esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Bio-Sourced Epoxies for Bio-Composites
## 3 Epoxy Resins Based on Itaconic Acid
In addition, after co-monomers such as divinyl benzene (DVB) and acetylated epoxidized soybean oil (AESO) were introduced into the EIA/MHHPA system, dual-curing systems were formed, and the rigidity and toughness could be manipulated further via the various contents of rigid DVB or soft AESO, as shown in Figure 4, compared with data from reference published [10]. As shown in Scheme 3, direct reactions between itaconic acid and epichlorohydrin generate resin EIA, which is a mixture of different oligomers of varying molecular weights. To make the best use of the  
Figure 4: Mechanical properties of the cured EP resins. EIA0 and DGEBA refer to the cured samples without comonomers; D and A denote the DVB (divinyl benzene) and AESO (acylated epoxidized soybean oil) co-monomers, respectively. carboxylly groups and itaconic acid double bond, a trifunctional EP monomer (TEIA) was designed and synthesized (Figure 4), and it generated an extremely high epoxide value of 1.16 and a low viscosity of 0.92 Pa s at 25 \({}^{\circ}\)C. It is well known that low resin viscosities are beneficial for manufacturing. The lower viscosities in TEIA render it easier to process than DGEBA. In Table 5, the flexural properties of TEIA cured by different curing agents are listed. When flexible poly(propylene glycol) bis(2-aminopropyl ether) (D230) is used as the curing agent, the TEIA/D230 system shows higher flexural modulus, higher strain at break and comparable flexural strength to DGEBA/D230. When rigid MHHPA was employed as the curing agent, the flexural strength, modulus and elongation at break of the TEIA/MHHPA system exceeded those of the DGEBA/MHHPA system. These results denote that TEIA may be used as a high-performance epoxy resin [11]. Given the presence of double bonds in itaconic acid and the low flame retardancy of EP resins, a flame-resistant DOPO (6H-dibenz(C,E)(1,2)oxaphosphorin-6-oxide) was chemically incorporated into the itaconic-acid-based EP resin, and a phosphorus-containing EP resin (EADI) was obtained (Scheme 4). The property study showed that the cured EADI network presents a comparable glass transition temperature and mechanical properties to those of the DGEBA system. In addition, excellent flame retardancy capacities with the UL94 V-0 grade used during vertical burning tests were observed for the EADI system. EADI may be used as a bio-based candidate for EP flame retardancy [12]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Flexural Strength (MPa)** & **Flexural Modulus (MPa)** & **Elongation at Break (\%)** \\ \hline DGEBA/D230 & \(121\pm 1\) & \(2952\pm 18\) & \(13\pm 2\) \\ TEIA/D230 & \(117\pm 2\) & \(3603\pm 77\) & \(21\pm 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Flexural properties of cured EP resins with D230 curing agents.
# Spatially Adaptive Regularization in Image Segmentation
## 2 Defining Local Regularization by Exploiting Spatial Information
### Regularization Based on the Cartoon-Texture Decomposition  
We define \(f(\bar{u}_{i,j})\) by using the Cartoon-Texture Decomposition (CTD) of the image discussed in [29]. CTD splits any image \(u\) into the sum of two images, \(w\) and \(v\), such that \(w\) represents the cartoon or geometric (piecewise-smooth) component of \(u\), while \(v\) represents the oscillatory or textured component, i.e., \(v\) contains essentially textures, oscillating patterns, fine details, and noise. The algorithm for computing CTD acts as described next. For each image pixel, we decide whether it belongs to the cartoon or the textural part by computing a local indicator associated with an image window around the pixel. The main feature of a textured region is its high TV, which decreases very fast under low-pass filtering. This leads to the following definition of local total variation (LTV) at a pixel \((i,j)\):  
\[LTV_{\sigma}(u)_{i,j}=\left(L_{\sigma}*|\
abla u|_{i,j}\right), \tag{7}\]  
where \(L_{\sigma}\) is a low-pass filter, \(\sigma\) is a scale parameter, \(|\
abla u|_{i,j}=\sqrt{(\
abla_{x}u)_{i,j}^{2}+(\
abla_{y}u)_{i,j}^{2}}\), and \(*\) denotes the convolution product. The relative reduction rate of LTV,  
\[(\rho_{\sigma})_{i,j}=\frac{LTV_{\sigma}(u)_{i,j}-LTV_{\sigma}(L_{\sigma}*u)_ {i,j}}{LTV_{\sigma}(u)_{i,j}}, \tag{8}\]  
gives the local oscillatory behavior of the image. A value of \((\rho_{\sigma})_{i,j}\) close to 0 means that there is little relative reduction of LTV by the low-pass filter, thus the pixel \((i,j)\) belongs to the cartoon. Conversely, \((\rho_{\sigma})_{i,j}\) close to 1 indicates that the relative reduction is large and, hence, the pixel belongs to a textured region.  
We use (8) for defining the weights \(\lambda_{i,j}\). The basic idea is that a large regularization parameter is needed if the pixel \((i,j)\) belongs to the cartoon, while the parameter must be reduced in texture regions. Therefore, we set the function \(f\) in (6) as  
\[f(\bar{u}_{i,j})\equiv f^{\text{CTD}}(\bar{u}_{i,j})=\max\left\{\frac{\lambda_ {min}}{\lambda_{max}},\,1-(\rho_{\sigma})_{i,j}\right\}\lambda_{max} \tag{9}\]  
and \((\rho_{\sigma})_{i,j}\) is defined by using the given image \(\bar{u}\). Following [30], we set \(L_{\sigma}\) equal to the Gaussian filter.
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 2 Materials and Methods
### Feature Selection  
In order to limit the computational complexity of the solution without compromising the classification performance, the maximum number of features actually considered for the training phase of this study is arbitrarily set to ten. This is in line with expert opinions and previous works which have shown that the number of discriminative features can be very small [19; 24; 28; 32]. For filtering and using only the most informative features relative to the heartbeat type classes, the mutual information (MI) ranking criterion as described by [PERSON] et al. [28] is followed. The function _mutual_info_classif_ provided by the Scikit-Learn package for Python [30] is used for estimating the MI between each feature and the class labels using the training set DS1. Table 2 shows the top ten MI ranked features.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Pure Mode Generation Using ABT-ABT  
The tuning angle of the 2-mm quasi-isotropic CFRP composite plate was calculated for the pure-mode guided wave generation. The material properties were measured experimentally using the ultrasonic immersion technique described in Reference [31] (given in Table 2).  
Dispersion curves of the 2-mm quasi-isotropic CFRP composite plate were obtained using the semi-analytical finite element (SAFE) method [5]. For the SAFE approach, 32 one-dimensional (1D) quadratic elements across the thickness direction were used to ensure the convergence of the solution. Phase-velocity dispersion curves of the 2-mm quasi-isotropic CFRP composite plate in the 0\({}^{\circ}\) direction are shown in Figure 8a. In this study, the wedge velocity of the adjustable wedge was 2720 m/s, and the ABT central frequency was 500 kHz. The theoretical tuning angle was calculated based on Equation (1), as shown in Figure 8b. It was found that the wave modes could not be excited when the phase velocities were below \(c_{w}\). At 500 kHz, S0 mode and SH0 mode could be excited, and the ABT tuning angles were around 26\({}^{\circ}\) and 49\({}^{\circ}\), respectively. Therefore, the incident angles should be set to 26\({}^{\circ}\) and 49\({}^{\circ}\) to excite pure S0 mode and SH0 mode in the 2-mm quasi-isotropic CFRP composite plate. In addition, the group-velocity dispersion curves are given in Figure 8c, and the group velocities of S0 mode and SH0 mode at 500 kHz were 5.49 mm/\(\upmu\)s and 3.39 mm/\(\upmu\)s, respectively. These group velocities were used to validate the generated guided waves in the composite plate.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(E_{\mathbf{11}}\) & \(E_{\mathbf{22}}\) & \(E_{\mathbf{33}}\) & \(\
u_{\mathbf{12}}\) & \(\
u_{\mathbf{13}}\) & \(\
u_{\mathbf{23}}\) & \(G_{\mathbf{12}}\) & \(G_{\mathbf{13}}\) & \(G_{\mathbf{23}}\) & \(\rho\) \\ \hline
153 GPa & 8.7 GPa & 8.7 GPa & 0.37 & 0.37 & 0.5 & 6.9 GPa & 6.9 GPa & 2.9 GPa & 1625 kg/m\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 2: Engineering constants of the unidirectional prepreg.  
Figure 7: Ultrasonic NDT inspection results of the impact damage in the 2-mm quasi-isotropic composite coupon: (**a**) B-scan image; (**b**) C-scan image.
# Numerical Investigation of the Collapse of a Steel Truss Roof and a Probable Reason of Failure
## 3 Analysis
### Analysis of the Columns and Supports
#### 3.2.1 Analysis of the Steel-Reinforced Columns  
The main purpose of this analysis was to understand the effective material properties of the steel-reinforced concrete columns by using three-dimensional finite elements. Here, the column was assumed to be a composite beam and its material properties were calculated with homogenisation. Thus, the aim was to obtain a more realistic global model. For this purpose, a model was created considering the steel reinforcement of the concrete columns. In this model, the column material was considered as a composite material with concrete and reinforcing steel rods. The steel rods were placed in the concrete as shown in Figure 7. The rods and the concrete were rigidly bonded to each other. According to the general theory of mechanics of composite materials, lateral reinforcement steel binders, which are predicted to have no significant effect on axial stiffness, were not included in the analysis for the sake of simplicity. The steel reinforcing rods were taken as given in the project and as described in the previous section.  
To represent the behaviour, a cross-sectional face was subjected to axial displacement restrictions whereas the opposite face, which is the other cross-sectional face, was subjected to a uniform axial displacement. Using the results of the analysis, the effective Poisson's ratio and Young's modulus were determined. A similar procedure was also performed for the effective thermal expansion coefficient. The results of this analysis were used in the global analysis.  
Figure 6: Three-dimensional finite element model prepared for finite element (FE) analysis.
## 3 Multi-Beam Fast Tracking and Satellite Capture Method
### Overall Scheme of Multi-Beam
The detection component performs beam pointing and energy characteristic analysis on each sub-beam and sends the analyzed sub-beam pointing position coordinate values in the directional pattern to the optimal pointing analyzer. The optimal pointing analyzer analyzes and calculates an optimized array of antenna pointing information, which includes azimuth and elevation angles. This analysis is based on the coordinate values of the sub-beam pointing directions in the directional pattern. Figure 8: A sketch of the selecting point in the multi-beam antenna pattern. Figure 9: Structure of the multi-beam scheme.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 5 Evaluation  
To verify RSTS's performance, we conducted simulations in Matlab R2021b and implemented a 10-hop chain topology with 11 nodes, as shown in Figure 4. For comparison, we also performed FTSP with or without threshold detection, i.e., the difference between two consecutively receiving timestamps did not exceed the preset threshold of 1.001. The clock skew \(\alpha_{i}\) and offset \(\beta_{i}\) of each node were randomly selected from the sets [0.9999, 1.0001] and [0, 0.0002], respectively, as the maximum clock drift was 100 ppm [9]. The synchronization period \(T\) was 1 s. The length of the linear regression table was 8 [23]. Each node recorded its time based on a 32.768 kHz crystal oscillator. We also defined the clock difference between each ordinary node and the time source to denote the synchronization performance, including clock skew and offset. To simulate a real network environment, we assumed that the communication delay satisfies a normal distribution with a mean of 2.5 \(\times\) 10\({}^{-4}\) and a variance of 1 \(\times\) 10\({}^{-8}\)[9].
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 5 Conclusions  
In this paper, the iterative interpolation method and cluster analysis method has been presented for improving the positioning efficiency of indoor fingerprinting acoustic localization. In the fingerprinting acoustic localization process, the calibration efforts in offline phase can be reduced due to the sparse sampling treatment, and the satisfactory positioning accuracy can be guaranteed by virtual RPs  
Figure 8: The positioning results of the fingerprinting acoustic localization based on iterative interpolation method. The green, pink, blue and yellow dots are the RPs clustered to different clusters, red dots denote the test points, and the black dots are the estimation results of each interpolation.  
Figure 9: The positioning results comparison of the acoustic localization without interpolation process and acoustic localization base on iterative interpolation process.  
generated by the iterative interpolation method. Meanwhile, the K-Means cluster analysis method was adopted for database partition, and a two-level RPs matching method was used to speeding up the online positioning phase. The results show that the fingerprinting acoustic localization method can achieve satisfactory accuracy with few initial RPs sampling in offline phase and a more rapid RPs matching process in online phase by iterative interpolation and cluster analysis. As future works, an extension of the clustering method to reduce the location results deterioration of the frontier points and various types of complex tasks for further verification of the novel method are being considered.  
[PERSON] conceived and designed the experiments and wrote the paper. [PERSON] and [PERSON] contributed to project research scheme formulation. All authors contributed to the final version.  
This research was funded by the National Natural Science Foundation of China (No. 61373017), Natural Science Foundation of Hebei Province (No. F2014202121), and Graduate Student Innovation Funding Project of Hebei Province (No. 220056).  
The authors thank all the reviewers and editors for their valuable comments and work.  
The authors declare no conflict of interest.
## 3 Performance Analysis for Single SWIPT Relaying System
### MGF Expression for the Indirect Link  
The MGF of \(\gamma_{\rm id}\) is derived as  
\[\begin{split} M_{\gamma_{\rm id}}(\mathsf{s})&=\int_ {y=0}^{\infty}\left[\int_{x=0}^{\infty}\frac{1}{\bar{\gamma}_{\rm sr}}e^{- \frac{\gamma_{\rm sr}}{\bar{\gamma}+1}\mathsf{s}}e^{-\frac{\chi}{\bar{\gamma} _{\rm rd}}}dx\right]\frac{1}{\bar{\beta}_{\rm rd}}e^{-\frac{\chi}{\bar{\beta}_{ \rm rd}}}dy\\ &=\frac{1}{\bar{\gamma}_{\rm sr}\mathsf{s}+1}+\frac{\bar{\gamma}_ {\rm sr}\mathsf{s}}{\bar{\gamma}_{\rm sr}\mathsf{s}+1}\frac{\exp(1/\alpha)}{ \bar{\gamma}_{\rm sr}\bar{\beta}_{\rm rd}\mathsf{s}+\bar{\beta}_{\rm rd}}E_{1} \left(\frac{1}{\alpha}\right)\end{split} \tag{14}\]  
with \(\alpha=a\bar{\beta}_{\rm rd}\) and \(a=\bar{\gamma}_{\rm sr}\mathsf{s}+1\)[41; 42; 43]. Furthermore, an upper bound for the MGF is given by [42]  
\[M_{\gamma_{\rm id}}(\mathsf{s})<\frac{1}{a}+\frac{a-1}{a}\frac{\ln(1+\alpha)} {\alpha}=M_{\gamma_{\rm id}}^{\rm APP}(\mathsf{s}). \tag{15}\]
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
## 2 Main Concepts  
### Apparent Track Stiffness  
#### 2.1.1 Light Falling Weight Deflectometer (LFWD)  
The LFWD is a portable, simplified version of the FWD to measure the displacement and load (refer to Figure 1). A geophone and load cell are in the LFWD unit. So, the displacement and external load at the sleeper can be measured using them. The elastic modulus or stiffness of the ground can be calculated from the load and displacement. The elastic modulus is directly linked to the bearing capacity and compaction of the roadbed or used to evaluate the stiffness. [PERSON] et al. (2018) applied LFWD to ballasted tracks and used it to evaluate the stiffness of the ballasted track. The stiffness is the so-called apparent track stiffness (\(\mathbf{k_{ap}}\)), expressed as Equation (1), because displacement is measured by applying load to the sleeper while the sleeper and fastener are connected. Figure 1,b shows the LFWD equipment and field test, respectively, and Figure 1c illustrates the load and displacement measured using LFWD. For a hanging sleeper, the displacement measured reflects the hanging level of the sleeper. That is, the hanging level is proportional to the displacement. Consequently, small apparent stiffness would indicate a hanging sleeper, whereas significant apparent stiffness can be classified as a supported sleeper.  
\[k_{ap}=\frac{P_{max}}{\delta_{max}} \tag{1}\]
## 5 Results
### Final Results
#### 5.2.1 Accuracy per Resolution Time  
Initially, the correlation between the resolution time of the tasks, the mean accuracy of the visual variable types perception, and the level of partial overlap applied were analyzed for each group of combined values; Figure 7 shows the results of the data collection. The following presents some analysis of the scenarios:  
* Mean accuracy levels of hue, lightness, text, and shape visual variables were classified as GOOD and MEDIUM (consistently over or equal to 90%);
* The hue visual variable had the best resolution time (constantly less than 2.5 s);
* The partial overlap effect on the mean accuracy of the saturation visual variable when mapping three different values was minimal;
* The lightness and shape visual variables showed very similar performances, which had to be distinguished by resolution time analysis, which demonstrated that lightness had the superior performance;
* The number of encoded values had the most significant impact on the resolution time of the orientation visual variable (consistently over ten seconds);
* The progressive increase in the number of different visual encoding values significantly impacted the mean accuracy of the saturation visual variable in all of the proposed scenarios (mean accuracy ranged from 74% to 82%);
* The visual variable most affected by the progressive increase in partial overlap level was the text visual variable, whose mean accuracy presented values around 90% (the 70% partial overlap scenario).  
Statistical tests based on hypotheses regarding the correlation between the accuracy and the resolution time were applied to each studied visual variable to determine the degree to which partial overlap influenced each.  
Figure 8 (Accuracy) and Figure 9 (Resolution Time) illustrate the influence of partial overlaps on each visual variable, as determined by the selection and comparison of twenty thousand samples (bootstrapping method).  
Figure 8 shows that the text visual variable was most affected by the partial overlap (\(p\)-value = 0 and a 6.33% reduction in its accuracy), followed by the orientation visual variable (\(p\)-value = 0.013 and a 7.41% reduction in its accuracy). The other visual variables did not reach a sufficient statistically significant difference to draw a conclusion.  
Considering the resolution time of tasks, Figure 9 shows that the text visual variable was the most affected by the partial overlap (\(p\)-value = 0 and a rise of 2.54 s), followed by the shape visual variable (\(p\)-value = 0 and an increase of 1.12 s), the lightness visual variable (\(p\)-value = 0 and a rise of 0.87 s), and the hue visual variable (\(p\)-value = 0.0005 and an increase of 0.31 s). The other variables did not show statistically significant differences.  
Figure 7: Resolution time, mean accuracy, and partial overlap for each proposed scenario of visual variables studied, partial overlap levels, and the number of different visual coding values.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 3 Simulation Platform Design
### Capacitor Bank  
Capacitor banks are crucial in LV industrial facilities for voltage support and power factor (\(PF\)) correction. In the simulation scheme of Figure 3, a capacitor bank can be connected to the PCC. To determine the necessary capacitive reactive power (\(Q_{cb}\)) for achieving a new \(PF\), (20) can be used.  
\[Q_{cb}=P_{\text{PCC}}\Big{[}\tan\Big{(}\cos^{-1}PF_{existing}\Big{)}-\tan\Big{(} \cos^{-1}PF_{target}\Big{)}\Big{]}, \tag{20}\]  
where \(P_{\text{PCC}}\) is the active power at the PCC, \(PF_{existing}\) is the current \(PF\), and \(PF_{target}\) is the desired \(PF\).  
The capacitor bank's connection affects the system voltage, with voltage change (\(\Delta U\)) calculated by (21). To ensure minimal impact on customer loads, the voltage change is typically constrained to around 2% to 3% for distribution networks [26].  
\[\Delta U=\frac{Q_{cb}}{S_{s}}. \tag{21}\]  
Figure 6: \(I_{m}\) versus \(-di/dt\) for thyristors in LV applications (adapted from ref. [23]).  
Capacitor banks may cause resonance, amplifying harmonics. The resonance frequency (\(f_{res}\)) is given by (22).  
\[f_{res}=f_{s}\sqrt{\frac{S_{s}}{Q_{cb}}}, \tag{22}\]  
Detuned reactors are added to prevent harmonic resonance. The tuning frequency (\(f_{t}\)) for detuned capacitor banks is expressed in (23).  
\[f_{t}=h\cdot f_{s}=\frac{1}{2\cdot\pi\cdot\sqrt{L_{cb}\cdot C_{cb}}}, \tag{23}\]  
where \(f_{s}\) is the fundamental power frequency, \(h\) is the harmonic order, and \(L_{cb}\) and \(C_{cb}\) are the reactor inductance and capacitor bank capacitance, respectively. Typical \(h\) values range from 4.5 to 4.85.  
Detuned capacitor banks include a resistor (\(R_{cb}\)) to dampen resonance and broaden the filter's bandwidth [27; 28]. The quality factor (\(QF\)) measures damping and bandwidth are expressed in (24).  
\[QF=\frac{X_{L,f_{t}}}{R_{cb}}=\frac{X_{C,f_{t}}}{R_{cb}}, \tag{24}\]  
where \(X_{L,f_{t}}=2\cdot\pi\cdot f_{t}\cdot L_{cb}\), and \(X_{C,f_{t}}=1/\left(2\cdot\pi\cdot f_{t}\cdot C_{cb}\right)\).  
Typical values of \(QF\) are categorized as follows [27; 28; 29; 30; 31]:  
* Low: 5-20 (broader bandwidth and moderate attenuation).
* Medium: 20-50 (balanced attenuation and bandwidth).
* High: 50-100 (high attenuation and narrower bandwidth).