esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 3 Design of Pantograph-Catenary Current-Carrying Friction Wear Experiment
### Correlation Analysis  
To analyze the degree of correlation between pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear, a correlation analysis is conducted. The Pearson correlation coefficient, \(r\), is introduced, and its expression is given as follows:  
\[r=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt {\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}\sum\limits_{i=1}^{n}{(y_{i}- \overline{y})^{2}}}}} \tag{7}\]  
In the equation, \(x_{i}\) and \(y_{i}\) represent the sample values of pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear, respectively; \(\overline{x}\) and  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter Name** & **Contact Wire** & **Contact Slide** \\ \hline Brinell hardness /(N/mm\({}^{2}\)) & 96.2 & 49.4 \\ Electrical resistivity /(\(\Omega\cdot\)mm) & \(2.4\times 10^{-5}\) & 0.01 \\ Thermal conductivity /(W/mm\({}^{\circ}\)C) & 0.398 & 0.006 \\ Modulus of elasticity /(GPa) & 119 & 13 \\ Poisson’s ratio & 0.326 & 0.425 \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameters of slide and contact wire.  
Figure 1: Sliding electrical contact experimental machine.  
\(\overline{y}\) represent the mean values of pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear, respectively.  
The relationship between pantograph-catenary current-carrying frictional energy dissipation and wear under different sliding speed conditions, with a contact pressure of 100 N and a contact current of 200 A, is shown in the curve in Figure 2.  
As shown in Figure 2, when the pantograph-catenary current-carrying frictional energy dissipation is low, the pantograph slide wear is also small. As the pantograph-catenary current-carrying frictional energy dissipation increases, the wear of the pantograph slide also increases. The calculation results indicate that the correlation coefficient between pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear is 0.989396, which suggests a significant correlation between the two. The greater the pantograph-catenary current-carrying frictional energy dissipation, the greater the wear of the pantograph slide.
## 3 Methodology
### Simulation Scenario: Monte Carlo  
To assess the performances of GNSS ranging for a lunar scenario, two types of environments have been set up enabling both theoretical as well as experimental analyses. The first setup resorts to a Monte Carlo analysis implemented in Matlab on measurements extracted from STK while the second is based on the measurements made by a real spaceborne receiver that simulates the reception of GNSS signals in lunar orbit. Both environments use exactly the same scenario at the same epoch and for the same duration, on the 9 November 2025 00:00:00.00 UTC for a duration of four hours for comparison purposes. While the experimental environment is based on the actual NaviMoon spaceborne receiver in a HWiL architecture, the theoretical simulation is modeling a receiver based on the NaviMoon parameters [71]. Namely, the considered receiver sensitivity is 15 dB-Hz. The theoretical simulations allows to individually assess specific factors involved in the ISR estimation process of a space scenario, by isolating the source of errors one by one.  
The scenario described in Section 3.1 was created using STK. The ephemerides used for the Galileo and GPS constellations were obtained from SpacePNT and contained 21 and 32 satellites, respectively. The antenna patterns for GPS were obtained online from the U.S. Navigation Center [72] while the Galileo patterns were obtained internally at ESA. L5/E5a is the focus frequency band of the present contribution, given its inherent advantages for spaceborne GNSS users, as discussed in [42]. The user's trajectories were obtained in STK by giving it an initial state vector and propagating it with its high-fidelity orbit propagator to generate the reference trajectories of the VMMO and of the LPF. Once the ranges, spacecraft positions and \(C/N_{0}\) levels are extracted from STK, these data are then post-processed in Matlab to characterize the quality of the ISR estimator.  
The \(C/N_{0}\) levels are converted to pseudorange standard deviations based on the Betz theory, providing an estimation of the code tracking accuracy as a function of the RF front-end parameters and the \(C/N_{0}\)[73]. The pseudorange noise is then generated for each Monte Carlo simulation. The receiver's local clock biases are set with values observed during the NaviMoon experiments in the laboratory. The clock bias and position of each orbiter are then estimated individually. The time series of each user is then corrected based on the estimated clock bias. The measurements of the _aiding user_ are then time-compensated to match the respective closest timestamps of the _aided user_, according to Equation (4). Finally, the ISR are estimated based on the set of visible GNSS satellites common to both users.
## 2 Methods
### Trend Analysis, Correlation Analysis, and Regression Model Establishment  
The Mann-Kendall trend analysis was used to determine the trends of WUE and drought over the period of 2000-2014 [50]. For the Mann-Kendall non-parametric test method, the advantage is that the data series does not require normality. This method is widely applied in trend studies of climate data and vegetation data [51; 52]. The significance level was assessed at \(p<0.05\). The relationships between ecosystem WUE and TVDI over the period of 2000-2014 were examined using linear correlation analysis.  
To investigate the legacy effect of drought on WUE, we built three linear regression models between WUE and the TVDI in different years.  
\[WUE_{current}=a\times TVDI_{current}+b \tag{4}\]  
\[WUE_{current}=a\times TVDI_{current}+b\times TVDI_{previous\ one\ year}+c \tag{5}\]  
\[WUE_{current}=a\times TVDI_{current}+b\times TVDI_{previous\ one\ year}+c\times TVDI_{previous\ two\ year}+d \tag{6}\]where \(\mathit{WUE}_{current}\) is the current year WUE, and \(\mathit{TVDI}_{current}\), \(\mathit{TVDI}_{previous}\)_one_year, and \(\mathit{TVDI}_{previous}\)_two_year represent the current, previous one, previous two year TVDIs, respectively. We compared the regression coefficients of the different models to further analyze whether the fitted model increased the fit. The Akaike Information Criterion (AIC) was also calculated to evaluate the benefit of adding the TVDI from the previous year to the regression model between TVDI and WUE. The new model (using two/three years of TVDI data) is considered an improvement over the old model (using one year/two years of TVDI data) if the AIC value is reduced by more than 3.0 [6].
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
## 2 SIW Variable Phase Shifter
### SiW 3-dB Coupler  
A hybrid coupler with high isolation and 3 dB coupling is designed on RT/duroid 6002 substrate with relative permittivity of 2.94, thickness of \(h\) = 0.508 mm, and loss tangent of 0.0012 [20]. The component is designed and simulated in CST Microwave Studio 2018. Figure 1 presents the SIW 3 dB coupler structure. The required coupling and isolation are achieved by optimizing the length of the gap in the middle, the location of the vias on both sides, and also the middle vias. As shown in Figure 2a, this design has a reflection coefficient better than \(-22\) dB in the entire frequency range. It has an isolation between the adjacent ports of higher than 30 dB at mid-band frequency and 3 dB power division between 16 GHz and 18 GHz. Figure 2b demonstrates that the transmission coefficient, when including dielectric and metallic losses in the analysis, amounts to an insertion loss of less than 0.5 dB.
###### Abstract  
An important challenge for air-ground unmanned systems achieving autonomy is navigation, which is essential for them to accomplish various tasks in unknown environments. This paper proposes an end-to-end framework for solving air-ground unmanned system navigation using deep reinforcement learning (DRL) while optimizing by using a priori information from search-based path planning methods, which we call search-based optimizing DRL (SO-DRL) for the air-ground unmanned system. SO-DRL enables agents, i.e., an unmanned aerial vehicle (UAV) or an unmanned ground vehicle (UGV) to move to a given target in a completely unknown environment using only Lidar, without additional mapping or global planning. Our framework is equipped with Deep Deterministic Policy Gradient (DDPG), an actor-critic-based reinforcement learning algorithm, to input the agents' state and laser scan measurements into the network and map them to continuous motion control. SO-DRL draws on current excellent search-based algorithms to demonstrate path planning and calculate rewards for its behavior. The demonstrated strategies are replayed in an experienced pool along with the autonomously trained strategies according to their priority. We use a multi-stage training approach based on course learning to train SO-DRL on the 3D simulator Gazebo and verify the robustness and success of the algorithm using new test environments for path planning in unknown environments. The experimental results show that SO-DRL can achieve faster algorithm convergence and a higher success rate. We piggybacked SO-DRL directly onto a real air-ground unmanned system, and SO-DRL can guide a UAV or UGV for navigation without adjusting any networks.  
air-ground unmanned system; deep reinforcement learning; deep deterministic policy gradient; navigation; path planning; obstacle avoidance
## 3 Results and Discussion
### Configuration Analysis
Additionally, the operational cost in serial operations (configurations 1 and 2) is lower than in parallel operations (configurations 3 and 4) due to the heat pump's higher share in heat production (Figure 5). An interesting point in Figure 7 is that, for the HHP system, series operation is more efficient than parallel operation because it results in lower costs and reduced emissions. The main reason for this is the faster ability of the system to reach the Two,sp before the buffer tank, which cannot be reached in the case of parallel configurations. This also forces the biomass boiler to work for more time periods since its activation occurs with more frequency, as we see in Figure 5.  
In a nutshell, and based on the results in this section, we conclude that configuration 2 is the best option for the HHP system, as it offers the highest efficiency, lowest emissions, and the most reduced costs compared to the other configurations. Figure 7: The Equivalent Warming Impact on the System and the operation cost of different configurations (EUR/year).
## 2 Study Site and Methodology
### Data Processing and Analyses  
As with the conventional meteorological sign notation, downward and upward fluxes are considered negative (-) and positive (\(\pm\)), respectively. The first step of data processing involved the removal of erroneous spikes and their associated CO\({}_{2}\) fluxes when latent heat flux (LE\({}_{\mathrm{wpl}}\)) \(<\) -100 or \(>\) 800 W m\({}^{-2}\); sensible heat flux (\(H_{\mathrm{s}}\)) \(<\) -150 or \(>\) 500 W m\({}^{-2}\); and precipitation events occurred [10]. For both hourly daytime and nighttime CO\({}_{2}\) fluxes, descriptive statistics were given, and best-fit cumulative distribution function (CDF) was selected. [PERSON]'s multiple comparison was performed after one-way analysis of variance (ANOVA) for the entire dataset in order to test significant differences in hourly, nighttime _versus_ daytime and monthly means. All the statistical analyses were performed with Minitab 15.1 (Minitab Inc. 2006). Second, EC data were separated into daytime (\(R_{\mathrm{n}}\) and/or \(R_{\mathrm{s\_dn}}\)\(>\) 10 W m\({}^{-2}\)) and nighttime (\(R_{\mathrm{n}}\) and/or \(R_{\mathrm{s\_dn}}\)\(\leq\) 10 W m\({}^{-2}\)) periods since EC data are more reliable during daytime hours than during nighttime hours, and nighttime EC data (\(F_{\text{c\_wpl}}^{night}\)) can be used to estimate daytime as well as nighttime ecosystem respiration (both plant and soil respiration) (\(R_{\text{E}}=F_{\text{c\_wpl}}^{night}\)). Negative night CO\({}_{2}\) flux data were deleted as no gross primary productivity (GPP = 0) occurs during the nighttime. The site-specific threshold value of friction velocity (\(u^{\star}\)) was determined as 0.03 m s\({}^{-1}\) below which low vertical wind velocity led to underestimation of the nighttime CO\({}_{2}\) fluxes [11]. Likewise, nighttime periods where horizontal wind velocity was less than 1 m s\({}^{-1}\) were removed from the dataset. Finally, night fluxes where CO\({}_{2}\) density data had the standard deviation of \(>\) 14 mg m\({}^{-3}\) (0.6 \(\mu\)mole m\({}^{-3}\)) were eliminated from further analyses [12]. Multiple (non-)linear regression models were fitted to the resultant night CO\({}_{2}\) dataset, and best-fit M(N)LR models with and without the inclusion of temporal variables (hour, and month) were chosen using best subsets procedure (low Mallows' \(C_{\text{p}}\), high adjusted \(R^{2}\), and low SE). The following soil respiration equations of RothC [13] and CENTURY [14] models were also used to model daytime \(R_{\text{E}}\):  
\[f(R_{\text{soil}})=\frac{47.9}{1+\exp\biggl{(}\frac{106}{\text{ST}+18.3} \biggr{)}}\text{ for RothC model} \tag{1}\]  
\[f(R_{\text{soil}})=0.56\pm 0.465\text{*}\text{arctan}(0.097\text{*}(\text{ST} -15.7))\text{ for CENTURY model} \tag{2}\]  
where \(R_{\text{soil}}\) is soil respiration (mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)), and ST is soil temperature (\({}^{\circ}\)C). Net ecosystem exchange (NEE, mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) can be expressed as follows:  
\[\text{NEE}=-\text{GPP}\pm R_{\text{E}} \tag{3}\]
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 2 Related Work
The scheme proposed in [14] can prevent leakage of local model parameters due to the noise contained in the local parameter but can be vulnerable to insider attacks because participants must actively cooperate. Recently, [PERSON] et al. offered a technique in which participants verify the integrity of the updated results in the system that updates the global model parameters using the Secret Sharing scheme in [15]. The algorithms using the HE scheme in [2] and Secret Sharing in [14] have shown that neural networks can be safely trained without personal information leakage in FL scenarios. In [2], all training process participants owned the same private key, although the distributed deep learning system using the HE scheme was designed to protect shared data. For this reason, all channels between participants and servers must be protected using transport layer security or secure socket layer. However, as the number of participants increases, the cost to establish secure channels becomes very high. In addition, since the probability of one participant's private key being leaked is the same as the probability of all participant's private keys being leaked in this system, the risk of private key leakage increases as the number of participants grows. In the proposed system, each participant can own different private keys on the same FL-based system. In [14], at least half of the participants must guarantee their honesty for privacy preservation. If the number of participants in FL is large, this assumption is reasonable, but there can be a variety of FL scenarios. The proposed system allows participants to preserve data privacy regardless of the number of honest participants and can be utilized as a solution to build a flexible PPFL-based system. This paper presents a PPFL algorithm based on a distributed cryptosystem using the AHE scheme to protect the local and global model parameters. The participant uses the HE scheme to encrypt the local model parameters with its private key, and the cloud server updates the global model parameters with the local model parameter encrypted with different keys based on the distributed cryptosystem. The proposed PPFL-based system can achieve robust privacy protection because the proposed algorithm can allow each node to use a different private key for the HE scheme in the same FL-based system. Furthermore, a highly flexible FL-based system can be built using our algorithm because clients only need to encrypt and decrypt model parameters to protect them.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis
### Comparison of FBCCA and CCA  
The evaluation criterion includes the average accuracy and \(ITR\) of 30 subjects. For different application scenarios, the standards are inconsistent. \(ITR\) is more suitable for online testing and evaluation and has a relatively considerable effect on the actual application of SSVEP and BCI. However, accuracy evaluation is also a critical criterion in some scenarios that need higher precision but not a quicker acquisition time. As a result, this study analyzes these two commonly used criteria.  
The FBCCA algorithm is compared to the traditional CCA algorithm, using the best parameter in harmonic and subband numbers above. The extracted data length takes 2 s, and the comparative results are shown in Figure 9.  
As shown in Figure 9, the algorithm accuracy of FBCCA is nearly 20% higher than CCA, while \(ITR\) is nearly 30% higher, indicating a significant improvement in the algorithm performance.
## 5 Cellular Network Analysis
### Basic Analyses  
A number of straightforward analyses to extrapolate statistics concerning the cellular network and its configuration can be carried out, involving different components. For instance, suppose that we want to obtain the number of PLMNs available in each country. Since each \(mcc\) can be related to one or more country boundaries, and given the fact that a country may have several associated \(mcc\), the solution is to group PLMNs (couples of \(mcc\), \(mmc\)) by the corresponding country _iso_ codes to obtain a list of PLMN for each country. Starting from the above grouping, a straightforward representation is the chorople map where each country is colored from light to dark following the number of PLMN operating in that country, as shown in Figure 12. In this map, dark colored countries have higher numbers of PLMNs according to the breaks described in the legend. Note that for this analysis we used all recognized PLMNs, independently from the associated geometry.  
Figure 11: Integration of different sources: a cell reconstructed from 37 observations of the OpenCellID dataset and 87 observations of the u-blox dataset.  
One might be interested in finding the 10 countries with the highest number of PLMN operators, leading to Figure 13. The specific legend breaks have been chosen because, as is visible in Figure 13, India and the Unites States have very large numbers of PLMNs (182 and 111, respectively) compared to other countries, which have at most 17 PLMNs. As for the density of the PLMNs (column _density_), by dividing the number of the PLMNs over the areas of each country, we can observe immediately that, except for very small countries (e.g., Monaco, Gibraltar, or Macau), most have a density of under 0.0089 PLMN per Km2 (more than 50%).
## 3 Simulation and Experiment
### Simulation Test
The BER of the signal subjected to FrFT plus DFE is compared with that of the signal directly demodulated by DFE. Figure 7 shows the comparison of demodulation effects between the two methods. It reveals that the BER of the signal that subjected to FrFT plus DFE is much lower than that of the signal directly demodulated by DFE, highlighting the significantly improved demodulation performance with fractional Fourier transform equalization compared to direct decision feedback equalization. Hence,  
Figure 5: The signal autocorrelation results without FrFT equalization. Figure 6: The signal autocorrelation results after FrFT equalization. the simulation result shows that the channel equalization method based on FrFT can suppress multipath effects and obtain a lower BER, shows better performance and applicability in demodulation.
## 3 Research Design and Procedure
### Proposed Methodology (LADTree)
Return left_subset and right_subset. Function: CalculateDeviance(left_subset, right_subset)
1. Calculate the deviance using logistic regression models based on the given subsets. 2. Return the calculated deviance. ``` ```  
**Algorithm 2**StoppingCriteria(Dataset)  
These functions are essential to the LADTree technique, which is used to build logistic regression-based decision trees. The \"StoppingCriteria\" function defines the conditions that determine when to stop the tree construction process, such as reaching a maximum depth or having a minimum amount of samples per node. It returns true if these requirements are fulfilled, suggesting that further splitting of the dataset is not required. The \"FindBestSplit\" function determines the optimum split in the dataset that maximizes class separation. It iterates over each feature and value combination, dividing the dataset and computing deviation with logistic regression. The split that produces the lowest deviation is chosen as the best split and returned. The \"SplitDataset\" function partitions the dataset into left and right subsets depending on a given feature and value. Instances with feature values less than or equal to the provided value are placed in the left subset, while those with higher values are placed in the right subset. The subsets are then returned for further processing. Finally, the \"CalculateDeviance\" function calculates the deviation using logistic regression models based on the subsets created by the \"SplitDataset\" function. This deviation measurement quantifies the difference between observed and expected class probabilities, which helps to determine the appropriate split for the decision tree. These functions work together to simplify the iterative process of building a decision tree model with the LADTree algorithm, ensuring that optimal splits are located while adhering to set stopping conditions.