esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustable Structure for Feedback Active Headest System Using the Virtual Microphone Method
## 5 Real-Time Experimental Results and Discussion
### Experiment 4: Varying Test Listeners  
In previous experiments, the effectiveness of the proposed active headrest system was validated using a dummy head. To further investigate the robustness of the proposed system against varying users, 10 volunteers of various statures were invited to assess the attenuation performance.  
The second disturbance configuration in Section 5.3 was used to set up the primary source. To evaluate the performance of the noise reduction, microphones were attached to the ears. After the participant was seated, the proposed headrest structure was realigned so that the distances from the secondary loudspeakers to the physical monitoring microphones and ears were approximately consistent with when the dummy head was employed. The fixed headrest structure and movable microphone structure with the same setup as illustrated in Section 5.2 were also added for comparison. The required transfer functions that were derived from those measured with the dummy head were not re-estimated. Figure 9 demonstrates the attenuation of the total SPL at the left and right ears of all participants before and after the control with the VMM.  
As shown in Figure 9, the noise reduction results using fixed and movable microphone structures were all below 15 dB and the latter was generally better than the former. In several cases of fixed structure, the attenuation was less than 6 dB. When applying the proposed structure, the noise reduction performance achieved for all participants was at the same level and the disturbance was nearly attenuated to the background noise level. In addition, the secondary paths and other plant responses changed slightly when the support frames were repeatedly adjusted. However, the results indicate that the maximum difference in performance was less than 4 dB attenuation. Consequently, it could be concluded that the proposed active headrest system is robust and applicable for users with varying statures.  
Figure 9: Noise reduction levels at the left and right ears of 10 participants. (**a**) Performance of fixed headrest structure. (**b**) Performance of movable microphone structure. (**c**) Performance of the proposed adjustable structure.
## 3 Results and Discussion
### Specimen Experiment
#### 3.1.6 Thermodynamic Performance  
After testing, the thermal conductivity of sample A-3, B-1 and sample C-1 under dry conditions are about 0.114 W/(m\(\cdot\)K), 0.117 W/(m\(\cdot\)K) and 0.115 W/(m\(\cdot\)K), respectively. The results show that the thermal conductivity of the two groups all meet the requirements of a thermal insulation material [37].  
After completing the thermal conductivity test under dry condition, the thermal conductivity of the test specimens after reaching the moisture absorption equilibrium is tested under the relative humidity of 5%, 15%, 30% and 50%. The results are shown in Figure 9.  
It can be seen from the test results that the thermal conductivity increases with the increase of the relative humidity of the environment. The relative humidity of the environment has a great influence on the thermal conductivity of the mortar. The maximum effective thermal conductivity over the tested range is 0.2533 W/(m\(\cdot\)K), which is two times higher than the dry value of 0.1173 W/(m\(\cdot\)K). The experimental data are curved fitted with a continuous relationship that is represented by a polynomial given below:  
\[k_{eff}=a+b\phi+c\phi^{2}+d\phi^{3} \tag{8}\]  
where \(a=0.1167\), \(b=0.0076\), \(c=-3\times 10^{-4}\), \(d=3\times 10^{-6}\), R\({}^{2}=0.9823\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mortar Samples** & **A-3** & **B-1** & **C-1** \\ \hline Flexural strength (MPa) & 4.31 & 4.37 & 5.23 \\ Compressive strength (MPa) & 16.35 & 15.21 & 19.49 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Testing of mechanical property.  
Figure 8: Equilibrium moisture content of specimens (20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 40 \({}^{\circ}\)C).
# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 4 Partitioned Co-Kriging Based Performance Prediction
### Performance Prediction with Partitioned Co-Kriging
When the auxiliary variable is readily available and changes smoothly, such variable can be introduced into the co-kriging method as an auxiliary influencing factor. In this work, we employ co-kriging method to predict the performance of new configuration in an updated database version (target). Introducing the performance response in an older database version (source) as an auxiliary variable is conducive to the prediction result. In the application of co-kriging, a first step is to model the variogram for each variable as well as a cross-variogram for the two variables. Under the second-order stationary hypothesis, its expectation is  
\[E[Z_{2}(x)]=m_{2}. \tag{2}\]  
The cross-variogram is  
\[\gamma_{12}(h)=E[Z_{1}(x+h)-Z_{1}(x)][Z_{2}(x+h)-Z_{2}(x)]. \tag{3}\]Thus, the interpolation formula of co-kriging method is  
\[Z_{2}^{*}(x_{0})=\sum_{i=1}^{N_{1}}\lambda_{1i}Z_{1}(x_{1i})+\sum_{j=1}^{N_{2}} \lambda_{2j}Z_{2}(x_{2j}). \tag{4}\]  
where \(Z_{2}^{*}(x_{0})\) is the performance prediction value of configuration \(x_{0}\) in target; \(Z_{2}(x_{2j})\) indicates the performance measurement of each configuration in target; \(\lambda_{2j}\) is the weight coefficient for performance measurement of each configuration in target; \(Z_{1}(x_{1i})\) is the performance measurement of each configuration in source; \(\lambda_{1i}\) is the weight coefficient for performance measurement of each configuration in source; \(N_{1}\), \(N_{2}\) refer to the sample size of source and target, respectively; and \(N_{1}>N_{2}\). Two Lagrange multipliers \(u_{1}\) and \(u_{2}\) are introduced for derivation:  
\[\sum_{i=1}^{N_{1}}\lambda_{1i}\gamma_{11}(x_{1i}-x_{1})+\sum_{j=1 }^{N_{2}}\lambda_{2j}\gamma_{21}(x_{2j}-x_{1})+u_{1}=\gamma_{21}(x_{0}-x_{1}),\] \[\sum_{i=1}^{N_{1}}\lambda_{1i}\gamma_{21}(x_{1i}-x_{1})+\sum_{j=1 }^{N_{2}}\lambda_{2j}\gamma_{22}(x_{2j}-x_{1})+u_{2}=\gamma_{22}(x_{0}-x_{1}),\] \[i=1,2,\cdots,N_{1},\qquad j=1,2,\cdots,N_{2},\] \[\sum_{i=1}^{N_{1}}\lambda_{1i}=0,\qquad\sum_{j=1}^{N_{2}}\lambda _{2j}=1. \tag{5}\]  
where \(\gamma_{11}\) and \(\gamma_{22}\) are the variograms of \(Z_{1}\) and \(Z_{2}\), separately. \(\gamma_{12}\) and \(\gamma_{21}\) are the cross-variograms for the two variables, and \(\gamma_{12}=\gamma_{21}\). By solving linear Equation (5), the weight coefficients (\(\lambda_{1i},i=1,2,\cdots,N_{1};\lambda_{2j}\), \(j=1,2,\cdots,N_{2}\)) and two Lagrange multipliers \(u_{1}\) and \(u_{2}\) can be obtained. Thus, the performance estimation of any configuration in the configuration space can be acquired from Equation (4). **Performance prediction**. Given any unseen configuration in target, we first identify the region where it belongs according to the clustering results. The performance of the configuration is then predicted by co-kriging method using the samples and variogram model in this region, as described in Algorithm 1 (lines 5-8).
## 5 Adaptive Load Vectors
### The Adaptive Modal Combination Method  
The adaptive modal combination (AMC) approach, proposed by [71], is an innovative method for evaluating the earthquake resilience of building structures. It integrates elements of the ATC-40's capacity spectrum approach, [PERSON] and [PERSON]'s adaptive pushover technique, and [PERSON] and [PERSON]'s modal pushover analysis [54]. The AMC method uniquely employs lateral forces to mimic the instantaneous inertia force distributions during seismic events and dynamically updates target displacements using energy -based modal capacity curves and constant-ductility capacity spectra. This feature eliminates the need for pre-estimating target displacement before conducting the pushover analysis. The method has been rigorously tested on steel moment-frame buildings, demonstrating accurate estimations of critical seismic performance parameters, such as roof displacement and interstory drift [71]. The procedure can be summarized as follows:  
1. Modal property assessment: analyze the structure to determine its natural frequencies, mode shapes, and the modal participation factors based on its current state.
2. Adaptive lateral load pattern construction: for each mode, devise an adaptive lateral load pattern using: \[S_{n}^{(i)}=m\Phi_{n}^{(i)},\] (50) where \(S_{n}^{(i)}\) is the load distribution, \(m\) is the mass matrix, and \(\Phi_{n}^{(i)}\) is the mode shape at step \(i\). This pattern can be recalculated at specific intervals, balancing computational demands with accuracy.
3. Energy-based incremental evaluation: determine the next step of the capacity curve for each ESDOF system using: \[\Delta D_{n}^{(i)}=\Delta E_{n}^{(i)}/V_{b,n}^{(i)},\] (51) where \(\Delta E_{n}^{(i)}\) is the increment of work done by the lateral force pattern and \(V_{b,n}^{(i)}\) is the base shear at step \(i\).
4. Inelastic response assessment: If the system exhibits inelastic behavior, approximate the global system ductility and post-yield stiffness ratio. A preliminary pushover analysis might be required to establish these parameters.
5. Generate capacity spectra: for the specific ground motion under consideration, produce capacity spectra in an acceleration-displacement response spectrum (ADRS) format.
6. Overlay the modal capacity curve with demand spectra: Plot the modal capacity curve alongside inelastic demand spectra. The intersection point represents the dynamic target for the pushover analysis, given by: \[S_{a,n}^{(i)}=V_{b,n}/\left(\alpha_{n}^{(i)}W\right),\] (52)
7. Extract the response parameters: at the step where the dynamic target point is achieved, retrieve the desired response parameters, such as displacements or member rotations.
8. Repeat the above steps for the necessary number of modes, typically the first few, for low to medium-rise buildings. Finally, combine the peak modal responses using an appropriate scheme, such as the square root of the sum of squares (SRSS), to determine the total response: \[r\left(\left(\sum_{n}\left(r_{n}^{(ip)}\right)^{2}\right)^{0.5}\right)\] (53)
# Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites
## 1 Introduction
For this reason, the I-Frequency and R-Frequency, alongside other AE signals, were considered for analysis. The selected signals were clustered using the fuzzy c-means data clustering technique into different classes. In this research work, the acoustic signals used were recorded from CFRP laminates bonded in the single-lap shear (SLS) configuration and then subjected to a static tensile load. An attempt was made to correlate the AE parameters (which were selected using PCA and clustered using fuzzy c-means) with the different damage modes. The novelty of this work is the usage of I-Frequency and R-Frequency, which has not been investigated extensively in the literature for damage characterization in CFRP. An introduction to CFRP composites and their damage modes can be found in several standard textbooks and the literature [10; 11; 12]. Similarly, a basic introduction about the AE technique and its application to the damage propagation analysis in CFRP can also be found in standard textbooks and several review articles [2; 3; 13; 14; 15]. To avoid any redundant information, basic introductions are not provided in this section. The definitions of the AE parameters that were used in this study are presented in the subsequent sections.
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 3 Dead-Beat Control for Single-Phase ESs
### Dead-Beat Control for Single-Phase ES  
The control system proposed in this paper is still based on the \(\delta\) control but replaces the PR and P controllers with a dead-beat controller. The scheme of the control system is drawn in Figure 3; its effectiveness, as well the comparison of its performance with that of the existing setup, are presented in the next sections.  
The operating principle of a dead-beat controller is illustrated in Reference [16]. Since here the dead-beat control is applied for the first time to an ES, the design of the relevant controller is exposed step-by-step.  
The first step is to discretize Equation (9). By using the zero-order-hold (ZOH) algorithm, the discrete-time state space model of Equation (9) can be expressed as  
\[\left\{\begin{array}{l}x(k+1)=Ax(k)+Bu(k)\\ y(k)=Cx(k)\end{array}\right. \tag{10}\]  
where, \(x(k)=[i_{L}(k)\;v_{ES}(k)\;i_{1}(k)]^{\mathrm{T}}\), \(u(k)=[v_{C}(k)\;v_{i}(k)]^{\mathrm{T}}\), and  
\[A=e^{A_{C}T}=\left[\begin{array}{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{array}\right],B=\int_{0}^{T}e^{A_{C}T}dtB_{C}=\left[ \begin{array}{ccc}b_{11}&b_{12}\\ b_{21}&b_{22}\\ b_{31}&b_{32}\end{array}\right].\]  
The system output at of the next control period is  
\[y(k+1)=Gx(k)+Hu(k) \tag{11}\]  
where, \(G=C^{*}A=[a_{1}\;a_{2}\;a_{3}]\) and \(H=C^{*}B=[b_{1}\;b_{2}]\). By making \(y(k+1)\) equal to the reference value at the next control period, designated as \(r(k+1)\), it follows that:  
\[r(k+1)=Gx(k)+Hu(k) \tag{12}\]  
Equation (12) holds on condition that the system is entered with a suitable value of the input \(v_{l}(k)\). Let this condition be satisfied. Equation (12) outlines that the output of the controlled system equates the reference value at each control period, which means that the tracking error of the control system is zero.  
Substituting the respective coefficients into Equation (12) yields  
\[r(k+1)=\left[\begin{array}{ccc}a_{1}&a_{2}&a_{3}\end{array}\right]\left[ \begin{array}{c}i_{L}(k)\\ v_{ES}(k)\\ i_{1}(k)\end{array}\right]+\left[\begin{array}{c}b_{1}&b_{2}\end{array} \right]\left[\begin{array}{c}v_{G}(k)\\ v_{i}(k)\end{array}\right] \tag{13}\]  
Solving Equation (13) gives the required input  
\[v_{i}(k)=\frac{r(k+1)-a_{1}i_{L}(k)-a_{2}v_{ES}(k)-a_{3}i_{1}(k)-b_{1}v_{G}(k)} {b_{2}} \tag{14}\]  
Figure 3: The proposed dead-beat control based on the \(\delta\) control.  
The working equation of the dead-beat controller is given by Equation (14). \(v_{i}(k)\) is calculated, it is made to enter the PWM generator at each control period. Note that \(r(k+1)\) in Equation (14), which is defined as \(v_{S\_ref}\) in Figure 3, is calculated by the \(\delta\) control.
# A Calculation Model of Grout Migration Height for Post-Grouting Technology
## 2 Solution of Grout Migration Height  
### Basic Assumptions  
The real behavior of upward migration of cement slurry in the weak layer zone is too sophisticated to be analyzed. Therefore, the posted-pile foundation (as shown in Figure 1) is simplified and simulated by a cylindrical model consisting of bored pile, weak layer zone, and surrounding soil, in which \(L\) is the length of bored pile, and \(r_{0}\) and \(b\) the radius of bored pile and the thickness of weak layer, respectively. The following assumptions are employed for making the theoretical solution of the grout migration height in this study:  
* soils around the bored pile are isotropic and homogeneous;
* cement slurry is simplified as the incompressible, isotropic and homogeneous Bingham fluid;
* surface of bored pile and hole is considered as cylindrical surface, and the annular cement slurry is homogeneously distributed.  
It should be acknowledged that the Herschel-Bulkley model is superior as compared to Bingham model in the calculation of grout migration height. Meanwhile, the Bingham model is much easier and the required calculation parameters are easily be obtained. Therefore, the Bingham model is selected in this manuscript for developing the calculation model of migration height.
## 2 System Model and Theoretical Analysis
### DM Model in SLAO  
We have designed and manufactured a 97-element continuous surface DM (CSDM) as the wavefront corrector in this study. By changing the surface shape of the mirror in real time according to the control voltage, the CSDM can efficiently correct wavefront aberrations. Generally, the influence of the CSDM is estimated with a Gaussian function [30; 31]:  
\[S_{j}(x,\ y)=\exp\biggl{\{}\ln\omega\biggl{[}\frac{1}{d}\sqrt{\bigl{(}x-x_{j} \bigr{)}^{2}+\bigl{(}y-y_{j}\bigr{)}^{2}}\biggr{]}^{a}\biggr{\}}, \tag{1}\]  
where \(\omega\) is the coupling coefficient determined by the size of the electrode driver and the CSDM, \((x_{j},\ y_{j})\) is the center coordinate of the \(j\)th actuator, \(\alpha\) is the Gaussian index and \(d\) is the normalized interval between adjacent actuators. The phase compensation \(\varphi(x,\ y)\) produced by the CSDM with 97 actuators is expressed as:  
\[\varphi(x,\ y)=\sum_{j=1}^{97}u_{j}S_{j}(x,\ y), \tag{2}\]  
where \(u_{j}\) is the voltage of the \(j\)th actuator in the range of the maximum possible voltage. It can be seen that \(\varphi(x,\ y)\) is linear with the voltages applied to the actuators.
## 4 Analysis
The charge accumulation may also affect the streaming potential during the first ten seconds of the pressure holding period. Before the pressure is applied, the electric charge accumulation is in equilibrium. When the pressure is applied, the charges in fluid begin to move and can balance the unlike charges on the surfaces of bone. This process is equivalent to decreasing the charge densities on the surfaces and also reduces the increase of the streaming potentials for which the leading potential curves become flat. After a moment (such as ten seconds), more and more unlike charges are induced from the bone bulk by the moving charges, which is equivalent to charge accumulation. Then the slopes become increased, as shown in \[\frac{1}{r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial\psi(x,r)}{ \partial r}\bigg{)}=\frac{2n_{0}ze}{\varepsilon}\sinh\bigg{(}\frac{ze\psi(x,r) }{k_{B}T}\bigg{)} \tag{3}\]0  
Based on the above analysis, we can see that, even the steady fluid flow in bone, it can induce non-steady streaming potentials and the potentials may increase with time in some locations. In other words, the steady fluid flow in bone can cause the streaming potentials to change with time and the amplitude changes with time may be greater than the classical values calculated by Equation (1) or (19). From Equation (1) and taking the parameter values used in the experiment shown in Table 1, the streaming potential \(\phi_{0}\) is found to be 2.3 mV, a value of the same order of magnitude as presented in \[\frac{1}{r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial\psi(x,r)}{ \partial r}\bigg{)}=\frac{2n_{0}ze}{\varepsilon}\sinh\bigg{(}\frac{ze\psi(x,r) }{k_{B}T}\bigg{)} \tag{3}\]1 Equation (19) describes the relationship of streaming potentials and the microcanal radius. A plot of normalized streaming potentials versus radius is illustrated in Figure 6B. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\varepsilon\) & \(6.99\times 10^{-10}\) CV\({}^{-1}\)m\({}^{-1}\) & \(T\) & 293.15 K \\ \(\mu\) & \(1.5\times 10^{-3}\) Nsm\({}^{-2}\) & \(M\) & 0.145 mol \\ \(\lambda_{b}\) & \(3\times 10^{-3}\) Sm\({}^{-1}\) & \(e\) & \(1.602\times 10^{-19}\) \\ \(\zeta_{0}\) & 0.1 mV & \(z\) & 1 \\ R & \(10^{-5}\) m & \(k_{\rm B}\) & \(1.38065\times 10^{-23}\) JK\({}^{-1}\) \\ \(P\) & 150 kPa & N\({}_{\rm A}\) & \(6.022\times 10^{23}\) mol\({}^{-1}\) \\ n\({}_{0}\) & \(8.732\times 10^{25}\) & \(\kappa^{-1}\) & \(7.945\times 10^{-10}\) m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used in the calculation. The curve in Figure 6B shows that when the radius R of the microcanal is beyond 20 nm, the streaming potential approaches \(\phi_{0}\), calculated from Equation (1). In fact when R = 20 nm, the ratio of the two Bessel functions \(I2(kR)/I0(kR)\) = 0.922, which means that the electro-viscous resistances described by the Bessel functions in Equation (19) can only play minor roles. Thus, the streaming potentials in the canaliculi, lacunas, Haversian and Volkmann's canals are usually not affected by the electro-viscous resistance in the fluid.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 3 NGeneApp: An Android-Based Real Time RFI Detector for Smartphones  
In order to detect the interference in the surrounding environment in a quick and practical way, we ported a GNSS software receiver to a pocket-size and portable device, i.e., onto the microprocessor of a smartphone. We developed an Android app to run a GNSS software receiver, able to detect in real-time RFI and send the collected data to a remote server. The Android-based real-time GPS/EGNOS/Galileo single frequency fully software (SW) receiver, named NGeneApp, has been obtained porting the source code of an ARM-based SW receiver [12] to the Android Operating System (OS). The smartphone is then connected to a mass-market USB-based FE and a classic hemispherical patch antenna.
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology  
The presented work employs a machine learning-based approach to tackle the fault classification problem. The primary goal was to classify the fault type in transmission line faults, which involved 10 different labels: AG, BG, CG, AB, BC, CA, ABG, BCG, CAG, and ABC, where A, B, C, and G stand for phase A, phase B, phase C, and ground, respectively. These faults are classified as single line to ground faults (SLG), line to line faults (LL), double line to ground faults (2 LG), and line to line to line faults (LLL). Only LLLfaults are symmetrical, while the remaining faults are asymmetrical as shown in Figure 1. Classification is a fundamental task in machine learning aimed at assigning labels to data instances. For instance, given a dataset where each event is associated with a label, such as \"AG\" for event 1, the goal is to predict the correct label for each event. The accuracy of a classification model is typically assessed by comparing the predicted labels to the ground truth labels. For example, if a model accurately predicts the labels for 90 out of 100 events, the accuracy is computed as 90%. This evaluation metric reflects the proportion of correctly classified instances out of the total number of instances, providing a measure of the model's performance in terms of label prediction accuracy.  
The overview of the proposed method is illustrated in Figure 2. The input data for our classifier consist of three-phase voltage and current measurements from a single end of a two-terminal transmission line, resulting in six time series datasets. This approach differs from previous work that used data from both ends of the transmission line [15]. The data then undergo preprocessing, which includes window selection, resampling, and normalization. In this study, fault classification along the transmission line is performed using the Conformer model. The classifier outputs a predicted label, which in this case is the type of fault occurring on the transmission line.