esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Color Properties  
Figure 8 shows the resulting visual aspect of BioEP resin and BioEP/FF composites filled with coarse particles-CFF (Figure 8a) and fine particles-FFF (Figure 8b) after the curing/post-curing cycle.  
Table 5 gathers the color parameters for the BioEP resin and BioEP/FF composites. As can be seen in Figure 8, BioEP/FF composites obtained by casting show dimensional uniformity as well as a uniform and defect-free surface appearance. This situation is even more evident in composites with FFF. As can be seen, the unfilled BioEP resin sample has some degree of transparency, which is reflected by the low \(a^{*}\) and \(b^{*}\) values. After the addition of the FF filler, composites become brown due to the natural color of the lignocellulosic filler (see Figure 1), which produces an increase in the values in the \(a^{*}\) and \(b^{*}\) coordinates (Table 5). As it can be seen in Figure 8, and quantitatively analyzed by the CIElab coordinates, the color of composites filled with FF is very similar in all of them except that with 10 wt% of FFF, which presents a lighter brown color, obtaining a lower \(\Delta E\) with respect to the BioEP than the rest of the composites. Comparing both types of composites according to the filler size (CFF or FFF), it can be seen how the surface appearance of FFF-filled composites is much more homogeneous than CFF-filled composites. In the case of CFF-reinforced composites, random whitish spots are present on the surface, which may be due to the formation of large filler aggregates, which negatively affect both aesthetics and mechanical properties. The brown wood color acquired by the samples after the filler incorporation, and the excellent surface appearance acquired mainly in the samples reinforced with FFF can be attractive from the aesthetic point of view for applications in sectors such as furniture, construction, or automotive [85; 86].  
Figure 8: Visual aspect of BioEP and BioEP composites reinforced with different FF content; (**a**) (left to right) BioEP resin and BioEP composites reinforced with 10, 20, 30 and 40 wt% of CFF; (**b**) (left to right) BioEP resin and BioEP composites reinforced with 10, 20, 30 and 40 wt% of FFF.
## 3 Results
### Ex-Vivo Experiment
#### 3.1.2 Isoperimetric Ratio of Ablation Area  
Lung tissue is the respiratory organ of the human body, and the air volume in the lung changes periodically with the breathing movement, resulting in changes in its physical parameters. In the current clinical application of microwave ablation, there is a lack of accurate prediction of the thermal field of microwave ablation. The choice of ablation parameters only based on the doctor's subjective experience may lead to poor ablation of the lesion, postoperative tumor recurrence, or excessive ablation of the lesion, causing serious complications and additional harm to the patient. Therefore, it is very important to accurately predict the size and shape of the ablation area and reserve a reasonable safety  
Figure 5: Average long diameter.  
Figure 6: Average short diameter.  
margin. Considering that most lung tumors are spherical or irregular in shape, it is ideal that the shape of the ablation area tends to be round, so that the lesion can be completely covered, and the damage to the surrounding normal tissue can be minimized. Therefore, this study cites the isoperimetric ratio [31] as an index to evaluate the ablation area. The isoperimetric ratio is a measure to describe the circularity of a two-dimensional object, and the calculation method is shown in Equation (5):  
\[\text{isoperimetric ratio}=4\pi\cdot\text{S}/\text{A}^{2} \tag{5}\]  
In the formula, S is the area of the graph (cm\({}^{3}\)), and A is the perimeter of the graph (cm). When the value of isoperimetric ratio is equal to 1, the figure can be regarded as an ideal perfect circle, on the contrary, when the value of circularity is close to 0, it means that the shape is an oblate or a crack-like shape. By calculating the data in Tables 5-7, the isoperimetric ratio of the ablation area under different ablation parameters is obtained, as shown in Table 8.  
From Table 8, it can be concluded that the isoperimetric ratio of the inflated group is in the range of 0.81-0.89, and the isoperimetric ratio of the deflated group is around 0.89-0.94. Under the same ablation power and ablation time, the isoperimetric ratio of the deflated group is greater than that of the inflated group, and the shape of the ablation area is closer to the regular circle. The shape of the ablation area in the inflation state is more inclined to the slender ellipse, which is mainly due to the large difference in the short diameter of the ablation area between the two groups.
## Appendix D CDS-Hops: Detailed Derivation Steps  
By putting \(\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha=b\pi\), in Equation (16), we have the following expression:  
\[\sum_{i=1}^{CDS-hops}h_{i}=\left(\frac{b\pi}{\pi}+\frac{b\pi}{\pi}\ \beta\ \right)\pi[1-\sum_{i=1}^{\infty}\frac{(2i)!^{2}}{(2^{i}.i)!^{4}}\ \frac{(\beta^{2}+2\beta)^{i}}{(2i-1)(1+\beta)}] \tag{11}\]  
\[\sum_{i=1}^{CDS-hops}h_{i}=[b\pi+b\pi\beta)[1-\sum_{i=1}^{\infty}\frac{(2i)!^{ 2}}{(2^{i}.i)!^{4}}\ \frac{(\beta^{2}+2\beta)^{i}}{(2i-1)\left(1+\beta\right)}] \tag{12}\]  
\[\sum_{i=1}^{CDS-hops}h_{i}=b\pi(1+\beta)[1-\sum_{i=1}^{\infty}\frac{(2i)!^{2}} {(2^{i}.i)!^{4}}\ \frac{(\beta^{2}+2\beta)^{i}}{(2i-1)(1+\beta)}] \tag{13}\]  
By replacing the value of \(b\) in Equation (13), we have:  
\[\sum_{i=1}^{CDS-hops}h_{i}=[\ \sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+ \alpha](1+\beta)[1-\sum_{i=1}^{\infty}\frac{(2i)!^{2}}{(2^{i}.i)!^{4}}\ \frac{(\beta^{2}+2\beta)^{i}}{(2i-1)(1+\beta)}] \tag{14}\]  
**Table E2. ** List of symbols. \begin{tabular}{l l} \hline \(G\) & Graph \\ \(V\) & Set of vertices \\ \(E\) & Set of edges \\ \(A_{DC}\) & Direct communication area \\ \(A_{MC}\) & Multi-hop communication area \\ \(A_{ellipse}\) & Area of ellipse \\ \(A_{FQ-ellipse}\) & Area of the first quadrant of the ellipse \\ \(n\) & Number of sensor nodes \\ \(D_{a}\) & Acceptable delay \\ \(V_{CDS}\) & Set of CDS nodes \\ \(V_{non-CDS}\) & Set of non-CDS nodes \\ \(E_{CDS}\) & Set of edges between CDS nodes \\ \(E_{non-CDS}\) & Set of edges between non-CDS nodes \\ \(E_{MST}\) & A subset of \(E_{CDS}\) that forms an MST \\ \(E_{non-MST}\) & A subset of \(E_{CDS}\) that does not form an MST \\ \(i\) and \(j\) & Node indices \\ \(S^{\prime}\) & Spanning tree \\ \(S^{\prime\prime}\) & A sub-graph of \(S^{\prime}\) that forms an MST \\ \(e(i,j)\) & Edge between \(i\) and \(j\) \\ \(a\) & Major axis of an ellipse \\ \(b\) & Minor axis of an ellipse \\ \(l(i,j)\) & Link between \(i\) and \(j\) \\ \(d\) & Euclidean distance \\ \(T_{GN}\) & Total number of GNs \\ \(\alpha\) & Offset factor that is used to compensate the distance margin \\ \(x_{i}\) & x-coordinate of node \(i\) \\ \(y_{i}\) & y-coordinate of node \(i\) \\ \(r\) & Radius of a circle \\ \(C\) & Circumference of a circle \\ \(A\) & Area of a circle \\ \(L_{p}\) & Length of AUV path \\ \(h_{CDS}\) & CDS hops \\ \(h_{non-CDS}\) & Non-CDS hops \\ \(N\) & Total number (of CDS or non-CDS edges) \\ \(P\) & Perimeter of the elliptical path traversed by an AUV \\ \(e\) & Eccentricity of an ellipse \\ \(\beta\) & A control variable for different trajectories of an AUV \\ \(dB\) & Decibels \\ \hline \end{tabular}   
**Table E3. ** List of symbols.
## 3 Results
### Transition Frequency  
The relation of COM-COP and ankle-hip angle was analyzed by using CCF coefficient data, and the transition frequency was determined with the sigmoid model. The COM-COP and ankle-hip sigmoid models of the representative subject are shown in Figure 6. All subjects showed the changes from upper to lower distribution and vice versa of CCF coefficient data, which can be explained as the degree of freedom of human balance being changed from one degree of freedom (DOF) to two DOF. The COM-COP and ankle-hip transition frequency for every subject and perturbation conditions are summarized and shown in Figure 7. Even though all subjects were free from neurological disease, they showed different transition frequencies at both COM-COP and ankle-hip transitions.  
For the COM-COP transition, the transition frequency varied between subjects. From the total of 40 transitions frequencies of COM-COP and ankle-hip for the 20 subjects, 26 transitions frequencies showed a transition of both COM-COP and ankle-hip transition at lower frequencies during increasing frequency perturbation conditions. For decreasing frequency, 16 transition frequencies showed the transition in the lower frequencies. The mean transition frequency of COM-COP was 0.44 Hz (\(\pm\)0.12) during increasing frequency, while it was 0.55 Hz (\(\pm\)0.12) during decreasing frequency. Furthermore, the mean transition frequency of ankle-hip during increasing frequency was 0.42 Hz (\(\pm\)0.12) and  
Figure 6: The representative of CCF coefficient data distribution with the sigmoid model during (**a**) increasing frequency and (**b**) decreasing frequency translation perturbation condition. Each blue point represents a CCF coefficient data for each frequency. The black line represents the sigmoid model. UD: Upper distribution; LD: Lower distribution.  
0.56 Hz (\(\pm\)0.12) during decreasing frequency. From the mean value of transition frequency, it was observed that the COM-COP transition and ankle-hip transition occurred almost at the same frequency during both increasing and decreasing frequency. A significant difference was observed between increasing and decreasing frequency for COM-COP (\(p\) = 0.003) and ankle-hip transition frequencies (\(p\) = 0.008). However, no significant difference was observed between COM-COP and ankle-hip transitions frequency in both frequency conditions (increasing (\(p\) = 0.56, ns); decreasing (\(p\) = 0.17, ns)). No significant difference was observed between subjects during both frequency order conditions (increasing (\(p\) = 0.08, ns) and decreasing (\(p\) = 0.34, ns)). A significant difference was observed between increasing and decreasing frequency perturbation for head peak-to-peak amplitude (\(p\) = 0.0361). All subjects showed \(r^{2}\) value more than 0.039, indicating that the model significantly fit with the CCF coefficient data distribution.
# Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography
## 4 Discussion
This is most easily controlled via measurements obtained from a homogeneous elastography phantom and therefore may not be fully transferable to the clinical setting. The study phantom material has a low value of viscosity [50]. Human tissue typically has greater viscosity which can result in variations in the attenuation and speed of the generated shear wave [6]. Measurements were only acquired at two depths--3 cm and 6 cm. Increasing depth is a cause of SWV discrepancy when imaging the same target due to shear wave attenuation [13; 15]. Thus, it is likely that the intermediate and harder targets may demonstrate less variability at depths between 3 cm and 6 cm, however, this work did not address this. In this study, the greatest intra-system variability occurred because of phantom target depth and stiffness--these factors are likely to be exacerbated in human tissue.
## 3 Imaging Attitude Control
### Coordinate Frames
#### 3.1.5 Sensor (S) Frame  
The origin \(O_{S}\) of this frame is also at \(O_{N}\), and it is the center of the sensor's sensitive area. While the boresighting has been done and the three gimbal angles are zero, the roll axis \(X_{S}\) is coincident with the aircraft roll axis and the longitudinal direction of the pixel array. The sensor pitch axis \(Y_{S}\) is coincident with the aircraft pitch axis and the latitudinal direction of the pixel array. \(Z_{S}\) is aligned with the LOS and forms an RH orthogonal set.
## 3 Results and Discussion
### Structure Verification of 2,4-DTu by NMR  
The \({}^{1}\)H NMR spectrum of 2,4-dithiouracil showed two singlets at 12.90 ppm and 13.64 ppm for the protons in both NH groups (H-1 and H-3), respectively. The lack of HSQC correlations confirmed that these two singlets were for protons that are not bound to carbons. Additionally, there were two doublets at 6.50 ppm and 7.27 ppm in the \({}^{1}\)H NMR spectrum that were assigned correspondingly to the H-5 and H-6 protons. In support of this assignment, there was one \({}^{1}\)H-\({}^{1}\)H COSY correlation found between the signals at 6.50 ppm and 7.27 ppm. Also, there were two HMBC correlations for the H-5 proton with the C-4 and  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Complexes** & **Color** & **Yield (\%)** & **Melting Point (\({}^{\circ}\)C)** & **Solubility** \\ \hline
2,4-DTu & yellow & & 279–281 & soluble in DMSO \\ \hline Cu(II) & yellow-green & 56 & \(>\)350 \({}^{\circ}\)C & limited solubility in DMSO, C\({}_{6}\)H\({}_{12}\), and DMF; and insoluble in EtOH, H\({}_{2}\)O, THF, and EIOAc. \\ \hline Au(III) & beige & 52 & \(>\)350 \({}^{\circ}\)C & limited solubility in DMSO and DMF; insoluble in H\({}_{2}\)O, THF, EtOH, EIOAc, and C\({}_{6}\)H\({}_{12}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical and physical characteristics of metal complexes with 2,4-dithiouracil and its derivatives.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Metal** & **Composition \({}^{\ast}\)** & **Formula** & **Molecular Weight** & **W(M)\% Calc/Exp.** & **W(S)\% Calc/Exp.** \\ \hline Au(III) & [2,4-DTu.(2- & \(\mathrm{Tu_{2}}\)Au].U.5 DMSO.5H\({}_{2}\)O & C\({}_{26}\)H\({}_{51}\)N\({}_{6}\)O\({}_{14}\)S\({}_{9}\)Au & 1185.28 g/mol & 16.62/16.7 \(\pm\) 1.5 & 24.35/25.2 \(\pm\) 2.1 \\ Au(III) & [2,4-DTu.(2-Tu)\({}_{2}\)Au].U.2-Tu. & C\({}_{28}\)H\({}_{37}\)N\({}_{12}\)O\({}_{10}\)S\({}_{9}\)Au & 1187.22 g/mol & 16.59/16.7 \(\pm\) 1.5 & 24.31/25.2 \(\pm\) 2.1 \\ Cu(II) & 2,4-DTu.2 DMSO.3H\({}_{2}\)O & [2-Tu.U.Cu]4 DMSO.4H\({}_{2}\)O & C\({}_{16}\)H\({}_{36}\)N\({}_{4}\)O\({}_{11}\)S\({}_{5}\)Cu & 684.35 g/mol & 9.29/9.23 \(\pm\) 0.65 & 23.43/21.9 \(\pm\) 1.8 \\ \hline \hline \end{tabular} \({}^{\ast}\)Tentative average composition of different complexes. 2,4-DTu = 2,4-dithiouracil; 2-Tu = 2-thiouracil; U = uracil.  
\end{table}
Table 2: Elemental analyses data for the metal ions and sulfur of the complexes.  
C-6 carbons, as well as three HMBC correlations for the H-6 proton with C-2, C-4 and C-5 carbons. The carbon assignments for 2,4-dithiouracil were additionally verified by using the option provided in the NMRshiftDB database for a \({}^{13}\)C NMR chemical shift prediction based on hierarchically ordered spherical environment (HOSE) codes [40]. The complete NMR signal assignments for 2,4-dithiouracil are given in Table 3.
# Impact of Audio Delay and Quality in Network Music Performance +
## 1 Introduction
We present the largest NMP study with actual musicians and performances that we are aware of and the first ever study covering audio quality for NMP. The remainder of this paper is structured as follows. We present related work in Section 2. Section 3 describes our experimental setup, while Section 4 presents our questionnaire. Section 5 presents and analyzes the subjective results from our experiments,while Section 6 offers a discussion on the study's findings and some lessons learned. We summarize our findings and discuss future work in Section 7. An extended version of this work is available in the PhD thesis of [PERSON], Quality of Musicians' Experience in Network Music Performance, Department of Informatics, Athens University of Economics and Business, 2022.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Comparison with Classic DL Methods  
In this section, we make a comparative analysis of several classical deep learning networks with the proposed DECC. The DECCs put forward by the test group are our network and the control group, including seven other DL models, i.e., Vit_b_16 [39], ResNet18 [18], Vgg [31], Densenet [40], Mnasnet [41], GoogLeNet [19], and Mobilenet_v3 [21]. The DECC we used for comparative analysis has 3 layers of Dual-Encoder and 15 layers of Conv-for-Sum. For the other networks, to obtain the best positioning precision, we stopped when it converges to the best result of smooth.  
In terms of time overheads, as shown in Figure 13a, the DECC has 4.5 h of training cost, and Vit_b_16 [39], ResNet18 [18], Vgg [31], Densenet [40], Mnasnet [41], and GoogLeNet [19] are 40%, 64%, 66%, 75%, 62%,68%, and 75% less than Mobilenet_v3 [21] %, respectively.  
As shown in Figure 13b, the network positioning accuracy of DECC is 0.26 m, and the mean square error is less than 0.30, compared with Vit_b_16 [39], ResNet18 [18], Vgg [31], Densenet [40], Mnasnet [41], GoogLeNet [19], and Mobilenet_v3 [21], which increased by 73%, 50%, 488%, 330%, 176%, 62%, and 438%, respectively. This shows that, while it improves the positioning accuracy, it also improves the training efficiency, and so our proposed DECC has high practical application value.
# Numerical Homogenization of Single-Walled Corrugated Board with Imperfections
## 2 Materials and Methods  
### Material Parameters and Corrugated Cardboard Geometry  
Corrugated cardboard is a fibrous material, and therefore it is highly orthotropic. The direction of the fibers in the individual cardboard layers determines the mechanical properties of the board components. For wood-based materials, two main directions can be distinguished: the machine direction (MD) and the cross direction (CD). The strength of paper and paperboard is more than twice as high in the MD than in the CD because the fibers are arranged along the MD in the production process (see Figure 1). In the cross direction, the material is more ductile, while in the machine direction, it is more resistant to tearing and crushing.  
The linear behavior of an elastic orthotropic material can be represented by the strain-stress relationship:  
\[\begin{bmatrix}\varepsilon_{11}\\ \varepsilon_{22}\\ 2\varepsilon_{12}\\ 2\varepsilon_{13}\\ 2\varepsilon_{23}\end{bmatrix}=\begin{bmatrix}1/E_{1}&-v_{21}/E_{2}&0&0&0\\ -v_{12}/E_{1}&1/E_{2}&0&0&0\\ 0&0&1/G_{12}&0&0\\ 0&0&0&1/G_{13}&0\\ 0&0&0&0&1/G_{23}\end{bmatrix}\begin{bmatrix}\sigma_{11}\\ \sigma_{22}\\ \sigma_{12}\\ \sigma_{13}\\ \sigma_{23}\end{bmatrix}, \tag{1}\]  
Figure 1: The force-displacement curves in MD, CD and \(45^{\circ}\).  
where \(E_{1}\) and \(E_{2}\) are the [PERSON]'s moduli in the MD and the CD, respectively; \(\
u_{12}\) and \(\
u_{21}\) are the [PERSON]'s coefficients; \(G_{12}\) is the [PERSON]'s modulus; \(G_{13}\) and \(G_{23}\) are the transverse shear moduli. The symmetry of the material stiffness/compliance matrix allows us to determine the relationship between the [PERSON]'s coefficients and the [PERSON]'s moduli:  
\[\frac{\
u_{12}}{E_{1}}=\frac{\
u_{21}}{E_{2}}. \tag{2}\]  
The material orientation is always the same in all layers of corrugated cardboard. The machine direction coincides with the cardboard wave direction, which results from the technological conditions of the production process (see Figure 2). The lower strength in the cross direction is compensated by a take-up factor of the corrugated layer.  
All corrugated board layers were modeled while using classical linear elastic orthotropy, see Equation (1). The material data were taken from the literature [17] and are presented in Table 1. The thickness of both flat layers (liners) and the corrugated layer (fluting) was assumed to be 0.30 mm.
# Validation of a Modal Work Approach for Forced Response Analysis of Bladed Disks
## 3 Results and Comparisons
### LPT Bladed Rotor Test Case
#### 3.2.2 Pre-Stressed Modal Analysis  
The first FEM computation is a static analysis needed for evaluating the pre-stress state of the sector. The influence of the thermal load was neglected, while the centrifugal load was considered by imposing a rotational speed of 3100 rpm. This value is obtained by means of an iterative procedure to obtain a resonance crossing at the 1 xBPF of the IGV in the ZZENF diagram.  
The pre-stressed modal cyclic symmetry analysis was performed to extract only the first three mode families for a selected set of nodal diameters. The modal displacements were extracted at the nodal diameters identified in the ZZENF diagram. The ZZENF diagram for the rotor is shown in Figure 7, reporting the eigenfrequencies as a function of the nodal diameter for the first three families. The speed line related to the rotational speed of 3100 rpm is reported as well. This diagram allows identifying the potential frequencies where resonances occur.  
All three mode families present the typical behavior of bladed rotor systems: at lower nodal diameters, the system shows a relevant disk participation with a sudden frequency increase. Moving to a higher value of nodal diameters, the disk does not participate anymore to the overall deformation that is concentrated to the blade, so there is no more significant difference in the frequency with the variation of nodal diameters. This effect tends to become more evident for high-frequency families.  
The validation of the Tremor tool was performed for two forcing distributions, which consider the effect of both IGV and stator wakes on the rotor. In the first case (IGV wake forcing), the resonance crossing occurs at \(\text{ND}\,=\,-7\) (backward), where the natural mode frequency corresponds to the first BPF of the IGV (\(\text{EO}\,=\,7\)). In the second case (rotor wake forcing), the first BPF of the stator (\(\text{EO}\,=\,126\)) does not correspond to the mode frequency at \(\text{ND}\,=\,+21\) (forward), so this is an out of resonance condition for first mode family, as shown in Figure 7.  
Figure 6: FEM mesh of the single sector low-pressure turbine bladed rotor.  
The first family (bending mode) contours for ND \(=\)\(\pm\)7 are shown for the whole system in Figure 8, where the traveling wave deformation is clearly visible.  
The modal displacements for the blade surface points and two control nodes are thus extracted for ND \(=\)\(-\)7 and ND \(=\)\(+\)21.