esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## Abbreviations  
LPR  
Lunar Penetrating Radar  
CE-3  
Chang'E-3  
CE-4  
CH-1  
The low-frequency channel  
CH-2  
The high-frequency channel  
HF  
high-frequency  
CEEMD  
Complete Ensemble Empirical Mode Decomposition  
DFR  
dual-frequency radar on the rover  
SAR  
synthetic aperture radar  
LRS  
Lunar-Radar Sounder  
SELENE  
Japan's Kaguya probes  
LRO  
Lunar-Reconnaissance-Orbiter  
YR  
Yutu rover  
NAOC  
National Astronomical Observatory of the Chinese Academy of Sciences
# Comparing Human Activity Recognition Models Based on Complexity and Resource Usage
## 6 Conclusions  
In this paper, based on other research, we trained and evaluated our deep neural nets (CNNs and RNNs) and classic machine learning models (SVM, RF and KNN). The employed techniques and architectures were chosen and defined to be representative concerning common state of the art approaches. We chose a comprehensive public dataset, the UCI HAR dataset [31] to train them using preprocessed sensor data for the deep neural nets and extracted features for the machine learning models. We used metrics for performance (macro-averaged F1-Score and Accuracy) and model complexity (mean prediction time, memory usage and the number of trainable parameters) to compare the models and chose the most favourable to be used in an application. For a strictly memory-limited use case, the Random Forest that achieved an F1-Score of 88.34%, a mean prediction time of 0.22 ms and memory usage of only 0.1 MB, performed best. For a non-memory-limited use case, the SVM with linear kernel performed best with an F1-Score of 95.62%, a mean prediction time of 0.47 ms and memory usage of 2 MB.  
As our results show, it does not always have to be the most complex model or the newest of architecture to perform the best. The SVMs clearly perform better than the CNN and especially the RNN. Not only is the performance better in terms of prediction accuracy, prediction speed and memory usage, but normally more complex models also tend to run  
Figure 14: Number of trainable paramaters for each of the best models.  
a higher risk of overfitting. This is why in many cases it makes sense to start with simpler approaches to get a baseline and then try out more complex ones to then make informed decisions whether an increased performance is worth the added complexity.  
Even though in our case the deep neural nets were not on par with some of the machine learning models when it comes to either prediction performance, memory consumption or mean prediction time, they still performed very well. Additionally, it needs to be mentioned again that the deep neural nets were able to do this with just the preprocessed sensor data instead of the extracted features. However, the complexity of the activities and the data also needs to be taken into account. Our dataset only features six very simple activities and only one simple data source with the smartphone. Having more and more complex activities may be combined with data recorded from multiple sources like ambient sensors or even cameras could drastically increase the need for such complex models.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results  
### UC1: Rural Environment  
Figure 7 shows an example of the rural solution in summer without rain. In the figure, 100 CPEs (blue circles) are served with only one PoP (green square) and 116 ENs (red triangles). The served CPE links have an average service distance of 35 m with a maximum of 372 m. The longest backhaul has nine hops, carrying an aggregated traffic of 1.126 Gbps for the last link connected to the PoP. The following simulation results from the UC1 describe the impact of environmental factors on the network performance.  
Figure 7: Use Case 1 solution example. Snapshot of the rural Leest area.
## 2 Results and Discussion
### Effects of Spray-Drying (SD) on the Volatiles Composition of Red Mombin Pulp  
The volatiles profile of red mombin pulp dehydrated by the SD process was also evaluated. In order to allow a comparison between the integral pulp and the dry material, the powder was reconstituted in distilled water until it reached the same amount of soluble solids of the pulp before the dehydration (17 \({}^{\circ}\)Brix). Results showed a significantly difference between the volatile profiles of the pulp before and after the SD process, as can be seen in the total chromatogram areas (Table 1). The amount of volatiles decreased 70.56% after the SD process, especially for the ketones and esters, which showed a reduction of chromatogram area of 98.04% and 95.81%, respectively (Figure 1b).  
With regard to the major volatile compounds of red mombin pulp, the losses during the drying process were highly correlated (r\({}^{2}\) = 0.99; Pearson correlation) with the degree of volatility of these compounds. Ethyl acetate, a relatively highly volatile compound (T\({}_{\mathrm{boiling}}\) = 77.2 \({}^{\circ}\)C) was not observed after the SD process, while hexanal (T\({}_{\mathrm{boiling}}\) = 129.0 \({}^{\circ}\)C) suffered a loss of approximately 70% and the alcohols 2-hexen-1-ol (T\({}_{\mathrm{boiling}}\) = 159.0 \({}^{\circ}\)C) and 3-hexen-1-ol (T\({}_{\mathrm{boiling}}\) = 157.0 \({}^{\circ}\)C) lost 41% and 42%, respectively (Figure 1c).  
Only 24 out of 36 compounds detected in the pulp remained in the dried product and only limonene (peak 25; Table 1) was detected in larger amounts after drying. Two other compounds were identified only in the processed product: ethyl-3-methylbutanoate (peak 18; Table 1) and the \(\beta\)-pinene (peak 23; Table 1). Although the amounts of the majority of the volatile compounds detected in the pulp decreased after the SD process, the use of a high temperature (120 \({}^{\circ}\)C) could promote a disruption of the fresh pulp cells, modifying the molecular interactions between flavor compounds and matrix constituents [22] and releasing products that were bound to the matrix. This could explain the higher amounts of limonene and the appearance of new volatile compounds after the SD process.  
The results showed that, as already observed for roselle extracts (_Hibiscus sabdariffa_ L.) [23], only part of the volatile compounds from the pulp is retained in the powder after the SD process. The degradation induced by the heat can lead the formation of substances which were not previously detected, suggesting that SD may have a significant effect on the qualitative and quantitative composition of red mombin pulp and possibly in their acceptance by the consumer. Further studies are needed to assess the effects of this processing method in the sensory quality of the product.
## 4 Results and Discussion  
### Parameter Tuning of the RF Model  
The main purpose of this section is to determine the optimal hyperparameters of the RF model, including the number of decision trees and the maximum depth of decision trees. In addition, before conducting the model training, the original dataset was standardized to eliminate the negative effect of magnitude between the data and speed up the model's training. The formula for standardization of data is given as follows:  
\[X_{i}^{*}=\frac{X_{i}-\mu_{i}}{\sigma_{i}},\ i=1,\ 2,\ \ldots,\ 6 \tag{39}\]  
where \(X_{i}\) denotes the data samples belonging to feature \(i\), \(\mu_{i}\) and \(\sigma_{i}\) denote the mean and standard deviation of \(X_{i}\), respectively, and \(X_{i}^{*}\) signifies the standardized datasets that are prepared for constructing the RF model.  
The parameters tuning of RF consist of two steps. First, the scale of the RF is optimized to determine the number of decision trees in a forest. In this regard, the number of trees increases from 1 to 200 with 1 increment each time. Meanwhile, other parameters are set to default values. Figure 5 shows the performance of the RF model with respect to the number of trees. Intuitively, it can be seen that the mean squared error decreases when the number of trees increases from 1 to 50, and then with the increase in the number of trees, the mean squared error only shows small fluctuations. The results show that the minimum mean squared error is reached when the number of trees is 51. Consequently, the scale of the RF model is set to 51 decision trees. Furthermore, using the testing set to validate the performance of the RF model when the number of trees is 51, the results show that the R\({}^{2}\), RMSE, MAE, and VAF values on the testing set are 0.915, 0.275, 0.224, and 93.504, respectively.  
After that, the optimal maximum depth of decision trees was determined. The number of the maximum depth increases from 1 to 10 with 1 increment each time, while the number of decision trees is 51 currently. Figure 6 depicts the performance of the RF model concerning the maximum depth of decision trees. It can be found that the minimum mean squared error is reached when the maximum depth is set to 7. When the maximum depth values are larger than 7, there is no apparent decrease in the mean squared error. At the same time, the performance evaluation results of the current RF model on the testing set are R\({}^{2}\) of 0.923, the RMSE of 0.262, the MAE of 0.209, and the VAF of 93.994, respectively. Thus, according to the aforementioned results, the maximum depth is set to 7. Now, the RF model has been successfully constructed, and the later work uses five techniques to optimize the weights of decision trees, aiming to improve the performance of the current RF model.
# Multi-Pass Stamping Forming a Concave Ring
## 5 Conclusions  
Through reasonably designed stamping dies, the multi-pass stamping technology can replace the spinning technology to process the concave ring. The stamping dies includes a first pass upper die, a second pass upper die, a set of inner die and a lower die. The first pass upper die bends the workpiece by 45\({}^{\circ}\). The second pass upper die bends the workpiece 90\({}^{\circ}\). To facilitate the removal of the inner after processing, the internal inner and the external inner were divided into six pieces.  
In the first pass, the stress was distributed in the contact area between the blank and the upper-1. In the second pass, the stress was distributed in the bending area of the workpiece. In the first pass and the second pass, the effective plastic strain was concentrated in the bending area. The workpiece only undergoes plastic deformation in the bending area, which means that the design of the multi-pass stamping die is reasonable.  
In the first stamping experiment, the bending direction and bending angle of the workpiece are consistent with the theory. The final shape of the experimental workpiece is the same as the simulated workpiece. The height of the final workpiece is 3.31 mm. The width of the final workpiece is 10.82 mm. The dimensional error is less than 0.5%. The dimensional of the concave ring formed by multi-pass stamping meets the design requirements.  
An improper mold design can cause ruptures, bends and cracks in the final workpiece. The groove profile of the upper-1 and the depth of the lower groove are the main factors affecting the defects of the concave ring.  
Conceptualization, S.Z. and X.S.; methodology, S.Z.; software, S.Z.; validation, X.S., S.Z. and J.S.; formal analysis, S.Z.; investigation, S.Z.; features, S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z. and J.S.; visualization, Z.L. and J.S.; supervision, Z.L.; project administration, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51975301 and the Natural Science Foundation of Zhejiang, grant number LZ17E050001.  
The authors declare no conflict of interest.  
Figure 15: Defects of the concave ring formed by multi-pass stamping. (**a**) Rupture; (**b**) depression; (**c**) crack.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were conducted using IBM SPSS Statistics 22.0 for Windows (SPSS Inc., Chicago, IL, USA). Normality and homogeneity of variances were assessed using the Shapiro-Wilk test and Levene's test, respectively. The primary analysis involved a two-way ANOVA considering leg dominance, whole-body vibration (WBV) and stick conditions. Specific differences between conditions were determined using the Bonferroni post hoc test. The significance level was set at \(p<0.05\). Results were reported as the mean \(\pm\) standard deviation. \(F\)-values; \(p\)-values; effect-size (ES) values, represented as the eta partial square (\(\eta_{\mathrm{p}}\)\({}^{2}\)); and the 95% CI were reported. The magnitude of ES was categorised as small (\(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.01), medium (\(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.06) or large (\(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.14) [41; 42].
###### Abstract  
The electroporation technique utilizes pulsed electric fields to induce porous defects in the cell membrane, and the technique can be used for delivering drugs into cells and killing cancer cells. To develop an electric pulse protocol in the clinic with this technique, the key issue is to understand the evolution of pores in the cell membrane during the process of electroporation. This paper presents a study to address this issue. Specifically, a mathematical model of single-cell electroporation (SCE) was developed, which includes pore area ratio (PAR) as an indicator of the electroporation dynamics and area weight for considering the 3D nature of cells. The model was employed to simulate the electroporation of a single cell with different high-frequency irreversible electroporation (H-FIRE) protocols. The simulation result has found that the change of PAR with respect to the time duration of electroporation follows a sigmoid pattern to increase under specific protocols, which is called the cumulative effect of PAR. Subsequently, the relationship between the protocol of H-FIRE, described by a set of pulse parameters such as pulse width, pulse delay, electric field strength, and pulse burst duration, and the cumulative effect of PAR was established, which thereby allows designing the protocol to kill cells effectively. The study concluded that the proposed SCE model, along with the cumulative effect of PAR, is useful in designing H-FIRE protocols for the ablation of cancer tumors in the clinic.  
single-cell electroporation; high-frequency irreversible electroporation; pore evolution Article
## 3 Prediction Method of Ground Heave Considering the 3D Space Effect
### Ground Deformation Volume  
Due to the space effect of the considered ground deformation, a transverse deformation curve (\(x\)-direction) perpendicular to the diaphragm wall and longitudinal deformation curve (\(y\)-direction) parallel to the diaphragm wall should be introduced to calculate the deformation volume. Many experimental and numerical results show that the transverse deformation curve has a good agreement with normal distribution [3; 38], as shown in Figure 5a. Hence, the expression of transverse deformation curve is  
\[S_{x}=S_{x\text{max}}e^{-\pi(x-x_{m})^{2}/(x_{0}-x_{m})^{2}} \tag{5}\]  
where \(S_{xmax}\) is maximum value of transverse deformation, \(x_{0}\) is influence range of transverse deformation, and \(x_{m}\) is distance from the position where it has the maximum deformation value. Based on field monitoring and theoretical results, [PERSON]'s [14] raised that ground deformation of sandy clay is in range of 2 times the excavated depth, and soft soil in 2.5 to 5 times the excavated depth. [PERSON]'s [14] suggestion does not give the value of influence for the range of deformation. [PERSON] [38] studied ground and wall movements induced by excavation of foundation pits. They developed an empirical expression to determine the value x\({}_{0}\) of ground movement:  
\[x_{0}=H\tan(45^{\circ}-\varphi/2) \tag{6}\]  
where \(\varphi\) is friction angle of soil. It is also used by [PERSON] et al. [3], [PERSON] [40], [PERSON] and [PERSON] [41]. Likewise, the \(x_{m}\) can be calculated with an empirical expression  
\[x_{m}=\frac{H}{\tan\bigl{(}82^{\circ}-2.36\varphi\bigr{)}} \tag{7}\]  
Longitudinal deformation curves can be fitted better with the Boltzmann function based on a large number of measured data of ground deformation in Beijing, China [42].  
Figure 4: The simplified calculation model of the tunnel for Metro Line 12 passing through the diaphragm wall.  
Figure 5b shows the longitudinal deformation calculated model. Hence, ground longitudinal deformation can be expressed as  
\[S_{y}=S_{y\max}\frac{1}{1+e^{(y-y_{0}/2)/12}} \tag{8}\]  
where \(S_{ymax}\) is maximum value of longitudinal deformation, and \(y_{0}\) is influence range of longitudinal deformation. Taking \(x_{0}\) as reference, the value of \(y_{0}\) can be determined as  
\[y_{0}=H\tan(45^{\circ}-\varphi/2)+L \tag{9}\]  
where \(L\) is the width of the diaphragm wall. Considering transverse deformation and longitudinal deformation, the deformation \(S\) at any point in ground is  
\[S=S_{\max}e^{-\pi(x-x_{m})^{2}/(x_{0}-x_{m})^{2}}\frac{1}{1+e^{(y-y_{0}/2)/12}} \tag{10}\]  
where \(S_{max}\) is the maximum value of deformation. In the influence range of deformation, integrating Equation (10) to obtain the deformation volume \(V_{S}\)  
\[V_{S}=\int_{0}^{x_{0}}\int_{0}^{y_{0}}S_{\max}e^{-\pi(x-x_{m})^{2}/(x_{0}-x_{m })^{2}}\frac{1}{1+e^{(y-y_{0}/2)/12}}\mathrm{d}x\mathrm{d}y \tag{11}\]  
Equation (11) does not have primary form, but can be solved by double numerical integration function using MATLAB 2015a code.
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## Nomenclature  
\(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(\alpha_{4}\)  
\(A\alpha A\)  
Angle of attack (\({}^{\circ}\))  
\(A\alpha A_{(C_{M_{CG}}=0)}\)  
Angle of attack at zero pitching moment condition (\({}^{\circ}\))  
\(A\alpha A_{(VFB)}\)  
Angle of attack at Vertical Force Balance condition (\({}^{\circ}\))  
\(\beta\)  
Shock wave angle (\({}^{\circ}\))  
\(\beta^{n\alpha}\)  
Shock wave angle at nose optimised configuration (\({}^{\circ}\))  
\(c\)  
Line chord m  
\(C_{D}\)  
Drag coefficient  
\(C_{L}\)  
Lift coefficient  
\(C_{M_{CG}}\)  
Pitching moment coefficient  
\(D\)  
Drag force (N)  
\(E\)  
Aerodynamic efficiency  
\(E_{max}\)  
Maximum aerodynamic efficiency  
\(F_{T}\)  
Thrust force (N)  
HRUF  
Horizontal Rectilinear and Uniform Flight  
\(L\)  
Lift force (N)  
\(\gamma\)  
Adiabatic index  
\(M\)  
Mach number  
\(M_{CG}\)  
Pitching moment (Nm)  
\(M_{d}\)  
Downstream Mach number  
\(M_{u}\)  
Upstream Mach number  
\(M_{u_{u}}\)  
Upstream Mach number perpendicular to the shock wave  
\(\mu_{\infty}\)  
Freestream dynamic viscosity (Nsm\({}^{-2}\))  
\(M_{\infty}\)  
Freestream Mach number  
\(\hat{m}_{S}\)  
Mass flow rate throughout the scramjet (kgs\({}^{-1}\))  
\(n\)  
Polynomial degree  
\(v_{PM}\)  
Prandtl-Meyer wall inclination angle (\({}^{\circ}\))  
Pressure Pa  
\(p_{d}\)  
Downstream pressure Pa  
\(p_{u}\)  
Upstream pressure Pa  
\(p_{\infty}\)  
Freestream pressure Pa  
\(Pr_{\infty}\)  
Freestream Prandtl Number  
\(q_{\infty}\)  
Dynamic pressure Pa  
\(R\)  
Air gas constant (Jkg\({}^{-1}\)K\({}^{-1}\))  
\begin{tabular}{l l} \(Re_{\infty}\) & Freestream Reynolds number \\ \(\rho\) & Density (kgm\({}^{-3}\)) \\ \(\rho_{\infty}\) & Freestream density (kgm\({}^{-3}\)) \\ \(S\) & Aircraft surface (m\({}^{2}\)) \\ \(T\) & Temperature (K) \\ \(T_{d}\) & Downstream temperature (K) \\ \(T_{u}\) & Upstream temperature (K) \\ \(T_{\infty}\) & Freestream temperature (K) \\ \(\theta\) & Deflection angle (\({}^{\circ}\)) \\ \(\overrightarrow{\bigcup_{\infty}}\) & Freestream velocity vector (ms\({}^{-1}\)) \\ VFB & Vertical Force Balance \\ \(W\) & Aircraft weight (N) \\ \(x_{1},x_{23},x_{4},x_{5}\) & Design distances m \\ \((x_{b},\,y_{b},\,z_{b})\) & Body axes \\ \((x_{w},\,y_{w},\,z_{w})\) & Wind axes \\ \(y^{+}\) & Non dimensional number \\ \(Y,y_{S}\) & Design distances m \\ \(z\) & Flight altitude m \\ \end{tabular}
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 4 Three-Dimensional Slope Stability Analysis  
### Analysis of Results of Pore Pressure Simulations  
Under the working conditions of Case 1 (reservoir static water level is 175 m), the results for the pore pressure shown in Figure 9a demonstrate that the pore pressure in the zone below the 175 m water level was directly affected by the reservoir's water level.  
Combining the pore pressure simulation results for Case 2 (a slow decrease in water level from 175 m to 145 m) and Case 4 (a sudden decrease in water level from 162 m to 145 m) (Figure 9b,d), the calculation results show that in the absence of rainfall, the  
Figure 9: (**a**) Pore pressure map for Case 1 (kPa). (**b**) Pore water pressure map for Case 2 (kPa). (**c**) Pore water pressure map for Case 3 (kPa). (**d**) Pore water pressure map for Case 4 (kPa). (**e**) Pore water pressure map for Case 5 (kPa).  
pore pressure within the slope was solely affected by the decreased water level. The wading area at the anterior border of the landslide was the main affected zone. As the water level decreased, the seepage field at the anterior border changed significantly, and the groundwater level also decreased, resulting in the slow dissipation of pore pressure, producing stress hysteresis. As the posterior border of the slope was far away from the zone where the reservoir water level changed, the pore pressure in the central and posterior regions changed only slightly and was negligible.  
For the 50-year rainfall data coinciding with a drop in water levels, the results for Case 3 (slow water level drop from 175 m to 145 m) and Case 5 (a sudden water level drop from 162 m to 145 m) (Figure 9c,e) show that the groundwater distribution at the front of the slope was affected by both the change in the reservoir water level and rainfall. By contrast, the pore pressure in the middle and rear parts of the slope was mainly affected by rainfall. However, for the entire slope mass, the changes in pore pressure were relatively small.  
The pore pressure cloud diagrams for the five working conditions shown in Figure 9 indicate that a drop in the water level had a significant impact on the wading area at the anterior border of the slope, while having a lesser effect on the non-wading area in the middle and rear of the slope. Rainfall had a more significant impact on the surface layer of the slope.
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-2 Remote-Sensing Products  
We applied Sentinel-2 data from the Harmonized Landsat and Sentinel-2 (HLS) project to provide high-frequency multispectral data with moderate-to-high spatial resolution [70]. The HLS dataset  
Figure 1: False-color composite images of the study area at each site (band 8 for the red channel, band 4 for the green channel, and band 3 for the blue channel) in January 2018. The region in each subfigure is a \(3\times 3\)-km footprint range. The yellow box in the middle of the image is the carbon flux footprint region. CUM—Cumberland Plain; WOM—Wombat Forest; TUM—Tumbarumba; RIG—Riggs Creek; YNC—Yanco.  
provided remote-sensing reflectance with high spatial resolution that was atmospherically corrected, and the data were mapped to the Military Grid Reference System [71]. We used the HLS land surface reflectance product for Sentinel-2 data, which were resampled to 30-m spatial resolution from the HLS v4 dataset (Figure 1). We evaluated the effect of cloud cover on the satellite image by using the cloud and shadow masks provided in the HLS data.