esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the evolution of wireless communications, especially market demand for a smaller communication device, there is a tremendous interest and need for integrated, less costly and more efficient electronic components. Tunable capacitors were the only known technology that could utilize these incredible market chances. Recently, the demand for the tunability feature of the inductor increased, especially in radio frequency (RF) application, to create standard reconfigurable wireless systems utilizing single miniaturized on-chip solution. This includes discrete and micro electro-mechanical system (MEMS) inductors that have a wide range of applications in RF transceiver block, such as voltage controlled oscillator (VCO) [1; 2], low-noise amplifier (LNA) [3], wireless matching network [4; 5; 6; 7], multi-band filters [8], multi-band RF circuits [9], RF power amplifier [10], and reconfigurable antenna [11; 12; 13; 14; 15; 16]. Beside using the tunable inductor for these RF applications, the presence of its variable functions can compensate a temperature or frequency drift, system performance due to process variation and malfunction because of component aging. However, research to create such a component is still at early stages, since obtaining high inductance tuning range and high quality factor simultaneously is very challenging.  
Previous work has proposed various discrete inductor tuning techniques, such as switched turns [16], switched mutual inductance [17], bimorph-effect-based varied-coupling [18] and switched magnetic field [19]. Unfortunately, narrow tuning range observed plus the obtained quality factor is quite low. Later, the research in this field has moved to MEMS technique, where tunable inductors are sometimes based on the self-assembling variable inductor, the transformer-type interleaved spiral or stacked coils inductor [20; 21]. Although the fabrication for this type of MEMS is cost effective due to single layer metal utilization, the obtained quality factor is low because of skin effect. As for other types of MEMS tunable technique, which are switch-controlled inductor [22], thermal actuator that controls the spacing between main and secondary coils [23], and inductor tuned by the means of bimorph actuation [24], these mechanisms suffer from a few constraints, such as discrete-step tuning capability, lower tuning range, and high fabrication cost. The goal of this work is to design a novel tunable inductor with high tuning range and high-quality factor for radio frequency applications using microfluids. It is achieved by manipulating and the precise control of fluid behavior, which result changes to the inductor characteristic, thus, changing the inductance value. In this work, Section 2 describes principle of inductor theory. Design, implementation and measurement results are presented in Sections 3-5, respectively. Finally, a conclusion of the work is given in Section 5.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling
### Rotational Speed Control  
The rotational speed variation of the twin-spool rotor system when the aero-engine PW6000 is in service is divided into three phases: ramp-up, stable operation, and ramp-down. The LP and HP rotors rotate in opposite directions, and the speed ratio is relatively stable when the PW6000 is in service.Therefore, if the LP rotor shaft rotation speed function \(\dot{\Phi}_{\mathrm{LP}}\) and the speed ratio \(r\) are determined, the HP rotor shaft rotation speed could be calculated \(\dot{\Phi}_{\mathrm{HP}}=r\dot{\Phi}_{\mathrm{LP}}\). The rotation speed function of the LP rotor shaft \(\dot{\Phi}_{\mathrm{LP}}\) is:  
\[\dot{\Phi}_{\mathrm{LP}}=\begin{cases}a\,t&t<t_{1}\\ V_{\mathrm{max}}&t_{1}\leq t\leq t_{2}\\ V_{\mathrm{max}}-a(t-t_{2})&t_{2}<t\end{cases} \tag{16}\]  
where \(V_{\mathrm{max}},a\) are control parameters, denoting the maximum rotation speed and rotation acceleration, respectively. When \(t<t_{1}\), the rotor is in the acceleration phase; when \(t_{1}<t<t_{2}\), the rotor is in the stable rotation phase with speed \(V_{\mathrm{max}}\); when \(t_{2}<t\), the rotor is in the deceleration phase.
## 3 Experimental and Calculation Procedures
### Investigation Methods
#### 3.3.3 Small-Angle X-ray Scattering (SAXS)  
Small X-ray scattering (SAXS) experiments were carried out by the Empyrean X-ray diffraction platforms (PANalytical), in transmission geometry CuK\(\alpha\) radiation with a wavelength (\(\lambda\)) of 1.5418 A was used at 40 kV and 40 mA configuration at room temperature. The X-ray source consisted of an anode Cu source with a line focus type. SAXS measurements were performed using a 10 mm fixed mask, Cu 0.2 mm beam attenuator and PIXcel3D area detector. SAXS is an analytical technique that measures the X-ray intensities scattered by a sample as a function of the scattering angle (scattering vector q). Scattering vector q is given by q = 4\(\pi\)sin(\(\theta/2\))/\(\lambda\), where \(\lambda\) is the wavelength of the X-ray beam and \(\theta\) is the scattering angle. Measurements were made at a small angle in the range of 0.2 to 4.0 deg of 20.  
The Porod approximation was applied to SAXS data processing to assess the interface between the matrix and the distracting objects (pores). Porod constant (k\({}_{\text{p}}\)) can be applied for determination of the specific surface area of the two-phase system using X-ray scattering at small-angle:  
\[\frac{\text{S}}{\text{V}}=\frac{4\text{p}(1-\text{p})\text{k}_{\text{p}}}{ \text{Q}_{\text{p}}} \tag{1}\]  
where p and (1-p) are the volumes of two phases, k\({}_{\text{p}}\) is the Porod's constant, and Q\({}_{\text{p}}\) is the Porod's invariant which is proportional to the mean-square density fluctuation of the whole scattering volume. If S/V is calculated by D\({}_{\text{v}}\)(R) or Porod algorithm, the specific surface area from SAXS (S\({}_{\text{SAXS}}\)) can be calculated according to the equation:  
\[\text{S}_{\text{SAXS}}\left[\frac{\text{m}^{2}}{\text{g}}\right]=\frac{10000.\hat{\
abla}[A^{-1}]}{\text{d}\left[\frac{\text{g}}{\text{cm}^{2}}\right]} \tag{2}\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Monomers**} & \multicolumn{2}{c}{**Pore-Forming Diluents**} \\ \cline{2-5}  & **DVB-TEVS** & **Molar Ratios** & **Toluene and Decano-1-ololol Volumes [cm\({}^{3}\)]** \\ \hline DVB:TEVS = 1:2 & 1 & 2 & 5 & 5 \\ DVB:TEVS = 1:1 & 1 & 1 & 5 & 5 \\ DVB:TEVS = 2:1 & 1 & 0.5 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Compositions of reaction mixtures in organic-inorganic materials synthesis.  
where S/V is the surface divided by volume ratio calculated from the distribution curve and d is the mass density [54; 55].
## 3 Damping Effect of Saturated Sandy Soil
### Theoretical Model of Liquefied Saturated Sandy Soil  
Liquefied saturated sandy soil exhibits such strong fluidity that its mechanical behavior is challenging to describe using conventional soil constitutive models. This study adopts a \"non-Newtonian fluid\" and viscous fluid approach to characterize the properties of liquefied saturated sandy soil. Dynamic viscosity \(\eta\), a key parameter for characterizing viscous fluid properties, measures fluid viscosity. For a given fluid, viscosity is directly proportional to density, inversely proportional to temperature, and nearly independentof pressure. Assuming no temperature changes, dynamic viscosity \(\eta\) is expressed by Equation (6) [19].  
\[\eta=\mu\rho \tag{6}\]  
In the formula, \(\rho\) signifies the material density, while \(\mu\) denotes the kinematic viscosity coefficient, which is correlated with the characteristic wave velocity \(c\) of the liquefied sandy soil.  
The characteristic wave velocity \(c\) is related to the thickness of the liquefied sandy soil layer \(h\), as shown in Equation (7) [15].  
\[c=\mu/(\rho h) \tag{7}\]  
Therefore, the dynamic viscosity \(\eta\) can also be expressed as Equation (8).  
\[\eta=c\rho^{2}h \tag{8}\]  
Within this model, temperature effects are not considered. The stress-strain behavior of liquefied sand is predominantly characterized by the Us-Up curve within the EOS (equation of state) subroutine. The Us-Up equation of state is a mathematical model specifically designed to accurately describe the complex behavior of fluid materials under extreme or dynamic loading conditions. In this context, Us signifies the velocity of stress wave propagation, while Up indicates the velocity of particle motion.  
The bulk sound speed \(k_{0}\) is related to the material's bulk modulus \(K\)[20], and can be determined from the intercept value of the Us-Up curve.  
\[k_{0}=\sqrt{K/\rho} \tag{9}\]  
The bulk modulus \(K\) is described by two distinct expressions, each grounded in the principles of material mechanics and dynamics. Equation (10) [19] relates \(K\) to the elastic modulus \(E\) and Poisson's ratio \(\
u\), while Equation (11) [19] links \(K\) to pressure \(P\) and volume \(V\).  
\[K=\frac{E}{3(1-2v)} \tag{10}\]  
\[K=\frac{P_{0}+dP}{V_{0}-dV} \tag{11}\]  
Consequently, the bulk sound speed \(k_{0}\) is associated with two distinct formulas. The first formula is utilized to compute the theoretical value, whereas the second formula allows for the calculation of the experimental value derived from empirical data.  
\[k_{0}=\sqrt{\frac{E}{3(1-2v)\rho}} \tag{12}\]  
\[k_{0}=\rho^{-\frac{1}{2}}\bigg{(}\frac{P_{0}+dP}{V_{0}-dV}\bigg{)}^{\frac{1}{ 2}} \tag{13}\]  
Numerical Experiments on the Seismic Mitigation Effects of Hollow Concrete Gravity Dams Containing Saturated Sandy Soil
###### Abstract
The goal of terrain recognition is to achieve the classification of terrain types, the perception of ground conditions, and path planning through automatic detection and identification of surface features around the robot, which has important application value in the field of intelligent robots [6, 7, 8]. Terrain recognition technology is particularly important for robot applications, especially outdoor robots. It is mainly based on advanced convolutional neural network (CNN) frameworks, such as VGGNet [9], ResNet [10] and DenseNet [11], etc., which can automatically learn feature representations from terrain images to obtain excellent performance [12]. [PERSON] et al. [13] proposed a deep coding pool network based on ResNet to identify flat, obstacle-laden and complex terrain, and assist the robot in gait switching during movement. In addition, for mobile deployment, some lightweight networks have been recently proposed, such as ShuffleNet [14], GhostNet [15] and MobileNet [16]. These networks require fewer parameters and calculations, but are more accurate and convergent. The speed is not as good as the above models with larger parameters. In this work, we designed a stability control system for a two-wheeled heavy-duty self-balancing vehicle to achieve the stable operation of the self-balancing vehicle on complex terrain. First, its kinematic characteristics are analyzed and modeled to understand the vehicle's dynamic properties and control requirements. Secondly, a real-time terrain recognition method was established based on deep learning technology to accurately identify the terrain of the self-balancing vehicle and determine the friction coefficient, thereby ensuring the stability of the self-balancing vehicle and achieving precise control. Finally, the effectiveness and stability of the proposed control system in complex terrain were verified through experiments. Figure 1 is a structural diagram of the stability control system of a two-wheeled self-balancing vehicle. The main contributions of this paper are summarized below. 1. Design a heavy-duty two-wheeled self-balancing vehicle modeling method to make the center of mass calibration more accurate. Determine the friction coefficient through terrain recognition results to ensure the stability of the self-balancing vehicle and achieve precise control. 3. Propose a lightweight terrain recognition method based on deep learning, introduce a coordinate attention mechanism to improve the network's feature extraction capabilities for different types of terrain, and construct an auxiliary loss function to optimize the network. The remainder of this paper is organized as follows. Section 2 introduces related work, including wheel-legged robots, terrain recognition, and self-balancing control strategies. Section 3 introduces the research methods, including establishing a dynamic model of a two-wheeled self-balancing vehicle, a self-balancing control algorithm and a lightweight terrain recognition network. Section 4 introduces the terrain recognition results and experimental results. Section 5 summarizes the work of this paper.
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs  
### Inertial-Based Positioning Algorithm Using IMU  
IMU provides measurements of acceleration \(\left(\overset{\rightarrow}{a}\right)\) and angular velocity \(\left(\overset{\rightarrow}{\omega}\right)\) in three-dimensional space. The inertial-based positioning algorithm computes position, velocity, and orientation by integrating these measurements over time. It is divided into the five following steps.
## 3 Results and Discussion
### Optimal Extraction Conditions
#### 3.3.2 Antioxidant Capacity of Watercress Extracts  
Plant extracts have attracted the interest of industries and the scientific community worldwide owing to the functional benefits offered by their antioxidant activity [39]. A notable example is the cosmetics industry, where a plethora of cosmetics are preferred due to their natural antioxidant ingredients. Another great example is the food industry, where natural antioxidants [40], which are mainly derived from plant materials [39], are used as food ingredients [41]. This is the reason the present study focused on the antioxidant activity of watercress leaves. Regarding the results obtained by the FRAP method, by applying appropriate extraction procedures and parameters, the antioxidant activity increased from 14.60 to 72.10 \(\upmu\)mol ascorbic acid equivalents (AAE)/g dw, i.e., a 393.84% increase, as shown in Table 2. These results are in perfect agreement with a previous study, where the antioxidant activity in watercress was found to be 74.54 \(\pm\) 10.81 \(\upmu\)mol AAE/g dw [42]. According to Tables 1 and 3, ST is the most suitable for enhancing the antioxidant activity of watercress leaf extracts. This finding may be attributed to the fact that the use of US and PEF results in extended processing time, increasing the risk of compound oxidation [43]. Moreover, this could also be the reason only the combination of US with ST was deemed appropriate for the maximum acquisition of antioxidant activity with DPPH, as shown in Tables 1 and 3. By following the conditions detailed in Table 2, an increase in the DPPH radical scavenging capacity of 556.19% can be achieved. At the same time, as shown in Table 4, applying US and ST as an extraction technique for 30 min at 65 \({}^{\circ}\)C with 50% ethanol solvent, a 43.98 \(\pm\) 9.51 \(\upmu\)mol AAE/g dw antioxidant capacity can be obtained, confirming, among others, the importance of an increased ethanol concentration to enhance the extraction efficiency [44]. Furthermore, it is of paramount importance that ethanol is not only an easily recoverable solvent but also deemed suitable for human consumption, making it a widely preferred choice in the food and pharmaceutical industries [45; 46].
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.1 Influence of the Parameter Related to the Erosion Module  
The parameters a and b of the hyperbolic erosion model have a direct impact on the pace at which the breach bottom erodes vertically. These parameters are crucial in the downcutting process of the breach bottom. The hyperbolic model uses parameters a and b with values of 1.1 and 0.0007, respectively. In the comparison case, parameters a and b have values of 1.0 and 0.0005, and 0.9 and 0.0003, respectively. These values reflect two soils that are more susceptible to erosion. They are used to assess the effect on the breach development caused by the outflows.  
As shown in Figure 14, it displays the breach bottom elevation for various erodibility parameters. The rate of vertical erosion increases as the values of parameters a and b decrease. However, the eventual height of the bottom of the breach remains unaffected by the values of a and b. As shown in Figure 14, as the soil erodes further, the eventual breach becomes bigger. As shown in Figure 14, the maximum discharge of the breach is significantly influenced by the parameters a and b. As the parameters a and b decrease, the discharge curve of the breach becomes steeper. This is primarily because the soil is more susceptible to erosion with lower values of a and b. Consequently, the vertical erosion rate of the breach and the enlargement speed of the lateral breach are accelerated, leading to a larger peaking discharge and an earlier appearance time.
## 4 Discussion
### Soil Moisture Content, Bulk Density, and Water Holding Capacity  
The findings of this study show that increasing the rate of organic amendments application improved soil water retention. The increased organic matter content (Figure 1c) may be contributed to improved soil moisture and water holding capacity. These findings were consistent with the previous study of, which found that adding biochar to sandy-loam soil increased water holding capacity linearly [49] also discovered an increase in water retention with the addition of poultry litter, which may be attributed to the high carbon content. Soil organic matter binds the individual clay particles together to form micro aggregate, which then cluster into macro-aggregates, improving drainage and moistureretention. Moreover, the increased soil moisture content may be due to a combination of more micropores that form pore spaces following the incorporation of poultry litter and its biochar into the soil. Furthermore, biochar produced at high pyrolysis temperatures has a high surface area, hence water retention was greatly improved in soils treated with BC600 and PLBC600. Higher bulk density reduces pore spaces, which means fewer spaces for water retention and a more stable soil structure [40]; hence the control samples had lower moisture content than treated soils. Ross & Ketterings [47] observed that using 0.5% and 1% biochar rice husak a reduced bulk density and increased in water retention.
## 2 Theoretical Aspects
### Identification Method of Chaotic Characteristics
#### 2.1.1 Saturation Correlation Dimension  
The correlation dimension characterises the compactness of a dynamic system and is used to reflect the system's complexity. When the saturation correlation dimension is fractional, the system is said to have chaotic properties. For an m-dimensional phase space, its correlation function can be defined as follows:  
\[C(r)=\lim_{M\to\infty}\frac{2}{M(M-1)}\sum_{1\leq i\leq j\leq M}H\big{(}r-\|Y_ {i}-Y_{j}\|\big{)} \tag{1}\]  
where \(M=N-(m-1)\tau\) is the number of phase points, \(H(u)\) is the Heaviside function, \(N\) is the time series; \(r\) is the vector point in the time series; \(M\) is the embedding dimension; Tau is the time delay; \(Y\) is the reconstruction vector.  
When the time series is chaotic, for the positive, the relationship between the correlation function \(C(r)\) and \(r\) is  
\[C(r)\propto\alpha r^{D_{2}} \tag{2}\]  
where \(\alpha\) is a constant, \(D_{2}\) is the correlation dimension which can be obtained by the slope of the \(\log_{2}C(r)\sim\log_{2}r\) curve, that is  
\[D_{2}=\lim_{r\to 0}\frac{\log_{2}C(r)}{\log_{2}r} \tag{3}\]  
Due to the noise in the measured signal, the embedding dimension is generally controlled to rise gradually. The apparent straight line segments in the \(\log_{2}C(r)\sim\log_{2}r\) curve are fitted using the least square method for each embedding dimension. The slope of each segment increases with the rising embedding dimension, and eventually reaches saturation, the saturation correlation dimension.
## 4 Conclusions  
AM metal parts exhibit a high surface roughness. To improve the surface quality of AM materials, post-processing is required. In this paper, we proposed a laser-aided machining and polishing process to reduce the surface roughness of AM metals. The conclusions of this research work can be summarized as follows.  
* In this study, multiple lasers were integrated for fabrication, machining, and polishing of AM materials in the same build chamber. A unique scan pattern was also introduced to implement low energy input for machining and polishing of metals. A two-step polishing process using CW and pulsed laser was employed for rough and fine polishing.
* A systematic approach was discussed and implemented to obtain optimal process parameters for machining and polishing of AM aluminum alloy. A regression analysis was performed, and a mathematical model was derived from the experimental results to predict the material removed during the machining process.
* The roughness and waviness in the surface topography were minimized using the combined machining and polishing process and a more than 97% improvement in surface quality was achieved.  
Future works may include implementing the process to obtain geometric dimensions and tolerances (GD&T) for custom parts, applying the method for other AM processes, materials, and alloys. The influence of the laser-aided machining and polishing process on the microstructure and mechanical properties such as hardness, corrosion resistance, and fatigue performance can also be a part of future studies.  
The contribution of each author is summarized as conceptualization, M.M.P., S.P. and S.P.I.; methodology, M.M.P. and S.P.; software, M.M.P.; data curation, S.P.; validation M.M.P.; formal analysis, M.M.P. and S.P.; investigation, M.M.P. and S.P.; writing--original draft, M.M.P.; writing--review and editing, M.M.P., S.P.I. and F.L.; visualization, M.M.P.; supervision, S.P.I. and F.L.; project administration, S.P.I. and F.L.; funding acquisition, S.P.I. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Science Foundation Grants CMMI-1625736 and EEC 1937128. Part of the work was also funded by the Department of Energy's Kansas City National Security Campus, which is operated and managed by Honeywell Federal Manufacturing Technologies, LLC, under contract number DE-NA0002839, the Intelligent Systems Center, and the Material Research Center at Missouri University of Science and Technology. Their support is greatly appreciated.  
The authors declare no conflict of interest.