esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Torque Calculation and Generator Behavior
### Generator Behavior on the Variable and Smooth Torque  
Although a smooth torque is obtained, which ultimately smooths the output of the PAT, in most cases, the PAT and generator are directly coupled. However, a Simulink model was developed to observe the behavior of the generator with variable and smooth torques, as shown in Figure 13.  
A built-in asynchronous machine block rated at 37 kW was selected from the Simulink library to simulate the behavior of the generator. This block can simulate both motors and generators, using (+) and (-) signs with the torque, respectively. The variable and smoothed torques obtained from ANSYS CFX, as shown in Figure 14, were used as the input signals in the Simulink model [32,33]. The model was simulated for 25 s, and each duration of five seconds corresponds to a different torque value.  
Figure 14: Torque input signal to the generator.  
Figure 13: Overall system to observe the behavior of the generator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Before PWC Integration**} & \multicolumn{3}{c}{**After PWC Integration**} \\ \hline
**Flow (L/s)** & **Torque (Nm)** & **Deviation (\%)** & **Flow (L/s)** & **Torque (Nm)** & **Deviation (\%)** \\ \hline
192.1 & 226.559 & 0 & 192.1 & 226.559 & 0 \\
182.5 & 204.519 & 9.73 & 190.2 & 222.105 & 1.97 \\
172.9 & 183.693 & 18.92 & 188.9 & 219.085 & 3.3 \\
167.1 & 171.664 & 24.23 & 191.7 & 225.619 & 0.41 \\
153.7 & 145.042 & 35.98 & 193.7 & 230.342 & 1.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Torque of PAT regarding different flow values.  
The simulation results of the peak value of the generator current are shown in Figure 15, which demonstrates the variations in the current with respect to the torque obtained from the PAT. In a hydro system, the torque varies with the flow variation. The current depends on the torque value. Therefore, any fluctuation in the flow rate affects the output current of the generator. However, the PWC technique provides a smooth flow, which results in a smooth output.
# Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries
## 2 Literature Review
For experimental evaluation, three different datasets were used, namely, Labeled Faces in the Wild (LFW) [39], SMFD [38], and World Masked Face Dataset (RMFD) [38]. The SVM classifier reached an accuracy of 99.64% during training. It appears from the literature review that several researchers used different mechanisms for face mask detection and also achieved superior results; however, the limited amount of training data and computational complexity restrict their systems from a real-time implementation. Therefore, firstly, we create a novel large-scale dataset that consists of two classes, i.e., face mask and no mask. Secondly, we developed a lightweight CNN model that can be easily deployed over resource-constrained (edge devices) to efficiently and accurately detect face masks in a real-time scenario.
## 4 EAT Segmentation Methods via Pericardium Delineation  
Depending on the method, delineating the pericardium can be an easier task than segmenting EAT outright. EAT and PAT are both unevenly distributed around the heart, leading to wide variation from slice to slice and patient to patient in CT and MRI images. They also have an uneven shape that is hard to easily describe in an algorithm. However, on CT and MRI images the pericardium is a smooth, thin, almost elliptical contour. Methods like active contours or ellipse fitting are naturally suited for segmenting this kind of shape. Once the pericardium is segmented, EAT can be more easily segmented as all adipose tissue within the pericardium.  
The biggest challenge with segmenting the epicardium is how thin it is. When collecting CT scans for CAC scoring, slice thickness is usually set to around 2-3 mm [40]. The pericardium is normally less than 2 mm thick, so the pericardium can often be blurred or faint on CT images due to partial volume averaging, especially because the heart is a constantly moving organ.  
There are several methods proposed for pericardium delineation. Some are methods presented purely as pericardium delineation methods, while others are part of methods to segment and quantify EAT. In this section, we present papers that describe pericardium delineation or are methods to segment EAT by first segmenting the pericardium.
## 3 Results and Discussion
### Comparison of the Overall Heat Transfer Coefficient with/without Modification  
With the results discussed in Section 3.1, it is clear that nonuniformity of the radial fuel temperature led to the nonuniformity of the parameters such as the effective thermal conductivity and turbulent viscosity. To calculate the overall heat transfer coefficient, the fuel temperature in a cross section cannot be averaged into a single value. As a result, the modified method proposed in Section 2.2 was used to calculate the overall heat transfer coefficient. The method was compared with the unmodified one in this study. The relative difference in the overall heat transfer coefficient was expressed as shown in Equation (23).  
\[\delta_{k}=\frac{k_{mod}-k_{unmod}}{k_{unmod}}\times 100\% \tag{23}\]  
As seen in Figure 7, the unmodified overall heat transfer coefficient showed a trend of decreasing at first and then increasing. The relative difference between them was high in general, and it appeared at its maximum point near the inlet and outlet of the heated section, with a maximum value about \(-71\%\).  
Figure 7: Comparison of the OHTC without modification and after modification.  
The unmodified overall heat transfer coefficient s calculated by the average temperature difference of the cross section, while the modified overall heat transfer coefficient is calculated by the flow state. As seen in Figure 4b, in the outlet of the cooling channel, the fuel temperature on the adjacent wall was in the supercritical temperature region, while the axis fuel temperature was in the transcritical or subcritical temperature region. The overall heat transfer coefficient calculated by Equation (4) not only considered the influence of the temperature on the thermophysical property of the fuel but also considered the influence of the flow state on the fuel heat convection.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Continuous Wavelet Transform (CWT):  
\[W_{x}(a,b)=\int_{-\infty}^{\infty}x(t)\,\psi^{*}\bigg{(}\frac{t-b}{a}\bigg{)}\,dt \tag{4}\]  
where:  
* \(x(t)\) is the EEG signal.
* \(\psi(t)\) is the mother wavelet.
* \(a\) is the scale parameter.
* \(b\) is the translation parameter.
* **Morlet Wavelet*
* (Complex Morlet Wavelet): \[\psi(t)=\pi^{-\frac{1}{2}}\,e^{i2\pi f_{0}t}\,e^{-\frac{t^{2}}{2\sigma^{2}}}\] (5) where:
* \(f_{0}\) is the central frequency of the wavelet.
* \(\sigma\) controls the width of the wavelet in time.
* **Scale and Frequency Relation**: The scale \(a\) is related to the central frequency \(f_{0}\) and \(\sigma\) as: \[a=\frac{1}{2\pi f_{0}\sigma}\] (6) The corresponding frequency for each scale is given by: \[f(a)=\frac{f_{0}}{a}\] (7)
* **Scalogram**: The scalogram is the absolute value of the CWT coefficients: \[\text{Scalogram}(t,f)=|W_{x}(a,b)|\] (8)Figure 1 illustrates examples of the signal image, spectrogram, and scalogram used in this study.
## 2 Related Works
### Data Collection Schemes Based on Mobile Data Collector
#### 2.1.2 Mobile Data Collectors with Predictable or Deterministic Mobility  
In this case, the static and mobile nodes agree on a specific time at which the data transfer may initiate. Mobile nodes follow a very strict schedule and other nodes know exactly when mobile nodes will be in their communication range. For instance, in [24], mobile nodes are assumed to be on board of public transportation shuttles that visit sensor nodes according to a schedule. In this way the sensor nodes calculate the exact active time and wake up accordingly to transfer data.  
In [25] a scheme called Multiple Enhanced Specified-deployed Sub-sinks (MESS) for WSNs is proposed for data collection using multiple sub-sinks. The sub-sinks are considered as enhanced nodes having more resources and deployed at equal distances along the accessible path, creating a strip in the sensing field and providing a set of meeting points with the mobile sink. Each sub-sink working as an access-point to the mobile sink notifies the underlined network segment about the service it is offering.  
Similarly in [26] [PERSON] _et al._ proposed a greedy algorithm called Wireless HIgh SPEed Routing (Whisper) to forward data to a high speed sink. The scheme is based on the assumption that all nodes know their own locations, their neighbors' locations, and the path and displacement of the mobile sink. In this case, the sink, moving at high speed, does not stay permanently at the location of interest, and therefore refreshes its future location based on its speed and trajectory. The interested sensor nodes cannot directly send message to the fast moving sink and therefore forward their data towards the estimated meeting point with the sink. The meeting point is estimated on the basis of various delays in message transmission together with the node's own location, the neighbors' locations, and the estimated sink location.
## 3 Results and Discussion
### Flavor Compounds in Coffee Beans Grown at Various Altitudes
#### 3.3.2 Electronic Sensory Evaluation  
Electronic sensing was conducted using an electronic nose, which provided a comprehensive evaluation of the flavor profile of the coffee powder while minimizing artificial errors. The volatile composition was analyzed to identify differences in specific flavor components, allowing for mutual verification. The results from the electronic sensing are illustrated in Figure 3. The fit indices for the independent variable (R\({}^{2}\)x), the dependent variable (R\({}^{2}\)y), and the model prediction index (Q2) were 1.000, 0.626, and 0.187, respectively. Both the R\({}^{2}\)X and R\({}^{2}\)Y values exceeded 0.5, indicating a satisfactory fit of the predicted data and reasonable accuracy in data visualization [40].  
The low value of Q2 indicated that although each sample could be distinguished, the difference was not significant. After 200 permutation tests, the intersection point of the Q2 regression line and the vertical axis was \(-\)0.769, indicating that there was no over fitting in the model and that the model verification was effective. T70/2 and PA/2 could be identified by the VIP as the key sensors that form the difference. Moreover, the principal composition analysis and cluster analysis revealed differences in flavor between high-altitude coffee and low-altitude coffee.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Compounds** & **VIP** & **Tendency** & **Flavor Characteristic** \\ \hline Pyrazine, methyl- & 6.19 & decrease & Nutty, roasted, sweet, chocolate \\
2-furanmethanol & 5.08 & decrease & Mild, slightly caramellic \\ Furfural & 3.13 & increase & Sweet, bread-like, caramellic \\ Pyridine & 2.77 & decrease & Pungent, penetrating, diffusive \\ Pyrazine, 2,6-dimethyl- & 2.24 & decrease & Sweet, tried, nutty, roasted, chocolate \\ Pyrazine, 2,5-dimethyl- & 1.78 & decrease & Roasted, nutty, grassy, chocolate \\ Pyrazine, 2-ethyl-6-methyl- & 1.76 & decrease & Roasted hazelnut-like \\
2-furancarboxaldehyde, 5-methyl- & 1.70 & increase & Sweet-spicy, warm, caramellic \\ Pyrazine, 2-ethyl-3-methyl- & 1.41 & decrease & Nutty, roasted \\ Pyrazine, 3-ethyl-2,5-dimethyl- & 1.36 & decrease & Hazelnut, roasty, earthy \\ Pyrazine, ethyl- & 1.25 & decrease & Nutty, roasted, buttery, rum \\ \hline \hline \end{tabular}
\end{table}
Table 2: Components that significantly change with increasing growing altitudes.  
Figure 3: _Cont_.
# What Is a Causal Graph?
## 2 Probabilistic DAG
### Reification  
It is important to keep in mind that a probabilistic DAG is nothing but a very indirect way of representing a collection of conditional independence properties. In particular, the arrows in the DAG have no intrinsic meaning; they are there only to support the moralisation procedure. It is indeed somewhat odd that the property of conditional independence, which is essentially a symmetric one, can be represented at all by means of arrows, which have built-in directionality. The example of Markov equivalence between 1, 2, and 3 in Example 1 shows that the specific direction of an arrow in a DAG should not be taken as meaningful itself. Rather, an arrow has a transient status, rather like that of a construction line in an architect's plan or of a contour line on a map: instrumentally helpful but not representing anything visible in the house or on the ground.  
Nevertheless, on looking at a DAG, it is hard to avoid the temptation to imbue its arrows with a direct meaning in relation to the system studied. This is the philosophical error of reification, which confuses the map with the territory [26], wrongly interpreting a purely instrumental property of a representation as if it had a direct counterpart in the external system. In the case of a probabilistic DAG model, this frequently leads to endowing it with an unjustified causal interpretation, where the presence of an arrow \(A\to B\) is considered to represent the presence of a causal influence of \(A\) on \(B\). Such a confusion underlies much of the enterprise of \"causal discovery\", where observational data are analysed to uncover their underlying probabilistic conditional independencies; these are represented by a probabilistic DAG, and that DAG is then reinterpreted as representing causal relations.  
This is not to say that a DAG cannot be interpreted causally, but to do so will require a fresh start with new semantics.
## 5 Conclusions  
The effect of carbonization pressure on the rheological properties of CGBS was studied in this paper, and the evolution mechanism of the CGBS microstructure was explored from a microscopic perspective. The main conclusions are as follows:  
Figure 13: LCA boundary of CGBS [37].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**NO.** & **C\({}_{\rm e}\)** & **C\({}_{\rm t}\)** & **C\({}_{\rm p}\)** & **C\({}_{\rm r}\)** & **C\({}_{\rm a}\)** & **C** \\ \hline CGBS1 & 38.72 & 85.40 & 1.26 & \(-\)92.17 & 0 & 33.21 \\ CGBS4 & 38.72 & 85.40 & 1.26 & \(-\)92.17 & \(-\)33.85 & \(-\)0.64 \\ \hline \end{tabular}
\end{table}
Table 5: Calculation results of carbon emissions, kg/t.  
1. Increasing the carbonization pressure in the reactor can increase the CO\({}_{2}\) sequestration capacity of CGBS. When the carbonization pressure is 0.7 MPa, the CO\({}_{2}\) sequestration capacity is 0.38%. When the carbonization pressure is further increased, the CO\({}_{2}\) sequestration capacity tends to be stable.
2. After increasing the carbonization pressure, the calcium carbonate produced will make the slurry structure dense, resulting in the deterioration of the rheological properties of CGBS. Combined with the CO\({}_{2}\) sequestration capacity, 0.7 MPa can be used as the optimal carbonization pressure.
3. When the carbonization pressure is 0.7 MPa, the yield stress, plastic viscosity and hysteresis loop area of CGBS are 181.66 Pa, 0.1024 Pa-s and 1376 Pa/s, respectively.
4. After increasing the carbonization pressure, the calcium carbonate and hydration products produced fill the pores of the sample, which reduces the specific surface area and pore volume of CGBS and makes the microstructure denser.  
Data curation, L.Z., Z.L., Y.Z. and M.L.; Formal analysis, L.Z., Q.G. and N.Z.; Funding acquisition, B.H.; Investigation, Z.L., Q.G. and B.H.; Methodology, L.Z., Q.G., B.H., N.Z., Y.Z. and M.L.; Resources, B.H.; Software, Z.L. and W.G.; Validation, Y.Z.; Writing--original draft, L.Z. and M.L.; Writing--review and editing, L.Z., Z.L., Q.G., B.H. and N.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (52304160), Key Project of National Natural Science Foundation of China (52130402) and Higher Education Discipline Innovation Introduction Plan (supported by the 111 Project) (B21016).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
CGBS coal gauge-based backfilling slurry  
CG coal gauge  
FA fly ash  
XRF X-ray fluorescence  
XRD X-ray diffraction  
BT Brunauer-Emmett-Teller  
SEM scanning electron microscope
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results  
This section discusses the simulation results of the proposed autoencoder-based image communication system presented in Section 3. PSNR and classification accuracy are considered under different channel SNR conditions over a range of different images to measure the efficacy of the proposed architecture. Finally, the performance is compared against an equivalent JPEG-based image communication system to complete the study. The equivalent JPEG system is defined such that the bitrate of both systems is approximately maintained at the same level.
# Two-Phase Deep Learning-Based EDoS Detection System
## 6 Result Analysis
### Quality of Service
#### 6.1.1 Evaluate the Period Detector  
We first evaluate the performance of the period detector. Despite the fact that the period detector cannot classify which exact flow is abnormal, it helps the system detect when an attack occurs and reduces the sequence length of the input data for the flow detector, which leads to a speedup of the detection time of the whole system for each flow. We need to evaluate the period detector to prove the efficiency of our chosen parameters for this ANN model.  
Figure 8 presents the detection performance of the period detector in three common types of EDoS attacks using the model parameters listed in Table 3.The results are summarized in Table 7. Accuracy, detection rate, false alarm rate, and F1-score, when detecting ICMP flooding attack are 95.896%, 94.208%, 4.104%, 95.831%, respectively. The results show that our model can accurately detect when an EDoS attack occurs, which will help the flow detector quickly detect abnormal flows.
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 5 Unsupervised Learning Application in Precision Medicine
### Dimensionality Reduction Application in PM
Traditional methods like PCA and t-SNE, which are constrained by linearity or visual representation, often fall short of capturing complex relationships. In contrast, the HAE framework optimized the latent space by clustering similar data points more effectively, providing a lower-dimensional, informative representation of the raw medical data. This allowed for more efficient prognosis prediction, as the autoencoder uncovered non-linear patterns in the data that would otherwise remain hidden. Another application involved the use of deep autoencoders to analyze transcriptomic data for cancer diagnosis [75]. The DeepT2 Vec autoencoder significantly reduced high-dimensional gene expression data into smaller feature vectors while retaining critical biological information. This compressed representation was employed in classification tasks, such as distinguishing between normal and tumor tissues. In the study of [PERSON] et al. [76], deep learning-based dimensionality reduction, in the form of convolutional neural networks (CNNs), was employed to classify different cell types and states based on morphology and immunofluorescence data from malignant serous effusions (MSEs). These CNNs processed data from millions of individual cells, effectively identifying critical features related to cancer biology. By leveraging these techniques, the study could capture the diversity of cell types within MSE samples and correlate them with genomic and transcriptomic profiles. In another work [77], DL is applied to process high-dimensional mass spectrometry data, facilitating the identification of disease-specific metabolic profiles without the need for intermediate steps like peak extraction and annotation. The model uses a pre-pooling module to reduce the dimensionality of the raw MS data, transforming it from a three-dimensional structure into a two-dimensional matrix. The reduced data are passed through a CNN, which extracts features relevant to classifying disease states. This CNN-based feature extraction is critical for reducing the complexity of the data while maintaining its most important characteristics for identifying disease-related metabolic signals. By using an ensemble deep learning strategy, the method presented handles large and complex datasets across different hospitals, effectively reducing batch effects and enhancing classification accuracy.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 3 Method
### Matching and Feature Extraction
\begin{matrix}\mathbf{w_{1}}*\mathbf{U}\left(\mathbf{P_{t}^{i}},\mathbf{ Id}\mathbf{s}_{\mathbf{t},i}\right)+\mathbf{w_{2}}*\mathbf{HG}\left(\mathbf{P_{t}^{i}},\mathbf{ Id}\mathbf{s}_{\mathbf{t},i}\right)\\ +\mathbf{w_{3}}*\mathbf{MT}\left(\mathbf{P_{t}^{i}},\mathbf{Id}\mathbf{s}_{\mathbf{t},i}\right)\end{matrix} \right) \tag{8}\]  
\(w_{1},w_{2},w_{3}\) are the weights of each feature. \(\mathbf{U}\left(\mathbf{P_{t}^{i}},\mathbf{Id}\mathbf{s}_{\mathbf{t},i}\right)\) is a function to obtain the IoU. \(\mathbf{HG}\left(\mathbf{P_{t}^{i}},\mathbf{Id}\mathbf{s}_{\mathbf{t},i}^{\mathbf{k}}\right)\) is a function to compare two histograms. \(\mathbf{MT}\left(\mathbf{P_{t}^{i}},\mathbf{Id}\mathbf{s}_{\mathbf{t},i}^{\mathbf{k}}\right)\) is a function to obtain the motion features by KF. If the \(p\) is greater than the set threshold, the match is successful, and then, we update the trajectory of the target and record the ID for the target \(\mathbf{P_{t}^{i}}\) and region by \(\mathbf{PcId}_{\mathbf{t}}^{i}\) and \(\mathbf{RcID}_{\mathbf{t},i}^{\mathbf{k}}\). If the match fails, we put this target in the last stage to match. In the third matching and feature extraction module, the matching process is similar to the second stage, except for the difference in the matching range. The matching range of the target in the second stage is in the region where the target belongs, while the third stage matches outside of this range. Therefore, we need to use \(\mathbf{UID}_{\mathbf{t}}^{i}\):  
\[\mathbf{Id}^{\mathbf{k}}_{\mathbf{t},i}=\mathbf{UID}_{\mathbf{t}-\mathbf{1}}^{i} \tag{9}\]  
The matching method is the same as Equation (8). But when the probability \(p\) is greater than the set threshold, the matching stops. Then, we update the trajectory of the target and record the ID for the target \(\mathbf{p_{t}^{i}}\) and region by \(\mathbf{PcId}_{\mathbf{t}}^{i}\) and \(\mathbf{RcID}_{\mathbf{t},i}^{\mathbf{k}}\). If a target fails to match, we regard this target as a new target and record the ID for the target \(\mathbf{p_{t}^{i}}\) and region using \(\mathbf{PcId}_{\mathbf{t}}^{i}\) and \(\mathbf{RcID}_{\mathbf{t},i}^{\mathbf{k}}\). Finally, after all matches are completed, store the unmatched target IDs of the previous frame into \(\mathbf{RID}_{\mathbf{t},i}^{\mathbf{k}}\) or \(\mathbf{UID}_{\mathbf{t}}^{i}\). If a region disappears, the \(\mathbf{RID}_{\mathbf{t}-\mathbf{1},i}^{\mathbf{k}}\) of this region is stored in \(\mathbf{UID}_{\mathbf{t}}^{i}\). For a re-detected target, the ID of this target is removed from the sets \(\mathbf{RID}_{\mathbf{t},i}^{\mathbf{k}}\) and \(\mathbf{UID}_{\mathbf{t}}^{i}\). Also, in the sets \(\mathbf{RID}_{\mathbf{t},i}^{\mathbf{k}}\) and \(\mathbf{UID}_{\mathbf{t}}^{i}\), after some frames, this ID is eliminated.