esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Numerical Calculation of Strain Influence Lines  
The simply supported steel-concrete composite beam bridge on Shanghai Metro Line 3 operates with double-track trains traveling in both forward and reverse directions. When a concentrated unit load moves longitudinally on the rail, the longitudinal strain function at a measurement point on the composite section represents its strain influence line. The AC03 type train on the simply supported beam bridge is modeled as a series of axles with fixed spacing \(p_{i}\) (Figure 2). Using strain influence line theory, the effect of each axle load \(p_{i}\) on the bridge structure can be calculated, and the total load effect of the entire train is obtained through the superposition principle. Structural dead load effects are considered when calculating the strain influence line of the measurement points. In the validated global FE model of the simply supported composite beam bridge, vertical unit forces of 1.0 kN are applied on the steel rail along the longitudinal direction of the girder for analysis. Figure 5 illustrates the longitudinal strain influence lines of six measurement points (Q2-3, Q3-4, Q4-3, M2-3, M3-4, M4-3) located on the quarter and mid-span sections of the simply supported composite beam bridge in the forward direction. From Figure 5a, it is evident that the longitudinal strain influence lines of the three measurement points on the bottom plate of the I-beam at the quarter-span exhibit a single-peak shape. The strains at these points peak when the moving unit load is near the quarter-span (\(x=11.25\) m) in the longitudinal direction, with peak strains around 0.26 \(\upmu\)\(\upmu\), 0.18 \(\upmu\)\(\upmu\), and 0.13 \(\upmu\)\(\upmu\), respectively. Additionally, the peak strain at measurement point Q4-3 slightly lags behind the other two points, likely due to its location on the outermost I-beam. Figure 5b shows the longitudinal strain influence lines of three measurement points on the bottom plate of the I-beam at the mid-span section, also displaying a unimodal shape. The strains at these points reach their peak values when the moving load is near the mid-span (\(x=22.5\) m), with peak strains approximately 0.39 \(\upmu\)\(\upmu\), 0.28 \(\upmu\)\(\upmu\), and 0.13 \(\upmu\)\(\upmu\), respectively.  
Figure 5: Strain influence lines of measurement points of the simply supported composite beam bridge on Shanghai Metro Line 3: (**a**) on the quarter-span section; (**b**) on the mid-span section.  
Identification Method of Train Axle Loads Using Strain Influence Line Theory with Mathematical Optimization Techniques
## 2 Summary of \(p\)-Multipliers and Group Effect Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Soil Type** & **Pile Head** & \multirow{2}{*}{_SD_} & **Pile** & \multicolumn{6}{c}{_p_-Multipliers for Rows**} & \multicolumn{1}{c}{**Group Effect**} \\  & & **Condition** & **Condition** & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **6 th** & **7 th** & **Parameter** \\ \hline [PERSON] et al. [32] & Sand & Fixed & 3 & 2 \(\times\) 2 & 0.81 & 0.5 & - & - & - & - & - & 0.655 \\  & & & 6 & 2 \(\times\) 2 & 0.83 & 0.69 & - & - & - & - & - & 0.76 \\  & & 3 & 5 plies & 0.71 & 0.6 & 0.51 & - & - & - & - & 0.655 \\  & & & 6 & 5 plies & 0.9 & 0.73 & 0.75 & - & - & - & - & 0.815 \\ [PERSON] et al. [50] & Clay & Fixed & 4.4 & 3 \(\times\) 4 & 0.56 & 0.39 & 0.41 & 0.53 & - & - & - & 0.47 \\  & Stiff Clay & Fixed & 3 & 2 \(\times\) 2 & 0.89 & 0.6 & - & - & - & - & 0.245 \\ [PERSON] [57] & Fixed & 7 & 2 \(\times\) 2 & 1 & 1 & - & - & - & - & - & 1 \\  & & 3 & 2 \(\times\) 2 & 0.84 & 0.43 & - & - & - & - & - & 0.635 \\  & & 7 & 2 \(\times\) 2 & 1 & 1 & - & - & - & - & - & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerically derived \(p\)-multipliers in previous studies. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Soil** & **\(\varphi\)** & **Test** & **Pile** & \multicolumn{6}{c}{**Pipped \(p\)-Multipliers for Rows**} & \multicolumn{1}{c}{**Group**} \\  & **Type** & **(\({}^{\circ}\))** & **Type** & **Layout** & **Pile Type** & **D (cm)** & _SID_ & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **6 th** & **7 th** & **Effect** \\  & & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Sand & 33 & Centrifuge & \(3\times 3\) & Square Steel & 42.9 & 3 & 0.8 & 0.4 & 0.3 & - & - & - & - & 0.5 \\  & Sand & 33 & Centrifuge & \(3\times 4\) & Square Steel & 42.9 & 3 & 0.8 & 0.4 & 0.3 & 0.3 & - & - & 0.45 \\  & Sand & 33 & Centrifuge & \(3\times 5\) & Square Steel & 42.9 & 3 & 0.8 & 0.4 & 0.3 & 0.2 & 0.3 & - & 0.4 \\  & Sand & 33 & Centrifuge & \(3\times 6\) & Square Steel & 42.9 & 3 & 0.8 & 0.4 & 0.3 & 0.2 & 0.2 & 0.3 & 0.37 \\  & Sand & 33 & Centrifuge & \(3\times 7\) & Square Steel & 42.9 & 3 & 0.8 & 0.4 & 0.3 & 0.2 & 0.2 & 0.2 & 0.3 & 0.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Previous experimental studies conducted on fixed-head pile groups (modified after [PERSON] [14]).
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
###### Abstract  
In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.  
cognitive radios; multiband spectrum sensing; machine learning; wavelets; software-defined radio +
Footnote †: journal: _Sensors_  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
###### Abstract  
The conventional frequency domain method (CFDM) and dual-force-based time domain method (DTDM) are often used to solve the steady-state response of system with complex damping under an arbitrary force. However, the calculation efficiency of the DTDM is low due to the straightforward summation operation of series even if the solution of the DTDM is the exact real part of the solution. In addition, since the CFDM only can obtain the real part of solution not the complete solution, it gives misleading information that the solution does not have an imaginary part. In this paper, a fast frequency domain method (FFDM) is proposed to calculate the complete response of complex damping system including the imaginary part with a higher accuracy in a much faster manner. The new FFDM uses half of the Fourier series of the discrete Fourier transform of the actual arbitrary force to construct the Fourier series of the dual force, followed by calculating the time history response using the inverse fast Fourier transform. The new developed method is validated through three numerical examples with harmonic and seismic excitations. The numerical results show that the accuracy of the new FFDM is compatible to the DTDM but with much higher computational efficiency.  
complex damping; forced vibration; steady-state solution; frequency domain method; dual force
_applied sciences_  
_Article_  
**Precise Configuring of Actuators/Sensors for Active Control**  
**of Sound Quality in Cabs with Modal Vibration Energy**  
**and LA-PSO**  
**[PERSON] 1,2,3, [PERSON] 1, [PERSON] 2, [PERSON] 2,3, [PERSON] 2 and [PERSON] 1,*(c)**  
1 School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (H.Z.)  
2 State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; [EMAIL_ADDRESS] (K.Z.); [EMAIL_ADDRESS] (E.X.); [EMAIL_ADDRESS] (Q.H.)  
3 Dongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, China  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Active control of structural modal vibration is an effective strategy to enhance the sound quality of cabs in commercial vehicles. However, accurate determination of the positioning and quantity of modal active control sensors and actuators is crucial for cabs with intricate structures, owing to the presence of multiorder modes and their coupling. The study presented herein focuses on the cab of a commercial vehicle and contemplates the features of the irregular large-space structure of the cab. By capitalizing on the modal frequency and mode shape of the cab, utilizing the piezoelectric control principle and modal vibration energy as the assessment index, an advanced multimode composite control criterion is postulated to ascertain the configuration of primary sensors and actuators. The particle swarm optimization (PSO) objective function is constructed to accomplish the optimal position matching of the actuator/sensor, using the multimodal surface velocity vector of the vibration sensor as the core parameter. Furthermore, an improved linear adaptive particle swarm optimization (LA-PSO) technique is advanced to satisfy the requirements of optimal convergence performance and accuracy of the complex cab structure. The optimization culminates in a 9 \(\times\) 9 multichannel active control scheme for determining the optimal position of the actuators/sensors. This investigation provides a technical foundation for the active control of sound quality in automotive cabs and presents an innovative method for implementing effective noise control systems in large-scale machinery and equipment.  
**Keywords:** actuators/sensors configuration; active control of sound quality; linear adaptive particle swarm optimization; composite control of multiple modes
## 3 Field Tests  
### Site and Ground Conditions  
The field test area is located in the Daxing district of Beijing, China. The diameter of the shaft is 4.2 m, and the excavation depth is 6.5 m. There are no buildings within 30 m.  
The field geology investigation and soil test have been carried out. Figure 4 shows the detailed physical properties and geotechnical parameters of the soil. There are five layers of soil with a total depth up to 9 m. The top layer is filled with a depth of 1 m. The stratum of 1.0-3.3 m is sandy silt with a high void ratio. The stratum of 3.3-5.2 m is clayey silt with higher moisture content and plasticity index. The stratum of 5.2-6.8 m is also sandy silt, but has a lower void ratio. Below this stratum is also clayey silt. The ground water is 14 m underground.  
Figure 4: In situ geotechnical profile and soil properties (from left to right: Soil layer, moisture content (\(w\)), bulk density (\(\gamma_{\mathrm{I}}\)), cohesion (\(c\)), angle of internal friction (\(\varphi\)), void ratio (\(e_{0}\)), plasticity index (\(I_{\mathrm{P}}\)) and liquid index (\(I_{\mathrm{L}}\)) varying with depth).  
Figure 3: Schematic diagram of the prestress construction.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 2 EA Chemical Structure and Solubility  
EA is a chromene-dione derivative (2,3,7,8-tetrahydroxy-chromeno [5,4,3-cde]chromene-5,10-dione), encompassing both a hydrophilic moiety with 4 hydroxyl groups and 2 lactone groups, and a planar lipophilic moiety with 2 biphenyl rings. This particular structure has both hydrogen bonding acceptor (lactone) and donor (-OH) sites (Figure 1). Due to the weak acidic nature of its four phenolic groups (pKa1 = 5.6 at 37 \({}^{\circ}\)C), around neutral pH it is mainly deprotonated on positions 8 and 8\({}^{\prime}\), while above pH 9.6 lactone rings open to give a carboxyl derivative [36]. EA low oral bioavailability is mostly due to its poor water solubility (9.7 \(\mathrm{\SIUnitSymbolMicro g}\)mL), which increases with pH, as well as the antioxidant action [37]. However, in basic solutions, phenolic compounds lack stability as these molecules, under ionic form, undergo extensive transformations or are converted to quinones as a result of oxidation. A stability study on pomegranate fruit peel extract demonstrated that EA content significantly decreases in few weeks regardless of the pH of the solution, due to the hydrolysis of the ester group with hexahydroylphenic acid formation, suggesting that EA should not be stored in aqueous medium [38]; this aspect reinforces the need to develop novel systems also for EA stabilization.  
Prior to facing the route of the development of a new delivery system, the knowledge of solvents, co-solvents and substances in which EA could be consistently soluble and stable represents an indispensable step for designing a good formulation. In this regard, with the aim of providing a useful guidance for further research studies, Table 1 gathers published data concerning EA solubility. Concerning organic solvents, EA is slightly soluble in methanol, soluble in DMSO and shows maximum solubility in N-methyl-2-pyrrolidone (NMP), confirming the effect of basic pH on EA dissolution. An analogous trend is also observable in aqueous solutions, where results implied that the solubility of EA depends on the pH values of the media. While EA is almost insoluble in acidic media and distilled water, its water solubility is significantly improved by basic pH. As highlighted further ahead, one of the mostly exploited vehicles is polyethylene glycol (PEG) 400, as it is endowed with satisfactory biocompatibility and, at the same time, is miscible with both aqueous and organic solvents. EA solubility in oils and surfactants is also provided, helpful for developing emulsifying-based techniques.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 2 Experimental Procedure  
### Composite Sandwich Specim  
In this study, the facesheets for the FRPSSs were made from E-glass plain weave fabric with 160 g/m\({}^{2}\) from Samson Composites Ltd. (Shenzhen, China) with epoxy resin from EPOCHEM Ltd. (Lagos, Nigeria) at a volumetric ratio of 2:1. EASYCll 75 closed-cell PVC foam core, EASY Composites (Stoke-on-Trent, UK) was used as the core. The samples were fabricated at room temperature (27 \({}^{\circ}\)C in Nigeria) and allowed to cure for 18 h with 4 layers each for the top and bottom facesheets (the stacking sequence is assumed to be 0\({}^{\circ}\)/90\({}^{\circ}\) in each laminate). This balanced layup approach ensured that the neutral axis of the structure was at its midpoint. Furthermore, since the facesheets were made from plain weave fabric, care was taken to ensure that the longitudinal axis of the samples was maintained in the same direction for all the plies during their manufacture to eliminate pseudo-ductility effects. The fabrication process used a combination of the hand lay-up and a vacuum-bagging technique (about 18 h), and sample plates of 80 mm \(\times\) 80 mm with 3 different core configurations (i.e., without adhesive, horizontal and vertical adhesive layers, respectively) were produced as presented in Figure 1. The application of adhesive layers in the core is commonly used when the design requires a relative increase in stiffness at a lower cost in contrast to reinforcements in lattice-webbed cores. This manufacturing process was adopted in the study since it is one of the most common low-cost methods used in small-scale projects. However, it could result in more manufacturing imperfections compared with more optimised processes. Furthermore, multiple impacts were used in this study to emulate real-life scenarios of foreign-object damage (FOD) that affect marine structures with velocities and energies, causing stresses below the strength of the structure. The FODs could be caused by marine debris--metal, wood, cables and nets, small ice fragments, as well as rocky debris. This manufacturing approach was selected due to its
# Interactive Teaching in Virtual Environments: Integrating Hardware in the Loop in a Brewing Process
## 5 Experimental Design
### System Interaction Assessment
The use of InTouch as a SCADA interface provides an interactive and visual platform to monitor and control key variables in real time. In addition, the integration of scripts and programming logic allows efficient data acquisition from the PLC, facilitating the transmission of real-time information. For subsequent analysis, direct connection to Microsoft Excel is established through specialized tools or data export to CSV files (Comma-Separated Values), offering a comprehensive and effective solution for data collection and management in the teaching-learning process. In this context, the efficiency of the system is accentuated by the optimized  
Figure 11: Operation of the brewing stages. Figure 12: Audio in the virtualized process. communication time provided by the OPC protocol, which enables fast and reliable data transfer between the SCADA and the S7-1200 PLC. The use of this protocol contributes significantly to real-time response, crucial for accurately simulating industrial operations. In addition, the system efficiently handles various types of data, including analog and digital variables, by structuring and transmitting complex information. This versatile approach adapts to the diversity of data present in industrial processes, providing students with a comprehensive and realistic experience that addresses the complexity inherent in modern industrial environments. The experimentation demonstrated successful and synchronized interaction between the controller and the virtual environment, which allowed data generated by sensors and actuators to be accurately reflected in real time. This two-way communication allowed the engineering students not only to observe the industrial process but also to intervene in it. Through the programs developed, students were able to control and manipulate the system, providing them with the opportunity to apply control theories and concepts in a practical environment. This approach proved valuable in improving students' understanding of industrial processes and provided them with hands-on experience that fostered the development of engineering skills. In summary, the results indicate that the successful implementation of this real-time teaching-learning methodology holds promise for the training of future engineers in the field of industrial process control.
## 2 Materials and Methods
### Proposed Model  
In the suggested Regularized Contrastive Masked AutoEncoder (RC-MAE) model, the loss function is computed by combining the contrastive loss \((L_{\mathit{con}})\) multiplied by the weight \((\lambda_{\mathit{con}})\), the Variance--Covariance loss \((L_{\mathit{VCR}})\) multiplied by the weight \((\lambda_{\mathit{VCR}})\), and the reconstruction loss \((L_{\mathit{MAE}})\) multiplied by the weight \((\lambda_{\mathit{MAE}})\). Hence, the overall loss, denoted as \(L_{\mathit{RC-MAE}}\), can be seen in Equation (8),  
\[L_{\mathit{RC-MAE}}=\lambda_{\mathit{Con}}L_{\mathit{Con}}+\lambda_{\mathit{ VCR}}L_{\mathit{VCR}}+\lambda_{\mathit{MAE}}L_{\mathit{MAE}} \tag{8}\]  
where \(\lambda_{\mathit{Con}}\), \(\lambda_{\mathit{VCR}}\), and \(\lambda_{\mathit{MAE}}\) are the hyperparameters of contrastive loss, Variance--Covariance loss, and reconstruction loss. In the proposed method, due to the scale of the gradient of \((L_{\mathit{con}})\) being larger than \((L_{\mathit{MAE}})\), we empirically find that performance is robust when we set the mentioned hyperparameters to 0.3, 0.3, and 0.4, respectively.
## 2 Methodology
### Numerical Model
#### 2.2.2 Turbulence Model  
The turbulence model used in this study is a k-\(\omega\)-based shear-stress transport (SST) turbulence model. The k-\(\omega\) model consists of two transport equations of the turbulence kinetic energy (\(k\)) and turbulence frequency (\(\omega\)), which are linked to the turbulence viscosity to determine the energy of the turbulence and the specific dissipation rate to predict the turbulence scale, and its properties are as follows [16]:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho U_{j}k\big{)}=P_{k}-\beta^{\prime}\rho k\omega+\frac{\partial}{\partial x _{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k} {\partial x_{j}}\Bigg{]}+P_{kb} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions** & **Conventional** & **Newly Proposed** & **Change (\%)** \\  & **INVELOX** & **Wind Funnel** & **Change (\%)** \\ \hline Overall height (m) & 19.81 & 45.30 & +128\% \\ Upper-funnel diameter (m) & 12.20 & 25.00 & +105\% \\ Lower-funnel diameter (m) & 1.86 & 2.00 & +7.5\% \\ Inlet height (m) & 6.10 & 7.80 & +28\% \\ Venturi diameter (m) & 1.86 & 1.70 & \(-8.6\%\) \\ Elbow diameter (m) & 3.05 & 2.70 & \(-11.4\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of changes in dimension and design between conventional INVELOX and newly proposed omni-directional wind funnel.  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho Uj\omega\big{)}=\alpha\frac{\omega}{k}P_{k}-\beta\rho\omega^{2}+\frac{ \partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{\omega}} \bigg{)}\frac{\partial\omega}{\partial x_{j}}\Bigg{]}+P_{\omega b} \tag{4}\]  
where \(\mu_{t}\) is turbulence viscosity given as \(\rho\frac{k}{\omega}\), \(P_{k}\) is the production rate of turbulence, \(P_{kb}\) and \(P_{\omega b}\) are the buoyancy production terms in k-equation and \(\omega\)-equation, respectively, and \(\beta^{\prime}=0.09\), \(\alpha=0.556\), \(\beta=0.075\), and \(\sigma_{k}=\sigma_{\omega}=2\).  
The k-\(\omega\)-based SST model accounts for the transport of the turbulent shear stress and gives highly accurate predictions of the onset and the amount of flow separation under adverse pressure gradients. The k-\(\omega\)-based SST turbulence model combines the advantages displayed by both k-\(\varepsilon\) and k-\(\omega\) turbulence models. The k-\(\omega\) turbulence model is used when solving the inner parts of the boundary layer, making the model directly usable all the way down to the wall through viscous sub-layer. A k-\(\varepsilon\) turbulence is switched when the flow behaves like a free stream away from the boundary layer. Therefore, by using k-\(\omega\)-based SST turbulence model, low Reynolds number turbulence flow can be calculated without any wall functions and prevent over-prediction of inlet free-stream turbulence properties [17].