esa-sceva/satcom-chunk-collection huggingface dataset:
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 1 Introduction  
Composite materials with unique mechanical properties were widely applied long before the discipline of materials science existed. Depositing multiple materials in turns can produce the multilayered structure, among which metal/metal and metal/ceramic multilayers are well studied [1; 2]. Particularly, owing to their unusual mechanical, electrical and magnetic properties provided by different metallic phases, nanoscale metallic multilayer films (NMMFs) are promising for micro-electro-mechanical systems. Additionally, the tunable length scale and interface features enable NMMF a basic model for the fundamental studies of length-scale and interface-structure dependent mechanical properties in metal and alloys. Generally, the strength of NMMFs often exhibit an order-of-magnitude increasement [1; 2; 3; 4] without overly compromising ductility [5; 6; 7; 8]. Generally, the strength of NMMFs can approach 1/3 of the theoretical strength \(\mu\)/30 of GPa-level [3]. The strengthening effect is always related to the structure nature of the bicrystal interfaces [9; 10; 11].  
Therefore, the essential concept of mechanical behavior in NMMFs is that, unlike bulk composites following the rule-of-mixtures law, the volume fraction of the constituent phase is not the pivotal factor. In particular, as numerous studies concerning the strengthening effects, recent studies have focused on the role of interface in material toughening [5; 8; 10; 12; 13; 14; 15; 16]. Similar to the strengthening effect identified in NMMFs, both the layer thickness and the specific interface structures become the decisive parametersto control fracture behavior and toughening process in NMMFs. Then the interface engineering, which is considered as achieving the desired properties by effectively controlling the distribution of heterophase interface and interfacial character, has been a main job in material science and engineering. However, pioneer understanding stays in a phenomenal level that interfaces act as obstacles to cracks. From this aspect, the well-defined layer structure and continuous interface in NMMFs provide essentially conditions for accurate understanding the crack-interface interaction. Thus, the remainder of this paper focuses on selected efforts in the field, classified by microstructure, mechanical as well as fracture-related properties in NMMFs, aiming to uncover the underlying plasticity deformation and toughening mechanism, which is considered essential for designing multilayers.
## 3 Results and Discussions
### Data Response to the Model at Each Site
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Total Score** & **Count** & **Measure** & **Model Error** & \multicolumn{3}{c}{**Infit**} & \multicolumn{3}{c}{**Outfit**} \\ \cline{5-10}  & & & **Badajoz** & & **MNSQ** & **ZSTD** & **MNSQ** & **ZSTD** \\ \hline Mean & 30.9 & 13 & \(-\)0.75 & 0.31 & 1.01 & \(-\)0.1 & 1.03 & 0.1 \\ Standard Deviation & 5.7 & 0 & 0.53 & 0.05 & 0.63 & 1.1 & 1.29 & 0.9 \\ Maximum & 44.0 & 13 & 0.32 & 0.68 & 6.40 & 4.7 & 9.90 & 7.8 \\ Minimum & 17.0 & 13 & \(-\)3.18 & 0.27 & 0.26 & \(-\)2.4 & 0.23 & \(-\)1.1 \\ \hline \multicolumn{10}{c}{Cáceres} \\ \hline Mean & 29.9 & 13 & \(-\)0.68 & 0.30 & 1.01 & \(-\)0.2 & 1.16 & 0.1 \\ Standard Deviation & 5.1 & 0 & 0.50 & 0.06 & 0.74 & 1.3 & 1.40 & 1.3 \\ Maximum & 42.0 & 13 & 0.24 & 0.59 & 5.51 & 5.0 & 9.90 & 7.5 \\ Minimum & 17.0 & 13 & \(-\)2.81 & 0.26 & 0.15 & \(-\)2.8 & 0.18 & \(-\)1.6 \\ \hline \multicolumn{10}{c}{Monfragüe} \\ \hline Mean & 30.2 & 13 & \(-\)1.04 & 0.34 & 1.01 & \(-\)0.2 & 1.07 & 0.0 \\ Standard Deviation & 5.1 & 0 & 0.59 & 0.04 & 0.81 & 1.4 & 1.33 & 1.2 \\ Maximum & 41.0 & 13 & 0.14 & 0.56 & 7.04 & 5.6 & 9.90 & 6.7 \\ Minimum & 18.0 & 13 & \(-\)3.02 & 0.31 & 0.12 & \(-\)3.2 & 0.13 & \(-\)2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall model fit information; summary of all 365 days in 2016 for the three locations: sum of points of the common scale (total score), days or items taken into account (count), logit position of the days, and items along the straight line that represents the latent variable, atmospheric pollution (measure), and standard error of measurement (model error). Infit and outfit MNSQ are mean-square fit statistics to verify if items fit the model; infit and outfit ZSTD are standardized fit statistics to verify if items fit the model. statistics lower than the suggested limit for days, they are very close to the recommended values; moreover, the fact that item reliabilities are very high is indicative of the adequate consistency of data, and probably measures do not have significant errors. It is also necessary to verify how the assignment scale has been utilized [20]. There is no general rule to initially define the correct number of categories. Following the experience in previous works [12; 13], five categories were chosen. After analyzing some parameters, it was clear that this number was appropriate for the three locations. Thus, according to Table 3, the \"observed average\" and the \"structure calibration\" increase by category value, all infit and outfit MNSQ values are between 0.6 and 1.5, and the \"observed average\" values are very close to the \"sample expected\" ones, as it is recommended [22].
## 3 Results  
### Anthropometric Characteristics  
Swimmers' body weight, body height, body fat, and LBM were similar between groups (\(F_{(2,21)}\) = 0.86, \(p\) = 0.43, \(\eta_{\text{p}}\)\({}^{2}\) = 0.08 [medium], Table 2). In addition, the body height increased (\(F_{(2,21)}\) = 8.83, \(p\) = 0.01, \(\eta_{\text{p}}\)\({}^{2}\) = 0.30 [large]), while body fat decreased in all groups (\(F_{(2,21)}\) = 30.39, \(p\) = 0.01, \(\eta_{\text{p}}\)\({}^{2}\) = 0.49 [large]) after the 6-week training period (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Time** & **G-MS** & **G-ME** & **G-CON** \\ \hline \multirow{3}{*}{Body weight (kg)} & Pre & 60.8 \(\pm\) 8.0 & 59.4 \(\pm\) 8.5 & 60.3 \(\pm\) 12.5 \\  & Post & 59.1 \(\pm\) 7.5 & 59.5 \(\pm\) 8.5 & 59.9 \(\pm\) 11.9 \\  & \%\(\Delta\) & \(-\)2.6 \(\pm\) 2.1 & 0.2 \(\pm\) 2.5 & \(-\)0.2 \(\pm\) 4.7 \\ \hline \multirow{3}{*}{Body height (cm)} & Pre & 170.1 \(\pm\) 5.3 & 170.9 \(\pm\) 8.2 & 168.5 \(\pm\) 12.0 \\  & Post & 170.3 \(\pm\) 5.2 * & 172.0 \(\pm\) 8.9 * & 169.4 \(\pm\) 12.4 * \\  & \%\(\Delta\) & 0.1 \(\pm\) 0.2 & 0.6 \(\pm\) 0.8 & 0.5 \(\pm\) 0.8 \\ \hline \multirow{3}{*}{Body fat (\%)} & Pre & 15.5 \(\pm\) 4.5 & 15.3 \(\pm\) 3.4 & 17.6 \(\pm\) 3.6 \\  & Post & 14.8 \(\pm\) 4.7 * & 14.7 \(\pm\) 3.4 * & 16.6 \(\pm\) 3.4 * \\ \cline{1-1}  & \%\(\Delta\) & \(-\)5.4 \(\pm\) 3.8 & \(-\)3.6 \(\pm\) 4.3 & \(-\)5.6 \(\pm\) 3.9 \\ \hline \multirow{3}{*}{LBM (kg)} & Pre & 49.2 \(\pm\) 5.6 & 49.0 \(\pm\) 6.8 & 48.3 \(\pm\) 9.0 \\ \cline{1-1}  & Post & 48.7 \(\pm\) 5.2 & 49.4 \(\pm\) 7.1 & 48.4 \(\pm\) 9.0 \\ \cline{1-1}  & \%\(\Delta\) & \(-\)1.0 \(\pm\) 1.0 & 0.9 \(\pm\) 1.6 & 0.5 \(\pm\) 2.8 \\ \hline \end{tabular} \%:\(\Delta\): Post- vs. pre-measurements, LBM: lean body mass; *\(p\) < 0.05, post- vs. pre-measurements.  
\end{table}
Table 2: Swimmers’ anthropometric characteristics in pre- vs. post-training period. The group of maximum strength (G-MS), the group of muscular endurance (G-ME), and the control group (G-CON).
## 3 System Description
### Smart PV Modules
#### 3.1.1 Power Supply from the PV Panel  
The power supply built includes two steps: one to charge the supercapacitor and another to supply the wireless communication stage as shown in Figure 2. A prototype of such a power supply and wireless communication system can be seen in Figure 3.  
This power supply must guarantee that all the electronic circuitry is correctly supplied from the PV panel--even under low or no irradiation conditions--which means that it must be able to correctly charge the supercapacitor. Since the voltage provided by the PV panels is typically larger than the operating voltage of supercapacitors, a step-down converter must be included in the power supply. This converter controls the charge of the supercapacitor so that it is achieved in a reasonably short time.  
Figure 3: Supply system from PV panel to obtain the 3.3 V and the communication circuitry.  
Figure 2: Schematic of the power supply and its connection to the Smart PV Module.  
Inversely, the operating voltage of the supercapacitor chosen (2.5 V) is lower than that required by the communication circuitry to work properly, namely 3.3 V. Therefore, a step-up converter is also necessary after the supercapacitor [30]. It is very important that this converter optimize the use of energy so as to make sure the supercapacitor stores enough energy to make the communication system operate during the night.  
Although it is obvious that, as far as energy production is concerned, no useful information will be delivered during the night, the system is still operating when the sun is gone for safety purposes--protection against possible theft of the panels. In the same way, all the circuitry will be switched off during the idle periods to reduce consumption.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 6 Results
### Output SIR vs. Reverberation Time  
The impact of the reverberation time on the output SIR can be seen in Figure 10. To avoid saturating the plots, the standard deviation vertical bar is shown only every 10 reverberation time values. Again, no interferences were present for this evaluation.  
Figure 9: Output SIR vs. input SNR.  
The slight decrease of enhancement performance of Demucs-Embed compared to the original Demucs is now observed throughout all reverberation times, while Demucs-PhaseBeamform outperforms Demucs in every reverberation scenario. Since the only difference between them is the beamformer that precedes the Demucs-Denoiser model, it appears that this filter is complementing the model very well, resulting in reverberation robustness.
# Dielectric Spectroscopy Studies and Modelling of Piezoelectric Properties of Multiferroic Ceramics
## 3 Results and Discussion
### Modelling of Impedance--Frequency Characteristics of the Piezoelectric Equivalent Circuit
The quality of the fitting procedure was estimated according to the following parameters: _\"chi-squared\"_ (\(\chi^{2}\)) and weighted sum of squares (_WSS_) [29]. In the case of the fitting results shown in Figure 10, the parameters were as follows: _\"chi-squared\"_ was \(\chi^{2}\) = 2.78 \(\times\) 10\({}^{-3}\) and \(\chi^{2}\) = 3.23 \(\times\) 10\({}^{-3}\) for \(U\) = 0.1 Vrms and \(U\) = 2.0 Vrms, respectively. The weighted sum of squares was _WSS_ = 0.314 and WSS = 0.177 for \(U\) = 0.1 Vrms and \(U\) = 2.0 Vrms, respectively. Figure 11 shows the dependence of the fitting quality parameters, namely _\"chi-squared\"_ (\(\chi^{2}\)) and weighted sum of squares (_WSS_), on the temperatures at which the impedance/frequency characteristics of 67 BFBT ceramics were recorded. One can see in Figure 11 that both \(\chi^{2}\) (_\"chi-squared\"_) fitting quality parameter (Figure 11a) and WSS parameter (Figure 11b) change within one order of magnitude. Upon visual inspection, it can be seen that the linear approximations used for the \(\chi^{2}\) and WSS data approximation show that a higher electric field strength of the measuring signal (and, at the same time, a higher value of the circumferential magnetic field strength) improves the  
Figure 11: Fitting quality parameters \(\chi^{2}\) (_“chi-squared”_) (**a**) and weighted sum of squares (_WSS_) (**b**) obtained as a result of fitting the experimental resonance spectra and response of the electric equivalent circuit given in Figure 8; amplitude of the measuring signal: \(U\) = 0.1 Vrms (blue squares) and \(U\) = 2.0 Vrms (red circles). Figure 10: Impedance/frequency characteristics of 67 BFBT ceramics (empty symbols) studied under the following conditions: temperature—20 \({}^{\circ}\)C and measuring voltages—**(a)**\(U\) = 0.1 Vrms. ; (**b)**\(U\) = 2.0 Vrms. Fit result (green solid line) was limited to the frequency range of the first resonance. quality of the fitting procedure. This also means that the data is less susceptible to external interference. A comparison of the statistical characteristics of the obtained \"_chi-squared_\" fitting quality parameters showed that the standard deviation was SD = 0.00192 and SD = 0.00212 for the low value of the electric field strength \(E\) = 166 V/m (\(U\) = 0.1 V\({}_{\rm rms}\)) and high electric field strength \(E\) = 3333 V/m (\(U\) = 2.0 V\({}_{\rm rms}\)), respectively. The \(WSS\) parameter showed that the standard deviation was SD = 0.21743 and SD = 0.11077 for \(E\) = 166 V/m and \(E\) = 3333 V/m, respectively. An increase in the intensity of the measuring field led to a substantial improvement in the quality of further data simulation.
###### Abstract  
Composite slab systems have become increasingly popular over the last few decades because of the advantages of merging the two building materials, profiled steel sheets and concrete. The profiled composite slab's performance depends on the composite interaction at the longitudinal direction of the concrete-steel interface. Geopolymer concrete has emerged over the last few years as a potential sustainable construction material, with 80% less carbon dioxide emissions than cementitious concrete. Recently, self-compacted geopolymer concrete (SCGC) has been developed, synthesised from a fly ash/slag ratio equal to 60/40, micro fly ash (5%), anhydrous sodium metasilicate solid powder as the alkali-activator and a water/solid content ratio equal to 0.45. The production of SCGC eliminates the need for an elevated temperature during curing and high corrosive alkali-activator solutions, as in traditional geopolymer concrete. The bond characteristics of the profiled composite slab system incorporated with the SCGC mix have not yet been thoroughly investigated. The cost-effectiveness of small-scale tests has popularised its usage by many researchers as an alternative technique to large-scale testing for assessing composite slab load shear capacity. In this paper, small-scale push tests were conducted to investigate the load slip behaviour of the SCGC composite slab compared to the normal concrete (NC) composite slab, with targeted compressive strengths of 40 and 60 MPa. The results indicate that SCGC has better chemical adhesion with profiled steel sheets than NC. Additionally, the profiled composite slab incorporated with SCGC possesses higher ultimate strength and toughness than the normal concrete composite slab.  
Keywords:SCGC; Bondek() profiled steel sheet; small-scale test; bond-slip relationship +
Footnote †: journal: Applied
###### Abstract  
In the prediction of the remaining useful life (RUL) of rolling bearings, feature extraction and selection are critical prerequisites for accurate prediction, while the construction of the prediction model is the core. However, existing RUL prediction methods face two main challenges: (1) feature construction methods based on predefined indicators often ignore the correlation among features; and (2) single models typically yield limited prediction accuracy. To address these issues, this study proposes a feature selection method based on hierarchical clustering combined with the elbow method and a hybrid Transformer-GRU (Gated Recurrent Unit) model for RUL prediction. Specifically, the initially filtered feature set is further clustered using hierarchical clustering, and the optimal number of clusters is determined by the elbow method to construct a compact and representative feature set. This feature set is then input into a Transformer-GRU model, where the Transformer encoder captures temporal dependencies across time steps to generate rich feature representations, and the GRU network models their dynamic evolution over time to predict the bearing RUL. The proposed method is validated on the PHM2012 dataset. The experimental results show that after removing redundant features, the model's training time is reduced by 8.61% and the number of parameters decreases by 23.26%. Compared with other benchmark models, the proposed Transformer-GRU model achieves a lower mean absolute error (MAE) of 0.0836 and a root mean square error (RMSE) of 0.1137, demonstrating superior predictive performance. These results confirm that the proposed feature selection method effectively eliminates feature redundancy, enhances training efficiency, and reduces model complexity, while the hybrid model significantly improves prediction accuracy.  
rolling bearing hierarchical clustering; transformer; GRU; lifetime prediction; feature redundancy +
Footnote †: journal: applied sciences
## 2 Related Work
proposed two studies focused on end-to-end corneal ulcer segmentation, utilizing the SUSTech-SYSU dataset [2] for training and testing. The first work, CU-SegNet [16], utilized the Multiscale Adaptive-aware Deformation Module (MAD) and the Multiscale Global Pyramid Feature Aggregation Module (MGPA) to reduce the influence of large variations in the morphology and size of ulcers and guide the model towards multiscale deformation. In a subsequent study [4], they utilized the Multiscale Self-Transform Network (MsSTNet) for corneal ulcer segmentation. They enhanced performance by feeding the results into a Generative Adversarial Network (GAN) discriminator to predict whether images were real or fake during training. [PERSON] et al. [11] selected ResNet-18 to develop a corneal ulcer segmentation model. The model was trained and evaluated using 354 fluorescein-stained images from the SUSTech-SYSU dataset. In the context of our study, we propose a novel end-to-end algorithm for identifying the severity degree of corneal ulcers in fluorescein-stained images. This algorithm leverages a combination of techniques:  
1. Cornea Segmentation Module: This component is developed through the integration of deep learning techniques, image processing, and the HCT. 2. Corneal Ulcer Segmentation Module: This module is developed using deep learning methodologies, utilizing the predicted cornea mask during the model learning phase. The utilization of these two components is fundamental to our approach to accurately determining two grading results:  
1. The percentage of the ulcerated area on the cornea. 2. The severity degree based on the ulcer's distribution across five corneal zones.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 1 Introduction
Phase-based optical flow estimation provides full-field vibration information in sub-pixel accuracy under marker-free conditions. In light of the fact that the damage will lead to localized chaotic changes, a novel damage index, the multi-scale local information entropy (MS-LIE), is introduced to effectively characterize the complexity of mode shapes in Gaussian multi-scale space while mitigating measurement noise. Moreover, according to the assumption that damage feature spatial distribution remains consistent across all scales, a data fusion technique grounded in Dempster-Shafer (D-S) evidence theory is developed to fuse the damage information at all scales, ultimately eliminating the noise-induced uncertainty and enabling the precise determination of the damage location. The remainder of the paper is organized as follows: Section 2 introduces the workflow and the theory of the proposed method. Section 3 numerically investigates the efficiency and robustness of the proposed method under a range of noise levels and damage states. Section 4 demonstrates experimental verifications on multiple damage detection scenarios. Section 5 draws the conclusions and suggests avenues for future research.
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 3 Results
### Experiments  
Particular care was taken to manually align the probe to the tube and obtain the same conditions used for simulation. However, from the analysis of the results, we noticed a slight misalignment of about \(1^{\circ}\) in a couple of cases, which is within the accuracy of the position system. Hence, the actual angle (\(\vartheta_{\text{D}}\), \(\vartheta_{\text{A}}\)) configurations were [(91,0), (85,0), (80,0), (91,44), (85,45), (81,43)]\({}^{\circ}\).  
Figure 7 shows the velocity profiles experimentally obtained by using the Vermon probe. Qualitatively, the overall trend of the estimated profiles was the same observed from the simulations: \(v_{z}\) was estimated with good accuracy, while \(v_{z}\) and \(v_{y}\) suffered from higher underestimation. The errors reported in Table 3 indicate that, on average, the relative error \(\overline{B}_{v_{Module}}=11.5\%\) was lower (roughly 3%) compared with simulations, even if the standard deviation was higher (10.9%). As highlighted by the plots and confirmed in Table 3, when \(\vartheta_{\text{A}}=45^{\circ}\), there was no mismatch between the estimates of the \(v_{z}\) and \(v_{y}\) components. The difference between the bias of the two velocity components was lower than 1%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**CMUT Probe**} \\ \hline \(\vartheta_{\text{D}}\) & \(\vartheta_{\text{A}}\) & \(\overline{B}_{v_{z}}\pm\overline{\sigma}_{v_{z}}\) [\%] & \(\overline{B}_{v_{x}}\pm\overline{\sigma}_{v_{x}}\) [\%] & \(\overline{B}_{v_{y}}\pm\overline{\sigma}_{v_{y}}\) [\%] &