esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Evaluation of EmoSC  
#### 5.2.1 Datasets Analysis  
Table 3 and Figure 6 present the statistics and emotion distributions of StyleTalk, DailyTalk, and EmoSC. StyleTalk consisted of 2696 samples from nine speakers, with neutral and friendly emotions dominating, while cheerful and unfriendly emotions were underrepresented (Figure 6a). DailyTalk, despite its 21,232 samples, had only two speakers and exhibited severe class imbalance, with happiness comprising the majority (17,480 samples), while anger, fear, and disgust were significantly underrepresented (Figure 6b). In contrast, EmoSC, the largest dataset with 32,203 samples from 10 speakers, maintained a well-balanced emotion distribution, reducing class bias and enhancing generalization (Figure 6c).  
To assess how distinguishable emotions were in speech, we fine-tuned an emotion predictor (Section 4.1) using a balanced dataset by manually splitting the data. EmoSC achieved the highest classification accuracy (97.0%), confirming clearly separable emotional cues. StyleTalk (91.1%), despite its smaller size, also demonstrated well-defined emotions. DailyTalk, with the lowest accuracy (60.7%), had less distinct emotional expressions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **BLEU \(\uparrow\)** & **BERTScore \(\uparrow\)** & **ROUGE-L \(\uparrow\)** & **Resp. F1 1 \(\uparrow\)** \\ \hline Spoken-LLM & StyleTalk & 2.9\% & 75.5\% & 15.7\% & 45.5\% \\ StyleTalker & DailyTalk & 4.6\% & 90.4\% & 16.1\% & - \\ \hline \multirow{3}{*}{EmoSDS} & StyleTalk & 8.5\% & 86.8\% & 35.5\% & 46.8\% \\  & DailyTalk & 8.3\% & 89.9\% & 35.9\% & - \\ \cline{1-1}  & EmoSC & 10.4\% & 88.0\% & 40.5\% & 48.6\% \\ \hline \hline \end{tabular} 1 Weighted F1 score on response emotion prediction.  
\end{table}
Table 2: Response generation results on each dataset. An upward arrow (\(\uparrow\)) indicates a higher value, signifying better performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Total** & **Total** & **Accuracy** & **Same Text 1** & **Real Human** \\  & **Samples** & **Speakers** & & & **Audio 2** \\ \hline StyleTalk & 2696 & 9 & 91.1\% & O & X \\ DailyTalk & 21,232 & 2 & 60.7\% & X & O \\ EmoSC & 32,203 & 10 & 97.0\% & O & O \\ \hline \hline \end{tabular} 1 Whether it included the same text in different emotions. 2 Whether it included real human audio.  
\end{table}
Table 3: Data statistics of each dataset.
## 5 Numerical Parametric Study on Tunnel Displacement
### Modeling and Validation  
Numerical modelling is performed with the finite element commercial package FLAC [30] to identify the tunnel displacement responses to the overlying foundation pit excavation. A three-dimensional model with dimensions of 140 m \(\times\) 140 m \(\times\) 60 m is finally developed to minimize the boundary effects. Soil, diaphragm wall and segment lining are modelled by solid elements, while steel supports are modelled using beam elements. Soil parameters are presented in Table 1, and the supporting structures' parameters utilized in the modelling are illustrated in Table 4. The three-dimensional finite element-model mesh adopted for numerical analysis is displayed in Figure 13.  
A comparison of the displacement and earth pressure of the left tunnel with the experimental tests for numerical validation is plotted in Figures 14 and 15. It can be seen that the numerical results are larger than the model test results, but the two results are highly comparable in terms of variation trends, which can effectively provide assurance and support for in-depth parametric analysis on tunnel responses. Additionally, it confirms that the tunnel heave at the crown is larger than that at the invert. Meanwhile, the horizontal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \(\gamma\) **(kN/m3)** & \(E\) **(GPa)** & \(\mu\) \\ \hline Diaphrag wall & 22.54 & 28.0 & 0.2 \\ Steel support & 78.5 & 206.0 & 0.2 \\ Segment liner & 25.0 & 34.5 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material parameters of supporting structures.  
Figure 13: 3D finite element mesh, (**a**) the whole mesh, (**b**) location relationship between foundation and twin-tube tunnels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parametric Setup for Influence** & \multicolumn{3}{c}{**Parametric Setup for Influence of**} & & & \\ \multicolumn{3}{c}{**of \(H/H_{e}\) Ratio**} & & \multicolumn{3}{c}{\(B/H_{e}\) Ratio**} & & & \\ \hline \(H/\)**m** & \(H/H_{p}\)** & **Ratio** & \(L_{p}/\)**m** & \(W_{p}/\)**m** & \(L_{p}/H_{p}\)** & **Ratio** & \(C_{t}/\)**m** & \(D_{t}/\)**m** & \(C_{t}/D_{t}\)** & **Ratio** & **Unloading Ratio** \\ \hline
3 & & 0.375 & 8 & 1 & 11 & 1.83 & 0.18–0.72 \\
6 & & 0.75 & 16 & & 2 & 14 & 2.33 & 0.14–0.56 \\
9 & & 1.125 & 24 & & 3 & 17 & 2.83 & 0.12–0.47 \\
12 & 8 & 1.5 & 32 & 20  
displacement at the near-pit side (L13) is slightly smaller than that at the away-from-pit side (L5).
## 3 Stability Analysis
\begin{array}{l}\frac{1}{(1-\phi)^{2S}}H_{0}^{\prime\prime}+\left(1-\phi+ \phi\frac{\rho_{5}}{\rho_{f}}\right)\left[\frac{n+1}{2}\left(f_{0}H_{0}^{\prime \prime}+h_{0}H_{0}^{\prime\prime}+h_{0}^{\prime\prime}\ F_{0}+h_{0}^{\prime \prime}\ H_{0}\right)\right.\\ \left.-n(2f_{0}^{\prime}\ H_{0}^{\prime}+f_{0}^{\prime}\ F_{0}^{\prime}+h_{0}^{ \prime}H_{0}^{\prime})\left.+\gamma H_{0}^{\prime}\right]-\left[1+\frac{3 \left(\sigma_{s}/\sigma_{f}-1\right)\phi}{\sigma_{s}/\sigma_{f}+2-\left(\sigma_ {s}/\sigma_{f}-1\right)\phi}\right]MH_{0}^{\prime}=0,\end{array}  \tag{32}\]  
\[ \begin{array}{l}\frac{1}{\mathrm{Pr}}\frac{k_{nf}}{k_{f}}G_{0}^{\prime\prime} \ +\left(1-\phi+\phi\frac{\left(\sigma C_{p}\right)_{nf}}{\left(\rho C_{p}\right)_{f}} \right)\left[\frac{n+1}{2}(f_{0}G_{0}^{\prime}+h_{0}G_{0}^{\prime}+\theta_{0}^{ \prime}F_{0}+\theta_{0}^{\prime}H_{0})\right.\\ \left.-(2n-1)(f_{0}^{\prime}G_{0}+h_{0}^{\prime}G_{0}+\theta_{0}F_{0}^{\prime}+ \theta_{0}H_{0}^{\prime})\left.+\gamma G_{0}\right]=0,\end{array}  \tag{33}\]  
subject to the boundary conditions:  
\[ \begin{array}{l}F_{0}(0)=0,\ \ \ H_{0}(0)=0,F_{0}^{\prime}(0)=NF_{0}^{\prime\prime} (0),\ \ \ H_{0}^{\prime}(0)=NH_{0}^{\prime\prime}\ (0),\ \ \ G_{0}(0)=KG_{0}^{\prime}(0),\\ F_{0}^{\prime}(\eta)\to 0,H_{0}^{\prime}(\eta)\to 0,\ \ \ G_{0}(\eta)\to 0\ \ \mbox{as}\ \ \eta\to\infty.\end{array}  \tag{34}\]  
The solutions \(f_{0}(\eta)\), \(h_{0}(\eta)\), and \(\theta_{0}(\eta)\) are determined from the steady-state problem (9)-(12) before plugging back into Equations (31)-(33), in which the linear eigenvalue problem (31)-(34) could be solved. Moreover, [PERSON] et al. [47] suggested relaxing an appropriate boundary condition on \(F_{0}(\eta)\), \(H_{0}(\eta)\), or \(G_{0}(\eta)\) to better find the range of \(\gamma\). Thus, the condition \(\,F_{0}^{\prime}(\eta)\to 0\ \ \mbox{as}\ \eta\to\infty\ \mbox{is relaxed and for a fixed value of}\ \gamma\), the linear eigenvalue problem (31)-(34) are solved, along with the new boundary condition; \(F_{0}^{\prime\prime}\ (0)=1\). The obtained solutions offers an infinite set of possible eigenvalues \(\gamma_{1}<\gamma_{2}<\gamma_{3}<\ \ldots\), where \(\gamma_{1}\) is the minimum eigenvalue. Furthermore, if \(\gamma_{1}>0\), then there is an initial decay of disturbances and the flow becomes stable. Other than that, if \(\gamma_{1}>0\), then there is an initial growth of disturbances and the flow becomes unstable.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 2 Materials and Methods
### AMC Structure Design  
The AMC structure is a class of metamaterial that has a 0\({}^{\circ}\) phase reflection to an incident phase upon the surface. It is a variant of the Perfect Electric Conductor (PEC), which has a \(-180^{\circ}\) phase to an incident wave. The AMC region is usually accepted when the reflection phase is 0 \(\pm\) 90\({}^{\circ}\) for the range of frequencies.  
Figure 10 exemplifies the antenna close to the PEC and AMC structure. When an antenna was placed in front of the PEC with space less than \(\lambda\)/4, its 180\({}^{\circ}\) phase shift caused destructive interference, which results in poor reflection coefficient value and low total efficiency. Alternatively, when the antenna was placed in front of the AMC structure at spacing less than \(\lambda\)/4, its 0\({}^{\circ}\) phase shift caused constructive interference, resulting in good return loss and sufficient bandwidth [34].  
The unit cell was arranged periodically in a 3 \(\times\) 3 array. Figure 11 portrays a 3 \(\times\) 3 periodic array structure of the designed unit cell. The design geometry is shown in Figure 11, and the parameter specifications are listed in Table 3 wherein L\({}_{\text{u}}\) represents the length of the unit cell's periodic array. The array's thickness was considered to be 1.6 mm and gap between the unit cell was 2.5 mm. The design consists of rectangular slots in the ground, and u\({}_{\text{w}}\), u\({}_{\text{l}}\) are the space gaps between the slots. Figure 11a,b presents the front and rear views of the periodic array. Figure 11c,d depicts the array's front and back views of the fabricated unit cell structure.  
Figure 10: An Antenna placed flat against the PEC and AMC.  
Figure 9: Results of Unit Cell Structure, (**a**) Permeability, (**b**) Permittivity.  
The designed antenna was placed against the AMC with a spacing of a 25 mm gap. Figure 11(a) illustrates the front view of the AMC structure. Figure 11(b) is the true image of the fabricated AMC Backing antenna.  
Figure 11: 3 \(\times\) 3 Array Structure, Proposed 3 \(\times\) 3 Array (**a,b**), Snapshot of Fabricated 3 \(\times\) 3 Array. (**c,d**), (**a,c**) Front view, (**b,d**) Back view.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Dimension (mm)** \\ \hline L\({}_{\text{U}}\) & 112 \\ U\({}_{\text{g}}\) & 2.5 \\ U\({}_{\text{w}}\) & 32 \\ U\({}_{\text{l}}\) & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter specifications of 3 \(\times\) 3 Array periodic structure.  
Figure 12: Prototype AMC Backing Antenna (**a**) Antenna top view (**b**) Fabricated Antenna.
## 1 Introduction
### Novelty and Contributions  
The previous works of WR were carried out without taking into account the transmission limitations of IQDs. Nevertheless, IQDs are always present in real-world wireless deployments. The key remarkable elements and contributions of this work are listed here.  
* We will design a novel SFBC recognition algorithm for deployment in OFDM networks when IQDs are present. Developing a channel estimator will be an integral service we will adopt to enable the recognition process.
* We will consider that IQDs will take place at both the transmitter and recipient. The overall channel taps will be determined by incorporating the effects of IQDs at both endpoints into the actual link paths.
* This work's analytical investigation will illustrate that the exact maximum likelihood strategy in SFBC waveform recognition of OFDM systems will be too expensive for practical deployment. As a consequence, we will apply the expectation maximization algorithm as an innovative and cost-effective iterative technique.
* The proposed recognizer will leverage the a posteriori probabilities of the conveyed symbols provided by a channel control decoder to assess the a posteriori expectations of the broadcast symbols, which will be processed as if they are training data.  
The following is a list of the many benefits that the suggested approach offers.  
* It is consistent with any error control decoder mechanism that can determine the soft information of the broadcast symbols. This involves convolutional, turbo, and low density parity check decoders.
* It applies to any collection of SFBC signals, irrespective of the nature of their broadcast patterns, which include the number of transmit antennas and subcarriers employed in a space frequency block.
* It keeps up a reliable performance while maintaining the computational overhead within reasonable bounds.  
The preceding is a description of the upcoming tasks. Section 2 describes the system structure and the challenge. Section 3 discusses the proposed maximum likelihood recognizer along with its practical deployment. Section 4 outlines the simulation findings as well as pertinent interpretations. Section 5 introduces the concluding remarks of the work along with the possible future tasks.
# Simplified Calculation Method for Active Anti-Floating of Elliptical Basements by Relief Wells
## 2 Simplified Calculation of Multi-Well System for Elliptical Pit
### Seepage Field Inside a Circular Pit  
For the simplified calculation of unevenly distributed wells in non-circular pits, the first step is to find the analytical solution of the seepage field of unevenly distributed wells in circular pits and then to map it to the non-circle by means of a conformal transformation.  
In order to obtain the seepage field on the inside of the cut-off wall of a circular pit, this paper adopts the conformal transformation [33; 34] to solve the potential function at any point in the pit. First, a pumping well \(M_{1}\) with a distance \(e\) from the center of the circle and a flow rate \(q\) is placed in a circular pit with a radius \(R\). As shown in Figure 1, its coordinates are given as the following:  
\[z=x_{1}+iy_{1} \tag{1}\]  
Next, an injection well \({M_{1}}^{\prime}\) with the same flow rate is placed symmetrically around the circumference of well \(M_{1}\); the inverse mapping of well \(M_{1}\) gives well \(M_{2}\), i.e.,:  
\[z_{2}/R=R/z_{1} \tag{2}\]  
since well \({M_{1}}^{\prime}\) is symmetric with well \(M_{2}\) about the real axis, the coordinates of well \({M_{1}}^{\prime}\) are obtained:  
\[\overline{z_{2}}=R^{2}/\overline{z_{1}} \tag{3}\]  
then the potential function between well \(M_{1}\) and well \({M_{1}}^{\prime}\) is calculated as follows:  
\[\omega=\frac{q}{2\pi}\ln(\frac{z-z_{1}}{z-R^{2}/\overline{z_{1}}})+C \tag{4}\]  
since in the IDPR conditions, the point on the cut-off wall is taken for the zero point of the potential function, that is, when \(|z|=R\), the point on the cut-off wall potential function \(\omega=0\). At this time, the characteristic point is taken on the circumference of \(z=R\) and with substitution into the above equation, the following solution is obtained:  
\[C=\ln(R/|z_{1}|)\]  
Figure 1: Schematic diagram of a seepage field inside a circular pit.  
According to the principle of well superposition, in the case of mutual interference between several relief wells on the site, the potential function at any point within the site is equal to the superposition of the potential functions of several relief wells at that point. Therefore, by the principle of superposition, the potential functions of \(n\) relief wells within the circular pit cut-off wall, sorted as \(i=1,2,3,\ldots\), with coordinates \(\omega_{wi}\), are superposed to obtain the potential function of the circular pit multi-well system for a cut-off wall at any point \(\omega\) on the inside of the curtain, as shown below:  
\[\varphi=\sum_{i=1}^{n}\frac{q_{i}}{2\pi}\ln(\frac{\Xi-\Xi_{wi}}{\Xi-R^{2}/ \Xi_{wi}}\frac{R}{|\Xi_{wi}|}) \tag{6}\]
## 1 Introduction
Supelecl-lipschitz is a special polygon model for non-spherical particles that was first introduced by [PERSON] et al. [24] and later extended to 3D by [PERSON] et al. [25]. It can describe 80% of solid shapes [26; 27; 28; 29]. In this study, the cube, which is a unique shape of supeleclipsoid is used to characterize non-spherical particles. The discrete element method (DEM), initially proposed by [PERSON] and [PERSON] [5], has emerged as a powerful tool for investigating the mechanical behavior of granular materials [6; 7; 8]. The discrete element method offers several advantages over the finite element method, a numerical simulation technique designed for continuous media. Specifically, when studying the physical and mechanical properties of granular media, the discrete element method proves to be more advantageous [9]. At the macroscopic level, DEM can capture essential response features of granular media, including those relevant to large-displacement geomechanics problems. Furthermore, it enables the investigation of microstructural changes, such as contact force chains, displacement and rotation field distribution, and coordination number fluctuations [10; 11]. DEM has been widely employed to simulate binary mixtures and explore the influence of particle shape. Non-spherical particles can be effectively modeled using clump particles, where multiple spherical particles combine to form a single clump particle [12; 13; 14; 15]. Alternatively, polygon particles, where one spherical particle represents a polygon particle, have also been utilized [16; 17; 18; 19]. In some cases, the rolling friction coefficient is adjusted to reduce the rolling efficiency of spherical particles when modeling non-spherical particles [20; 21; 22; 23]. To investigate the impact of particle shape on binary mixtures, The concept of polygon particles, specifically employing the cube, a unique shape derived from the supeleclipsoid model introduced by [PERSON] et al. [24] and extended to three dimensions by [PERSON] et al. [25] is adopted. The supeleclipsoid model has been demonstrated to accurately represent approximately 80% of solid shapes [26; 27; 28; 29]. In this study, the cube, a distinctive supeleclipsoid shape, is utilized to characterize non-spherical particles within the binary media. This study aims to explore the influence of the volume proportion of cubic to spherical particles on the shear behavior of binary mixtures, considering the impact of particle shape. Binary samples comprising both spherical and cubic particles with varying volume fractions are prepared, and their shear strength and volumetric strain are analyzed from a macroscopic perspective. In addition to the macroscopic analysis, the microscopic metrics such as mechanical coordination number, contact force chains, and the anisotropy of normal and tangential forces are applied. These comprehensive analyses shed light on the underlying mechanisms governing the behavior of binary mixtures and provide valuable insights into the role of particle shape in shaping their macro and micromechanical properties.
# Hybrid GA-SOCP Approach for Placement and
## 3 Proposed Hybrid GA-SOCP Optimization Approach
### Master Stage: Genetic Algorithm
#### 3.2.1 Initial Population  
The most of nature-inspired optimization algorithms work with a list of potential solutions named \"population\". This list has the complete information regarding the solution of the problem under study. Here, we consider an integer population to determine the location of the DGs in the Dc network. This population \(W^{t}\) (being \(t\) the iterative counter) takes the following form:  
\[W^{t}=\begin{bmatrix}w_{11}&w_{12}&\cdots&w_{1a}\\ w_{21}&w_{22}&\cdots&w_{2a}\\ \vdots&\vdots&\ddots&\vdots\\ w_{b1}&w_{b2}&\cdots&w_{ba}\end{bmatrix}\]  
where \(a\) is the number of variables of interest and \(b\) is the number of the individuals in the population.  
Note that the component \(w_{km}\) in the initial population matrix is calculated, as follows:  
\[w_{km}=\text{round}\left(1+\text{rand}(\cdot\left(n-1\right)\right),\ \forall k=1,2,\ldots,b,\ \forall m=1,2,\ldots,n. \tag{19}\]  
where \(\text{round}(\cdot)\) determines the integer part of the argument and \(\text{rand}(\cdot)\) is a random number generated with a normal Gaussian distribution in the interval \([0,1]\). It is worth remembering that \(n\) is the number of nodes and it corresponds to the cardinality of the set \(\mathcal{N}\), i.e., \(n=|\mathcal{N}|\).  
**Remark 4**: _An essential stage during the creation of the initial population is that it is mandatory to guarantee that each potential individual \(W^{t}_{i}\) is different from each other \(W^{t}_{j};\forall j\
eq i\). This condition is known in genetic algorithms terminology as the diversity verification._
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the MAC Layer
#### 4.2.3 Uplink Transmission  
Uplink transmission is more complex than downlink transmission because the access point must learn about the stations' demands and inform the stations about the transmission parameters. For this purpose, there are two approaches that we present below: uplink OFDMA scheduled access and UORA.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 3 Applications in UAM
\begin{table}
\begin{tabular}{c c c} \hline
**Application** & **Requirements** & **Suitable Configurations** \\ \hline \multirow{6}{*}{Personal vehicle} & Up to 4 passengers & \\  & Short range/endurance & Multirotor/Dual \\  & Low service ceiling & system/ Helicopter \\  & Low-Medium speed & Considerable: \\  & Comfort and safety & Gyrodyne/Cyclocopter/Magnus- \\  & Small-medium dimensions & effect \\ \hline \multirow{6}{*}{Air-taxi} & Between 4–8 passengers & \\  & Short-medium range/endurance & Tilt-rotor \\  & Low-medium service ceiling & Helicopter \\  & Medium-High speed & Tilt-wing \\  & Comfort and safety & \\ \hline \multirow{6}{*}{Larger transportation} & More than 8 passengers & Fixed-wing \\  & Long range/endurance & Lighter than air \\  & Medium-high service ceiling & Considerable: \\  & High speed & Gyrodyne \\  & Comfort and safety & Morphing-wing \\ \hline \multirow{6}{*}{Emergency response} & Medium-high speed & Helicopter/Dual system/ \\  & Medium-long endurance/range & Tilt-wing/Tilt-rotor/Multirotor \\  & Medium cargo capacity & Considerable: \\  & Maneuverability & Gyrodyne/Tilt-body \\  & Hover flight & \\ \hline \multirow{6}{*}{Cargo delivery} & Low-high cargo capacity & VTOL and hybrid for \\  & Low-high endurance & small-medium cargo/HTOL for \\  & Low-high range & large cargo \\  & Low-high range & Considerable: \\  & High speed & Gyrodyne/Magnus- \\  & & effect/Morphing-wing/Tilt-body \\ \hline \multirow{6}{*}{Aerial surveillance} & Hover flight & \\  & Long endurance/Medium-long range & \\  & Low-high service ceiling & Lighter than air/Dual \\  & Low-medium payload & system/Multirotor/Tilt-rotor \\  & Robustness & \\ \hline \multirow{6}{*}{Entertainment} & Comfort & \\  & Broad view & Lighter than air/Gyroplane \\  & Low noise & \\ \hline \end{tabular}
\end{table} 
Table 16: Requirements and suitable configurations of different UAM applications. Figure 24: Development of PAV aircraft, by [PERSON] et al. [5].