esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Optical Constant and Thickness Analysis of Nb\({}_{2}\)O\({}_{5}\) Thin Film  
The transmission spectrum of the Nb\({}_{2}\)O\({}_{5}\) thin film was measured by the optical spectrophotometer. It can be seen that the curves of the transmission spectra almost overlap, as shown in Figure 10. There is no obvious waveform shift, which means that the thickness of the thin film deposited at different positions is good. The average transmittance in the wavelength of 400-800 nm is higher than 80%. Transmission spectra were measured from different positions. There was no significant difference in transmittance levels, and the transmittance difference was less than 1%. This result shows that the difference in refractive index or extinction coefficient of the Nb\({}_{2}\)O\({}_{5}\) thin films was not significant.  
We measured the optical constants of the Nb\({}_{2}\)O\({}_{5}\) films with an ellipsometer, and the results showed that the refractive indices of the five segments were 2.281, 2.261, 2.284, 2.272, and 2.275, as shown in Table 3. The highest refractive index is 2.261 for segment two. The lowest refractive index is 2.284 for segment three. The extinction coefficients of the Nb\({}_{2}\)O\({}_{5}\) thin films deposited at different positions were also analyzed. The analyzed extinction coefficients were 0.007, 0.009, 0.008, 0.008, and 0.006, respectively. The highest extinction coefficient is 0.009 for the position of segment two. The lowest extinction coefficient is 0.006 for the position of segment five. The analysis results show that the extinction coefficient is small. This suggests that the absorption of the Nb\({}_{2}\)O\({}_{5}\) thin film is smaller at the longer wavelength.  
The surface profiler was used to measure and analyze the film thickness of different segments. The film thickness distribution of different segments (from 1 to 5) is 420.86 nm, 421.82 nm, 421.41 nm, 421.93 nm, and 422.17 nm, respectively. The results show that the film thickness of segment 5 is 422.17 nm and its thickness is the thickest. The film thickness of segment one is 420.86 nm and its thickness is the thinnest. The average thickness of the deposited film at five different positions is 421.63 nm. The thickness difference between the thickest and thinnest films is 1.31 nm, so the uniformity of the film thickness distribution is good. If the film thicknesses of the five different segments are normalized, then film thickness uniformity is calculated to be 0.15%. The relative thicknesses of segments 1 to 5 are very close, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment** & **Refractive Index** & **Extinction Coefficient** & **Film Thickness (nm)** \\ \hline One & 2.281 & 0.007 & 420.86 nm \\ Two & 2.261 & 0.009 & 421.82 nm \\ Three & 2.284 & 0.008 & 421.41 nm \\ Four & 2.272 & 0.008 & 421.93 nm \\ Five & 2.275 & 0.006 & 422.17 nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optical constants of Nb\({}_{2}\)O\({}_{5}\) thin film with different segments.  
Figure 10: Transmission spectra of Nb\({}_{2}\)O\({}_{5}\) thin film deposited on different segments.
## 3 Results and Discussion
### Microstructural Characterization
#### 3.1.3 Microstructural Evolution After T6 Heat Treatment  
The microstructural and elemental distribution characteristics of Al-Si-(Cu)-Mg alloys (Alloy A and Alloy B) after the T6 heat treatment are illustrated in Figure 3. Both alloys displayed clear differences in the redistribution of alloying elements and in the evolution of strengthening phases, particularly those involving Cu and Mg.  
In Alloy A, Cu-rich intermetallics (e.g., Al\({}_{2}\)Cu) were either fully dissolved or significantly refined during the heat treatment. The EPMA elemental mapping (Figure 3a) revealed that Cu was homogeneously distributed throughout the Al matrix, indicating a substitutional solid solution. The uniform dispersion of Cu within the lattice suggests that Cu atoms diffused effectively into the Al matrix, inducing lattice distortion and thereby enhancing dislocation pinning at elevated temperatures--a mechanism conducive to solid solution strengthening.  
In contrast, Alloy B retained localized Cu-rich phases even after the heat treatment (Figure 3b), suggesting the insufficient dissolution of Cu due to its lower initial content and limited diffusivity. This incomplete solid solution process indicates that Cu was not fully incorporated into the Al lattice, thus limiting its strengthening contribution in Alloy B.  
Figure 3: SEM micrographs and EPMA elemental maps of (**a**) Alloy A and (**b**) Alloy B after T6 heat treatment.  
Mg distribution patterns also revealed significant differences between the two alloys. In Alloy A, Mg was relatively uniformly dissolved within the Al matrix, with only fine and sparsely distributed Mg\({}_{2}\)Si precipitates observed. The \(\alpha\)-Al(FeMn)Si phase remained thermodynamically stable after the heat treatment, indicating that Mn effectively modified the precipitation behavior of Fe by suppressing the formation of brittle \(\beta\)-AlFeSi, thus contributing to mechanical and microstructural stability.  
Conversely, in Alloy B, Mg\({}_{2}\)Si precipitates became coarser and more pronounced following the heat treatment, accompanied by widespread regions of Mg-rich agglomeration. Moreover, the lamellar \(\beta\)-AlFeSi phase persisted after the heat treatment with little observable change in the morphology or distribution. Such microstructural features--particularly coarse precipitates and sharp phase boundaries--may act as stress concentration sites and nucleation points for microcracks, potentially deteriorating the wear resistance and mechanical stability, especially under high-temperature conditions.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 3 Proposed DL-Based Detection Scheme
The first stage of the label encoding algorithm (Figure 1) is the mapping rule for the symbols to bit labels (opposed to modulation). For example, for a 4-QAM modulation, we assign a bit to each real and complex part of the symbols according to the sign value (positive or negative). Table 2 shows the binary representation of the labels for the 4-QAM constellation, which is the reference for the three analyzed labeling encoding cases. After obtaining the input data \(\mathbf{x}_{\Theta}=\{\Re(r_{j}),\Im(r_{j})\}\) and selecting any of the label encoding choices for \(\mathbf{y}\), we feed a neural network which consists of \(L\) fully-connected hidden layers, each activated with a ReLU activation function. However, we select different activation functions for the output layer according to the label encoding method to improve the detection of symbols. Throughout the training stage, the evaluation of the model is also \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Complex** & **Bit Label** \\
**Label** & **Value** & **Mapping** \\ \hline \(s_{1}\) & \(-1+1j\) & \(1\ 0\) \\ \(s_{2}\) & \(-1-1j\) & \(1\ 1\) \\ \(s_{3}\) & \(1+1j\) & \(0\ 0\) \\ \(s_{4}\) & \(1-1j\) & \(0\ 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reference binary encoding of labels for 4-QAM modulation symbols. carried out. The proposed MIMO signal detection consists of recovering the transmitted M-QAM symbols; therefore, it is considered a supervised learning classification task. The following metrics are commonly used for measuring the performance in DL-based classification tasks:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{8}\]  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{9}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{10}\]  
where \(TP\) is the number of positive values or correctly classified samples, \(FP\) is the number of false positive values, and \(FN\) is the number of false negatives. Note that in this case, multiclass classification, we need to evaluate an average value for each performance metric. The F1-score is calculated as the harmonic mean between precision and recall. It offers a balanced perspective of performance that adapts to different scenarios [44].
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 4 Conclusions  
In this study, the interfacial bond properties of CGM and a concrete substrate were studied through direct and slant shear tests. The following main conclusions were derived:  
1. For the direct shear specimen without shear key, the bond strength between CGM and the concrete interface was the highest when the adhesive interface form was triangular, followed by the trapezoidal and smooth interface (the lowest). Hence, the interface of assembly components should be set as a triangle, and the concrete surface should be roughened when conditions allow to effectively bond with CGM.
2. The interfacial bond strength of CGM and concrete increased with the increase in concrete strength, but the increase rate slowed down.
3. The interfacial bond strength of a specimen greatly improved when the specimen was under shear load in the slant section or when the shear key was set, and the interfacial bond strength increased with increasing slant shear angle. Thus, when repairing and strengthening concrete structures or connecting prefabricated components, the slant shear angle of the connection interface between CGM and concrete should be increased as much as possible, and when conditions allow, shear keys should be set at the contacting interface to improve the bond strength of the interface.
4. On the basis of the linear regression analysis of the bond-slip curve obtained from the experiment and in combination with the existing interfacial bond-slip model, an interfacial bond-slip model of CGM repairing the existing concrete structure was proposed. Bond strength, slippage, and bond stiffness values were also recommended for each group of specimens.  
Conceptualization, [PERSON] and J.W.; methodology, [PERSON]; investigation, [PERSON] and E.Z.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51878006.  
Not applicable.  
The data presented in this article are available from the corresponding author upon request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 10: Interface bond–slip model.
## 3 Results
### DMSP-F17 Ion Density Data  
On 4 August 2024, the DMSP F17 satellite passed near the Beirut port explosion site, travelling from the Southern to the Northern Hemisphere along the longitudes 30-45\({}^{\circ}\)E between 15:54 UT and 16:30 UT, approximately 46 min after the explosion at 15:08 UT. The satellite trajectory is represented by the solid red line on the geographic map in Figure 6a. The middle panel shows the ion density data along the path of the satellite, with the electron density shown in blue and the cubic spline fit in red. The right panel presents the first-time derivative and residuals, where the derivative is in blue and the residual is in red. The green lines in Figure 6a represent the traced IGRF magnetic field lines connecting the Northern and Southern Hemispheres. Owing to the high inclination angle (98.7\({}^{\circ}\)) of the DMSP orbit, the satellite covered a wide range of longitudes. Consequently, two IGRF magnetic field lines were traced to map the disturbance regions along the DMSP-F17 path to their corresponding conjugate regions, and the last line mapped the Beirut port site to the southern hemisphere. The two green lines trace the Southern Hemisphere disturbance point along the satellite's trajectory to its corresponding conjugate location in the Northern Hemisphere, while the other green line traces the Northern Hemisphere disturbance to its conjugate location in the Southern Hemisphere.  
Figure 5: The FAC data along the three Swarm-B paths: (**a**) path 1, (**b**) path 2, and (**c**) path 3, with their corresponding wavelet transformations shown in (**d**), (**e**), and (**f**), respectively. The colour bar to the right indicates the power spectral amplitude.  
Arrows along the traced magnetic field lines are included to clarify the point along the path of the satellite at which the traced magnetic field line begins and mark the disturbances along the satellite paths. The traced field lines clearly connect the latitudes where the ionospheric density disturbances were observed in both hemispheres. Notably, the northern conjugate point was positioned north (40-50\({}^{\circ}\)N) of the Beirut Port explosion site (33.901\({}^{\circ}\)N).
## 3 Results and Discussion
### Conventional Even-Spaced Array Results  
As a comparison, the data were sampled by regular geophones and stored without any filtering or filter bank. The raw data acquired with the impulsive sledgehammer source and the 50 vertical geophones are demonstrated in Figure 5a. In addition, the time-domain signal recorded by the number 10 geophone with a 5-m distance from the impact source was transformed to a time-frequency spectrogram using Gabor wavelet transform, as shown in Figure 5b. It is noted that the energy in the measured wave field concentrates in the frequency band of 40-80 Hz.  
Figure 4: **(a)** Olympic station in Shenyang metro line 9; **(b)** schematic of the experimental excitation and geophone array; and **(c)** in-site measurements setup.  
Figure 5: _Cont._To address the dispersion curve of phase velocity of the near-surface earth, waveform analysis is processing using frequency-wavenumber transform (_f-k_ transform) by two-dimensional fast Fourier transform (2D-FFT). This technique is extensively used for many near-surface applications as a full-waveform inversion approach. All the time-domain signals recorded by the even-spaced array were transformed to a wavenumber-frequency spectrogram and a dispersion image using 2D-FFT as a function of two variables: time and offset.  
The reference wavenumber-frequency spectrogram of the conventional technique is obtained by applying 2D FFT on the even-space signals transferring them from the time domain into the frequency (_f-_) domain, from spatial domain (offset) into wavenumber (_k-_) domain. The transform of the multi-sensed surface wave data is aimed at identifying the wavenumber with the energy propagating at each frequency. The _f-k_ transform provides an obvious image of the multiple modal propagations, as shown in Figure 6. The fundamental mode and the first higher mode could be clearly separated at the different dominant ranges. The resulting image represents the energy density as functions of the wavenumber: one wave mode is focused on 50 Hz with a wavenumber of 1 m\({}^{-1}\), and the other one is around 60 Hz with a wavenumber of 2 m\({}^{-1}\)(Figure 6a). Similarly, the phase velocity-frequency image of the soil layers yields two main dispersion curves: one mode ranges between 150 to 200 m/s with a central frequency of 60 Hz, the other curve is about 400 m/s with 50 Hz frequency, as shown in Figure 6b.  
Figure 5: Raw waveforms records: (**a**) waveforms acquired by the even-spaced geophone array; and (**b**) time-frequency spectrogram of the geophone located at 5 m.
## 4 Pre-Evaluation Based on the Spatio-Temporal Model  
### Input Parameters  
There are five modes of route adjustments, namely truncation, extension, cancellation, detour, and new opening. These can essentially be classified into two types: the addition or deletion of stops. According to the sub-network model and computational requirements, the input parameters for line adjustment can be specified, as shown in Table 1. When stops are deleted, only the ID numbers of the removed stops need to be entered. In contrast, when adding new stops, more detailed information is required, including the line ID, direction, stop order, unique stop ID, stop name, distance from the previous stop, distance to the next stop, and the longitude and latitude of each newly added stop.  
After that, based on the information regarding the deleted stops' IDs and the added stops' position, each stop's adjacent sub-stops are identified, and a bus sub-network model is constructed representing two scenes (before and after line adjustment) by clarifying all OD stops affected, including directly adjusted stops, walkable stops among directly adjusted stops, stops on the same line as direct stops, and all stops on all lines that intersect with the adjusted line (walkable transfers). It is important to ensure consistency in the scope of analysis between the old and new sub-networks, particularly when considering 'deleted stops in the new sub-network' and 'added stops in the old sub-network'. This ensures that spatial location searches remain consistent and accurate, avoiding discrepancies in the comparison of paths between the old and new networks. Such inconsistencies could skew the results of subsequent analyses.
## 2 Methods
### Simplified Static-Equivalent Model
#### 2.1.1 Dynamic Characterization of the System  
Although a static approach was considered to obtain the structural response in terms of stresses, one of the main requirements imposed by recommended practices and guidelines is to avoid structural resonance. For this purpose, the fundamental frequency of the system should avoid matching the rotor and blade-passing frequencies.  
In order to estimate the fundamental frequency of the system with the simplified model, distributed inertial properties were assumed for the beam elements, representing the monopile and tower mass. On the other hand, the mass of the rotor-nacelle assembly was considered a concentrated mass at the top node, while no punctual moments of inertia were considered, as their impact on the fundamental frequency of the system was expected to be negligible.  
In marine environments, fluid-structure interaction leads accelerated bodies in fluid to experience greater inertia forces due to the added mass of surrounding fluid particles. This added mass, varying with body geometry, can be mathematically considered as a weighted integration of these particles' masses [20]. Also, assuming that the monopile is filled with water, the structural oscillation mobilizes the internal fluid. For simplicity, these two fluid-structure interaction effects were considered as an additional punctual mass at each submerged node with the following value:  
\[m_{a}=A_{\text{int}}\,L_{e}\,\rho_{\text{w}}+C_{A}\,A\,Le\,\rho_{\text{w}}\approx 2 \,A\,Le\,\rho_{\text{w}} \tag{3}\]  
where \(A_{\text{int}}\) and \(A\) are the areas of the inner and outer circumferences of the monopile, \(L_{e}\) is the element length, \(\rho_{\text{w}}\) is the density of water, and \(C_{A}\) is the added mass coefficient, which, for cylinders in flows, takes a unitary value. Note that as the monopile thickness is significantly smaller than its diameter, the enclosed fluid area was assumed to coincide with the area of the outer circumference.  
Once the global mass matrix, \(M\), was obtained through the assembly of the elemental masses plus the punctual masses, the undamped fundamental frequency of the system could be obtained by solving the eigenvalue problem:  
\[|K-(2\pi f_{n})^{2}M|=0 \tag{4}\]  
where \(K\) is the global stiffness, including SSI effects, as presented in Equation (2).
# Environmental Effects on HV Dielectric Materials and Related Sensing Technologies
###### Abstract  
The increase in recent power failures, with negative impacts on humans and the economy, has been largely attributed to environmental effects and the aging of the power network. These have been accelerated in the last years due to two main factors: an increased load on the power network and material degradation owing to the presence of environmental pollutants. These factors together with specific weather conditions create the incipient conditions for power network degradation. In this paper, a review of the influence of environmental factors on high-voltage (HV) materials and components is provided. Sensing and artificial intelligence (AI) technologies developed to prevent the failure of the material structure and HV components are also reported.  
environmental effects; high-voltage materials; power network; sensing; AI 4  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], T.  
during the 1960s [3], and by 1990, they had become mature products and relatively popular in power networks [3; 4], with a larger adoption in current years due to their outstanding characteristics.  
The diversity, size and distribution of airborne pollution particles have been described by [PERSON] [5]. Typical organic, inorganic and water pollution particles and their size ranges are given in Table 2. Considering nonmagnetic particles, the forces that drive particles to the surface of insulators are gravity, wind and electric forces [6].
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 5 Results
### Dimension Reduction  
After intervalidation, the flight data were well compressed at the sample level. However, there were still a large number of variables, and the relationship between those variables was not clear. Besides, there may have been serious correlations. Therefore, before training the fault prediction model, the CIPCA in Section 2.2 was used to reduce the dimensionality of the intervalized flight data. In order to compare the effect of dimensionality reduction, we also used PCA to perform dimensionality reduction on the data. The data used by PCA are described in Section 4.  
It can be seen from Figure 6 that the cumulative variance interpretation curve of CIPCA was located above the PCA, indicating that CIPCA had a stronger ability to interpret data than PCA and could better cover the information of the data. When all five principal components were extracted, CIPCA could explain more than 80% of the flight data, while PCA could only explain less than 40% of the information.  
When retaining the number of principal components, we ensured that the retained principal components could explain most of the data information and chose as few principal component numbers as possible because too many principal components would have reduced the effect of dimensionality reduction. According to Table 2, the first five principal  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Number of** & **Number of** & **Number of** & **Imbalanced** & **Number of** \\  & **Samples** & **Faults** & **Normal** & **Ratio** & **Variables** \\ \hline Raw data & 253,885 & 16,471 & 237,414 & 14.41 & 56 \\ Data compression & 35,091 & 2973 & 32,118 & 10.80 & 56 \\ Dimension Reduction & 35,091 & 2973 & 32,118 & 10.80 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of data compression and dimension reduction.  
Figure 6: Comparison of CIPCA and principal component analysis (PCA) cumulative variance explained.  
components of CIPCA could explain nearly 85% of the data information, so we chose to retain the five to train BPNN for UAV failure prediction. Similarly, in order to compare the prediction effect, the first five principal components of PCA were also selected for modeling.
### Scatterer Distribution Models Applied to Optical Communication Systems
The performance of the proposed enhanced UM-VLC channel model was examined, evaluated, and compared with a state-of-the-art UM-VLC channel model in terms of CIR, received power, SNR, RMS delay spread, and BER. The remainder of this paper is organized as follows. In Section 2, a brief contextualization of the UM-VLC channel model used is presented. In Section 3, the analysis of the scattering phenomena in the UM scenario and the 3D hemispheric dust particles distribution model are presented. In Section 4, the most relevant evaluation metrics applied to the UM-VLC system through numerical results are evaluated and discussed. Finally, relevant conclusions are presented in Section 5.