esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed SE Framework with DWT Features
### Fusion of Two-Level DWT Features and Time-Domain Features
 \tag{16}\]  
where \(i\) is the channel index, \(M_{k}^{(i,j)}\) denotes the \(ij\)-th element of the matrix \(M_{k}\), \(N\) and \(K\) are, respectively, the feature size and the frame number, softmax is the softmax function, and \(\psi_{M_{s}}\) is a convolutional projection layer operation with parameters \(\theta_{M_{s}}\). Please note here that the softmax function is applied to the concatenation of three frame-wise DWT features _at the same channel_\(i\), and thus we call it an intra-channel softmax operation. As such, the obtained three masks satisfy the equation:  
\[M_{1}+M_{2}+M_{3}=\mathbf{1}, \tag{17}\]  
After that, we generate the DWT-wise MPF features by multiplying these masks with three DWT feature matrices:  
\[W_{DWT}=M_{1}\odot W_{D_{1}}+M_{2}\odot W_{D_{2}}+M_{3}\odot W_{A_{2}}. \tag{18}\]  
Figure 11: The diagram of integrating three DWT features with two BPF modules. * **Use an MPF module with inter-channel softmax for three DWT feature matrices:** This method is illustrated in Figure 13. The BPF and MPF modules used in the previous methods pursue the weights for different DWT-wise sub-band feature vectors _at the same channel_ (position in a vector) to show the relative importance of each sub-band features. Therefore, we use the sigmoid or softmax functions \(N\) times in the BPF or MPF module, where \(N\) is the is the number of channels (dimensionality) of each sub-band feature vector. By contrast, this method exploits the MPF module with a _single softmax function_ for _all of the channels_ of three sub-band feature vectors simultaneously. Accordingly, the obtained mask values are supposed to reflect the relative importance of different channels in different sub-band feature vectors. The whole process is formulated by: \[[M_{1}^{(:,j)},M_{2}^{(:,j)},M_{3}^{(:,j)}]=\texttt{softmax}(\psi_{M_{s}^{ \prime}}([W_{D_{1}};W_{D_{2}};W_{A_{2}}],\theta_{M_{s}^{\prime}})),\ \ j=1,2,\ldots,K,\] (19) where \(M_{k}^{(:,j)}\) denotes the \(j\)-th column of the matrix \(M_{k}\), \(\texttt{softmax}\) is the softmax function, and \(\psi_{M_{s}^{\prime}}\) is a convolutional projection layer operation with parameters \(\theta_{M_{s}^{\prime}}\). Here, the softmax function determines the weights of all channels of three frame-wise DWT feature vectors simultaneously, and thus we call it an inter-channel softmax operation. Therefore the obtained three masks satisfy the equation: \[\sum_{i=1}^{N}(M_{1}^{(i,j)}+M_{2}^{(i,j)}+M_{3}^{(i,j)})=1,\ \ j=1,2,...,K.\] (20) After that, the DWT-wise MPF features are generated by multiplying these masks with three DWT feature matrices: \[W_{DWT}=M_{1}\odot W_{D_{1}}+M_{2}\odot W_{D_{2}}+M_{3}\odot W_{A_{2}},\] (21)  
Figure 12: The diagram of integrating three DWT features with an MPF module with intra-channel softmax. After obtaining the DWT feature matrix from any of the three methods mentioned earlier, we concatenate it with the time-domain feature matrix \(W_{T}\) to build the final encoding feature matrix as in Equation (11).
## 3 Results  
Calculation of Full-Band Electromagnetic Parameters of Brain, Tissue, Heart, Lungs, Stomach, and Kidneys  
Electromagnetic parameters of tissues and organs from the cited reference are as shown in Table 1. Using a least-squares fitting method as per Equation (10), the function expression for full-band electromagnetic parameters of Brain Grey Matter was obtained, as shown in Figure 3:  
\[\varepsilon_{r}^{\prime}=59.93+\frac{380.75-59.93}{1+\left(50.94\cdot\omega \right)^{2}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Brain Grey Matter** \\ \end{tabular} } & \multicolumn{2}{c}{**Muscle**} & \multicolumn{2}{c}{**Heart**} & \multicolumn{2}{c}{**Lung Inflated**} & \multicolumn{2}{c}{**Stomach**} & \multicolumn{2}{c}{**Kidney**} \\ \cline{2-13}  & \(\sigma\) & \(\varepsilon\) & \(\sigma\) & \(\varepsilon\) & \(\sigma\) & \(\varepsilon\) & \(\sigma\) & \(\varepsilon\) & \(\sigma\) & \(\varepsilon\) & \(\sigma\) & \(\varepsilon\) \\ \hline
0.01 & 0.2917 & 319.7 & 0.6168 & 170.7 & 0.5014 & 293.5 & 0.2252 & 123.7 & 0.7844 & 246.  
In which, the correlation coefficient \(R^{2}=0.95605\). By comparing Equations (9) and (11), relevant electromagnetic parameters were identified as \(\varepsilon_{s1}=380.75\), \(\varepsilon_{\infty}=59.93\), and \(\tau_{1}=0.001587\).  
Meanwhile, values under a low-frequency limit and high-frequency limit of permittivity of the dispersion regime \(\varepsilon_{s1}=46.38243\) and \(\varepsilon_{\infty}=39.03617\) were highly consistent with corresponding values \(\varepsilon_{0.01\mathrm{GHz}}=319.7\), \(\varepsilon_{10\mathrm{GHz}}=38.11\). Thus, verifying that the method was reliable. In the same way, other tissue and an organs' electromagnetic parameters can be obtained:  
\[\mathrm{Muscle:}\ \varepsilon_{r}^{\prime}=58.41+\frac{223.28-58.41}{1+\left(12. 44\cdot\omega\right)^{2}}\ R^{2}\ =\ 0.98535\]  
\[\mathrm{Heart:}\ \varepsilon_{r}^{\prime}=68.70+\frac{349.22-68.70}{1+\left(9.46\cdot\omega\right)^{2}}\ R^{2}\ =\ 0.97652\]  
\[\mathrm{LungInflated:}\ \varepsilon_{r}^{\prime}=25.01+\frac{170.40-25.01}{1+ \left(12.49\cdot\omega\right)^{2}}\ R^{2}\ =\ 0.98566\]  
\[\mathrm{Stomach:}\ \varepsilon_{r}^{\prime}=69.16+\frac{379.02-69.16}{1+\left( 15.51\cdot\omega\right)^{2}}\ R^{2}\ =\ 0.99131\]  
\[\mathrm{Kidney:}\ \varepsilon_{r}^{\prime}=70.02+\frac{452.53-70.02}{1+\left(9.79\cdot\omega\right)^{2}}\ R^{2}\ =\ 0.97844\]  
By this time, the relation between full-band electromagnetic parameters of human tissues and frequency was derived using a Debye polynomial based on discrete test values of electromagnetic parameters under different frequencies. It should be noted that when conducting a test of tissue parameters at a specific frequency point, considering the uncertainties in the actual test, the system error could be obtained by testing with a reference material (e.g., dimethyl sulfoxide) with identified electromagnetic parameters, and be used for calibration of actual test results.
# Simplified Design of Magnetic Gear by Considering the Maximum Transmission Torque Line
## 3 Maximum Transmission Torque Line and Design Parameters of Magnetic Gear
### Design Process Based on MTTL  
An MG can be design in a simplified way since all the parameters of the MG can be obtained from the equations that were derived in Section 3.3. The design process based on the proposed approach is illustrated in Figure 5. Firstly, the transmission torque and size of the MG have to be decided by a designer based on the requirement of their application, where the size is consist of the outer radius, \(r_{\text{o}}\), and the axial length of the MG, \(\ell\). Secondly, for the total volume of PM, \(V_{\text{pm}}\) is estimated from the required transmission torque, and \(T_{\text{ls}}\) through the MTTL (as in Equation (3)). Thirdly, for the thickness of the PM, \(t_{\text{pm}}\) is obtained from Equation (19) from the substitution of \(r_{\text{o}}\), \(\ell\), and \(V_{\text{pm}}\). Lastly, other parameters, which are the thickness of outer yoke, \(t_{\text{o}}\), pole pieces, \(t_{\text{p}}\), and inner yoke, \(t_{\text{i}}\), are obtained from the substitution of the \(t_{\text{pm}}\) into Equations (5)-(7), respectively. From all the parameters obtained, a simplified design of MG is achieved, where the MTTL plays an important role in this approach, to gain the entire parameters of the MG.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 4 Conclusions  
In this work, the nonlinear dynamics of a resonant-impact DEA system were characterized in depth. Extensive numerical simulations and experimental validations were conducted to investigate the effects of several key parameters on the resonant-impact DEA system. The key findings of this paper are summarized as follows:  
1. The constraint introduced in the impact system results in strong nonlinearity in the equation of motion, which causes severe distortion of the frequency response curves and shifts in the resonant frequencies towards larger values.
2. Increasing the excitation voltage amplitude simultaneously increases the peak oscillation amplitude, frequency, and impact force.
3. Although adding a greater number of DEA layers to the system can effectively increase the peak frequency and impact force, it shows negligible effects on the peak oscillation amplitude.
4. A wider constraint gap increases the peak oscillation amplitude of the system while at the same time reducing its peak frequency, and shows negligible effects on the peak impact force in a wide range.
5. A stiffer constraint mainly contributes to increasing the peak impact force, while its effects on peak oscillation amplitude and frequency are minor.  
In our previous study, we demonstrated that the proposed resonant-impact DEA system is capable of driving a crawling robot at a peak velocity over ten times faster compared with DEA driving systems with no resonant impacts. The findings reported in the present paper can provide valuable guidance for optimizing the previously proposed crawling robot to further improve its crawling performance. This work can offer guidelines for the design and performance optimization of other resonant-impact DEA-driven devices as well, for instance, energy harvesters and vibro-tactile applications.  
Methodology, C.C. and C.W.; validation, C.W., A.C. and X.G.; investigation, C.W.; resources, C.C.; writing--original draft preparation, C.C. and C.W.; writing--review and editing, C.C., A.C. and X.G.; visualization, A.C. and X.G.; supervision, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (Grant No. 52105038), in part by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515110175), and in part by the Shenzhen Science and Technology Program (Grant No. JCYJ20210324115606018).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: Experimental and modelling results on the effects of constraint stiffness on (**a**) peak frequency and oscillation amplitude and (**b**) peak impact force. The discrete markers indicate the experimental data and the continuous curves are the modelling results.
## 4 Conclusions  
In the present study, we described the transformation of _Sarracenia purpurea_ L. insectivorous plants to generate composite plants with hairy roots. Firstly, we observed that only the _A. rhizogenes_ 15834 strain was efficient for the transformation of S. _purpurea_ L. The only effective method of transformation was the injection technique. Furthermore, as shown in other studies, genetic engineering of _S. purpurea_ L. roots is a potent method for the improvement of their biochemical composition. The obtained modified roots of line 7#1 exhibited an almost 9-fold increase in the level of phenolic compounds and a 4-fold increase in the amount of triterpenes when compared to the wild-type roots. The leaves of the modified plants also exhibited a higher level of the analyzed compounds, i.e., a 1.8-fold higher level of phenolics and a 1.5-fold increase in triterpenes in the most effective line 7#1  
Figure 4: Total triterpene content determined in the investigated hairy roots and composite plants of _S. purpurea_. The analyses were performed as described in Section 2. The data (\(\pm\)SD) resulted from three samples per line.  
Figure 5: Betulinic acid content measured in the composite plants _S. purpurea_ and in the investigated hairy roots by HPLC as described in Section 2. The data (\(\pm\)SD) were obtained from three samples per line.  
compared to non-transformed leaves. The transformation also resulted in an increased level of betulinic acid in the leaves of two other lines, 4#6 and 4#4, with the latter line showing an increased amount of this compound also in the generated roots. Thus, the transformation of plants with _A. rhizogenes_ 15834 had a positive effect on the content of compounds with pharmacological potential in the composite plants. Our further experiments will focus on studying the effect of plant extracts derived from transgenic _Sarracenia_ hairy roots on the proliferation of cancer cells and pathogenic microorganisms.  
Conceptualization, M.W.-K.; methodology, K.M.P., M.W.-K., M.P. and I.R.R.; formal analysis, K.M.P. and M.P.; writing, K.M.P. and M.W.-K. All authors have read and agreed to the published version of the manuscript.  
K.M.P. received financial support in the form of \"BioTechNan--Interdisciplinary Environmental Doctoral Program KNOW in the field of Biotechnology and Nanotechnology\". The project is co-financed by the European Union, European Social Fund, 3.2 Doctoral studies of the Operational Program Knowledge, Education and Development 201-2020.  
Data Availability StatementData sharing is not applicable.  
The authors would like to thank the Botanical Garden of the University of Wroclaw for _Sarracenia purpurea_ plants for this research.  
The authors declare no conflict of interest.
# Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots
## 1 Introduction
This adaptation is particularly beneficial for navigating uneven terrains, such as in climbing robots, where stability and precise foot placement are crucial. The linear foot path improves stability and balance, facilitating the easier control and prediction of foot movements, which are essential for precise and reliable navigation. Additionally, using a six-link leg mechanism offers several advantages over the more complex 8- or 12-link mechanisms. The simpler design and manufacturing process reduce production costs and assembly time, while the reduced weight enhances energy efficiency, which is particularly beneficial for mobile robots. The lower complexity to its control makes the system easier to implement and more reliable, especially in dynamic environments. Furthermore, the six-link mechanism's higher efficiency and speed, improved robustness, and ease of analysis and optimization contribute to its practicality and effectiveness. Its cost-effectiveness and space efficiency also make it an attractive option for various applications, providing a balanced approach between functionality and simplicity that meets the demands of walking robots. In this study, GIM software is employed for the dimensional synthesis of the leg mechanism, facilitating the generation of an accurate foot trajectory. Once the mechanism's dimensions are optimized, a comprehensive kinematic analysis is conducted using GIM to simulate the leg's motion, including its trajectory, velocity, and acceleration profiles. These simulations provide critical insights into the dynamic behavior of the mechanism. Simultaneously, the kinematic equations of the mechanism are derived using the vector loop method and implemented in MATLAB. These equations are used to calculate positional, velocity, and acceleration data, which are then compared with the GIM simulation results. Then, an inverse dynamic analysis is carried out to determine the actuator's driving torque, ensuring its efficient operation under expected load conditions. Additionally, SolidWorks is employed for the stress analysis and topology optimization of the leg mechanism. This walking leg mechanism shows significant potential for practical applications across various fields, including industries, all-terrain vehicles, and agriculture, underscoring its impact and versatility in enhancing robotic mobility in challenging environments. This paper is organized as follows. Sections 2 and 3 provide a detailed description and the construction of the proposed mechanism, respectively. Sections 4-6 cover the kinematic and dynamic modeling of the mechanism. Section 7 presents a comparison of the foot trajectory of this mechanism with existing designs. Section 8 presents the design of a four-legged walking robot and includes a static analysis with topology optimization. Finally, the paper concludes with a discussion of the findings and suggestions for future work.
## 3 Materials and Methods  
This section presents the architecture of the platform, which aims to monitor using both under- and aboveground sensors and a drone for smart farming. Figure 3 shows the conceptual work in a bird's-eye view of the proposed architecture, which consists of space segment and ground segment. The space segment comprises the drone, with its payloads including the camera, and LoRa module, which is responsible for collecting data from a wireless server gateway and then transmitting them to the cloud for storage and further analysis. The ground segment contains three parts: (1) ground control station that is controlled by the end user; (2) underground sensors gateway node that collects data from underground sensors (soil moisture); and (3) HOBO U30 Weather Station Starter Kit, which is an aboveground sensor that measures temperature/humidity, rain, and solar radiation. Clearly, in the proposed platform architecture, the drone acts as a focal point between ground devices including sensors, relays, and gateways, on the one hand, and cloud, on the other hand, which reflects the reliability and robustness of such a technology.  
The rest of this section discusses the proposed architecture from functions and specifications in five layers, namely, sensor nodes layer, wireless server gateway, drone-LoRa layer, cloud layer, and end-user layer.
## 2 Measurement of Spatial Correlation  
In this section, we first define several basic concepts and analyze the spatial correlation between target and adjacent links.  
Definition 1: As shown in Figure 1, a link is defined as the segment between every two intersections of road network.  
Definition 2: Single vehicle link travel time is defined as the time a probe vehicle takes to traverse a target link from a beginning intersection to an ending intersection.  
In our experiment, we choose one of the most commonly used indices, Pearson's coefficient [1; 16], to quantitatively measure the spatial and temporal correlations of travel time. The Pearson correlation coefficient, giving a value between \(-1\) and \(+1\), is used to measure the linear correlation between two variables \(x\) and \(y\). It is developed by [PERSON] and widely used in the sciences as a measure of the degree of linear dependence between two variables. Supposing two variables \(x\) and \(y\), the [PERSON]'s correlation coefficient is defined as follows:  
\[\rho_{x,y}=\frac{E\big{[}(X-\mu_{x})(Y-\mu_{y})\big{]}}{\sigma_{x}\sigma_{y}} \tag{1}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean average of variables \(x\) and \(y\), respectively. Similarly, \(\sigma_{x}\) and \(\sigma_{y}\) are corresponding standard deviations of variables \(x\) and \(y\). Therefore, according to Equation (1), we can calculate the spatial correlation coefficient between target link and adjacent link.  
Figure 2 reveals schematic diagram of traffic flow, where link 88 is upstream link, link 82 is target link and link 77 is downstream link. As seen in Table 1, which shows the pair-wise correlations among link 82, link 77 and link 88, the correlation coefficient of speed expectation in a certain direction and different time are significantly correlated at 0.01 confidence level (two-tailed). Figure 3 is a line chart of the relationships for speed expectations among link 77, link 82 and adjacent link 88 from Monday to Friday. In Figure 3, the correlation coefficient of speed on different days presents different values and varies with day. The speed expectation of link 82 increases when the speed expectation of adjacent link 88 increases, presenting a positive correlation. We also see in Figure 3 that it displays a rhythmic pattern among the speed expectation of link 77, link 82 and link 88. Hence, both Table 1 and Figure 3  
Figure 1: Visualization of the local road network in Wuhan City, China.  
illustrate the dynamic spatial correlations among target links and adjacent links. Consequently, travel information of adjacent links was selected as model inputs to predict target link travel time.
## 4 Experimental Validation and Analysis
### Experimental Validation of the Improved Algorithm Model
Figures 6-8 show the \(X\)-axis error, \(Y\)-axis error, and total residual distribution curve of the reference checkpoints of each algorithm and the improved algorithm. The distance residuals of the reference checkpoints of the SRD + CKF algorithm and the improved SRD + CKF algorithm are smaller than those of the SRD algorithm, and the improved algorithm demonstrates a much better accuracy than the SRD algorithm and the unimproved SRD + CKF algorithm. Comparing the SRD algorithm and the unimproved SRD + CKF algorithm, the accuracy was improved by a maximum of 39.29% and 30.08%. The specific values are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance Parameter** & **Reference Checkpoint** & **SRD** & **SRD + CKF** & **Improved SRD + CKF** \\ \hline \multirow{3}{*}{\(\delta_{1}/dm\)} & Point 1 & 4.6407 & 4.3768 & 3.3083 \\  & Point 2 & 3.3149 & 2.9288 & 2.1716 \\ \cline{1-1}  & Point 3 & 3.7049 & 3.2166 & 2.2489 \\ \cline{1-1}  & Point 4 & 4.2658 & 3.9851 & 3.1267 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of average distance residual values for each algorithm. Figure 8: Comparison of residuals of reference checkpoints.