esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Screening of Potential Antioxidant Components in Methanol Extracts of Marigold  
To rapidly screen the antioxidant components present in marigolds, an offline DPPH-HPLC-Q-TOF/MS method was used. Under optimized HPLC conditions, four main compounds of marigold were detected and isolated with retention times of 9.262 min (quercetagetin-7-O-glucoside), 12.144 (quercetagetin), 14.547 (quercetin) and 15.748 (patuletin) (Figure 1a). The strength of the antioxidant activity was judged by analysing the extent of the reduction in the peak area in the HPLC chromatogram after the active antioxidant components reacted with DPPH free radicals. In addition, to investigate the feasibility of the method, the results were calibrated with a PG positive control, and the retention time of the PG peak was 12.319, as shown in Figure 1b, and the peak area for PG was significantly reduced by 28.36% after reaction with DPPH. The data indicated that PG has a strong free radical scavenging ability and is a recognized antioxidant, further validating the feasibility of the offline DPPH-HPLC-Q-TOF/MS method. To evaluate the free radical scavenging ability of the four antioxidant components corresponding to peaks 1, 2, 3 and 4, the marigold extract was reacted with DPPH at different concentrations and then analysed by HPLC. With the peak areas (PAs) of peaks 1, 2, 3 and 4 in the untreated sample set as 100%, the relative PAs were calculated for the extract samples reacted with DPPH at concentrations of 0.625 mM, 1.25 mM, 2.5 mM and 5 mM, and the results are shown in Table 1. It was found that the areas of all four peaks decreased with increasing DPPH concentration. The peak 2 (quercetagetin) area decreased more sharply than those of the other three peaks. It was deduced that the antioxidant quercetin might be the greatest radical scavenging capacity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **Relative PAs (\%)** & \\ \hline
**No.** & **DPPH Concentration** & **Peak 1** & **Peak 2** & **Peak 3** & **Peak 4** \\ \hline
1 & Unreacted control sample & 100 & 100 & 100 & 100 \\
2 & Reacted with DPPH (0.625 mM) & 98.11 \(\pm\) 1.02 & 81.75 \(\pm\) 1.88 & 93.38 \(\pm\) 2.45 & 93.42 \(\pm\) 1.66 \\
3 & Reacted with DPPH (1.25 mM) & 85.5 \(\pm\) 1.68 & 14.42 \(\pm\) 0.75 & 50.95 \(\pm\) 1.68 & 61.17 \(\pm\) 2.03 \\
4 & Reacted with DPPH (2.5 mM) & 73.74 \(\pm\) 0.93 & 3.65 \(\pm\) 0.97 & 24.77 \(\pm\) 1.21 & 13.34 \(\pm\) 0.96 \\
5 & Reacted with DPPH (5 mM) & 2.81 \(\pm\) 0.06 & 0.47 \(\pm\) 0.03 & 4.26 \(\pm\) 0.47 & 12.37 \(\pm\) 0.85 \\ \hline \hline \end{tabular} Note: Values are means \(\pm\) SD (one-way ANOVA, \(n\) = 3, SPSS Statistics).  
\end{table}
Table 1: Relative PAs of peak 1 (quercetagetin-7-O-glucose), peak 2 (quercetagetin), peak 3 (quercetin) and peak 4 (patuletin) after reaction with different concentrations of DPPH.
## 3 SP-Rife Algorithm
### Spectral Refinement  
Due to the limitation in frequency resolution, it is not possible to obtain the frequencies between adjacent discrete points, which may cause some useful frequencies to be missed. Increasing the number of sampling points will solve this problem, but it will result in higher hardware costs. The spectral refinement technique can improve the signal's resolution, making it easier to identify and suppress noise signals. Therefore, to enhance the accuracy of frequency estimation at low signal-to-noise ratios, spectral refinement processing is performed on the signal before conducting interpolation estimation in the SP-Rife algorithm.  
Let the index corresponding to the maximum value in the spectrum be denoted by \(k_{1}\), and the index corresponding to the second-largest value be denoted by \(k_{2}\). The corresponding spectra are represented as  
\[X(k_{1})=\sum_{n=0}^{M-1}x(n)e^{-\beta\pi\frac{k_{1}}{N}n} \tag{1}\]  
and  
\[X(k_{2})=\sum_{n=0}^{M-1}x(n)e^{-\beta\pi\frac{k_{2}}{N}n}, \tag{2}\]  
where \(M\) is the signal length, \(N\) is the number of sampling points, and \(x(n)\) is the sine signal.  
Due to the fence effect, the frequency between points \(k_{1}\) and \(k_{2}\) has not been detected. Therefore, it is necessary to further refine the frequency resolution by a factor of \(L\) between the original adjacent spectral lines. In other words, the spectral amplitudes of the frequencies at the following frequency points need to be obtained:\[\begin{cases}\frac{L+k_{1}}{LN},\frac{L+k_{1}+1}{LN},\cdots,\frac{L+k_{1}+L-1}{LN}, \frac{L+k_{1}+L}{LN}&\text{if }k_{2}=k_{1}+1\\ \frac{L+k_{2}}{LN},\frac{L+k_{1}+1}{LN},\cdots,\frac{L+k_{1}+L-1}{LN},\frac{L +k_{2}+L}{LN}&\text{if }k_{2}=k_{1}-1\end{cases}. \tag{3}\]  
After performing spectral refinement, it is only necessary to calculate the spectra corresponding to the mentioned frequency points, without computing the entire spectrum. For the frequency spectrum of each frequency point \(\overset{\leftarrow}{k}\) between \(k_{1}\) and \(k_{2}\), use Equation (4) to calculate:  
\[\begin{cases}X\bigg{(}\frac{k_{1}+l}{L}\bigg{)}=\sum\limits_{n=0}^{M-1}x(n)e^{ -j\frac{2\pi}{N}(\frac{k_{1}+l}{L}-1)(n-\frac{M}{2})}&\text{if }k_{2}=k_{1}+1\\ X\bigg{(}\frac{k_{2}+l}{L}\bigg{)}=\sum\limits_{n=0}^{M-1}x(n)e^{-j\frac{2\pi} {N}(\frac{k_{2}+l}{L}-1)(n-\frac{M}{2})}&\text{if }k_{2}=k_{1}-1\end{cases}, \tag{4}\]  
where \(l\) is an integer from \(0\) to \(L-1\) indicating the deviation of the refined frequency point with respect to \(k_{1}\) and \(k_{2}\). In this way, a more refined spectral representation can be obtained between \(k_{1}\) and \(k_{2}\), thus enabling a more accurate estimation of the frequency.  
Subsequently, the search for the maximum and second-largest spectral values is performed again. In the SP-Rife algorithm, a 100-fold spectral refinement is conducted. Additionally, to find the true maximum spectral peak, the real part of the spectrum is used for determination.
## 3 Results
### Spatial Pattern and Interannual Dynamics of GPP across Europe from 2001 to 2016
#### 3.1.2 Proportional Increase in GPP and LAI with Rapid Forest Growth across Europe
The mean annual LAI increased by 0.0046 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\) (\(p<0.01\)) over the study area. Not accounting for the Russia region, the mainland of Europe showed a doubled greening rate (0.01 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\)) (Figure 3b). Among the five dominant vegetation types (i.e., forest, shrub, grass, crop, and wetland), the increasing rates for the LAI of forests (0.01 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\), \(p<0.001\)) and shrubland (0.008 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\), \(p<0.001\)) were more significant and higher than those for other vegetation types (Figure 3c). Although both the increasing LAI rates and the total vegetation cover for shrubs and forests were comparative, the increasing trend of the annual total GPP for shrubland (12.9 Tg C yr\({}^{-2}\), \(p<0.001\)) was only approximately half that for forests (22.2 Tg C yr\({}^{-2}\), \(p<0.001\)), while it was much lower for grass (5.6 Pg C yr\({}^{-2}\), \(p<0.001\)). In contrast, the annual mean LAI and GPP for crops and wetland showed no specific trends. Figure 2: Spatial patterns and interannual trends in the GPP and LAI and corresponding significance levels. (**a**) Spatial pattern of the linear trend in the annual total GPP from 2001 to 2016. (**b**) Spatial pattern of the linear trend in the annual mean LAI from 2001 to 2016 based on the GLASS V5 LAI product. (**c**) Spatial distribution of pixels with significant increasing/decreasing GPP trends. (**d**) Spatial distribution of pixels with significant increasing/decreasing LAI trends. In (**c**,**d**), green and red indicate significant land surface greening and significant land surface browning, respectively (with a significance level of \(p\) value < 0.05).
## 1 Introduction
Thus, the integration of numerical simulation and BP neural networks to obtain inverse analysis methods holds significant importance for diversion tunnel projects. In this study, taking the No.1 tunnel of the Xiaolangdi North Bank Irrigation Area Main Channel as an example and utilizing FLAC3D as the research tool, we conducted an analysis of the effects of continuous heavy rainfall on tunnel deformation characteristics and the sensitivity of numerical calculation model parameters. We revealed the variations of water level, soil cohesion, and internal friction angle under continuous heavy rainfall conditions, obtained sensitivity rankings, and combined with orthogonal design and BP neural networks, we conducted displacement inverse analysis of water level and soil mechanics parameters to obtain more reasonable and accurate values.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis  
The dynamic differential equations of integrated ball bearings have strong nonlinearity and are complex to solve. In this paper, the fine integration method is combined with the Adams-Bashforth-Moulton multi-step method to integrally solve the nonlinear dynamic equation of the integrated ball bearing, and the magnitude of the radial in-plane vibration and the axial in-plane vibration of the bearing mandrel are obtained. Taking a certain type of integrated ball bearing as an example, the relationship between the bearing structure, working conditions, and mandrel flexibility and the vibration characteristics of the integrated ball bearing was analyzed. The integrated bearing in this model uses grease lubrication, which results in a relatively small fluid dynamic pressure friction. Therefore, its impact is neglected in the calculations presented in this paper.  
The structural parameters of the bearing are presented in Table 1. The materials for the outer ring, core shaft, and steel balls are GCr15, while the cage material is PA46. Specific parameters can be found in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Outer Diameter (mm) & 18 \\ Inner Diameter (mm) & 8.5 \\ Width (mm) & 5.9 \\ Number of Steel Balls & 7 \\ Diameter of Steel Balls (mm) & 2.778 \\ Pitch Circle Diameter (mm) & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the integrated bearing.  
Figure 4: Solution process flowchart.
## 2 Current Works
### Index System Research  
The index system is the most critical part of comprehensive evaluation. Its construction principles are introduced in [8; 9]: (1) Completeness--the index system should cover as much attributes of the evaluation object as possible; (2) Measurability--the indicators in the system should be preferably measurable and objective; (3) Independence--the indicators are as unrelated as possible; (4) Simplicity--use as few indicators as possible to cover as many attributes as possible; (5) Hierarchical principle--the system should have a clear hierarchical structure; (6) Accuracy--an evaluation will reveal and convey technically adequate information about the features. Among them, Completeness, Independence, and Measurability are the three most important principles in the construction of the index system.  
There are large diversities due to the different evaluation targets and objects for the construction method and process of the indicator system. Representative solutions, such as those in [10], established a set of comprehensive evaluation index system based on multiple characteristics of evaluation objects. The authors of [11] provided a viable way to construct the index system based on the indicator importance sort algorithm, which has low algorithm complexity and can reflect the architectural constraints in the construction of the index system. The authors of [12] used the \"R-cluster + coefficient of variation analysis\" method to quantitatively screen the initial indicators, which is an efficient method for indicator selection and classification. The authors of [13] presented a simple and metric methodology for evaluating remote sensing satellite effectiveness, which focuses on product quality parameters, satellite availability, and predicted satellite reliability. The authors of [14] focused on the assessment of coverage effectiveness of remote sensing satellite, and proposed and designed a multi-index evaluation method based on index weight using entropy weight method and analytic hierarchy process. However, at present, there is no universal method to effectively reconstruct the index system's hierarchy.  
As far as the content of the proposed index system is concerned, the Internet Engineering Task Force (IETF) has given a series of authoritative documents for network performance metrics (such as delay, jitter, packet loss, connectivity, etc.) [15] that have laid the foundation of the network evaluation index system. Driven by various evaluation needs, corresponding evaluation index systems have been proposed from ground networks to wireless access networks and to space-based communication  
Figure 1: The logical diagram of this paper.  
systems [16; 17; 18]. The relevant subsystems and network node equipment of the SIN already have corresponding indicators and indicator systems. They can provide a large number of input indicators for the indicator system of the SIN demonstration platform. However, the platform still lacks a complete set of comprehensive evaluation index systems, which requires urgent research.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Communication Overhead
#### 5.4.3 Message Size for Bonawitz et al. [5]  
The client sends two messages to the server and \(n-1\) messages to other clients participating in the FL. If 1000 clients participate in FL, the total packet size for all communications in one iteration of FL is given in Equation (30):  
\[L_{(M,Bonawitz)} =L_{MID}+L_{Payload}+L_{TTL}+L_{TS}\] \[=16+1024+8+32 \tag{30}\] \[=1080\;\text{bits}=135\;\text{bytes}.\]  
In addition to the server, each participating client must communicate with \(n-1\) clients. Therefore, the total size of all messages is \(n\times M_{Bonawitz}\), which is \(1000\times 135=135,000\) bytes.
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 3 Method
### Post-Processing Procedure and Edge Connection  
Although we demonstrated the advantage of using an adaptive threshold over the use of a fixed value, Figure 2 reveals that the method still generates noisy edge pixels. Consequently, a median filter is used in the post-processing step to minimize the noise. The filter replaces the gradient at each identified edge pixel by the median of the gradients of the surrounding eight neighborhood pixels. This step proves to be efficient in reducing the spurious edge pixels. Finally, the precise geographical coordinates and projection information are automatically assigned to all edge pixels. This allows for edge segments extracted from different images around the Antarctic coast to be connected into one single polyline that can be further analyzed. To ensure consistency with the observed coastlines, the merged coastline is smoothed into the single polyline using the spatial analyst tool under ArcGIS environment.
## 3 Results and Discussion
### NOx Emissions  
This section evaluates the potential of improving the emission behaviour of the engine when adopting hydrogen-enriched fuels in CAI combustion systems. We compare NOx in terms of specific emissions in order to consider the effect of exhaust mass flow variation on the emitted NOx mass.  
Figure 10: Pressure traces versus crank angle (Biogas 1 made of CH\({}_{4}\) and CO\({}_{2}\); Biogas 2 with CH\({}_{4}\)/H\({}_{2}\) = 9:1; and Biogas 3 with CH\({}_{4}\)/H\({}_{2}\) = 8:2, see Table 2).  
Figure 9: Effect of initial gas temperature on impe. Biogas 1 made of CH\({}_{4}\) and CO\({}_{2}\); Biogas 2 with CH\({}_{4}\)/H\({}_{2}\) = 9:1; and Biogas 3 with CH\({}_{4}\)/H\({}_{2}\) = 8:2 (see Table 2). The best operating conditions are circled.  
Figure 11 show NOx emissions as a function of the initial gas temperature for methane, CH4H2_1, CH4H2_2 and for biogases, respectively. NOx emissions increase with the initial gas temperature, essentially because of a higher peak temperature attained in the combustion chamber. Initial gas temperature has a direct influence on both the in-cylinder peak temperature and the combustion ignition. An increase of initial temperature shifts auto-ignition towards top dead centre. The results of Figure 11 also show that, at a fixed initial temperature, the more the hydrogen in the fuel, the higher the NOx emissions. However, as previously explained, hydrogen addition increases fuels reactivity and influences the combustion phasing. NOx emissions, obtained at the optimal operating conditions, set in order to maximize imp, are shown in Figure 12. It is interesting to observe that hydrogen enrichment always allows a reduction of NOx emissions. This implies that the reduction of the initial gas temperature prevails over the increase of the peak temperature induced by hydrogen. It is also interesting to underline that at the optimal operating conditions, found to obtain the same maximum imp, the indicated efficiency remains the same by changing fuel, from pure methane to methane enriched with hydrogen, from conventional biogas to innovative ones.  
Figure 11: NOx [g/kWh] emissions as a function of the initial temperature, for methane and methane hydrogen blends. The best operating conditions are circled.  
Figure 12: NOx emissions and engine efficiency at optimal initial gas temperature.  
The best operating conditions of each fuel considered in this paper, with the relative impe, indicated efficiency and NOx emissions are summarised in Table 4.  
Table 4 summarizes the results for conventional and hydrogen enriched fuels at optimal initial gas temperature.
# Path Loss Measurement of Outdoor Wireless Channel in D-band
###### Abstract  
D-band (110-170 GHz) has received much attention in recent years due to its larger bandwidth. However, analyzing the loss characteristics of the wireless channel is very complicated at the millimeter-wave (MMW) band. Research on D-band wireless channels has been focused on indoor short-distance transmissions, with few studies looking at outdoor long-distance wireless channels. In this paper, we provide the design of the D-band outdoor long-distance transmission system, propose the outdoor line-of-sight (LOS) propagation measurements, and study the outdoor D-band propagation loss characteristics with distances up to 800 m. The path loss model uses the Floating Intercept (FI) and the Close-In (CI) model is established based on the least square method. In the CI model, the path loss exponent is greater than 2 and increases with frequency, while in the FI model, the path loss exponent has no apparent frequency dependence. The results show that D-band path loss in long-distance outdoor scenarios is greater than that in free space, indicating that the propagation condition is worse than in free space. The results show that both models have similar performance. Under this basis, the model with the smallest number of parameters would be the optimal choice. In addition, these results prospectively provide a theoretical model for designing and optimizing high frequency mm-wave propagation measurements at a distance of 200 m and beyond.  
D-band; path loss; outdoor channels; channel model +
Footnote †: journal: Sensors
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
###### Abstract  
This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32 Cube_AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm's robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.  
partial discharge; convolutional neural network; discharge phase mapping; microcontroller; pattern-recognition +
Footnote †: journal: Medical Engineering  
Xuewen Yan  
School of Electrical Engineering, Xi'an University of Post and Telecommunications, Xi'an 710121, China; [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (J.L.)
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
### Contribution and Overview of This Work  
In this work, we propose one way of applying DRL to the semi-active suspension control of a real street vehicle. We describe the entire process of the vertical dynamics RL controller design process, which is presented in more detail in Section 2. This process started with taking measurements of the whole vehicle on a four-post test rig and the measurements of individual components. Based on these measurements, we derived component models and optimized a QVM model that was later used for training. The modeling, identification, and evaluation are presented in Section 3. Thereafter, Section 4 describes the whole controller training process. The verification of the controller trained on QVMs in a full-vehicle model is discussed in Section 5. Finally, Section 6 presents the results of conducting real-world tests.  
As this work is partially based on the results of a funded project, parts of the contribution were published as a short summary in the German final report [20]. On top of that, the contributions of the paper at hand can be summarized as follows:  
1. We address the complete process of applying DRL to the semi-active suspension control problem in great detail. This process includes taking measurements, deriving a training model, training the controller, verifying the controller in simulation, and conducting real-world tests.
2. In our approach, we propose to optimize the QVM model structure as well as the QVM parameters in order to obtain an accurate training model. Additionally, we show that the optimized model structure is able to approximate the real measurement data better than a standard two-mass QVM.
3. We propose to train the controller on different QVMs, which represent the different corners of the vehicle to avoid overfitting. Additionally, we train on different excitation types to make the resulting controllers more robust. The whole training process,including the design of the reward function and the selection of the trained agent, is presented in great detail.
4. We evaluate the resulting controller in real-world tests on a four-post test rig. The selected RL agent was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average.