\) & 471.97 & kg\(\cdot\)m\({}^{2}\) \\ Stiffness & drill pipes and rotary table & \(k_{t}\) & 698.06 & N\(\cdot\)m/rad \\ Stiffness & drill pipes and drill collar & \(k_{tl}\) & 1080 & N\(\cdot\)m/rad \\ Stiffness & drill bit and drill collar & \(k_{tb}\) & 907.48 & N\(\cdot\)m/rad \\ Damping & drill pipe and rotary table & \(d_{t}\) & 139.61 & N\(\cdot\)m/s/rad \\ Damping & drill collar and drill pipe & \(d_{tl}\) & 190 & N\(\cdot\)m/s/rad \\ Damping & drill bit and drill collar & \(d_{tb}\) & 181.49 & N\(\cdot\)m\(\cdot\)s/rad \\ Damping & drill bit and mud drilling & \(d_{b}\) & 50 & N\(\cdot\)m\(\cdot\)s/rad \\ Weight on bit & drill bit & WOB & 100 & kN \\ Radius & drill bit & \(R_{b}\) & 0.156 & m \\ Factor & drill bit & \(\gamma_{b}\) & 0.9 & \(-\) \\ Limit velocity & & \(D_{\sigma}\) & \(10^{-6}\) & rad/s \\ Drive torque & & \(T_{\text{m}}\) & 10 & kN\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical values of the drill string system parameters [15].