esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### EEMD Theory  
The traditional EMD method has a mode-mixing problem when it is employed to analyze complicated vibration signals. To prevent this issue, an improved version, called EEMD, was proposed by [PERSON] and [PERSON] based on EMD in 2009 [20]. The fundamental basis of EEMD adaptively decomposing complicated signal signals are the added noise can make the impact interference smoother than that in original vibration signal; and the added noise components in an IMF can be canceled by averaging a set number of IMFs in several decomposition processes, because white noise has the properties of zero mean and consistency distribution in the time domain. That is to say, traditional EEMD mainly includes two parts that involve adding the appropriate noise; processing the noise-added signals with EMD several times; and then calculating the mean values of obtained IMFs. The main steps of EEMD are shown below [20; 21; 22; 23].  
(1) Determine the decomposition number (\(N\)) and add the noise amplitude (\(A\)).  
(2) Generate a white noise with amplitude \(A\), and add it to \(x(t)\) as  
\[x_{n}(t)=x(t)+e_{n}(t), \tag{6}\]  
where \(e_{n}(t)\) is the added white noise.  
(3) Employ the traditional EMD to process above noise-added signal to get  
\[x_{n}(t)=\sum_{m=1}^{M}c_{n,m}(t)+r_{n,m}(t), \tag{7}\]  
where \(c_{n,m}(t)\) and \(r_{n,m}(t)\) are the \(m\)-th IMF signal and residual signal of the \(n\)-th EMD decomposition.  
(4) Repeat steps (1) to (3) \(N\) times and each time, where the added noise signals are different, their amplitudes are all equal to \(A\).  
(5) The final IMFs can be expressed as  
\[\begin{cases}\overline{c}_{m}(t)=\dfrac{1}{N}\sum_{n=1}^{N}c_{n,m}(t)\\ \overline{r}_{M}(t)=\dfrac{1}{N}\sum_{n=1}^{N}r_{n,M}(t)\end{cases}. \tag{8}\]  
(6) Final, the original vibration signal is decomposed into  
\[x(t)=\sum_{m=1}^{M}\overline{c}_{m}(t)+\overline{r}_{M}(t). \tag{9}\]
# Thermoforming Simulation of Woven Carbon Fiber Fabric/Polyurethane Composite Materials
## 4 Results and Discussion
### U-Shaped Structure  
As mentioned, the thermoforming of U-shaped structures is like a one-dimensional deformation, especially for the [(0\({}^{\circ}\)/90\({}^{\circ}\))]\({}_{4}\) case. The geometric profile comparison of this [(0\({}^{\circ}\)/90\({}^{\circ}\))]\({}_{4}\) workpiece between the experiment and the simulation is shown in Figure 8a. As is shown, both results have a uniform and nonchanged width and only the length of the product is reduced due to the indentation. The original length of the blank is 255 mm, and it becomes 208.9 mm from the experiment and 205 mm from the simulation. This difference is less than 2% and could be considered as small. As for the [(+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\))]\({}_{4}\) case, the profile change from the original composite rectangle blanket is slightly large, as shown in Figure 8b. The center of the product, which is also the bottom of the U shape, has a uniform width. However, the width is reduced from 120 mm to either 102.8 mm for the experiment or 106.6 mm for the simulation. There is a 3.7% difference in the shortest width. The reason for this reduction is the shear deformation, just like the bias extension test. Similarly, both flanges of the U-shaped structure become the sector shape from both the experiment and the simulation, as shown in Figure 8b. Hence, the simulation still has a very similar profile to the experiment.  
The comparison of the fiber-included angle is shown in Figures 9 and 10 for both stacking sequence cases. The location to obtain the fiber-included angle is at the wall part of the U-shape as shown. One should note that the fiber-included angles have two values for the woven fabric, and their summation is 180\({}^{\circ}\). For the [(0\({}^{\circ}\)/90\({}^{\circ}\))]\({}_{4}\) case, the predicted value of the fiber-included angle is 89.60\({}^{\circ}\) (or 90.40\({}^{\circ}\)) as compared with the measured result of 92.06\({}^{\circ}\) (or 87.94\({}^{\circ}\)). These two values are very close to the original ones, and the change is small because the extension is along one fiber direction. For the [(+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\))]\({}_{4}\) case, the fiber-included angle encounters a big change after thermoforming. As shown in Figure 10, the measured value is 106.91\({}^{\circ}\) (or 73.09\({}^{\circ}\)) and the simulation result is 105.20\({}^{\circ}\) (or 74.80\({}^{\circ}\)). This large change in the fiber-included angle can also be attributed to the bias extension effect. According to the comparison for both the product profile and the fiber-included angle, the finite element analysis in this work could reasonably simulate the deformation behavior of thermoplastic composites under thermoforming.  
Figure 7: Force-displacement curves for bias extension tests under different temperatures.
# In-Process Measurement of Barkhausen Noise for Detection of Surface Integrity during Grinding
## 3 Results and Discussion
Since neither the micrograph nor the hardness indicate a tempering zone, this demonstrates again that the BN can already detect the initial changes in the residual stress state, which are visible in Figure 4. Slightly and very strong influenced surface states cannot be clearly distinguished. This problem is known from the literature and concerns in-process measurements as well as post-process measurements after grinding. Possibilities to still differentiate the states are mainly seen in multiparameter approaches or in the combination with process models, as described in [30]. Another point that affects both post-process and in-process measurements is the influence of the heat treatment states. For the evaluation of the measurement results, individual threshold values for the particular initial state are necessary. Figure 6: Envelope of the BN during grinding (full lines) and after spark-out (dashed lines) for different specific material removal rates (**a**) and values of M\({}_{\text{MEAN}}\) and M\({}_{\text{MAX}}\) (**b**). Figure 7: Ratio of BN values M\({}_{\text{MAX}}\) (**a,b**) and M\({}_{\text{MEAN}}\) (**c,d**) during and after grinding with different specific material removal rates and Q\({}^{\prime}\)\({}_{\text{w}}\) = 1 mm\({}^{3}\)/(mm-s). Because of the large ratio of up to 2.5 between \(Q^{\prime}{}_{w}\) = 12 mm\({}^{3}\)/(mm\(\cdot\)s) and \(Q^{\prime}{}_{w}\) = 1 mm\({}^{3}\)/(mm\(\cdot\)s) and at least 1.3 between \(Q^{\prime}{}_{w}\) = 6 mm\({}^{3}\)/(mm\(\cdot\)s) and \(Q^{\prime}{}_{w}\) = 1 mm\({}^{3}\)/(mm\(\cdot\)s), M\({}_{\rm MEAN}\) seems to be well suitable for the in-process detection of different thermo-mechanical surface states. Additionally the standard deviation is low, with a maximum of 0.08 compared with a maximum of 0.13 for M\({}_{\rm MAX}\). However, due to the mechanical excitation described before, it may be difficult to distinguish between the effects of grinding and the effects of the surface state influenced by grinding. This distinction will be part of future investigations. Some influences from grinding on the BN signal are described in [25]. Among others, this study indicates an influence of the workpiece rotation speed, which should be investigated without grinding as well. Despite the higher standard deviations, M\({}_{\rm MAX}\) is suitable for the detection of thermo-mechanically influenced surface areas both during and after grinding. For very high \(Q^{\prime}{}_{w}\), it should be mentioned that the in-process measured values might be lower than after grinding (see Figure 7a,b), which is probably an effect of the larger air gap between the sensor and the workpiece surface.
## 2 Computations of Surface-Wave Phase-Velocity DCs Based on the Reduced Delta Matrix Method  
We first give a brief overview of the reduced delta matrix method [14; 23; 35] since it constitutes the foundation of the following sections. We strictly follow the formulism of [PERSON] et al. [23] due to his modern mathematical representations compared with other earlier studies.  
This research involved a multilayered earth model (Figure 1), which consists of many homogeneous, isotropic, and elastic layers that overlie a half-space and lie beneath a free surface. Each layer is denoted by the P-wave velocity \(\alpha_{m}\), the S-wave velocity \(\beta_{m}\), the density \(\rho_{m}\) and the thickness \(h_{m}\). According to the reduced delta matrix method, the surface-wave secular (or dispersion) equation \(F\) can be described as an implicit function by the delta matrix recursion:  
\[\begin{array}{l}\overrightarrow{\mathbf{X}}_{m+1}=\overrightarrow{\mathbf{X }}_{m}\overrightarrow{\mathbf{T}}_{m},\ m=1,2,\cdots,n\\ F(\omega,c)=\overrightarrow{\mathbf{X}}_{n+1}\overrightarrow{\mathbf{V}}_{n+ 1}=0\end{array} \tag{1}\]  
where \(\omega\) is the angular frequency, \(c\) is the phase velocity, \(m\) is the sequence number for layers, and \(n+1\) indicates the half-space. \(\overrightarrow{\mathbf{X}}_{m}\) is a row vector with different lengths depending on which wave (Love or Rayleigh wave) is concerned. \(\overrightarrow{\mathbf{T}}_{m}\) and \(\overrightarrow{\mathbf{V}}_{n+1}\) can be regarded as the reduced delta matrices corresponding to the layer \(m\) and the half-space, respectively. The elements of these three matrices are listed in Appendix A. In order to obtain the phase-velocity DCs, the frequencies or periods of interest are substituted into (1), followed by a root-finding procedure consisting of the integrated use of the bisection method and the Brent method in this paper. Generally, there are multiple solutions or roots at a certain frequency or period, and the root corresponding to the lowest \(c\) is the fundamental mode while other roots are the higher modes.
# A Survey on Multi-User Conversational Interfaces
## 1 Introduction
Due to this, MUCIs are not implemented in a standardised way, which impedes further development and improvement of existing systems. As a consequence, the capabilities of contemporary systems are typically limited to specific use-cases, which may lead to an inadequate user experience. However, in this survey paper, we aim to identify common patterns and design guidelines to move towards more standardised approaches. Hence, the contributions of this survey paper are:  
* Development of a survey paper following the PRISMA flow method [1]. Following this approach, we filter 181 articles, analysing in detail 59 final candidates. From these candidates, we provide an analysis of the venues and journals in which they were published. Additionally, we outline possible existing connections between authors and countries of the analysed collection. Although dispersed, we observe a growing community and indicate several networks of potential collaborators. * Proposal of a definition of the MUCIs term based on related works and renowned dictionaries, given the lack of consensus in the literature. Identification of the most important aspects of contemporary MUCIs by highlighting patterns and common design approaches. To create a broad understanding, the relevant publications of the sample are categorised into four sections: Application Domains and Examples, User and Dialogue Modelling, Multi-Modality, and Evaluation Methods. * Provision of guidelines for future implementations of MUCIs and their current challenges to address. The paper is structured as follows: In Section 2, we define the scope and methodology of our survey, including the literature search strategy and its inclusion and exclusion criteria. Section 3 presents a comprehensive quantitative analysis of the relevant literature. In Section 4, we provide a detailed examination of the publications, organised by research fields and associated challenges. The key findings are subsequently discussed in Section 5, followed by a conclusion and an outlook on MUCIs in Section 6.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### Adhesive Strength Test  
In the literature, the POBS assay stands out as one of the most frequently employed methods for studying the dislodgment resistance of cements. This test is particularly suitable for specimens with parallel sides, as dislocation occurs parallel to the dentin-material interface. Widely regarded as a reliable method, the POBS assay is employed to measure the bond strength of root canal filling materials [22].  
On the other hand, nine articles utilized SEM, offering a valuable means of assessing the sealing capacity and adhesion of the sealer to the walls of the root dentin. SEM provides high magnification, enabling a detailed observation of surface morphology [11]. Furthermore, CLSM made an appearance in a single article.  
The selected articles used 1 mm, 1.5 mm, 2 mm and 3 mm slices. According to [PERSON] et al. [52], to correctly apply the push-out test, the thickness of the sample must be 0.6 times greater than the diameter of the seal to avoid influencing the values of the bond strength, that is, the thickness of the sample must be greater than 1.10 mm. They also proposed that the size of the plunger tip should be 0.85 times smaller than the size of the filling material [22]. The majority (25 articles) used the universal machine (Instron Corp., Norwood, MA, USA) (Schimadzu Corp., Tokyo, Japan) at a speed of 0.5 mm/min, while 16 articles used it at 1 mm/min. One article did not mention the speed used.  
A notable challenge in push-out tests lies in the lack of standardization. Significant variability exists in both the parameters of the test itself, such as the diameter of the orifice of the base/tip of the applicator and the testing speed, and in the methods of sample preparation, including thickness, incubation time, and root canal diameter. These variations can introduce alterations in stress distribution patterns, contributing to the substantial discrepancies observed in numerical results obtained with the same materials [44].
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 3 Nodal Accuracy Improvement Technology for Linear Elements
### Nodal Accuracy Improvement for 2D and 3D Problems
In the D-by-D approach, the first stage of the nodal accuracy improvement only slightly lowers the nodal errors but does not raise the convergence order at all, and only after the second stage of nodal improvement does the nodal accuracy gain convergence \(O(h^{4})\), which is reasonable because the first stage of nodal improvement only improves the nodal solutions for problem (8), while the overall accuracy is limited by the linear discretization error between \"mesh lines\" of FEMOL for problem (1). However, in the direct approach, the accuracy of the improved nodal displacements achieves convergence \(O(h^{4})\) once for all and is comparable to that of the two-staged D-by-D approach. Based on the above analysis and numerical experiments, it is evident that the direct approach is the choice for practical use due to its high performance in accuracy, simplicity and generality in formulation and computation for one- and multidimensional problems. In the following, the direct approach is designated as the exclusive nodal accuracy improvement strategy.
###### Abstract  
This paper presents a new layered dielectric leaky-wave antenna (LWA) for the sub-terahertz (THz) frequency range capable of efficient operation at the broadside with a wide beam scanning angle and stable gain. It consists of a conductor-backed alumina dielectric image line (DIL) with two different dielectric layers mounted on top of each other for performance improvement. The upper layer is a high permittivity RO6010 substrate to enhance the directivity as a superstrate and the lower layer is a low-permittivity RT/duroid 5880 substrate stacked on the alumina DIL to prevent the probable excitation of higher-order modes in the DIL channel. A 15-element linear array of radiating overlapped discs is used to mitigate the open stop-band (OSB) problem, fed by the mentioned waveguide, was designed and simulated at frequencies around 170 GHz. The dominant mode of the layered dielectric waveguide is perturbed by the infinite space harmonics generated by two sets of overlapped discs periodically sandwiched between the layers. It exhibited a relatively wide impedance bandwidth of 28.19% (157.5-206 GHz). Its radiation mechanism has been widely studied through simulations. The results revealed that the antenna provides a wide scanning capability through the broadside from \(-23^{\circ}\) to \(38^{\circ}\), covering the frequency range between 157.5 GHz and 201.5 GHz. For an array with 15 radiating elements, the simulated peak gain in the band is 15 dBi and the broadside gain is 13.6 dBi at 172 GHz.  
multilayer dielectric image waveguide; leaky-wave; sub-terahertz; open-stopband 25 July 2021  
30 August 2021  
6 September 2021  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] A Wide-Angle Scanning Sub-Terahertz Leaky-Wave Antenna Based on a Multilayer Dielectric Image Waveguide
# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
## 3 Methods  
### Limit Equilibrium Methods  
#### 3.1.1 Calculation of Safety Factor  
In this paper, the Spencer slice method was used to calculate the safety factor. It is assumed that the soil slope slides along a certain slip plane, and the soil reaches equilibrium along this slip plane. A certain vertical soil slice was considered in the sliding soil, and its forces were shown in Figure 9:  
On the considered soil slice, \(\Delta\)W is the dead weight, \(\Delta\)T and \(\Delta\)N are the tangential forces and the normal force at its bottom, and G is the interaction force between the slices. The differential form of the equilibrium equation was obtained by making \(\Delta\) x\(\rightarrow\)0, and the integral of the whole sliding surface was obtained. The force and moment balance equations of the whole sliding body [28] are shown in Equations (2) and (3), respectively:  
\[G_{n}(F,\lambda)=\int_{a}^{b}p(x)s(x)dx=0 \tag{2}\]  
\[M_{n}(F,\lambda)=\int_{a}^{b}p(x)s(x)t(x)dx=0 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slope Name** & **Actual Slope** & **Generated Slope** \\ \hline \hline
**Tianshengqiao** & & \\
**Secondary** & & \\
**Gate Head** & & \\
**2000** & & \\  
\[p(x)=\frac{dW}{dx}sin(\varphi_{e}^{\prime}-\alpha)+c_{e}^{\prime}secacos\varphi_{e}^ {\prime} \tag{4}\]  
\[s(x)=\sec(\varphi_{e}^{\prime}-\alpha+\beta)exp[-\int_{a}^{x}tansec(\varphi_{e} ^{\prime}-\alpha+\beta)\frac{d\beta}{d\zeta}d\zeta] \tag{5}\]  
\[t(x)=\int_{a}^{x}(sin\beta-cos\beta tan\alpha)exp\int_{a}^{\zeta}\left[tan( \varphi_{e}^{\prime}-\alpha+\beta)\frac{d\beta}{d\zeta}d\zeta\right]d\zeta \tag{6}\]  
where \(\frac{dW}{dx}\) is the bulk density of the soil, \(\alpha\) is the angle of inclination of the bottom of the soil slice, \(c_{e}^{\prime}=\frac{c^{\prime}}{F}\), \(\tan\varphi_{e}^{\prime}=\frac{tan^{\prime}}{F}\), \(c^{\prime}\) is the cohesion of the soil mass, \(\varphi^{\prime}\) is the internal friction angle of the soil, \(F\) is the safety factor, and is the unknown quantity to be solved, and \(\beta\) is the angle between the total force between the soil slices and the horizontal line, \(tan\beta=\lambda\). Among them, \(F\) and \(\lambda\) are the unknowns to be solved.  
The Newton-Raphson iterative method is used to solve the equilibrium equation of the above integral form to obtain \(F\) and \(\lambda\).
## 3 Results
### Active and Reactive Power Measurements
This is especially spectacular for the Sonoff ZigBee ZBMINI module, which may be due to its unusual software loaded into the microcontroller memory directly in the module. Then, the potential levels of consumption of additional active energy by such modules over a period of 1 day (\(E_{add\_day}\)) and 1 year (\(E_{add\_yr}\)) were calculated, assuming that one operating state of the smart home module was maintained throughout the entire period (relay on and off, respectively). There were two relations used:  
\[E_{add\_day}=P_{add}\times 24\;[\text{Wh}] \tag{2}\]  
\[E_{add\_yr}=E_{add\_day}\times\frac{365}{1000}\;[\text{kWh}] \tag{3}\] \begin{table}
\begin{tabular}{l c c} \hline \hline
**Module** & \(\mathbf{P_{far\_OFF}}\) **(W)** & \(\mathbf{Q_{far\_OFF}}\) **(VAr)** \\ \hline Blebox Switchbox & 0.814 & \(-\)0.027 \\ Gosund Socket S111 & 0.707 & 0.155 \\ Shelly 1 & 0.600 & 0.137 \\ Sonoff Basic R2 Supla & 0.592 & \(-\)0.701 \\ Sonoff Socket S26R2 TPE-FR & 0.559 & 0.148 \\ Sonoff ZigBee ZBMINI & 0.252 & \(-\)0.872 \\ tplink Socket HS110 & 0.673 & 0.298 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Instantaneous active and reactive power consumption levels of smart home modules— module located far from the Wi-Fi access point/router, with the relay turned off. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Module** & \(\mathbf{P_{far\_ON}}\) **(W)** & \(\mathbf{Q_{far\_ON}}\) **(VAr)** \\ \hline Blebox Switchbox & 1.053 & \(-\)0.095 \\ Gosund Socket S111 & 0.750 & 0.160 \\ Shelly 1 & 0.846 & 0.035 \\ Sonoff Basic R2 Supla & 1.298 & \(-\)0.730 \\ Sonoff Socket S26R2 TPE-FR & 0.821 & 0.119 \\ Sonoff ZigBee ZBMINI & 0.517 & \(-\)0.692 \\ tplink Socket HS110 & 1.289 & 0.030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Instantaneous active and reactive power consumption levels of smart home modules— module located far from the Wi-Fi access point/router, with the relay switched on. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Module** & \(\mathbf{P_{near\_OFF}}\) **(W)** & \(\mathbf{Q_{near\_OFF}}\) **(VAr)** \\ \hline Blebox Switchbox & 0.795 & \(-\)0.176 \\ Gosund Socket S111 & 0.427 & 0.006 \\ Shelly 1 & 0.535 & \(-\)0.031 \\ Sonoff Basic R2 Supla & 0.578 & \(-\)0.590 \\ Sonoff Socket S26R2 TPE-FR & 0.470 & \(-\)0.017 \\ Sonoff ZigBee ZBMINI & 0.069 & \(-\)0.881 \\ tplink Socket HS110 & 0.485 & 0.033 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Instantaneous active and reactive power consumption levels of smart home modules— module located close to the Wi-Fi access point/router, with the relay turned off.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 5 Conclusions  
In this paper, melody extraction from polyphonic music is addressed in the ELM framework. Pitch estimation and voicing detection are carried out simultaneously. More specifically, the musical signals are first down-sampled, processed by an equal loudness filter to mimic the human auditory system, and analyzed by CQT. The frame-wise CQT spectra are then concentrated at the center frame to build the input feature vectors for the ELM. The output one-hot vectors of the ELM are generated based on the labels. Next, during the testing stage, the output prediction matrix of the ELM is computed according to the pre-trained parameters, and the rough melody pitches are obtained by locating the maximum value of each frame. Finally, the rough pitches are fine-tuned by searching around the rough pitches. The proposed method can learn high-level melody features very efficiently. It performs pitch estimation and voicing detection simultaneously. Experimental results demonstrate that the proposed method achieves higher overall accuracies compared with reference methods.  
Investigation, W.Z., Q.Z., and J.D.; writing--original draft, W.Z.; writing--review and editing, Q.Z.; supervision, S.B. and S.F.; funding acquisition, W.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by [National Natural Science Foundation of China] grant number [61806178], [Natural Science Foundation of Liaoning Province] grant number [20170540197].  
The authors would like to thank authors of the reference methods for providing the codes, Vamp plug-ins, or executable programs for evaluating the performances of these methods.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **OA(\%)** & **RPA(\%)** & **RCA(\%)** \\ \hline BG1 & 45.58 & 58.56 & 62.79 \\ HCT & 47.60 & 62.76 & 67.47 \\ MEA & 45.18 & 57.49 & 63.48 \\ Melodia & 52.96 & 69.01 & **71.77** \\ PFDP & 54.22 & **69.41** & 71.71 \\ PM & 42.67 & 59.45 & 62.72 \\ Proposed & **63.93** & 54.43 & 56.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Averaged accuracies on three collections.