esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sediment Quality  
There were large differences in the sediment's characteristics among sectors, main systems and type of drainages (Table 2). The sediment pH displayed differences by sector (KW = 14.503, \(p\) \(<\) 0.05), with the Palma Real sector being the most acidic (6.302 \(\pm\) 0.391), differing from Muisne (7.01 \(\pm\) 0.354) and Cojima (6.979 \(\pm\) 0.197). This difference is transferred to the main estuaries (KW = 11.609, \(p\) \(<\) 0.05) and the type of drainage (KW = 12.078, \(p\) \(<\) 0.05). Natural drainages have a lower pH (6.412 \(\pm\) 0.473) compared to shrimp ponds drainages (6.955 \(\pm\) 0.307), while winter (6.89 \(\pm\) 0.37) showed higher pH levels than those of summer (6.58 \(\pm\) 0.50), evaluated through non-parametric tests. The redox potential of the superficial layer of sediments did not show differences among sectors (KW = 3.983, \(p\) \(>\) 0.05), main systems (KW = 2.812, \(p\) \(>\) 0.05), type of drainage (KW = 0.019, \(p\) \(>\) 0.05) or station (KW = 3.793, \(p\) \(>\) 0.05). However, the average value became negative during winter (\(-\)22.605 \(\pm\) 131 mV), while remaining positive during the summer (29.19 \(\pm\) 100.63 mV). The Cojima sediments were the only sediments that yielded a negative average (\(-\)52.69 \(\pm\) 115.78 mV). In a similar situation, the electrical conductivity with a global average of 8094 \(\pm\) 3107 uS/cm did not show differences in all the contrasts carried out. The ammonium content in sediments showed large differences by sector (F\({}_{3,34}\) = 9.308, \(p\) \(<\) 0.001). Cojima (14.875 \(\pm\) 7.772 mg/kg) showed the highest ammonium content among the sectors (PR = 4.45 \(\pm\) 2.871, TA = 4.850 \(\pm\) 3.958, MU = 5.250 \(\pm\) 3.924). The excess of ammonium in Cojima resulted in ammonium content differences among main systems (F\({}_{1,36}\) = 6.823, \(p\) \(<\) 0.05) and types of drainages (F\({}_{1,36}\) = 5.862, \(p\) \(<\) 0.05). The natural drainages or mangroves averaged 4.133 \(\pm\) 2.608 mg/kg and the modified drainages averaged 8.804 \(\pm\) 7.139 mg/kg, without any differences between sampling periods (F\({}_{1,36}\) = 0.121, \(p\) \(>\) 0.05). An opposite situation occurred with the total nitrogen content that presents sectorial differences (KW = 19.305, \(p\) \(<\) 0.001), where Palma Real (3325 \(\pm\) 980 mg/kg) exceeds Tambillo (2189 \(\pm\) 962 mg/kg), Muisne (1179 \(\pm\) 754 mg/kg) and Cojima (1174 \(\pm\) 730 mg/kg), and Tambillo exceeds Muisne and Cojima, who did not differ from each other. The differences are transferred to systems (KW = 16.277, \(p\) \(<\) 0.001) and drainage type (KW = 15.895, \(p\) \(<\) 0.001) where natural drains (2992 \(\pm\) 1005 mg/kg) practically double in total nitrogen to shrimp drains (1367 \(\pm\) 899 mg/kg), there are also seasonal differences (KW = 4.807, \(p\) \(<\) 0.05) where the nitrogen content increases during winters.
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## 4 Discussion  
In the use of CEEMD to process radar data, [PERSON] et al. used the CEEMD decomposition method not only to perform time-frequency analysis but also to perform denoising and target recognition on GPR data [26; 27]. They mainly used CEEMD to decompose GPR data, used the Hilbert transform to analyze the data, and then performed denoising accordingly. The method in this paper not only uses CEEMD to decompose the data, but also uses the STFT and Hilbert transform to analyze the single component and the overall data comprehensively, which improves the application of CEEMD in ground-penetrating-radar processing.  
On the other hand, regarding the use of CH-1 data from the lunar radar, although [PERSON] et al. [20] believed that [PERSON] et al. [6] and [PERSON] et al. [10] had objections to the use of CH-1 data, and used the ground experimental data and S transform to support their view, they did not perform in-depth decomposition and analysis of the overall CH-1 data. In this paper, the non-linear decomposition method is used to deeply analyze the false reflectors and available information of CH-1 data.  
Figure 14: Stratification on CE-3 landing site by CH-2 data.  
Figure 15: Stratification on CE-3 landing site by CH-1 data.  
The most important significance of this paper is that the LPR data of CE-4 is being collected on the moon, and the CH-1 data collected by CE-4 can be used by the CEEMD combined with the short-time Fourier transform and the Hilbert in the case of an extremely low SNR. The idea of special transformation is analyzed to improve the credibility of the CE-4 data.
# Cable-Driven Mechanism Models for Sensitive and Actuated Minimally Invasive Robotic Instruments
## 3 Physical Implementation of MM2  
### Test Benches for MM2  
Test benches for the implementation of the formulations for MM2 were conceived with two fundamental parts: one external hollow cylinder (mobile part A) and one internal  
Figure 9: **Left:** planar scheme with four cables, four pulleys, and four springs for mechanism model MM2. Mobile part in the starting position (dashed lines: points \(C_{0}\), \(E_{0}\), \(A_{0}\), \(B_{0}\), \(F_{0}\), \(D_{0}\)) and new balancing configuration (continuous lines: points \(C_{1}\), \(E_{1}\), \(A_{1}\), \(B_{1}\), \(F_{1}\), \(D_{1}\)). **Right**: scheme for the comparison between MM1 and MM2.  
part (fixed part B). The external hollow cylinder is connected with the internal part by using cables.  
We conceived an innovative formula for eight cables, shown in [5], able to measure (as an output) external loads (applied on the tip of the mobile part) and its position and rotation, using as input the displacement of each spring connected to each cable. MM2 is the model used for the conceived formulation.  
Our final objective is to reduce the dimensions of the cable-driven sensitive mechanism in order to include it in one part of the tube of the MIRI (near the forceps). In this case, the external dimensions of the sensitive mechanisms must be not more than 8 mm. However, in order to validate the formulation presented in [5], two test benches were developed (for MM2) with bigger dimensions with respect to 8 mm. The formulation presented in [5] is able to describe well the mechanical behavior of the two different test benches shown in Figures 10 and 11, which used the same type of spring, with an external diameter of 9.4 mm, number of wraps 25, stiffness coefficient 0.6786 N/mm, initial length of spring 24.8 mm, and diameter of wire 1 mm. The diameter of the cable in nylon is 0.6 mm.
## 2 Materials and Methods
### Creation of Training and Test Data for Cropland Suitability Prediction  
The study involved the systematic creation of training and test datasets for predicting cropland suitability, specifically for soybean cultivation in continental Croatia from 2015 to 2021. Input data were sourced from two primary repositories: the Paying Agency for Agriculture, Fisheries and Rural Development (APPRRR), which provided vector shapefile and textual data of ground truth soybean agricultural parcels, and the PROBA-V/Sentinel-3 satellite missions, offering biophysical variables including the LAI and FAPAR in raster format. LAI and FAPAR data were downloaded from the Copernicus Global Land Service FTP server for the months of April through October, capturing the complete vegetative period for all soybean varieties cultivated during the defined seven-year timeframe [21]. Preprocessing steps in a geographic information system (GIS) included resampling and reprojection of the satellite-derived data to the Croatian Terrestrial Reference System (HTRS96/TM, EPSG:3765) at a spatial resolution of 300 m, followed by clipping to the study area extent. The analysis proceeded with a raster-vector overlay, which facilitated the identification of valid LAI and FAPAR pixels essential for training and test dataset creation. This selection adhered to three critical criteria: ensuring at least 50% coverage of ground truth parcels specific to each study year relative to 300 m spatial resolution reference grid, a shape index of each APPRRR parcel higher than 0.5 [25], compliance with crop rotation systems to mitigate the risks of monoculture [26], and verification of cultivated soybean coverage within individual parcels, as confirmed by data from the APPRRR. A minimum  
Figure 2: Workflow of the proposed machine learning-based method for determining cropland suitability.  
threshold of 0.25% for the training and test data according to the entire agricultural land in study area was determined according to [PERSON] [27]. Additionally, peak LAI and FAPAR values were computed for each pixel annually, reflecting the maximum vegetative potential necessary for robust soybean yield assessment, as variations in growth stages among different soybean cultivars lead to a critical temporal overlap during the full seed growth stage (R6), which closely correlates with soybean yield [28].
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### Efficiency Evaluation  
Cross-validation is a popular ML model evaluation approach that divides data into multiple groups for training and validation. The k-fold cross-validation approach often splits the data into k equal parts and trains the model on k-1 parts while validating on the rest. Each part becomes the validation set once during this process.  
Cross-validation helps to prevent overfitting and underfitting by accurately assessing the model's performance on unseen data. This research evaluates the regression model's performance using 5-fold cross-validation, which divides the data into 5 equal parts and repeats the training and validation process 5 times [51].  
The approaches' performance was assessed using prediction errors, and four mathematical metrics were employed to control and compare them. The first metric, the correlation coefficient (\(\mathbb{R}^{2}\)), assesses the strength of two variab'es' association as they vary. The coefficient of determination (\(\mathbb{R}^{2}\)) measures model fitting accuracy by dividing real data variance by predicted value variation. \(\mathbb{R}^{2}\) values between 55% and 75% are considered satisfactory, but \(\mathbb{R}^{2}\) values below 30% are cause for concern. The mean squared error (MSE) takes bias and error variation into account but is more susceptible to outliers than the mean absolute error. Moreover, root mean square error (RMSE) is a measure of the average distance between the predicted values and the actual values. It is calculated by taking the square root of the mean of the squared residuals. The lower the RMSE, the better the model predicts the actual values. RMSE is often used to compare the performance of different models [52].  
Figure 1: Shows the various steps that make up the proposed approach.
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 4 Simulation Result and Analysis
### Power Distribution Results and Analysis  
The simulation results of the proposed power management strategy in performing HESS power distribution is shown in Figure 11. The figure shows simulation result where power demand is supported by the SC and battery simultaneously. For instance, from around t = 250 s to t = 300 s, there are two high power demand spikes, and the SC supports the battery to provide a fast burst power demand. This result indicates that the proposed power management strategy successfully satisfied the objective of HESS.  
The SOC of the battery is shown in Figure 12 which demonstrates that the battery performs without any charging state and the initial SOC is 80%. Meanwhile, during the simulation, the SC supports the battery and absorbs energy while the EV slows down. For example, from around t = 230 s to t = 250 s, the EV slows down, and the system recycles  
Figure 11: HESS power distribution result.  
Figure 10: Power demand prediction result.  
-temperature energy to charge the SC. The changes in the SOC of the SC for 300 s simulation is also shown in Figure 13. Compared to the SOC level of the battery, the SOC of the SC is more volatile which indicates the SC performs fast and varied energy transfer than the battery during the simulation.  
The comparison study to evaluate the performance of the proposed power management strategy is also carried out in this section. Two performance indicators that are corresponding to the discharging battery behavior will be used to assess the result. The first indicator is the average of the discharging battery power as described in Equation (5).  
\[\overline{P}_{batt}=\frac{1}{N}\cdot\sum_{t=1}^{N}P_{batt}(t) \tag{5}\]  
where \(\overline{P}_{batt}\) is the average discharging power and \(N\) is the number of total data. This paper uses standard deviation as the second indicator, described in Equation (6), to calculate power variation which is normal and widely used to represent variance in many cases and applications.  
\[\sigma=\sqrt{\frac{1}{N}\cdot\sum_{t=1}^{N}\bigl{(}P_{batt}(t)-\overline{P}_{ batt}\bigr{)}^{2}} \tag{6}\]  
The proposed power management strategy will be compared to battery-only strategy by using the same WLTP class 3 driving cycle for 300 s. The battery-only strategy works only with battery pack as the main energy source. Thus, the battery-only strategy has  
Figure 12: SOC level of battery.  
Figure 13: SOC level of SC.  
no power split process and future power demand prediction. Figure 14 demonstrates the battery discharging power comparison between the proposed power management and battery-only strategy and the results analysis of the performance are summarized in Table 3. From the result, by taking the future power demand and power split into account, the proposed power management strategy has lower average battery power and power variation compared to battery-only. The average battery power is reduced by 46.1% and the battery power variation is reduced by 52.3%.
## 3 Dynamic Error Modeling and Uncertainty Analysis
### Uncertainty Simulation and Analysis  
Basing on the mathematical solution in Section 3.1, simulation experiments were carried out in MATLAB. A measurement network with four transmitters was built and the layout was the same as Section 2.2. A receiver moved at different speeds, 30, 60, and 120 mm/s respectively. We set 4800 points at three horizontal planes (z = 0, z = 1000, and z = 2000) uniformly as Figure 7. The coordinate uncertainty components of each point, i.e., \(u_{x}\), \(u_{y}\) and \(u_{z}\), were calculated. In Figures 8-10 we have displayed the compound uncertainty \(u\) which indicates the distance between the theoretical point and measurement result.  
\[u=\sqrt{{u_{x}}^{2}+{u_{y}}^{2}+{u_{z}}^{2}} \tag{10}\]  
Figure 8: Coordinate uncertainty distribution of points at z = 0.  
Figure 7: Points distribution in the measurement zone.  
We first constructed a measurement network with four transmitters. The rotation speeds of transmitters were set as 1600, 1800, 2000, and 2200 rev/min respectively. They were consistent with those in simulations in Section 2.2. The orientation parameter of transmitters was calibrated according to a coordinate control network [17]. A linear guide with a maximum travel distance of 800 mm was applied to generate a standard linear trajectory and its straightness error was better than 0.07 mm after calibration with an interferometer. It is much smaller than dynamic error and the linear guide can be seen as a reference in experiments. A receiver was located on the platform and moves with it synchronously. Movement speeds were set as 30 mm/s, 60 mm/s, and 120 m/s respectively. Experiments were carried out in two conditions successively: the linear guide was 5 m/7 m in front of the primary transmitter. To break the specificity in speed direction, in both conditions the linear guide had a horizontal angle of 45\({}^{\circ}\) and a vertical angle of 20\({}^{\circ}\) in the global coordinate system.  
The real-time coordinates of the receiver during each motion were recorded. Then we fitted a spatial line. The dynamic error was evaluated by the straightness error and the root-mean-square errors at each case were also calculated in Figures 12 and 13.  
Figure 11: Experiment configuration: (**a**) Overview; (**b**) linear guide and receiver.  
Figure 12: Straightness error of practical trajectory at 5-m at different speeds.  
For further comparison, Table 1 lists the deviations between practical experiment results and corresponding simulation results. We can see that the experimental results match with the simulation results, and the maximum deviation between them is 0.091 mm. The dynamic error model of wMPS is validated to be reasonable.
# Positively Charged Lipid as Potential Tool to Influence the Fate of Ethosomes
## 3 Results and Discussion
### Physico-Chemical Characterization Studies
As can be seen in Table 2, formulations B and C, which respectively, contained DSPE and DOPE, showed no relevant variations in the physico-chemical properties of the samples, if compared to the results recorded for formulation A, because all had a net negative surface charge and a narrow size distribution with vesicular dimensions below 250 nm. On the contrary, the addition of DOTAP to the sample at the same molar ratio (formulation D) led to a clear change in the surface charge of the nanosystems, which converted to a net positive charge. Probably, the persistence of the negative surface charge for formulations B and C was due to the positioning of lipid in the bilayer, which induced masking of the positive \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formulation** & **Mean Size (nm)** & **Polydispersity Index** & **Zeta Potential (mV)** \\ \hline A & 224 \(\pm\) 3 & 0.16 \(\pm\) 0.01 & \(-\)25 \(\pm\) 0 \\ B & 208 \(\pm\) 1 & 0.20 \(\pm\) 0.02 & \(-\)24 \(\pm\) 1 \\ C & 241 \(\pm\) 5 & 0.23 \(\pm\) 0.02 & \(-\)27 \(\pm\) 1 \\ D & 152 \(\pm\) 2 & 0.16 \(\pm\) 0.02 & +56 \(\pm\) 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physico-chemical properties of conventional and positive charged ethosomes. functional groups of lipid molecules. Moreover, the simultaneous presence of phosphate groups in DSPE and DOPE, which on the contrary was missing in DOTAP, could have counterbalanced the positive charge and strongly influenced the final surface charge of nanos vesicles. Compared to formulation A, a reduction in vesicle sizes occurred in the case of formulation D, probably due to a better packing between the fraction of lipidic constituents having opposite charge. The low value of polydispersity index recorded in the case of formulation D also confirmed that the narrow distribution was retained by the new realized system, compared to conventional ethosomes. Moreover, the low value of polydispersity index and the net surface charge suggested a good stability of the system [40]. The great stability of the obtained cationic ethosomes (formulation D) was further confirmed by investigating \(\Delta\)Trasmission and \(\Delta\)Backscattering profiles over 3 h using Turbiscan Lab(r). As can be seen in Figure 1, \(\Delta\)Backscattering profiles were within the range of \(\pm\)1%, indicating that no creaming, sedimentation and flocculation phenomena occurred during the analysis, suggesting a great stability of samples over time. Any positive and negative variations in backscattering profiles over 8 mm and under 2 mm, that are visible in Figure 1, were not related to destabilization phenomena but were dependent on enclosed air, respectively, at the top and bottom of the glass vial [41].
## 4 Simulation Results and Performance Analysis
### Simulation Settings  
Table 3 Lists of the simulation parameters. The simulation was run for 2 hours. The transmission range of a vehicle was limited to 250 meters. For the warning messages, the region of interest was considered to be 5 km from the place of incident and period of validity of the message to 30 minutes.  
A sample snapshot of the simulation run for a length of the road of 500 m is provided for all three scenarios. Figure 7 depicts the snapshot of the simulation run in an average traffic scenario. Figure 8 depicts the snapshot of the simulation run in a dense traffic scenario. Figure 9 depicts the snapshot of the simulation run in a sparse traffic scenario.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total No. of vehicles injected & 1000 \\ No. of lanes per direction & 4 \\ No. of vehicles per km per direction & 0–15 (Sparse), 16–30 (Average), 31–50 (Dense) \\ Beacon interval & 100 ms (Sparse), 150 ms (Average), 300 ms (Dense) \\ Transmission range & 250 m \\ MAC Protocol standard & IEEE 802.11p \\ Propagation model & Two-Ray Ground \\ Vehicle speed & 7 m/s to 27 m/s \\ Simulation time & 2 h \\ Lifetime for message & 30 min \\ Region of interest & 5 km from the place of incident \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Design of Broadband High-Frequency Multi-Throw RF-MEMS Switches
## 4 Conclusions  
This research presents a 3P3T RF MEMS switch operating across frequencies from DC to 380 GHz. ANSYS HFSS 2023 R2 simulations validate its exceptional RF performance. Achieving insertion losses below 0.66 dB across such a wide frequency band and up to high frequencies is particularly commendable. Moreover, maintaining low insertion loss while elevating isolation to over 32 dB demonstrates the advanced level of our switch. The switch's six-port configuration enhances signal modulation capabilities. COMSOL simulation indicates an optimized maximum stress of 26 MPa for the switch, achieved through the innovative power-up stage and folded-beam design. This has led to a reduced actuation voltage of 6.8 V and a quick response time of 2.28 us, surpassing most RF MEMS switches. In our study, we also provide S-parameter plots and current density plots under typical operating conditions, further substantiating the switch's robust RF performance. Given its balanced efficiency and reliability, this switch shows potential for driving advancements in 6G communications, phased-array antennas, and satellite communications.  
Conceptualization, J.Y.; performance optimization, M.Z., J.L., Y.S. and Z.Z.; validation, J.Y. and M.Z.; writing--original draft preparation, J.Y.; writing--review and editing, M.Z., J.L., Y.S. and Z.Z.; supervision, Q.W. and M.L.; project administration, Q.W. and M.L.; manuscript review, Q.W. and M.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Double First-Class Talent Plan Construction (11012315), Double First Double First-Class disciplines National first-class curriculum construction (11013168), Double First Class Disciplines Construction (11013351), and National Future Technical College Construction Project (11013169). The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflicts of interest.