esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Copper Oxide Particle Size on the Properties of Epoxy Resin
## 3 Results and Discussion
### Microscopic Observations  
As part of this work, an analysis of the microstructure of the base material and the produced composites was carried out. Figure 4a,b show the structure of epoxy resin. This material is characterized by typical brittle fractures. Figure 4c-h present microscopic photographs of both the copper oxides used and the composites modified with them. The copper oxides with particle sizes of 1-10 \(\upmu\)m and 10-30 nm show similar types of fine-grained particles which are characterized by a whisker-like structure, while the copper oxide particles with a size of 40-60 nm are characterized by a more developed, dendritic structure. An analysis of the photographs of the microstructures of the produced composites showed that composites with copper with particle sizes of both 1-10 \(\upmu\)m and 40-60 nm are brittle, while the use of copper oxide with a particle size of 10-30 nm changes the  
Figure 3: Comparison of experimental results for different particle sizes with theoretical evaluations using the Voigt and Reuss models (**a**) and estimations of the composite Young’s modulus based on Equation (1), where \(\kappa=4\) (**b**).  
nature of the matrix to a more developed one, which suggests some plasticization of the material. Additionally, strength tests seem to confirm this effect because the materials were characterized by the highest deformation at break and the highest impact strength value.  
Figure 4: Structure of copper oxide powders and manufactured composites. (**a**) Structure of epoxy resin fracture. (**b**) Structure of fracture structure of epoxy resin. (**c**) Structure of copper particles with  
a size of 1-10 \(\upmu\)m. (**d**) Fracture structure of resin composite reinforced with copper particles with a size of 1-10 \(\upmu\)m. (**e**) Structure of copper particles with a size of 40-60 nm. (**f**) Fracture structure of resin composite reinforced with copper particles with a size of 40-60 nm. (**g**) Structure of copper particles with a size of 10-30 nm. (**h**) Fracture structure of resin composite reinforced with copper particles with a size of 10-30 nm.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 6 Experiments on Shapes
### Changing Width in Gaussian Initial Conditions  
In Section 6.2, we saw that increasing \(\sigma\), the width of the Gaussian initial condition, will lead to better hit rates. We wanted to refine these results by fixing the damping parameter to \(k=0.1\) and changing \(\sigma\) from \(1\) to \(20\). The results can be seen in Figure 13.  
Figure 12: Resized version from Figure 11. We only plotted the IE method between \(k=10^{-3}\) and \(k=10^{-2}\). We resized the hit rate axes as well. With these adjustments, it is possible to distinguish the small changes caused by changing the width of the Gaussian initial condition.  
Figure 13: The hit rate of the centaur shape over different widths \(\sigma\) of the Gaussian initial condition. For this, we fixed the damping parameter at \(k=0.1\). The red solid line denotes the IE method, the blue indicates the CN method and the green line is for the \(\mathrm{SO}l_{0}\) method. The red dashed line shows us the hit rate of the wave equation solved using the IE method and \(\delta\)-peak initial conditions.  
As in the previous plots, we notice an increase in the hit rate. At \(\sigma\approx 10\), we reached the maximum value for the hit rate. The maximum value for the IE method is at \(\approx\)72% and for CN and SO\(l_{0}\) at \(\approx\)72.4%. Again, the difference between the second- and first-order methods is not that large. We added a dashed red line to the plot to indicate the hit rate from the IE method solving the wave equation (\(k=0\)). This setting belongs to the best-performing scenarios thus far; here, it reached a hit rate of \(\approx\)73.25%.  
As the reported hit rates are partially very small in their differences, one may consider again close solutions as numerically identical.
## 4 LC-Based SIW Phase Shifter Design
### Biasing System  
Various biasing systems have been proposed to change the relative permittivity of an SIW structure with LC driving [16; 30]. We used microstrip lines [31] as the biasing structure for verifying the LC director's driving in the proposed SIW structure. The microstrip conductors were set in copper that was 0.5 mm wide and 1 \(\upmu\)m thick.  
We checked the changes in the LC state by using an LC simulator (TechWiz LCD) [32; 33]. Simulations were conducted using ANSYS HFSS for RF simulation, and TechWiz for LC director alignment. Before voltage was applied to the biasing structure, LCs were in a perpendicular state (Figure 5a), and after a bias voltage \(V_{b}\) = 70 V was applied, the LCs were in a parallel state (Figure 5b). This change demonstrates that this biasing process drove the LC molecules between its extreme states. Furthermore, the RF field that propagated in TE10 mode was verified. Therefore, in simulations, we can assume that the LC layer is an isotropic material with relative permittivities \(\varepsilon_{r,\perp}\) and \(\varepsilon_{r,\parallel}\)[34; 35]. The SIW phase shifter using LC with a microstrip biasing line is depicted in Figure 6.  
Figure 3: Five layers of LC-based SIW phase shifter: (**a**) cross-section view, (**b**) perspective view. Components are described in the text.  
Figure 4: SIW structure with additional inserted posts: (**a**) SIW with one post, (**b**) SIW with two posts.  
Figure 5: Electrical biasing system for LC-based SIW phase shifter: (**a**) perpendicular state, (**b**) parallel state. \(V_{b}\) is bias voltage.  
Figure 6: Proposed LC-based SIW phase shifter. The following dimensions are used: \(s\) = 0.7 mm, \(h\) = 0.7 mm, \(l_{LC}\) = 10 mm, \(w_{LC}\) = 3.5 mm, \(l_{qw}\) = 3 mm, \(w_{qw}\) = 2.6 mm, \(l_{trans}\) = 1.4 mm, \(w_{trans}\) = 1.2 mm.
## 3 Proposed Method
### Adaptive Bin Estimator
#### 3.3.5 Hybrid Regression Layer (HREG) and Linear Combination (LCOMB)  
The final stage in the AdaDRBins estimator is the hybrid regression layer (HREG) and linear combination (LCOMB), which are detailed in Figure 7c. After the prediction of these MLPs, the final bin-division pattern \(\mathbf{b}_{3}\) is obtained. In the final stage of AdaDRBins, HREG is used on the final decoded feature \(\mathbf{D}_{4}\) to estimate the probabilities of each pixel falling into these bins. The hybrid regression layer consists of a \(3\times 3\) convolution followed by a Softmax activation unit, which results in the predicted probability \(p_{i}\) for the \(i\)th bin. Then, the predicted probabilities are applied to perform LCOMB with the predicted final centers of the bins \(c\left(b_{3}^{i}\right)\), which are given by  
\[c\left(b_{3}^{i}\right)=d_{\min}^{3}+\left(d_{\max}^{3}-d_{\min}^{3}\right) \left(b_{3}^{i}/2+\sum_{j=1}^{i-1}b_{3}^{j}\right), \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer type** & **Input Channels** & **Output Channels** & **Activation** \\ \hline FC & 32 & 32 & GeLU \\ FC & 64 & 2 & - \\ Global AvgPool & - & – & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the \(n\)th bias MLPs while keeping the same \(H_{n}\) and \(W_{n}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Input Channels** & **Output Channels** & **Activation** \\ \hline FC & 32 & 64 & GeLU \\ FC & 64 & \(2^{n-1}\cdot q\) & - \\ Global AvgPool & - & – & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the \(n\)th splitting MLPs while keeping the same \(H_{n}\) and \(W_{n}\).  
for achieving the final predicted depth map \(\hat{d}\). The predicted depth at (x, y) in \(\hat{d}\) can be obtained by  
\[\hat{d}(x,y)=\sum_{i=1}^{N}c\left(b_{3}^{i}\right)p_{i}(x,y), \tag{9}\]  
where \(p_{i}(x,y)\) is the probability of predicted depth at (\(x,y\)) falling in the \(i\)th bin, where N is \(2^{3}\cdot q=32\).
## 3 Methodology
### Multiple Linear Regression  
Multiple linear regression analysis defines the interdependence relationships between the important features selected from the random forest model and other factors in the data. Multiple linear regression analysis was used to determine how a dependent variable may be predicted using an independent variable or variables, as expressed in Equation (4):  
\[Y=B_{0}+B_{1}X_{1}+B_{2}X_{2}+\ldots+B_{n}X_{n}+\epsilon \tag{4}\]  
Variable \(Y\) is the dependent, while \(X_{1}\), \(\ldots\), \(X_{n}\) represent the independent variables for the observation \(i\). \(B_{1}\), \(\ldots\), \(B_{n}\) are the coefficients of regression, and \(\epsilon\) denotes the error component that accounts for any from the actual relationship due to external factors. The success of the regression model was evaluated using the root mean square error (RMSE), which places more emphasis on significant errors and is thus more sensitive to their presence. The model's performance was assessed using the RMSE formula presented in Equation (5):  
\[\sqrt{MSE}=\sum_{t=1}^{n}\frac{\left(\left|Y_{i}-\overline{Y}_{i}\right|\right) }{n} \tag{5}\]  
The value of \(R^{2}\), which is the coefficient of determination, was calculated using Equation (6), with n representing multiple observations and \(\overline{Y}\) representing the predicted value.  
\[R^{2}=\frac{SSR}{SST}=\frac{\sum(\overline{Y}-\overline{Y})^{2}}{\sum(Y- \overline{Y})^{2}} \tag{6}\]  
In Equation (7), the independent variables are denoted as SSR. These are the sum of squares regressions, while the total sum of squares regressions is represented by SST. SST represents the correlation between these two regressions. The correlation between these two variables is defined in the following manner:  
\[r=\frac{n\sum_{i=1}^{n}x_{i}y_{i}-\sum_{i=1}^{n}x_{i}\sum_{i=1}^{n}y_{i}}{ \sqrt{\left[n\sum_{i=1}^{n}x_{i}^{2}-\left(\sum_{i=1}^{n}x_{i}\right)^{2} \right]\left[n\sum_{i=1}^{n}y_{i}^{2}-\left(\sum_{i=1}^{n}y_{i}\right)^{2} \right]}} \tag{7}\]  
Numerous remaining criteria exist for selecting a model, including the Akaike information criterion (AIC), which allows for a trade-off between the model's fit and the number of parameters used [102]. AIC is commonly used in linear regression analysis and can be computed using the following mathematical formula as shown in Equation (8):  
\[AIC=n*log\left(\frac{RSS}{n}\right)+2k \tag{8}\]  
The residual sum of squares, also known as RSS, was calculated based on the total number of parameters \(k\) and the total number of observations \(n\). To assess multicollinearity, the variance inflation factor (VIF) was used. Equation (9) defined the VIF as:  
\[VIF_{i}=\frac{1}{1-R_{i}^{2}} \tag{9}\]
## 3 Methodology  
In this section, we propose our framework in detail. The proposed framework, as illustrated in Figure 2, comprises two primary modules: the multi-scale feature pyramid model and the global-local representation learning model. The multi-scale feature pyramid model consists of three sets of convolutional blocks (Conv Blocks), each characterized by distinct kernel sizes. Specifically, the first set employs \(3\times 3\) kernels, the second set utilizes \(5\times 5\) kernels, and the third set incorporates \(7\times 7\) kernels. This varied kernel configuration facilitates the effective extraction of emotional features across different scales. Furthermore, the groups interact and fuse features through both forward and backward fusion mechanisms, enhancing the robustness of the extracted multi-scale emotional characteristics. The global-local representation learning model is composed of three bi-directional long short-term memory (BiLSTM) networks, which are specifically designed to capture global features from the outputs of the multi-scale feature pyramid model.  
The process commences with the input speech signal being processed through a pre-trained Hidden-Unit BERT (HuBERT) model [35], which is a self-supervised learning model designed for speech representation. HuBERT captures high-level acoustic features by learning from large amounts of unlabeled speech data, providing robust embeddings for downstream tasks. These embeddings are subsequently input into both the multi-scale  
Figure 2: The overview of proposed multi-scale feature pyramid network.  
feature pyramid model and the global-local representation learning model to extract multi-scale discourse-level features. Ultimately, the framework generates the final emotional output through two fully connected layers.
## 5 Results
### Comparative Results with Previous Works  
One of the main drawbacks of comparing the results on RAVDESS was the multiple set-ups used in the literature. The most reliable and closed evaluation methodology for comparing our experiments was the publication of [PERSON] et al. [35]. Here, the researchers developed a speech emotion recognizer and applied a subject-wise 5-CV, obtaining an accuracy of 71.61%. Other works such as [76] used the last two participants in the validation and test sets, respectively, reaching an accuracy of 56.71% on the speech modality. With a variation of this set-up, we also found the work of [PERSON] et al. [34], which used as the test set only the last two participants and combined the 'Calm' and 'Neutral' emotions, passing from a problem with eight emotions to a problem with seven different classes. On these conditions, the top accuracy reached by their model is 77.5%, applying a global normalization.  
With our audio-based model, we achieved an accuracy of 81.82%, improving by 10.21% the audio-based solution of [PERSON] et al. [35], and by 15.09% if we compare this result to our best multimodal solution.  
Regarding our previous publications, we can see that both methods, the feature extraction and the fine-tuning of the xlsr-Wav2 Vec2.0, surpassed our previous proposals for the SER using CNNs in [13]. More specifically, for the feature extraction, we have obtained an improvement of 10.73 points, and for the fine-tuning, an increment of 5.24 with the current transformer-based approach. Regarding the visual modality, the AUs got a slight increment in comparison with the embeddings extracted from the STN on [13]. In our previous work, we reported an accuracy of 57.08%, and now we achieved 62.13%. As both modalities improved, the late fusion was also 6.62 points higher than in our previous study.
## 4 Experiment and Result Analysis
### Effect of the Four-Head ASFF Detector  
First, the original three-head ASFF detector (Detect-ASFF) was compared with the four-head ASHF detector (Detect-FASFF), which extends to the P2 feature layer for small objects. To this end, the conventional YOLOv8n was used, and both the three-head detector and the four-head version were inserted into the model. All other parameters were kept unchanged, and the model was evaluated on the blueberry maturity dataset.  
The experimental results are shown in Table 4. Although the use of the Detect-FASFF model slightly increases the computational complexity and memory requirements, its precision, recall rate, and average precision improved by 1.7%, 4.2%, and 4.0%, respectively, compared to the baseline model. Thus, the overall detection quality of the model was significantly improved. Although the frame rate (FPS) decreased, this setup can still meet the requirements of practical applications. Figure 8 shows the results on a representative image. Detect-ASFF and Detect-FASFF have fewer missed detections, especially when the blueberries are smaller or severely occluded.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Detection Heads** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]\%** & **Parameters/M** & **FPS** & **ModelSize/MB** \\ \hline YOLOv8 ref. & 86.3 & 75.2 & 84.0 & 3.4 & 161 & 6.0 \\ Detect-ASFF & 86.9 & 76.7 & 85.7 & 4.7 & 88 & 8.6 \\ Detect-FASFF & 88.0 & 79.4 & 88.0 & 4.3 & 99 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison experiment of adaptive feature fusion detection heads.  
Figure 7: Visualization of the dataset.
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 2 Materials and Methods
### Mean Average Precision  
Mean average precision (mAP) is a crucial metric for evaluating the effectiveness of machine learning models, especially in object detection. It integrates several sub-metrics to offer a thorough assessment:  
* Confusion Matrix: A tool that breaks down the model's predictions into true positives, true negatives, false positives, and false negatives, helping in the computation of other key metrics like precision and recall.
* Intersection over Union (IoU): This metric quantifies the overlap between the predicted and actual bounding boxes, providing insight into the model's localization accuracy.
* Precision: Represents the accuracy of the model's positive predictions, calculated as the ratio of true positives to the total of true positives and false positives.
* Recall: Measures the model's ability to identify all relevant cases, computed as the ratio of true positives to the total of true positives and false negatives.
* mAP averages the precision values across different classes and/or IoU thresholds, offering a single metric that reflects the model's precision and recall performance across its various categories:  
\[mAP=\frac{1}{n}\sum_{k=n}^{k=1}AP_{k} \tag{7}\]  
where \(n\) represents the number of classes and \(AP_{k}\) represents the average precision of class \(k\).
# Temporal EEG Imaging for Drowsy Driving Prediction
## 4 Experiment  
The EEG dataset used in this study includes 10,395 alert trials and 3080 drowsy trials collected from 38 subjects. According to the suggestion of the previous research, if the drivers are fully aware of the driving situation, the average time for them to respond to the lane-departure event is approximately 0.7 s [41]. According to the previous studies [42; 43; 44], drivers provide poor performance when they don't respond the lane-departure event in three times the mean alert RT. Therefore, this study adopts three times the average response time as the classification boundary of the drowsy prediction task. The EEG trials are considered as alert trials as long as their RT are less than 2.1 s. In addition, the EEG trials with an RT larger than 2.5 s are labelled as drowsy trials, and the EEG trials with an RT between 2.1 s and 2.5 s are not used in this experiment. The evaluation of our approach is performed using leave-one-subject-out cross-validation. We select the data from one subject for testing and the data from the remaining subjects for training. This process is repeated for each of the 38 subjects. To evaluate the predictive performance of the proposed method, the temporal EEG image method is compared with the single frame EEG image method--directly using the current trial of the EEG image for drowsiness prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Kernel Size** & **Stride** & **Num of Filters** \\ \hline Conv1a & \(3\times 3\) & 1 & 16 \\ Conv1b & \(3\times 3\) & 1 & 16 \\ Pool1 & \(2\times 2\) & 1 & - \\ Conv2a & \(3\times 3\) & 1 & 32 \\ Conv2b & \(3\times 3\) & 1 & 32 \\ Pool2 & \(2\times 2\) & 2 & - \\ Conv3a & \(3\times 3\) & 1 & 32 \\ Conv3b & \(3\times 3\) & 1 & 32 \\ Pool3 & \(2\times 2\) & 2 & - \\ FC1 & 500 & - & - \\ FC2 & 2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting of the CNN model.  
Figure 8: The convolutional neural network (CNN) architecture used in the experiment.
# Quantitative Ultrasound Analysis of Oral Mucosa: An Observational Cross-Sectional Study
###### Abstract  
(1) Background: Ultrasonography is gaining popularity as a diagnostic tool in the study of the oral mucosa. The precision of ultrasound has made it possible to identify the various layers, based on their echogenicity. The aim of this study was to perform a quantitative analysis of healthy oral mucosa based on the analysis of greyscale, echo levels (dB), and attenuation values (dB/cm). (2) Methods: Thirty-three patients (17 females and 16 males; 58.42 \(\pm\) 13.29 yo) were recruited for this study. The images were acquired with the GE Logiq-e R7 with a linear probe at 18 MHz frequency (harmonic). For each tissue (epithelium, rete ridges, connective tissue, muscle, and bone), regions of interest were traced for the analysis of echo levels, grey levels, and attenuation values. One-way ANOVA and pairwise comparison were performed. (3) Results: Three-hundred and thirty images were analyzed. Analysis of echo levels and grey levels showed a significant difference between epithelium and rete ridges (\(p\) = 0.001), and between rete ridges and connective tissue (\(p\) = 0.001), but not between epithelium and connective tissue (\(p\) = 0.831) or connective and muscle layers (\(p\) = 0.383). The attenuation values appeared to be specific for each tissue layer (\(p\) = 0.001). (4) Conclusions: Quantitative analysis applied to ultrasound imaging of the oral mucosa allows the definition of specific tissue areas.  
Keywords:ultrasound imaging; oral mucosa; quantitative analysis +
Footnote †: journal: _sciences_  
_Applied_  
_Acticle_  
Quantitative Ultrasound Analysis of Oral Mucosa: An Observational Cross-Sectional Study
# A Disposable Soft Magnetic Ribbon Impedance-Based Sensor for Corrosion Monitoring
## 1 Introduction
The impedance of the previously mentioned class of sensors is given by [18]:  
\[Z=R_{\mathrm{dcj}}\mathrm{ktcoth(jkt)} \tag{1}\]  
where \(k=\frac{1+j}{\delta}\) and \(\delta=\sqrt{\frac{\rho}{\pi\mu_{T}f}}\)\(\rho\) is given by \(\rho=R\Big{(}\frac{A}{T}\Big{)}\). \(R\) is the resistance of the ribbon, \(A\) is the cross-sectional area, and \(l\) is the length. For inductive materials, as the frequency is increased the skin depth decreases, which leads to an increase in the impedance of the system. When treated with different concentrations of corrosive chemicals, parts of the ribbon are etched away and holes accompanied by metal oxide form on the surface of the ribbon [16; 17]. This change in the surface topography of the ribbon decreases the magnetic permeability, as magnetic moments are removed from the surface as the ribbon is etched. According to the above equations, if it is assumed that the frequency of oscillation remains constant after acid treatment, this would lead to an increase in the skin depth. Plugging the skin depth into Equation (1), it can be noted that the \(\mathrm{coth(jkt)}\) section will increase along with the \(R_{\mathrm{dc}}\) section; this leads to an overall increase in the impedance, which allows the change in impedance to be analyzed to give the concentration of the corrosive chemical. In this study, the sensitivity--defined by the change in impedance of the soft ferromagnetic ribbons--is studied using frequencies ranging from 0.2 MHz to 7 MHz and concentrations of HNO\({}_{3}\) ranging from 0.9 M to 7.4 M. In addition, changes in the morphology and magnetic properties of the ribbon are studied using Scanning Electron Microscopy (SEM) imaging, X-ray Diffraction (XRD) data, and Physical Property Measurement System (PPMS) data. The proposed sensor would have low power consumption, be cost effective, disposable after use, and would not require the complexities of external magnetic fields or additional chemicals. By the creation of a simple-to-use corrosive chemical sensor, emergencies such as chemical spills in industry have the possibility to be dealt with in safer manners, and the safety of workers in chemical-using industries can be greatly increased.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 2 Materials and Methods
### Design Methodology  
In our work, we demonstrate a dual-band rectifier circuit that operates in the frequency bands of the 5G cellular network, specifically at 3.5 GHz and 5 GHz. The design methodology for the rectifier circuit consists of four major steps: determining the appropriate substrate, selecting the circuit topology, selecting the appropriate diode, and designing an efficient impedance-matching network. [53]. This methodology is detailed in Figure 2 ([54]).