esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
## 8 Results And Discussions
### Stratified K-Fold
#### 8.2.6 Scenario-6  
The SMIC dataset was used for training in Scenario-6 and CASME II was used for evaluating the model. Model-A again outperforms the two others with 44.7% accuracy, which is 1.0% and 5.6% higher in comparison with Model-B and Model-C, respectively.  
In contrast to Scenario-2, where intra-dataset evaluation was performed for CASME II, the model trained on SMIC has to cope with a drop of 11.8%.  
It can be observed that all models trained on SMIC perform similarly well on the different test sets. The accuracy of Model-A on the intra-dataset in Scenario-1 is 43.7%, while in Scenario-4 on SAMM, it is 44.3%. Such balanced results are found only with the model trained on SMIC. Due to the comparatively high number of samples of 164, the different ethnicities of the subjects, and a relatively balanced class distribution, the networks trained on SMIC show the best generalization and do not vary strongly across the datasets, which would be an indicator for a bias in the training data.
# Multifunctional Graphene-Concrete Composites: Performance and Mechanisms
## 3 Results and Discussion
### Photothermal Property
To assess the thermal-storage performance of the test blocks over an extended duration, the blocks were subjected to irradiation from a light source for 1 h, and the temperatures of the front and back surfaces were recorded at 5 min intervals. Following the removal of the light source, temperature readings were taken every minute. This temperature-variation study aids in analyzing the differences in heat-storage performance across the different test blocks. As shown in Figure 13a, the peak surface temperatures of test blocks from groups 2 to 4 are significantly higher than those of the test blocks in group 1. This indicates that a certain amount of graphene improves the heat-storage capacity of concrete. During the cooling phase, the rear surface temperature of the first group of test specimens exhibited the lowest final value. In contrast, the rear surfaces of the second, third, fourth, and fifth groups demonstrated comparatively higher final temperatures. This disparity further corroborates that the incorporation of graphene significantly enhanced the heat-storage capacity of the concrete, as evidenced by the delayed thermal dissipation in the modified specimens. Figure 13b and Table 4 indicate that the maximum heat-storage capacity is achieved when the mass ratio of cement to graphene is 1:004, resulting in a heat-storage capacity that is up to 0.8% greater than that of the control concrete. (The temperature exhibited an increase \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Peak Temperature of the Front Surface (\({}^{\circ}\)C)**} \\ \cline{2-5}  & **First Test** & **Second Test** & **Third Test** & **Average Value** \\ \hline
1-4 & 25.6 & 25.2 & 25.4 & 25.4 \\ \hline
2-4 & 25.5 & 25.4 & 25.6 & 25.5 \\ \hline
3-4 & 26.2 & 26.2 & 26.2 & 26.2 \\ \hline
4-4 & 25.9 & 25.8 & 25.7 & 25.8 \\ \hline
5-4 & 27.2 & 27.2 & 27.5 & 27.3 \\ \hline \end{tabular}
\end{table} 
Table 2: Peak temperature of the front surface. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Peak Temperature on the Upper Surface (\({}^{\circ}\)C)**} \\ \cline{2-5}  & **First Test** & **Second Test** & **Third Test** & **Average Value** \\ \hline
1-4 & 20.0 & 20.2 & 20.1 & 20.1 \\ \hline
2-4 & 19.6 & 19.5 & 19.4 & 19.5 \\ \hline
3-4 & 18.8 & 19.2 & 19.0 & 19.0 \\ \hline
4-4 & 19.5 & 19.6 & 19.4 & 19.5 \\ \hline
5-4 & 19.3 & 19.7 & 19.5 & 19.5 \\ \hline \end{tabular}
\end{table} 
Table 3: Peak temperature of the upper surface. In summary, graphene exhibited remarkable photothermal-conversion performance when utilized for wall insulation. Its high specific surface area enabled concrete to absorb and store a significant amount of heat while efficiently dispersing that heat across one side of the wall, thereby preventing local heat accumulation. However, excessive amounts of graphene can result in a reduced effective path for photothermal conversion.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Sensors
#### 3.3.3 Sensors for Security
Advanced monitoring, reporting, and verification (MRV) features will continue to play a major role in enhancing the transparency, environmental integrity, and credibility of subnational, national, and regional emissions trading systems (ETS) for the future integration of a global carbon market [96; 97]. Regarding infrastructure and buildings, continuous monitoring to detect corrosion and minor damages to prevent a possible failure takes advantage of the integration of surveillance cameras, humidity, atmospheric, and stress sensors, among others [98]. A simple combination of vibration and tilt sensing devices provides one of the low-cost and high-efficiency techniques proposed for a wide range of structures [99]. Personal and community security: To detect anomalies, violence, and unauthorized actions, biometrics and surveillance cameras are widely used. Smart lighting systems are  
Figure 6: Smart city architecture defined in five planes: application (connects city and citizens), sensing (sensors measurements), communication (cloud services), data (processing and analysis) and security and privacy planes (assurance of security and privacy) [85]. a useful and cost-effective tool that uses common sensors like light and motion detectors and can improve the security tasks [100]. Surveillance cameras, face-recognition systems, and global positioning systems (GPS), in combination with data handling systems, are increasingly common tools in the hand of law-enforcement agencies as smart public security strategy reported in [39]. Ref. [101] reviews the possible combination of different devices categorized in sensors, actuators, and network systems. The challenges presented by the growing use of such technologies and concerns for individual privacy is the topic of an emerging research area [102].
## 5 Simulation Results
### Case Analysis Results
Decoding the results, the optimal network reconstruction scheme is obtained as: turn on switches S\({}_{40}\), S\({}_{57}\) and S\({}_{58}\) and turn off switches S\({}_{2}\), S\({}_{44}\), S\({}_{49}\) and S\({}_{50}\). Under this scheme, the tie line between B\({}_{1}\) and B\({}_{4}\) is put into operation, and the transmitted power is 0.85 p.u. After S\({}_{2}\) is disconnected, load L\({}_{1}\) exits, which is a third-class load for the seawater purification. Apart from providing freshwater, it is mainly used for regulating system power and does not influence the system's operation safety. The connected loads of LB\({}_{5}\) are transferred to G\({}_{1}\), and other loads are normally supplied. The outputs of each generator are 7.85 p.u., 8 p.u., 1 p.u. and 0. The lowest operating efficiency of the generator is 98.13%. The average time of the network reconstruction process is 3.12 s. The revised system topology is shown in Figure 12.  
(3) Fault scenario 3. Generator G4 in the distribution network quits operation due to failure, and the line between LB\({}_{4}\) and B\({}_{4}\) is broken. Thus, switches S\({}_{35}\) and S\({}_{38}\) are disconnected. Moreover, the working transformer between B\({}_{1}\) and B\({}_{4}\) is faulty. Thus, S\({}_{57}\) and S\({}_{58}\) are disconnected and cannot be put into operation. The results of optimal convergence are the same after running multiple times, and it can stably converge to the optimal network reconstruction scheme; the optimal fitness is 0.91789. The corresponding optimal switch coding state is: 11100 10111 111111 111100 111110 01111 11110 11101 11101 11100 11111 100. Decoding the results, the optimal network reconstruction scheme is obtained as: turn on switches S\({}_{9}\), S\({}_{10}\), S\({}_{33}\), S\({}_{34}\), S\({}_{40}\) and turn off switches S\({}_{7}\), S\({}_{44}\), S\({}_{49}\), S\({}_{50}\). Under this scheme, the tie lines of B\({}_{2}\), B\({}_{3}\) and B\({}_{4}\) are put into operation, which is equivalent to transferring power from G\({}_{2}\) to B\({}_{4}\). The power transmitted on the two tie lines is 0.85 p.u. After switch S\({}_{7}\) is disconnected, load L\({}_{3}\) exits, which is a third-class load for the seawater purification. LB\({}_{5}\) is transferred to G\({}_{2}\), and other loads are normally supplied. The outputs of each generator are 8 p.u., 7.85 p.u., 1 p.u. and 0. The lowest operating efficiency of the generator is 98.13%. The average time of the network reconstruction process is 2.91 s. The revised system topology is shown in Figure 14.  
Figure 11: Convergence procedure of the MPGA under fault scenario 2. Figure 12: The revised system topology under fault scenario 2.
## 4 Simulation and Experimental Results
As the perfect timing offsets were not available, the DNN-based synchronization methods and the biased LFM synchronization methods were compared in the QPSK phase trajectories and symbol error rates (SERs), as shown in Figure 6. As seen in Figure 6a, for the bias LFM synchronization and linear interpolation, the symbols in the middle of the frame usually endured the maximum offset because of the uncompensated acceleration effect, which was first analyzed by [PERSON] [21]. The spline interpolation could suppress the acceleration, and thus the error of the phase trajectory was linearly changing between the boundaries of each frame (see Figure 6b) and the phase error was significant at the end of the frame because of the uncompensated synchronization bias. After the bias was learned from the DNN model, the QPSK symbols were well recovered, as shown in Figure 6d, with an SER of 0.002. This was considered an excellent result for the single-channel received scheme of LFM-based synchronization followed by a simple time-invariant equalizer. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Functional Model**} & **Proposed Scheme** & **Adaptive Equalizer Scheme** \\  & **(MAC)** & **(MAC)** \\ \hline LFM cross-correlation & 0.77 M & 0.77 M \\ \hline DNN for bias estimation & 20 k & - \\ \hline Spline interpolation & 0.17 k & - \\ \hline Resampling & 4.9 M & 9.9 M \\ \hline LS channel estimation & 4.0 k & - \\ \hline ZF equalization & 0.28 M & - \\ \hline Adaptive equalization & - & 14 M \\ \hline Total & 6 M & 25 M \\ \hline \hline \end{tabular} Note: MAC, multiply–accumulate operation.  
\end{table} 
Table 2: Complexities of the receivers for one packet.
### Information Theoretical Analysis of BSC-Based IoT Systems  
Theoretical advancements of communication channels for IoT systems modeled by BSC are helpful for characterization of their performance limits, which may be used in related system designs. For example, evaluation of explicitly constructed codes for such applications against such performance limit bounds may provide instructive recommendations/interpretations for the sake of efficient encoding/decoding procedures. In this context, for a given error probability and with no restriction imposed on the codeword length, the Shannon message transmission (TR) capacity of the BSC is studied in [17]. In [40; 41; 42; 43], for a specified codeword length and a fix rate less than the TR capacity, the error probability for the optimal TR code is investigated. The problem of construction of optimum or at least good codes for TR problem with a given rate and codeword length is addressed in [40; 44; 45; 46]. Furthermore, the TR capacity of the BSC is shown to be attained by Bernoulli input with \(1/2\) success probability, i.e., \(X\sim\text{Bern}(1/2)\)[35]. In [41], random linear code for the achievability proof with an exponential decoding search is investigated.  
However, in the research that is currently available, the BSC has mostly been studied for the TR problem. On the other hand, in [33], the DI for the BSC _without_ input constraint is studied, where the lower bound on the DI capacity is established. In addition, in [32], the DI for the BSC _with_ input constraint in a generalized context of the channel model, namely,DMCs is addressed and an extensive proof, dedicated for the BSC, was not provided. Based on the author's information, for the BSC _with_ input constraint, with the exception of this paper's conference version [47], the ultimate performance limits for the deterministic K-identification (DKI) problem have not yet been examined in the literature.
## 3 Results and Discussion
### Polymorphic Discrimination at 20 \({}^{\circ}\)C
#### 3.1.3 CB-AMF Mixtures with CBE  
From Figure 1 the positions of the diffraction peaks were identified: 64.8 A (q = 0.097 A\({}^{-1}\)), 44.2 A (q = 0.142 A\({}^{-1}\)) and 32.9 A (q = 0.191 A\({}^{-1}\)), with high orders at 16.1 A (q = 0.391 A\({}^{-1}\)), 14.6 A (q = 0.431 A\({}^{-1}\)) and 12.9 A (q = 0.486 A\({}^{-1}\)); and for WAXS: 4.00 A (q = 1.57 A\({}^{-1}\)), 3.89 A (q = 1.61 A\({}^{-1}\)), 3.79 A (q = 1.66 A\({}^{-1}\)), 3.70 A (q = 1.70 A\({}^{-1}\)) and 3.59 A (q = 1.75 A\({}^{-1}\)).  
We find the same peaks as the last two mixtures presented. The peak assignment is therefore the same: the peaks at 64.8 A, 44.2 A and 32.9 A correspond respectively to the structures 3L\({}_{001}\), 2L\({}_{001}\) and 3L\({}_{002}\); and the peaks observed in WAXS correspond to \(\beta_{1}\) form and to \(\beta^{\prime}\) form.  
At T = 20 \({}^{\circ}\)C, we measure a value for the parameter \(\rho\) for CB + 0.5 AMF + CBE of 0.694 while \(\rho_{\text{CBE}}\)(T = 20 \({}^{\circ}\)C) = 0.729, i.e., very similar values. Regarding the CB + AMF + CBE mixture, the parameter \(\rho\) reaches 1.233 while \(\rho_{\text{CB + 0.5 AMF + CBE}}\)(T = 20 \({}^{\circ}\)C) = 0.694, i.e., a 44% increase of this ratio. For this mixture, the proportion by mass of TGs from AMF hasincreased by 40% comparatively to CB + 0.5 AMF + CBE. It would seem that the \(\rho\) parameter is directly related to the mass distribution of TGs in IV form at the temperature T = 20 \({}^{\circ}\)C.
## 2 Islanding Detection Methods
### Signal Processing Based Methods
#### 2.5.2 Wavelet Transformer Method  
With this technique, a wavelet transformer is used to extract the characteristics of distorted current, voltage, or frequency. The measured signal wavelet coefficient is compared to a predetermined threshold value, and islanding is detected if the measured signal wavelet coefficient is greater than the specified value. This approach has some drawbacks, including the limitation to low-frequency bands, the difficult threshold value selection, the impact of the various sampling frequencies and the mother wavelet selection on the wavelet transformer. The wavelet packet transformer can be used to analyze high-frequency components utilizing the d-q axis of three-phase apparent power [39; 40]. The wavelet transformer method is classified into continuous wavelet transformer and discreet wavelet transformer.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Model Structure  
Many researchers have shifted their focus to the design of model structures for deep-learning-based image inpainting tasks. Currently, the most common structures are multi-stage image inpainting networks, single-stage image inpainting networks, and the newly proposed diffusion-model-based image inpainting networks. Below, we provide a detailed review of these three network structures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Reference** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **FID\(\downarrow\)** & **L1\(\downarrow\)/\%** & **Image Resolution** & **Mask Type** \\ \hline \multirow{4}{*}{Places2} & \multirow{4}{*}{[19]} & 25.1 & 0.8686 & 15.28 & & Irregular mask (20–30\%) \\  & & 22.89 & 0.8063 & 19.99 & 256 \(\times\) 256 & Irregular mask (30–40\%) \\  & & 21.22 & 0.7391 & 25.88 & & Irregular mask (40–50\%) \\ \cline{1-1} \cline{2-6}  & [67] & 24.42 & 0.87 & 1.47 & 256 \(\times\) 256 & Irregular mask (40–50\%) \\ \cline{1-1}  & [69] & 24.49 & 0.806 & 22.121 & 256 \(\times\) 256 & Irregular mask (50–60\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative evaluations of transformer-based image inpainting methods.
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.3 Effectiveness of Multiple Different Networks Integration  
This experiment is to verify the effectiveness of multiple different network integration. The size of the extracted image block is 23 \(\times\) 23 in the DRIVE dataset and 31 \(\times\) 31 in the CHASE_DB1 dataset. The integration of three different networks is compared with the integration of three identical networks. VGG-16-7 is used for the same network structure. VGG-16-2, VGG-16-4 and VGG-16-7 are used for the three different networks. Experiments in the two datasets of DRIVE and CHASE_DB1 and the results are shown in Tables 6 and 7.  
The integration results of the three identical networks and the three different networks are shown in Tables 6 and 7. In Table 6, compared with the integration of three identical net  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline The same nets & **96.64** & 81.16 & **98.17** \\ Three different nets & **96.64** & **82.06** & 98.06 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Integration of three identical networks is compared with the integration of three different networks on the DRIVE dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline Single network & 96.58 & 79.35 & **98.27** \\ Triple networks & **96.64** & **81.16** & 98.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Single network is compared with triple networks integration on DRIVE dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline Single network & 97.10 & 75.69 & **98.74** \\ Triple networks & **97.11** & **75.96** & 98.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Single network is compared with triple networks integration on CHASE_DB1 dataset.  
works, although the Acc of the three different networks integration remained unchanged, the SE increased by 0.9%.  
In Table 7, the Acc and SE of integration of the three different networks, which are 97.25% and 76.84% in the CHASE_DB1 dataset, compared with integration of the three identical networks, ACC and SE increased by 0.14% and 0.88%. Compared with multiple identical networks, different networks with different depths can extract vascular features in different regions, and the features extracted by the same network may be repeated. Therefore, the effect of the integration of multiple different networks is better than the integration of multiple identical networks.
# Comparative In Vitro Analysis of Composite Resins Used in Clear Aligner Attachments
## 5 Conclusions  
The mechanical, thermal, and tribological characterization performed in this study highlights the decisive influence of material composition on the performance of orthodontic attachments. The hybrid nanocomposite Aligner Connect (AC) demonstrated significantly superior performance compared to the flowable resin Connect Flow (CF), showing nearly double the maximum indentation load and a notably higher elastic modulus. The increased inorganic filler content of AC not only enhanced its mechanical strength but also improved its dimensional stability under compressive loading. These findings underscore that the most clinically relevant result of this study is the superior load-bearing capacity and long-term structural integrity of AC, which makes it a particularly suitable choice for cases requiring high retention and precise tooth movements.  
Moreover, the tribological analysis revealed that CF, while less rigid, exhibited lower friction coefficients and improved surface wear resistance, suggesting that its use may be preferable in less mechanically demanding clinical situations or where better adaptability to the aligner template is needed. This study therefore provides clinically meaningful guidance for selecting composite materials tailored to the biomechanical requirements of clear aligner treatments.  
The proposed experimental protocol, which integrates custom photmasks, layer-specific curing, and multi-modal mechanical testing, represents a novel and reproducible methodology for the evaluation of next-generation orthodontic materials. However, the in vitro conditions of experimental analysis do not fully replicate the complex oral environment, including thermal cycling, humidity, and masticatory loads, which may influence long-term material performance. Moreover, only two materials were tested, which limits the generalizability of the conclusions.  
Future research should focus on validating these findings under clinically simulated conditions, such as extended fatigue testing and thermocycling, and on conducting in vivo studies to assess the long-term behavior of attachment materials during actual orthodontic treatments. Such investigations will help to translate the present laboratory findings into concrete clinical recommendations and further improve the predictability and success of clear aligner therapies.  
Conceptualization, F.G. and R.L.; methodology, D.B.; validation, D.B., L.S. and P.C.; investigation, C.P.; writing--original draft preparation, F.G. and D.B.; writing--review and editing, R.L.; supervision, P.C. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 2 Materials and Methods
### Segmentation Methods
#### 2.4.1 FSS  
Our FSS approach employs a Mask R-CNN, which combines a Faster R-CNN as the object detection model with a fully convolutional network (FCN) to form the image segmentation model [14; 15]. Combining these algorithms and using them together improves the image segmentation performance. The FCN model is entirely composed of convolutional layers and is different from a general CNN in that it can output feature maps. The feature map output from the last convolutional layer of this model is upsampled to the same size as that of the input image, and the weights of the entire model are updated using the difference between the labelled ground truth mask and the output. The trained FCN divides the object from the background. Because the Faster R-CNN model performs the function of classifying objects along with detection, the FCN model eliminates the need to classify pixels into multiple classes. Therefore, instead of classifying pixels into multiple classes using softmax as an activation function, we employed the sigmoid function, which classifies a pixel as a mask pixel or non-mask pixel (i.e., 1 or 0) to yield clear predictions. Figure 6 shows the Mask R-CNN structure. The results of the cattle image segmentation using a Mask R-CNN are shown in Figure 7. The image segmentation task uses intersection over union (IoU) as an evaluation index, which is calculated by dividing the overlapping area by the combined area of the two masks to indicate the amount by which the ground truth mask and the model output mask overlap. The mean IoU of the cattle image segmentation using FSS was 0.92. As demonstrated by this result and related research, Mask R-CNN can effectively separate cows and the background with high performance, and its performance has been validated to the extent that it can be used as the backbone for subsequent segmentation models. Therefore, it was used as the segmentation model in this study.  
Figure 5: Difference between two ground truths; (**a**) FSS ground truth; (**b**) WSS ground truth.  
Figure 6: Mask R-CNN structure.