esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ET Simulation Results and Temporal and Spatial Evolution  
#### 3.2.1 ET Simulation Accuracy Evaluation  
In this study, five widely used ET datasets covering the QTP were collected. Analysis revealed notable differences in their annual averages and growth rates. A comparison of the accuracy of the five ET datasets with measured flux station data showed that significant variations among them, as shown in Table 3.  
Using multiple linear regression modeling, a new ET dataset, labeled as REG_ET, was created, as shown in Table 4, while an averaged dataset from the five datasets was labeled as MEAN_ET. A Comparison of regression modeling results with the mean dataset revealed that REG_ET (R\({}^{2}\) = 0.601, RMSE = 21.04 mm\(\cdot\)month\({}^{-1}\)) achieved higher precision than MEAN_ET (R\({}^{2}\) = 0.591, RMSE = 25.641 mm\(\cdot\)month\({}^{-1}\)).
## 2 Mechanisms for Generating Shaft Frequency Signals of Vessels
### Comparison of Signal Decomposition Algorithms Based on Shift Frequency Electric Field Experiments  
To deeply investigate the vessel's electric field signal extraction characteristics in different decomposition methods, the experiments based on the system acquisition of shaft frequency electric field signals in Section 3.1 and the four decomposition methods of EWT, EMD, EEMD, and ICCEMDAN are compared to decompose the signals into eight layers, i.e., components IMF1 to IMF8, to maximize the retention of the original signals and at the same time to extract the characteristics of each band. The time-domain waveforms and corresponding frequency spectra of signals processed through four distinct decomposition methodologies are comparatively presented in Figure 6.  
Figure 5: Empirical Mode Decomposition (EMD) flowchart.  
Figure 6: _Cont_.  
From the analysis of Figure 6, it can be seen that after EWT decomposes the shaft frequency electric field in Figure 6a, the signal features are mainly concentrated in component IMF8. In contrast, no target signal frequency is found in the frequency-domain signal except for the DC component. This indicates that the EWT fails to effectively decompose and extract the vessel's shaft frequency electric field signal features. Additionally, after EMD, EEMD, and ICEEMDAN, the time-domain features and energy of the shaft frequency E-field signal are mainly concentrated in components IMF3 and IMF4. From the frequency-domain perspective, the 4.73 Hz frequency signal in EMD appears in IMF3, which is the shaft frequency signal, and the 4.73 Hz frequency signal in EEMD appears in IMF3; the 4.73 Hz frequency signal in ICCEMAND decomposition appears in IMF3 and IMF4. In summary, comparing the four decomposition methods, in addition to the EWT decomposition method, the other three modal decomposition methods can initially extract the time-domain characteristics of the vessel's shaft frequency electric field signals. In contrast, these methods are only for the same vessel signals, and they cannot be effectively differentiated for the different vessel target signals.
## 4 Evaluation  
This section presents the performance evaluation of the DMMS. A comparative analysis is conducted with two popular schemes from the literature: MAP-ME (representing an Anchor-less proposal) and Kite with multiple rendezvous servers, denoted as multi-RV KITE (representing an Anchor-based proposal), which are designed to address the challenges of real-time content delivery in mobile network environments. We have faithfully reproduced the source code of MAP-ME and multi-RV KITE to establish reliable benchmarks. To emulate the named data networking environment, we employ the ndnSIM [41] simulator, an open-source NS-3-based simulator widely adopted for named data network research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameters** & **Value** \\ \hline \multirow{4}{*}{Network} & 5 \(\times\) 5 grid topology & 100 \(\times\) 100 m cell size \\  & Link delay & 5 ms \\  & Data rate & 10 Mbps \\  & Link layer protocol & IEEE 802.11 \\ \hline \multirow{4}{*}{NDN} & Interest frequency & 10 Inputs/s \\  & Packet size & 1024 bytes \\  & Numbers of Anchors & 4 \\  & Simulation duration & 100 s \\ \hline \multirow{2}{*}{Simulation} & Simulated repetitions & 100 for each speed \\  & Prediction time step & 0.1 s \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Mobility \\ (Gauss–Markov) \\ \end{tabular} } & Alpha & 0.85 \\  & Mean velocity & Variable 1–30 m/s \\ \cline{1-1}  & Mean direction & Uniform RV (min = 0, max = 6.28) \\ \cline{1-1}  & Normal direction & Gaussian RV (mean = 0, var = 0.2, \\ \cline{1-1}  & & bound = 0.4) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
We implemented the clustering algorithm using Python libraries. The resulting placements of Anchors are provided as input to our scenario for the DMMS and for the multi-RV KITE solution. It is worth noting that the author of the Kite solution did not provide explicit details regarding the method used for selecting the placements of rendezvous servers.  
Subsequent sections provide detailed insights into the simulation environment configuration, the selection of performance metrics for evaluating both consumer experience and network cost, and an extensive discussion of the obtained results. A summarized overview of the simulation parameters can be found in the accompanying Table 2.
## 3 Results
### Electrochemical Tests
#### 3.5.2 Electrochemical Impedance Spectroscopy  
A study of C-Mn steel corrosion behavior in 1 M HCl solution with and without St63 Gly37 at 298 K was studied by EIS after an immersion time of 30 min. The purpose was to compare and complete the results obtained by the previous weight loss and polarization methods [22]. Nyquist diagrams obtained in the presence of various concentrations of bio-copolymer are shown in Figure 10. The deduced impedance parameters, as charge transfer resistance \(R_{t}\) (\(\Omega\) cm\({}^{2}\)), double-layer capacitance \(C_{dl}\) (\(\mu\)Fcm\({}^{-2}\)), and inhibition efficiency (\(E_{R}\)%), are shown in Table 4.  
The charge transfer resistance values were qualitatively estimated from the difference in impedance at lower and higher frequencies [23]. The double-layer capacitance was obtained at the frequency \(f_{m}\), at which the imaginary component of the impedance is maximal (\(-Z_{i,max}\)) according the following Equation (6):  
\[C_{dl}=\frac{1}{2\pi f_{m}\cdot Rt}. \tag{6}\]  
The inhibition efficiency from the charge transfer resistance was calculated by the following Equation (7):  
\[E_{Rt}(\%)=\frac{R^{\prime}_{t}-Rt}{R^{\prime}_{t}}\times 100, \tag{7}\]  
where \(R^{\prime}_{t}\) and \(R_{t}\) are the charge transfer resistances with and without the St63 Gly37 bio-copolymer as a corrosion inhibitor, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**St63 Gly37 (mg L\({}^{-1}\))** & \(R_{t}\) (\(\Omega\)cm\({}^{2}\)) & \(f_{max}\) (Hz) & \(C_{dl}\) (\(\mu\)Fcm\({}^{-2}\)) & \(E_{R}\) (\%) \\ \hline
0 & 110 & 16.32 & 88.7 & - \\
5 & 146 & 12.88 & 84.6 & 24 \\
10 & 167 & 12.81 & 74.4 & 34 \\
50 & 200 & 11.39 & 69.9 & 45 \\
100 & 275 & 9.08 & 63.8 & 60 \\
200 & 458 & 6.52 & 53.3 & 76 \\
300 & 1100 & 4.57 & 31.6 & 90 \\ \hline \hline \end{tabular} \(R_{t}\)—charge transfer resistance, \(f_{max}\)—maximum frequency, \(C_{dl}\)—double-layer capacitance, \(E_{R}\)—inhibition efficiency from the charge transfer resistance.  
\end{table}
Table 4: Impedance parameters for corrosion of C-Mn steel in 1 M HCl at different concentrations of St63 Gly37 at 298 K.  
Figure 10: Nyquist plots for C-Mn steel in 1 M HCl at different concentrations of St63 Gly37 bio-copolymer (298 K).
## 6 Experimental Results
### Experimental Results of Suspension Control of the AMB Rigid Rotor System  
Figure 13 shows the floating process of the AMB high-speed motor, and evidently, the rotor can be stably suspended. The first four traces represent the voltage variations in the radial directions (BY, AY, BX, and AX), while the final trace corresponds to the axial (Z) direction. The horizontal axis is time (1 ms/div), and the vertical axis indicates the voltage (2 V/div). Each trace is annotated with the maximum voltage (_Umax_), minimum voltage (_Umin_), and average voltage (_Ularg_) to highlight the dynamic behavior in each direction.  
The setting of the central voltage (AX, AY, BX, and BY) of rotor stable suspension is AX = 5.8 V, AY = 5.2 V, BX = 7.8 V, and BY = 6.3 V. During the initial phase (0 ms to 3.7 ms), the rotor remains in its natural state with no applied bias voltage, resulting in relatively stable voltage profiles across all directions. At 3.7 ms, a bias voltage is applied, initiating rotor motion. The radial voltage traces show a distinct transition, and the rotor achieves a stable levitation state approximately 7 ms later. At 10.75 ms, the bias voltage is removed, and the rotor returns to its natural state, with the voltage traces reverting to their initial values. The axial (Z-direction) response follows a similar trend, with the bias voltage  
Figure 12: Experimental rig.  
Figure 13: Floating process of the AMB high-speed motor.  
applied at 3.7 ms, triggering a motion in the axial direction. The rotor stabilizes in the levitation state at 5.7 ms. The bias voltage is then removed at 10.75 ms, and the voltage returns to the natural state.
## Main Design of DERM
### Track Routing  
As introduced in Section 4.1, the sink floods an announcement message to the entire network in the initial phase. In the meantime, the shortest path tree can be constructed as in [49]. Then, along the tree, each RN replies to the sink with a message including its location. Here, we define the _access point_ of a node as the closest point to it on the sink path, which can be intuitively regarded as the site where the sink collects the data buffered in the node. We call two nodes \"adjacent\" if their access points are nearest in either direction along the path. The sink can find the adjacent nodes of each RN and send their locations to that RN through the corresponding reverse route. In this way, every RN is aware of its adjacent nodes, and thus a route sequentially connecting all the RNs along the path, called _adjacent route_, is established. Note that a route to an adjacent node outside the communication range can be discovered via geo-routing.  
Figure 5: An example of the sink location calibration in DERM.  
```
0:\(\alpha\), \(\Delta_{th}\); \(P_{uM}^{i}\), \(P_{IM}^{i}\), and their corresponding time; the sink mobility pattern;
0: newly calibrated sink location;
1:while Location check timer expires do
2:if\(i\) is the sink then
3:if\(dist(P_{uM},P_{eM})>\Delta_{th}\)then
4: Broadcast \(LocMsg(P_{uM},t_{uM})\) to its neighbors;
5: Send an \(AnyCastMsg(\widetilde{P_{uM}},R,P_{uM},t_{uM})\);
6:\(P_{IM}\gets P_{uM}\);
7:endif
8:else
9:if\(P_{uM}^{i}=P_{IM}^{i}\) and \(d_{prev}/d_{updt}>\alpha\)then
10: Broadcast \(LocMsg(P_{uM}^{i},t_{uM}^{i})\) to its neighbors;
11:\(P_{IM}^{i}\gets P_{uM}^{i}\);
12:endif
13:endif
14:endwhile
15:while Receiving a new \(AnyCastMsg(\widetilde{P_{uM}},R,P_{uM},t_{uM})\)do
16:if\(i\) is inside the target area then
17: Broadcast \(LocMsg(P_{uM},t_{uM})\) to its neighbors;
18:else
19: Forward the \(AnyCastMsg\) greedily;
20:endif
21:endwhile
22:while Receiving a new \(LocMsg(P_{uM},t_{uM})\)do
23:\(P_{uM}^{i}\gets P_{uM}\);
24:if\(d_{prev}/d_{updt}>\alpha\)then
25: Forward \(LocMsg(P_{uM},t_{uM})\) to its neighbors;
26:\(P_{IM}^{i}\gets P_{uM}\);
27:endif
28:endwhile
```  
**Algorithm 1** Sink location calibration algorithm at sensor node \(i\).  
When a packet finds that the sink has not visited the RN it buffered in before the deadline due to delayed location updating or unpredicted faults, it must be delivered via multi-hop routing. To address this problem, we propose a method named track routing, as shown in Algorithm 2. The design of Algorithm 2 is elaborated as follows.
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
## 1 Introduction
The equivalent circuit calculation leans on transmission line theory, which translates a complex coaxial cavity into multiple transmission lines, effectively rendering the resonant cavity as a standard microwave circuit model. This model streamlines the assessment of how each segment's structural parameters influence the cavity's characteristic parameters and offers a more direct route to design parameters. During this analytical process, pertinent circuit parameters are extracted, with the RF cavity's resonant frequency ascertained using the equivalent circuit [12; 13]. Both methodologies are concurrently employed in the design phase, with their results juxtaposed to bolster the precision and credibility of the resonant cavity analytical calculations.
## 3 Results of Systematic Literature Review
### Model Selection in Studies  
In the classification studies, it was seen that the most considered classifier was SVM (19%--21/109), followed by ANN (15%--16/109). When the models were considered independently of each other, the least considered models were DNN with 4% (5/109) and LOGREG with 6% (7/109). However, when we gathered the different neural network models (RNN, DNN, and ANN) into a single model, as neural-based models, it was observed that the total usage rate of neural-based models was 30% (33/109). Likewise, DT and RF had a lower usage rate compared to SVM and ANN when considered separately, but when we considered the two as tree-based models, it was found that the rate increased to 19% (20/109).  
Figure 3: The aims of student performance prediction studies in percentages (regression vs. classification).  
When all results were considered for the classification studies, it was observed that SVM and ANN were the most considered independent models in the studies, but, in general, neural-based models and tree-based models were also widely used.  
In regression studies, the use of ANNs stands out significantly compared to other models (28%--19/67), followed by SVR and LR (18%--12/67 each). If we categorize the models as in the classification studies, neural-based models increase to 37% (25/67) with the participation of LSTM, while tree-based models (RF + DT) reach 18% (12/67).  
If all results are considered without distinguishing between classification and regression and SVR and SVM are considered single models, neural-based models significantly take the lead in student performance prediction studies (32.95%--58/176). While SVR + SVM followed these models, tree-based models (RF + DT) also had a significant consideration rate. Figure 5 demonstrates the percentages of independent model usage in classification and regression studies.  
Since predicting which model would achieve superior results in artificial intelligence and machine learning applications is difficult, it is impossible to determine a specific model that would lead to future studies. However, the data created through the development of computer and data storage technologies will make artificial neural networks and deep learning methods that can process and learn big data more widespread for regression and classification tasks. Furthermore, other conventional and tree-based models might be considered more frequently in student performance evaluation studies' data analysis and data selection stages. However, the models' abilities should not be underestimated for particular datasets with limited inputs and samples [46].
## 5 Methods of Extraction of Bioactive Substances from Grape and Winery By-Products
### Microwave-Assisted Extraction (MAE)  
Microwave-assisted extraction belongs to the green methods for extracting bioactive substances without loss of stability. Microwave heating works mainly with non-ionizing electromagnetic waves, increasing the isolation and extraction of phytocomponents that are antioxidants. The operational viability and capability of MAE have been demonstrated in studies, with anthocyanin and proanthocyanidin yields varying based on factors such as extraction time/temperature, solvent type, microwave power, and solvent volume. MAE is a valuable process for commercialized polyphenol extraction [57]. The principle of operation of MAE includes the direct effects of microwave energy on the molecules of the material--their heating due to dielectric energy. This energy corresponds to the polarity of the medium, creating a rotary dipole and ion migration. This creates heat and collisions between molecules, increasing the energy of the medium. The whole process of microwave extraction is based on the interaction of the solid matrix with the solvent [58; 59].  
MAE is used for the extraction of bioactive compounds from grape by-products. [PERSON] et al. (2019) examined the feasibility of MAE at room temperature to extract polyphenols from Chardonnay grape marc. The optimization process involved RSM to determine the effects of solvent concentration, solid mass, and extraction time. The study found that optimal conditions included 48% ethanol, 10 min of extraction time, and 1.77 g of solid mass. The resulting extracts exhibited a high level of antioxidant activity, demonstrating potential applications as bioactive additives in food and pharmaceutical formulations [60]. Also, [PERSON] et al. (2022) explored the extraction of pectin from two grape pomace varieties (Feteasca Neagra and Rara Neagra) using microwave-assisted extraction. The study aimed to optimize the process, evaluating parameters such as microwave power, pH, and extraction time. Optimal conditions were established at 560 W, pH 1.8, and 120 s. The extracted pectin was characterized by its yield, galacturonic acid content, degree of esterification, and molecular weight, revealing that grape pomace is a viable and unconventional source of high-quality pectin [58]. [PERSON] et al. (2020) investigated the extraction of bioactive compounds from grape juice residue using microwave-assisted extraction (MAE) and UAE with an acidic aqueous solution. The study aimed to recover phenolic compounds while minimizing environmental impact. It was determined that MAE at 1000 W for 10 min effectively recovered 45% of anthocyanins, demonstrating that both UAE and MAE are effective in extracting valuable compounds while adhering to green chemistry principles [61].
# Design Optimization of a Hydrodynamic Brake with an Electrorheological Fluid
## 4 Optimization Method for the Hydrodynamic Brake Design
### Selection of Design Variables  
The calculations for the PHB are performed for the data shown in Table 1. The torque M and the voltage U\({}_{\rm h}\) are calculated, while one of the parameters r\({}_{\rm w}\), R, n is changed, and the remaining parameters are fixed. Examples of the calculation results for \(\omega_{1}\) = 200 rad/s, \(\Theta\) = 35 \({}^{\circ}\)C are shown in Figures 7-9.  
Figure 7 presents the influence of the radius r\({}_{\rm w}\) and the radius Ron the braking torque M. Figure 8 shows the dependence of the braking torque M on the blade number n. Figure 9 shows the influence that the number of blades nin the PHB has on the value of the voltage U\({}_{\rm h}\). As shown in Figure 7, increasing the inner radius r\({}_{\rm w}\) causes a decrease in the braking torque M, while increasing the outer radius R causes an increase in the torque M. This is related to the opposing influence of these radii on the flow velocity of the ER fluid within the rotors. Increasing the blade number n, Figure 8, causes a decrease in the torque M, which is connected to the decrease of the flow cross-sectional area A of the rotors. Figure 9  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Coefficient** & \(\alpha\) & \(\varepsilon\) & \(\varepsilon\) & \(\varepsilon\) & \(\varepsilon\) & \(\varepsilon\) & \(\varepsilon\) & \(\varepsilon_{\rm r}\) \\  & **-** & **-** & **-** & **kPa mm2/kV2** & \(\mu\)A mm2/(cm2 kV2) & **-** & **-** & **-** \\ \hline Value & 2.30 & 6.64 & 3.93 & 0.41 & 35.31 & 3.00 & 5.00 \\ Formula & (7) & (22) & (23) & (27) & (28) & (35) & (37) \\ \hline \end{tabular}
\end{table}
Table 2: Mathematical model coefficients for the researched PHB.  
Figure 7: Dependence of the torque on the radius r\({}_{\rm w}\) and on the radius R.  
shows a decrease in voltage \(\rm U_{h}\) as the number of blades increase, with a constant value of the torque M. The decrease is a result of decreasing average distance between blades d when the number of the blades increases. Decreasing the value of d causes an increase in the electric field strength E, which has a direct influence on the value of the yield stresses \(\rm\tau_{0}\). A significant (and different) influence of the radii \(\rm r_{w}\) and R, as well as the number of blades n, on the PHB characteristics justifies the purposefulness of selecting these parameters as design variables for optimization of the PHB design.
## 2 Data and Methods
### Analyses  
Three indices, including bias, RMSE and \(R^{2}\), were used to comprehensively evaluate the accuracy of MODIS-GPP and -ET in different land cover types over China. Bias can show the deviation between simulations and measurements. RMSE is the index reflecting the difference between overall samples. \(R^{2}\) is the similarity index between samples or fitting degree index between simulations and measurements. They can well evaluate the error of data.
## 3 Total Cross Section
### Joint Elastic and Inelastic Scattering  
Finally, we consider the joint contribution to the scattering cross section from both elastic and inelastic scattering. This cross section will, therefore, involve a sum over final states, where only the \(1s\to 1s\) contribution originates from elastic scattering processes. Explicitly, and for an arbitrary set of final exciton states \(f\), the total scattering cross section is given by  
\[Q_{\mathrm{Total}}=\sum_{f}Q_{1s\to f}. \tag{27}\]  
For the three transitions discussed previously, the sum in Equation (27) is restricted and is explicitly written as  
\[Q_{\mathrm{Total}}=Q_{1s\to 1s}+Q_{1s\to 2s}+Q_{1s\to 2p}. \tag{28}\]  
This total scattering cross section is plotted in Figure 3 for both types of free carriers, together with the dashed lines representing the thresholds for the inelastic scattering processes considered.  
Analyzing Figure 3, we can see that, when the same scattering process is allowed for both types of free carriers, electron-exciton scattering has a cross section roughly \(1.5\times\) larger. This trend is, however, inverted between the threshold momentum for hole-exciton \(1s\to 2p\) scattering, and the threshold momentum for electron-exciton \(1s\to 2p\), i.e., between \(k_{i}=0.0749\,\mathrm{\AA}^{-1}\) and \(k_{i}=0.0822\,\mathrm{\AA}^{-1}\). In this momentum range, the dominant \(1s\to 2p\) process is already allowed for hole-exciton scattering, leading to a vastly superior cross section relative to that for electron-exciton scattering.  
The final threshold included, visible in Figure 3 as the dashed purple lines, originates from \(1s\to 3d\) scattering, as \(E_{3d}=-3.74\,\mathrm{meV}\) is the lowest energy state after \(2s\). These take place at \(k_{\mathrm{min}}=0.0875\,\mathrm{\AA}^{-1}\) for electron-exciton scattering and \(k_{\mathrm{min}}=0.0798\,\mathrm{\AA}^{-1}\) for  
Figure 3: Total cross section for electron–exciton (black) and hole–exciton (red) scattering in hBN encapsulated in fused quartz as a function of the initial relative wave vector. Vertical dashed lines represent the electron–exciton and hole-exciton scattering thresholds for the \(1s\to 2p\) transition, located at \(k_{\mathrm{min},\cdot}=0.0822\,\mathrm{\AA}^{-1}\) and \(k_{\mathrm{min},h}=0.0749\,\mathrm{\AA}^{-1}\).  
hole-exciton scattering. These are, however, much smaller than the peaks in Figure 3, similar to what was observed in Ref. [37], and are invisible in Figure 3.