esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Response in Two Models of Socks with Different 3-D Weave Separations
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ROI’s** & **Sock Model** & **Mean** & **SD (\({}^{\circ}\)C)** & \(p\) Value** \\ \hline \multirow{2}{*}{Lesser toes} & AWC 1 & 32.2 & 2.6 & \\  & AWC 2.1 & 32.5 & 2.5 & 0.079 \\ \hline \multirow{2}{*}{Hallux} & AWC 1 & 31.9 & 2.7 & \\  & AWC 2.1 & 32.1 & 2.5 & 0.228 \\ \hline \multirow{2}{*}{5 th MTH} & AWC 1 & 32.5 & 1.9 & \\  & AWC 2.1 & 32.5 & 2.0 & 0.769 \\ \hline \multirow{2}{*}{2 nd–4 th MTH} & AWC 1 & 33.5 & 2.1 & \\  & AWC 2.1 & 33.7 & 2.0 & 0.091 \\ \hline \multirow{2}{*}{1 st MTH} & AWC 1 & 33.2 & 2.1 & \\  & AWC 2.1 & 33.6 & 2.0 & 0.014 \\ \hline \multirow{2}{*}{Lateral Midfoot} & AWC 1 & 32.9 & 1.8 & \\  & AWC 2.1 & 33.1 & 1.7 & 0.208 \\ \hline \multirow{2}{*}{Medial Midfoot} & AWC 1 & 33.5 & 1.8 & \\  & AWC 2.1 & 33.7 & 1.7 & 0.084 \\ \hline \multirow{2}{*}{Lateral Heel} & AWC 1 & 32.4 & 2.2 & \\  & AWC 2.1 & 32.4 & 2.2 & 0.954 \\ \hline \multirow{2}{*}{Medial Heel} & AWC 1 & 32.2 & 2.2 & \\  & AWC 2.1 & 32.5 & 2.1 & 0.186 \\ \hline \hline \end{tabular} ROI’s Regions of interest. MTH, metatarsal head.  
\end{table} 
Table 4: Comparison of post-exercise temperature between the AWC 1 and AWC 2.1 sock models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ROI’s** & & **Mean** & **SD (\({}^{\circ}\)C)** & **Delta** & \(p\) Value** \\ \hline \multirow{2}{*}{Lesser toes} & Pre & 26.72 & 3.21 & \\  & Post & 32.55 & 2.45 & 5.8 & \textless{}0.001 \\ \hline \multirow{2}{*}{Hallux} & Pre & 26.87 & 3.86 & \\  & Post & 32.12 & 2.52 & 5.3 & \textless{}0.001 \\ \hline \multirow{2}{*}{5 th MTH} & Pre & 28.35 & 2.36 & \\  & Post & 32.53 & 1.97 & 4.2 & \textless{}0.001 \\ \hline \multirow{2}{*}{2 nd–4 th MTH} & Pre & 29.16 & 2.48 & \\  & Post & 33.69 & 1.96 & 4.5 & \textless{}0.001 \\ \hline \multirow{2}{*}{1 st MTH} & Pre & 28.62 & 2.69 & \\  & Post & 33.55 & 2.03 & 4.9 & \textless{}0.001 \\ \hline \multirow{2}{*}{Lateral Midfoot} & Pre & 29.73 & 2.01 & \\  & Post & 33.11 & 1.71 & 3.4 & \textless{}0.001 \\ \hline \multirow{2}{*}{Medial Midfoot} & Pre & 30.53 & 1.82 & \\  & Post & 33.74 & 1.68 & 3.2 & \textless{}0.001 \\ \hline \multirow{2}{*}{Lateral Heel} & Pre & 28.40 & 2.55 & \\  & Post & 32.38 & 2.22 & 4.0 & \textless{}0.001 \\ \hline \multirow{2}{*}{Medial Heel} & Pre & 28.83 & 2.25 & \\  & Post & 32.49 & 2.08 & 3.7 & \textless{}0.001 \\ \hline \hline \end{tabular} ROI’s Regions of interest. MTH, metatarsal head.  
\end{table} 
Table 3: Pre- and post-exercise temperatures when using the AWC 2.1 model sock. sensation, which had a score of 3.6 \(\pm\) 1.3 points in the AWC 1 model and 3.5 \(\pm\) 1 points in the AWC 2.1. There were no statistically significant differences when comparing the two models of socks (\(p\) > 0.05 in all cases) (Table 5). There were no significant correlations between the temperature measured in any of the areas and the means of the humidity, thermal sensation. and cushioning scores given by the participants for either the AWC 1 or the AWC 2.1 models (Table 6, \(p\) > 0.05 in all cases).
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 1 Introduction
On the other side, the glide configuration benefits of the suppression of frequency dispersion over a wide passband as well as of the enhancement of stopband bandwidth and its corresponding field attenuation. Our study is carried out by using a mode-matching approach specifically formulated for mirror- and glide-symmetric metasurfaces. The need for this formulation comes from the fact that homogenized or simplified models applied to each surface separately are no longer valid in the presence of two strongly interacting surfaces. Furthermore, while commercial software can compute the phase constant of the Floquet modes, they cannot compute their attenuation constant in stopbands. In our approach, each hole is considered as a waveguide with triangular cross-section, with the field inside being decomposed as a sum of modes of this triangular waveguide. This gives a modal base (with the relevant cutoff frequencies) to express the field excited on each hole and allows us to explain the isotropy properties on the basis of this set of modes. While only one mode is sufficient to describe a single metasurface, a larger number of modes is required when two surfaces are interacting, as already discussed in [25]. The paper is organized as follows. In Section 2 we propose the analysis method with a detailed presentation of the modes retained in the triangular cross-section waveguides. Section 3 shows numerical results concerning the dispersion diagrams, including an in-depth discussion of the isotropic behaviour of the metasurfaces and of their band-gap properties. In Section 4 some conclusions are finally drawn.
## 3 Formulation and Numerical Modeling
### Numerical Methods  
The governing equations are solved using the finite element method as implemented in the commercial software COMSOL Multiphysics. Figure 4 shows the whole simulation model of the MPJCVD system, which is constructed in a three-dimensional computational domain with reactor geometry and adaptive meshes. The height and radius of the chamber are 600 mm and 500 mm, respectively. There are a total of 68,256 meshes used in the simulations. Since the estimated result is focused on microwave coupling and plasma generation, the meshes are refined near the region from the waveguide to the substrate. The minimum size of the mesh is 0.00989 mm. The input power is set at 700 W via the port boundary condition in the electromagnetic wave interface, and the frequency used is 2.45 GHz. The pressure is set at 70 Torr, and the environment's temperature is 1000 K. The initial electron density and electron energy are \(10^{13}\) m\({}^{-3}\) and 4 eV, respectively. The electron mobility and electron diffusion coefficient are \(4\times 10^{2}\) m\({}^{2}\)/(V\(\cdot\)s) and \(4\times 10^{2}\) m\({}^{2}\)/s, respectively.
###### Abstract  
In one study on vocal emotion recognition using noise-vocoded speech (NVS), the high similarities between modulation spectral features (MSFs) and the results of vocal-emotion-recognition experiments indicated that MSFs contribute to vocal emotion recognition in a clean environment (with no noise and no reverberation). Other studies also clarified that vocal emotion recognition using NVS is not affected by noisy reverberant environments (signal-to-noise ratio is greater than 10 dB and reverberation time is less than 1.0 s). However, the contribution of MSFs to vocal emotion recognition in noisy reverberant environments is still unclear. We aimed to clarify whether MSFs can be used to explain the vocal-emotion-recognition results in noisy reverberant environments. We analyzed the results of vocal-emotion-recognition experiments and used an auditory-based modulation filterbank to calculate the modulation spectrograms of NVS. We then extracted ten MSFs as higher-order statistics of modulation spectrograms. As shown from the relationship between MSFs and vocal-emotion-recognition results, except for extremely high noisy reverberant environments, there were high similarities between MSFs and the vocal emotion recognition results in noisy reverberant environments, which indicates that MSFs can be used to explain such results in noisy reverberant environments. We also found that there are two common MSFs (MSKT\({}_{k}\) (modulation spectral kurtosis) and MSTL\({}_{k}\) (modulation spectral tilt)) that contribute to vocal emotion recognition in all daily environments.  
m _Applied_  
_sciences_
## Abbreviations
### Nomenclature  
The following nomenclatures are used in this manuscript:  
\begin{tabular}{l l l}  & **Mathematical parameters** & \\ \(n_{\sigma}\) & critical speed of mixing the suspension & rev/s \\ \(N\) & power & W \\ \(n_{act}\) & rotation speed of activator & rev/s \\ \(n\), \(n_{actrpm}\) & rotational speed of activator & rpm \\ \(K_{N}\) & dimensionless power criterion & - \\ \(\xi_{m}\) & dimensionless resistance coefficient of the mixer & - \\ \(k_{N}\), \(k_{Nl}\), & & \\ \(k_{N1}-k_{N7}\) & dimensionless power consumption factor & - \\ \(I\) & corresponding stage of the study & - \\ \(r\) & radius of the inner edges of blades & m \\ \(\overline{r}\) & relative average distance from the vertical axis of vehicle & - \\ \(v(r)\) & circumferential velocity of liquid at a radius \(r\) & m/s \\ \(\overline{v}(r)\) & relative average peripheral velocity of stirred medium at a distance \(r\) from the vertical & - \\  & axis of apparatus & \\ \(\omega_{0}\) & angular velocity of the mixer & rad/s \\ \(N_{0}\) & power consumption of the mixer without loading the mixture & W \\ \(N_{consum}\) & power, consumed by the mixer for mixing the mixture & W \\ \(n_{smp}\) & sample number & - \\ \(t\) & time & s \\  & **Parameters of the material** & - \\ \(M_{\sigma}\) & fineness modulus of sand & - \\ \(d_{com}\) & average particle diameter of cement and sand & \(\mu\)m \\ \(M_{t}\) & torque applied to blades & Nm \\ \(K_{clm}\) & homogeneity coefficient of foam concrete mixture & - \\ \(\overline{p}_{max}\) & averaged maximum value of density & kg/m\({}^{3}\) \\ \(\overline{p}_{init}\) & averaged minimum value of density & kg/m\({}^{3}\) \\ \(\rho\) & medium density & kg/m\({}^{3}\) \\ \(\overline{p}\) & averaged value of density & kg/m\({}^{3}\) \\  & **Mixer geometrical parameters** & - \\ \(D\) & diameter of apparatus & m \\ \(H\) & height of liquid level in the apparatus & m \\ \(H_{mix}\) & mixer vessel & m \\ \(H_{R}\) & vertical distance between straight blades & m \\ \(H_{R1}\) & vertical distance between first straight blade and the base & m \\ \(H_{R2}\) & vertical distance between the second straight blade and the base & m \\ \(l\) & straight blade width & m \\ \(r_{b}\) & radius of the inner edges of blades & m \\ \(\overline{r}_{b}\) & relative radius of the inner edges of blades & - \\ \(r_{0}\) & stirrer radius & m \\ \(d_{a}\) & diameter of the straight blades of the mixer & m \\ \(h_{1}\), \(h_{2}\) & straight blade height & m \\ \(a_{hl}\) & length of blades of the mixer & m \\ \(b_{hl}\) & width of blades of the mixer & m \\ \(\alpha_{l}\) & inclination angle of the blades & degree \\ \(B\) & width of reflective partitions & m \\ \(H_{B}\) & height of reflective partitions & m \\ \(\alpha_{com}\) & inclination angle of the conical part of the mixer & degree \\ \(Z_{m}\) & number of stirrer blades & pcs \\ \(J\) & number of the reflective partitions in the mixer & - \\ \(V\) & mixer volume & L \\ \end{tabular}
## 3 Design of the Single Motor Speed Controller
### General Method for Designing GFTSMC  
Consider a second-order nonlinear system with uncertainties, described as follows [26]:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=f(x)+g(x)u(t)+d(x)\end{array}\right. \tag{7}\]  
where \(f(x)\) and \(g(x)\) are smooth defined functions in \(R_{n}\), with \(g(x)\
eq 0\), and \(u(t)\in R_{1}\); \(d(t)\) represents the disturbance term of the system.  
The fast sliding mode with a recursive structure can be expressed as follows:  
\[\left\{\begin{array}{l}s_{0}=x_{1}\\ s_{1}=\dot{s}_{0}+\alpha_{0}s_{0}+\beta_{0}s_{0}^{q_{0}/p_{0}}\end{array}\right. \tag{8}\]  
where \(\alpha_{0}\), \(\beta_{0}>0\), and \(p_{0}\), \(q_{0}\) (\(q_{0}<p_{0}\)) are positive odd integers. When the system state \(x\) is far from the equilibrium point, the convergence time is mainly determined by the fast terminal attractor. When the system state \(x\) approaches the equilibrium state, the convergence time is primarily influenced by the linear sliding mode.  
The sliding mode surface of traditional global fast terminal sliding mode control has limited adaptability when faced with complex nonlinear disturbances, especially when the error is large. The insufficient control force may result in a reduced convergence speed. Additionally, its suppression effect on high-frequency disturbances is poor, which can cause jittering in the control signal, affecting the system's dynamic performance and stability.  
To address these issues, this paper improves the sliding mode surface \(s_{1}\) by adding a quadratic nonlinear term \(\gamma_{0}s_{0}^{2}\). The modified sliding mode surface is written as follows:  
\[\left\{\begin{array}{l}s_{0}=x_{1}\\ s_{1}=\dot{s}_{0}+\alpha_{0}s_{0}+\beta_{0}s_{0}^{q_{0}/p_{0}}+\gamma_{0}s_{0}^ {2}\end{array}\right. \tag{9}\]  
To avoid singularity issues in GFTSMC, the parameters \(p_{j}\) and \(q_{j}\) must satisfy the following conditions:  
\[\frac{q_{j}}{p_{j}}>\frac{n-j-1}{n-j} \tag{10}\]  
where \(p_{j}\) and \(q_{j}\) are parameters in the control system that determine the system's convergence speed and stability; \(n\) is the system's dimension, representing the total number of state variables; and \(j\) is the index, indicating a specific dimension or state variable in the system.  
Figure 2: Structure diagram of the PMSM vector control system.  
GFTSMC ensures that the system reaches the sliding surface within a finite time. Furthermore, on the designed sliding mode, the system can converge to the equilibrium state from any initial state \(x(0)\
eq 0\), with the required convergence time being calculated as follows:  
\[t_{is}=\frac{p}{\alpha(p-q)}\ln\frac{\alpha(x_{i1}(0))^{(p-q)/p}+\beta}{\beta} \tag{11}\]  
The following control law is constructed:  
\[u(t)=-\frac{1}{g(x)}\big{(}f(x)+\alpha_{0}\dot{s}_{0}+\beta_{0}\frac{q_{0}}{p_ {0}}\dot{s}_{0}^{(p_{0}-q_{0})/p_{0}}+\phi s_{1}+\eta s_{1}^{q/p}\big{)} \tag{12}\]  
where \(\gamma>0\), and \(q,p(q<p)\) are positive odd integers.
## 5 Discussion
### Open Water Wetland Classification Based on HSWDC  
The short revisit period of Sentinel-1 sensor has a good advantage in open water wetland classification. For example, the inundated duration is important for many wetland-dependent animals and plants, and even the greenhouse gas sequestration and emissions of wetlands. We can use the HSWDC data (water occurrence) to further classify waters into permanent and seasonal water types, which is key to the above ecological and environmental issues. According to the classification system of Ramsar Wetland Convention, seasonal swamps show characteristics of water during the flood period during one year, mud flats are submerged during the rainy season and exposed during the dry season, and permanent water bodies are regions that always show the characteristics of the water body in a year. By referencing the above wetland definitions and [PERSON] et al. (2016), we can identify permanent water body, mudflats, seasonal marshes and rice fields in Dongting Lake by using the water occurrence information of the HSWDC (Figure 9a). Results show that the wetland classification results by water occurrence information are consistent with the classification results of [PERSON] et al. (2016). The difference between rice fields in Figure 9a and agricultural land in Figure 9b is that the agricultural land defined by [PERSON] et al. includes dry crop land. One challenge in wetland mapping is water dynamics; therefore, the HSWDC could be applied to future wetland mapping to improve the accuracy of wetland classifications.  
Figure 8: A comparison of time series of the surface water extent derived from HSWDC (shown in orange), GSWE (shown in gray), and water level from satellite altimetry (shown in blue) during 2016 to 2018.  
Figure 9: (**a**) Wetland classification of Dongting Lake based on water occurrences of the HSWDC. (The number of flooding 1 is defined as rice fields, 2–4 as seasonal marshes, 5–11 as mudflats, and 12 as permanent waters during one year). (**b**) Classification results of Dongting Lake from [PERSON] et al. (2016).
# Development of a Motorized Hospital Bed with Swerve Drive Modules for Holonomic Mobility
## 1 Introduction  
Hospital beds have forever been a crucial part of the healthcare system. Generally, transferring patients from one location to another is among the most important tasks of hospital beds [1]. However, this process is time consuming and inefficient because considerable workforce is needed for providing assistance. In addition, there have been reports that considered this task risky, as it can result in physical injuries to nurses and caregivers [2; 3; 4]. To address this issue, the idea of hospital beds with special mobility features has been around for a long time. In fact, since the 1990s, there have been many research efforts which resulted in considerable improvements of hospital bed. However, the focal points have been mainly in the design, notably in the addition of features such as side rails and adjustable parts [5; 6; 7; 8; 9; 10], whereas there has been hardly any improvement in the mobility aspect.  
[PERSON] et al. [11; 12] have developed a prototype of an automated hospital bed that emphasizes on navigation, mapping, and obstacle avoidance. However, its mechanical drivetrain is considerably rudimentary, as it has two differential wheels that limit its maneuverability to a nonholonomic one. Considering hospital environments, which are usually dynamic with a lot of people moving around, it is unusual to expect hospital beds to sport a holonomic feature that is capable of omnidirectional mobility. [PERSON] et al. [13; 14] developed a hospital bed with omnidirectional mobility by utilizing the active split offset caster (ASOC) module. Though powerful, the ASOC module is mechanically quite complex, as it has two independently driven coaxial wheels. In addition, employing the ASOC module would need a complete makeover of hospital beds. There are some advanced hospital bedsavailable in the market, such as the bed previously proposed by [PERSON] [15]. However, similar to the bed developed by [PERSON] et al., most off-the-shelf products have complex drivetrain mechanisms and are hence expensive.  
In this study, we propose a motorized hospital bed that utilizes a swerve drivetrain mechanism and operates with holonomic mobility. Unlike other drivetrain options, the swerve drive is simple in design and has the capability to provide omnidirectional mobility. It also resembles the casters that come with general hospital beds and hence, would allow for the minimum modification of the existing beds. Undoubtedly, such a feature would allow for a favorable larger-scale adoption in a more cost-efficient manner. Therefore, the aim of this study was to develop an initial prototype with the proposed approach and subsequently evaluate its performance. The focus of the evaluation was on the propulsion motors of the swerve drive, especially on the error between the planned and actual velocities.
## 1 Introduction
[17] investigated the effects of the oscillations of the free-stream velocity on pitch oscillation by the SST \(k-\omega\) model; [PERSON] et al. [18] studied the effects of free-stream turbulence on the aerodynamic characteristics of pitching airfoils by large eddy simulations. [PERSON] et al. [19] investigated the influence of reduced frequency on the dynamic stall characteristics of a NACA 0012 airfoil pitching in a turbulent wake with direct numerical simulations. In addition, [PERSON] and his partners made two efforts for decades to investigate the details of the dynamic stall phenomena [20; 21; 22]. The above results are extensive and continuous. However, the problem is that most of the research objects are wings or symmetrical airfoils. The literature lacks observations of specific asymmetric and blunt tail edge wind turbine airfoils, such as Delft University of Technology (DU) series airfoils. On the other hand, the flow field contour is a general method to explain the phenomenon of dynamic stall used by many previous researchers. It is summarized that a typical dynamic stall process consists of four major stages: attached flow at low angles of attack, the development of the leading edge vortex (LEV), the shedding of the LEV from the suction surface and reattachment of the flow to the suction surface [23]. However, there is a little attention given to the relationship between the frequency of shedding vortices and the frequency of airfoil pitching motion. The interpretation of the \"delay\" is always analyzed by the lift and drag coefficients or the qualitative expression of the flow field contours. The quantitative expression of the separation of the airfoil boundary layer is not obviously given. In order to avoid flow separation and reduce the dynamic stall effects, various devices have been proposed for flow control. Depending on the operating principle, they can be classified as active or passive [24]. Passive control includes vortex generators, microtabs, serrated trailing edges, and so on. Active control includes trailing-edge flaps, synthetic jets, and so on. [PERSON] et al. [25] overviewed 15 different devices for wind turbine control. [PERSON] et al. [26; 27] made long-term efforts to investigate the effects of vortex generators. Some other researchers focused on microtabs [28; 29] and obtained some good results. [PERSON] et al. [30] and [PERSON] et al. [31] reviewed some devices and discussed their effects. The flow control devices have good effects on avoiding flow separation, but the blades have a higher cost for their production. Based on the above, cases with different reduced frequencies (the most important influential factor) are compared regarding the aerodynamic forces and lift-to-drag ratio. In addition, the delay effect of dynamic stall is deeply analyzed from the perspective of two aspects: the pressure field and spectrum analysis.