esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of a Pneumatic Muscle Servo Drive Applied to a 6-DoF Parallel Manipulator
## 1 Introduction  
This article continues the description of work on the development of a delta-type parallel manipulator with six degrees of freedom and powered by proprietary pneumatic muscles. In the initial work [1], a comprehensive introduction to pneumatic muscles was provided, the research setup used for conducting experiments was described, and the construction of the proprietary muscle was presented. Additionally, in [1], the procedure for determining the static characteristics of the muscle and the characteristics themselves were outlined. The primary focus of [1] was on describing the process of determining the first mathematical model of the muscle, which describes the force generated by the muscle, as well as the description and results of research on the developed muscle servo drive. This drive consists of two muscles working in opposition and is used to generate the desired torque. Therefore, it can be used to drive a single arm of a parallel manipulator and can be utilized in situations where the manipulator is force-controlled. This article is the second in a series of three papers on the aforementioned topic. It provides an introduction to what pneumatic muscles are and their history and structure. Brief information about the developed muscle is presented to facilitate the reader's understanding of the research context. A concise description of the research setup and methodology for determining the static characteristics of the muscle is also provided; a comprehensive description of these topics can be found in [1]. Next, a comparison between the proprietary muscle and Festo's muscle is presented, justifying the use of the proprietary muscle. The subsequent section outlines the process and results of determining the dynamic characteristics of themuscle along with the controlling valve. A dynamic functional model describing the muscle position change in response to the control signal with a known load force is presented. Then, the process of synthesizing a muscle servo drive capable of achieving a desired angular position and the results of its testing are provided. The conclusion of the paper includes an assessment of the quality of the developed servo drive and its suitability for driving a single arm of a parallel manipulator under positional control.
## 2 Wideband Antenna Designing
### Results of Unit Elements
#### 2.2.4 Surface Current Density  
Figure 8 shows the surface current distribution of the proposed antenna operating over the ultra-wideband of 26.5-43.7 GHz. It can be seen from the figures that the current is highly distributed at feedlines and arm stubs for 28 GHz and the feedline and the lower part of the antenna for 38 GHz. This phenomenon refers to that larger the effective electrical length, which proves the generation of resonances. Moreover, the large number of current distributions at arms also verifies the wideband operation of the antenna by loading these stubs.
## 2 Materials and Methods
### Conductivity Measurements  
Preparation of compacted samplesFor electrical measurements, the powder samples were compacted to the pellets at 50 MPa on a PLG-12 laboratory hydraulic press (LabTools, Saint-Petersburg, Russian Federation). The solution of rubber in hexane was used as a plasticizer. The pellets were sintered at 1300 \({}^{\circ}\)C for 24 h. The diameters and thicknesses of the pellets were \(\sim\)7 mm and 1-2 mm, respectively. The density of the compacted samples was determined by the Archimedes' method in kerosene, the preparation of the samples was carried out according to the standard procedure [50]. The values of relative densities were found to be \(\sim\)95-97%. Then, the platinum paste electrodes were painted on polished surfaces of the sintered pellets and annealed at 900 \({}^{\circ}\)C for 3 h in air.  
_Impedance spectroscopy_. The conductivity of the samples was characterized by an impedance spectroscopy technique. The _ac_ conductivity of the samples was measured by the 2-probe method using the Z-1000P impedance spectrometer (Electrochemical Instruments (Elins), Chernogolovka, Russian Federation) within the frequency range of 1-10\({}^{\circ}\) Hz and amplitude of the perturbation signal 127 mV. The bulk resistance was calculated from a complex impedance plot using the Zview software fitting (Scribner Associates, Southern Pines, NC, USA). The accuracy of the impedance spectrometer was \(\pm\)3%. The measurement errors for a series of the samples did not exceed 10% for low temperatures (T \(<\) 500 \({}^{\circ}\)C) and 5% for high temperatures.  
_Conditions of the measurements_. The measurements of the temperature dependencies of conductivities were performed in the temperature range of 200-1000 \({}^{\circ}\)C with a cooling rate of 1\({}^{\circ}\)/min and taken every 20 \({}^{\circ}\)C until a constant resistance value was established. The conductivity measurements were carried out in dry and wet atmospheres. To minimize the experimental error, measurements were carried out on pellets with different geometries (thicknesses); each composition was tested three times on different pellets.  
_Preparation of \"dry\" and \"wet\" air_. In order to obtain the desired humidity and remove CO\({}_{2}\), the air was bubbled subsequently through 30% NaOH and saturated KBr solutions at room temperature to create \"wet\" air. For obtaining a \"dry\" atmosphere, the air was passed through P\({}_{2}\)O\({}_{5}\) powders. Partial water vapor pressure was measured by the HIH-3610 humidity sensor (Honeywell, Freeport, USA) and was 2\(\cdot\)10\({}^{-2}\) atm for \"wet\" and 3.5\(\cdot\)10\({}^{-5}\) atm for \"dry\" air.  
The main steps of the investigations are presented in Figure 2.
## 4 Experiments and Results
### Robustness Analysis
#### 4.3.5 Robustness of Change in Level of Detail  
Elements in BIM data contain point, line, and surface information. From the same perspective, adjusting the level of detail settings can alter the display form of element  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attack Type**} & \multirow{2}{*}{**Attack Ratio (\%)**} & \multicolumn{3}{c}{**NC Value**} \\ \cline{3-5}  & & **Office Building** & **Gymnasium** & **Structural Model** \\ \hline  & 10 & 1.00 & 1.00 & 1.00 \\  & 20 & 1.00 & 0.99 & 1.00 \\ Element & 30 & 1.00 & 0.97 & 0.98 \\ Deletion & 40 & 1.00 & 0.97 & 0.98 \\  & 50 & 1.00 & 0.96 & 0.95 \\  & 60 & 0.99 & 0.91 & 0.93 \\ \hline  & 10 & 1.00 & 1.00 & 1.00 \\  & 20 & 1.00 & 0.98 & 0.97 \\ Element & 30 & 1.00 & 0.97 & 0.95 \\ Addition & 40 & 0.98 & 0.96 & 0.95 \\  & 50 & 0.98 & 0.95 & 0.92 \\  & 60 & 0.96 & 0.92 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of element attack.  
Figure 10: Results of element attack. (**a**) Element deletion attack. (**b**) Element addition attack.  
points, lines, and surfaces, resulting in different geometric representations of building structures. In real-world scenarios, such changes in the level of detail can occur, which may affect the accurate extraction of the watermark information. To evaluate the algorithm's robustness against this type of attack, we conducted three experiments using BIM data with detailed, medium, and coarse levels of detail. The experimental results are presented in Table 8 and Figure 11.  
As shown in Table 8, changing the level of detail from detailed to coarse did not affect the algorithm's ability to extract watermark information from the BIM data. This is because the algorithm mapped the watermark bits to the center point of the bounding box of the image element and embedded watermark information using spatial location and at-tribute information. Changes in the level of detail only affected the morphological details of the element and did not impact the watermark bits or the watermark information embedded into the element. Therefore, the algorithm presented in this paper was resistant to changes in level of detail and could effectively extract watermark information from BIM data under different detail level settings.
## 2 Temperature Field Coupling Theory (Airflow to Lining/Heat-Insulating Layer to Surrounding Rock)
### Analytical Theory for Temperature Fields of Tunnel Lining and Surrounding Rock
Subsequently, the convective heat (\(q\)) can be calculated according to the air temperature near the tunnel lining surface (\(T_{p}\)), the temperature of the lining surface (\(T_{w}\)). And then the change of temperature (\(\Delta T\)) can be obtained. After that, a new time step is carried out based on the new airflow temperature near the tunnel lining (\(T_{p}\)) and the new temperature of the tunnel lining surface (\(T_{w}\)). The flowchart of the coupling heat transfer calculation is shown in Figure 1.  
Figure 1: Flowchart of the coupling heat transfer calculation.
## 8 Conclusions  
This study provides an RL solution called \"D3 ynQN\" for the single-ring RISC problem from the perspective of traffic engineers. The key to success lies in using traffic engineering expertise to handpick an RL method and improve it in terms of RL elements and training settings.  
There are four academic contributions in this study:  
1. We analyze the applicability of various categories of RL methods to the single-ring RISC problem. We found that such problem can be well solved by value-based off-policy deep RL methods.
2. We argue that the reward defined with vehicle departures is training-friendly, as the short-term rewards can properly encourage and punish the agent's behavior. We experimentally demonstrate that the agent can learn from such non-delay-based reward to gradually lower the average vehicle delay for the single-ring RISC problem.
3. We argue that the rewards should be discounted with time in the time-varying traffic environments. Accordingly, we define the time-step size as one second, and present an action-value updating algorithm TD(Dyn) to match the varying number of time steps between actions.
4. We propose a data augmentation algorithm based on intersection symmetry as an added bonus for training.  
In addition, this study has three practical contributions:  
1. The proposed RL solution is subject to the signal timing constraints in engineering, which include fixed phase sequence, minimum and maximum greens, yellow change and red clearance intervals.
2. The proposed RL solution is validated at a closer-to-reality intersection with varying vehicle turning ratios and pedestrian demand.
3. With the proposed RL solution, the trained agent leads to much smaller average vehicle delay than a properly configured fully-actuated control technique in a statistical sense.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 3 Overview of Neutral Host Framework  
In short, the 5G-enabled neutral host framework allows tenants to create and consume slices using a set of virtualized resources over a common infrastructure. The conceived framework, presented in Figure 1, is split vertically across three layers: Service/Application layer; Orchestration & Control layer; and Infrastructure layer.
## 3 Results and Discussions
### Temperatures
#### 3.2.2 Air Temperature  
Air temperature, \(T_{\mathrm{A}}\) (black dots in Figure 6), follows a similar sinusoidal behaviour. These values were interpolated to facilitate the interpretation of the phenomenon as well. The standard deviation, \(\sigma_{\mathrm{XY}}\), of the difference between measured and interpolated values was \(\approx\) 2.12 K. The amplitude and the average temperature were 9.48 and 291.99 K, respectively. Finally, the peak (301 K) was modelled on DOY 215, which is almost exactly in phase with the water surface temperature.  
Figure 4: Water levels measured in situ (\(H_{\mathrm{IS}}\)) versus levels obtained by classification (\(H_{\mathrm{RS}}\)) of TOPSAR ascending images.  
Figure 3: Daily water levels measured in situ, \(H_{\mathrm{IS}}\) (m a.s.l.), from 1 January to 31 December 2015. A linear interpolation curve is superimposed–imposed (continuous line).  
The sample covariance evaluated between the measured \(T_{\text{A}}\) and remotely sensed \(T_{\text{W}}\) was 47.84 K\({}^{2}\), while the Pearson coefficient and the Root-Mean-Square Error were \(\approx\) 0.95 and \(\approx\) 2.28 K, respectively.
## 4 Discussion  
The simulation test results show that the crushing rate of the machine designed in this research is 98.2%. Compared to the traditional corn crusher with only one crushing roller, it was found that the crusher designed in this research not only has a high crushing rate but also a uniform crushing size. The effects of the primary crushing roll speed, the secondary crushing roll gap, the secondary crushing roll speed on the crushing qualification rate were investigated through a one-factor test, and the better parameter intervals obtained were similar to the results of [PERSON] et al. [36]. Afterward, the variance and response surface analyses of the test factors through a multifactor test allowed us to obtain the \(p\) value of the regression model \(<0.0001\), indicating that the regression model is highly significant. The coefficient of determination of the regression equation, R\({}^{2}\) = 0.9758, and the corrected coefficient of determination, adjusted R\({}^{2}\) = 0.9448, are very close to 1, and the coefficient of variation, C.V = 0.82. This indicates that there is a significant correlation. In response surface analysis, the interaction of significant parameters also has a significant effect on the angle of repose; the test factors on the crushing pass rate in descending order are the secondary crushing roll speed \(>\) secondary crushing roll clearance \(>\) primary crushing roll speed. The results of this study are similar to the results of [PERSON] et al. [16]. At the same time, the optimal working parameters were obtained through a multifactor test, which is similar to the results of [PERSON] et al. [12]. Finally, we carried out simulations and physical tests, respectively, and the crushing qualification rate was more than 95%, which meets the crushing requirements.  
In this study, to determine the optimal combination of key parameters affecting the crushing rate of HCME crushing devices, single-factor tests and multifactor tests were carried out sequentially, based on the discrete element method. The impact of key test factors on the crushing rate is analyzed, the optimal parameter combination of high-moisture corn crushing is obtained, and finally, the bench test is carried out to validate the reliability of the simulation model. The test results show that the high-moisture corn crushing device designed through this research has a crushing rate of more than 95%, which meets the crushing requirements and can provide a theoretical basis for related research.  
It should be noted that there are some limitations to this study, and only one of the maize varieties widely grown at present was selected as the research object. However, there are some differences in the physico-mechanical properties of maize varieties grown in different regions. In the future, we will carry out a multi-species analysis of the physical and mechanical properties and crushing mechanisms of corn ears with different moisture contents, using the results of this study in the field of feed milling research and crushing equipment design to provide a theoretical basis.
## 3 Probabilistic Evaluation of ALSC Based on BASPCE
### Theory of Baspce [26]
Substituting the obtained ED sample set into the original model allows for the calculation of the corresponding output sample vector \(\mathbf{Y}=[g_{s}(\mathbf{\chi}_{1}),\)..., \(g_{s}(\mathbf{\chi}_{N_{\text{ED}}})]^{\text{T}}\). Thus, the least squares problem for calculating the expansion coefficients can be formulated as follows:  
\[\min\sum_{l=1}^{N_{\text{ED}}}\left[g_{s}(\mathbf{\chi}_{l})-\gamma^{\text{T}}\mathbf{ J}(\mathbf{\chi}_{l})\right]^{2} \tag{13}\]  
where \(\gamma=[\gamma_{0},\)...\(\gamma_{K_{q}^{m,p}}]^{\text{T}}\) represents the coefficient vector under the hyperbolic truncation scheme, and \(\mathbf{J}(\mathbf{\chi}_{1})=[\Psi_{0}(\mathbf{\chi}_{1}),\)..., \(\Psi_{\xi_{q}^{m,p}}(\mathbf{\chi}_{l})]^{\text{T}}\) is the polynomial matrix corresponding to the experimental design sample point \(\mathbf{\chi}_{l}\). Let \(\mathbf{J}=[\mathbf{J}(\mathbf{\chi}_{1}),\)\(\mathbf{J}(\mathbf{\chi}_{2}),\)..., \(\mathbf{J}(\mathbf{\chi}_{1}),\)...\(\mathbf{J}(\mathbf{\chi}_{N_{\text{ED}}})]^{\text{T}}\), then the solution to (13) can be obtained using the following expression:  
\[\hat{\gamma}=\left(J^{\text{T}}\mathbf{J}\right)^{-1}J^{\text{T}}\gamma \tag{14}\]In order to further reduce the higher-order interaction terms in the expansion under the hyperbolic truncation scheme, a penalty term can be introduced based on (13):  
\[\min\left\{\sum\limits_{l=1}^{N_{\text{ED}}}\left[g_{s}(\chi_{l})-\gamma^{T}I( \chi_{l})\right]^{2}+\lambda\sum\limits_{\mathbf{\alpha}\in\Theta_{q}^{m,p}}\left| \gamma_{\mathbf{\alpha}}\right.\right\} \tag{15}\]  
where \(\lambda\) is the penalty factor. Several algorithms can solve the penalized minimization problem in (15), including the LASSO method [29], forward stagewise regression [30], and least angle regression (LAR) [31], with the LAR method being successfully applied in [32] to obtain sparse PCE models. To sum up, it is necessary to set the value of \(p\), followed by determining the orthogonal basis functions, selecting the truncation strategy, and calculating the expansion coefficients to build the PCE model. However, the value of \(p\) corresponding to the PCE model with acceptable accuracy is typically unknown in advance. Therefore, combining the concept of adaptive algorithms with sparse PCE leads to the development of the BASPCE method. This method aims to select the most suitable value of \(p\) by using the leave-one-out (LOO) cross-validation error \(\epsilon_{\text{LOO}}\) as shown below. \[\epsilon_{\text{LOO}}=\frac{\sum\limits_{l=1}^{N_{\text{ED}}}\left(g_{s}(\chi_ {l})-g_{s}^{\text{PCE}(l)}(\chi_{l})\right)^{2}}{\sum\limits_{l=1}^{N_{\text{ED }}}\left(g_{s}(\chi_{l})-\frac{1}{N_{\text{ED}}}\sum\limits_{l=1}^{N_{\text{ED }}}g_{s}(\chi_{l})\right)^{2}} \tag{16}\]  
where \(g_{s}^{\text{PCE}(l)}(\cdot)\) denotes the PCE model constructed based on the new sample set \(\{\chi_{j}\); \(j=1,2,...,N_{\text{ED}},j\
eq l\}\), obtained by removing the \(l\)-th sample from the experimental design sample set.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
###### Abstract  
Plasma agriculture is a growing field that combines interdisciplinary areas with the aim of researching alternative solutions for increasing food production. In this field, plasma sources are used for the treatment of different agricultural goods in pre- and post-harvest. With the big variety of possible treatment targets, studied reactors must be carefully investigated and characterized for specific goals. Therefore, in the present study, a cone-shaped corona reactor working with argon was adapted for the treatment of small seeds, and its basic properties were investigated. The treatment of rapeseed using different voltage duty cycles led to an increase in surface wettability, possibly contributing to the accelerated germination (27% for 90% duty cycle). The discharge produced by the conical reactor was able to provide an environment abundant with reactive oxygen species that makes the process suitable for seeds treatment. However, operating in direct treatment configuration, large numbers of seeds placed in the reactor start impairing the discharge homogeneity.  
plasma agriculture; corona discharge; plasma source; non-thermal plasma (NTP); rapeseed; seed; germination acceleration Article
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 2 Related Works  
There are mainly two types of methods for single-image dehazing: prior-based methods and deep-learning-based methods.
## 4 Results and Analysis
### Impact with One Degree of Freedom  
The equation system is solved forcing \(\phi\ =\ 0^{\circ}\), \(\mathrm{v}\ =\ 0\), and \(\omega\ =\ 0\), in order to study the impact with one degree of freedom. Figure 9 shows the results for pressure distribution during the impact for a wedge section with symmetry entry and \(\beta\) = 20\({}^{\circ}\). The results of the mathematical model are compared with [PERSON] (1988) and CFD simulations. They show good agreement with CFD simulations, the error in the pressure peak respect to ([PERSON], 2017) is 2.99%, the correction factors suggested reduce the over prediction of the model by [PERSON] (1988). Figure 10 shows the force during the immersion of a wedge with \(\beta\) = 20\({}^{\circ}\). The results attained from the mathematical model were compared with [PERSON] (2018), it is  
Figure 8: Free surface during the free fall impact of a wedge with two degrees of freedom. (**a**) t = 0.0000 s. (**b**) t = 0.0044 s. (**c**) t = 0.0174 s. (**d**) t = 0.0234 s. (**e**) t = 0.0374 s. (**f**) t = 0.0654 s.  
noticed that the forces before and after the flow separation from the knuckle show a good agreement, the error in the vertical peak force is 2.04% respect to Tvenues [25] and 8.12% respect to [15].
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
## 2 Torque Ripple
### Initial Model of the PMSM  
The shape of the initial model of a PMSM with output power of 8 kW is shown in Figure 1. The motor has 6 poles and 36 slots and is an interior-type PMSM, in which the permanent magnet is located inside the rotor core. Table 1 lists the specifications of the initial model of the PMSM. As shown in Figure 1, the N42 UH grade NdFeB permanent magnets are arranged in a V-shape topology, and 35 PN440 grade electrical steels are used for the stator and rotor core. In addition, a hairpin winding arrangement with a rectangular cross section is used to improve the fill factor of the coil.