esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Different Extension Time Differences on the Collapse Process
The collapse pattern of Scheme 9 and Schemes 1-3 did not have a plastic hinge at the top of the rear row of the first-floor columns, which was due to the long time difference of 0.9 s. This made the plastic hinge between the rows of columns rotate sufficiently. When the plastic hinge was generated in the rear row of columns, the front-row structure had already touched the ground, resulting in insufficient force to make the plastic hinge of the rear row of columns form. The structure of the rear column bottom for the fixed-axis rotation can only form the collapse state of the rear column supported by the beam at D and E.  
Figure 10: Schematic diagram of the collapse process simulated numerically with different extension time differences. From the numerical simulation analysis of Figures 10 and 11, it can be seen that in these nine scenarios, when the blast-cut failure of each row of columns is removed, the transverse-force members of the structure (beams and slabs) are in the overhanging state, and at the combination of slabs, beams and columns, they will be subjected to larger moments. Since the column is much larger than the beam and floor in terms of the bearing capacity, lateral-bearing members such as the beam and floor will be damaged before the column. When a long time difference is used, for example, in the time difference of 0.7\(-\)0.9 s, due to the characteristics of this structure, the plastic hinge acts for a long time, and the  
Figure 11: Schematic diagram of the plastic-hinge formation position. (**a**) Plastic hinges are formed at both ends of D and E axis beams. (**b**) Plastic hinge appears at the top of the first layer of the last row of columns. rotation of the superstructure will produce a recoil force in the opposite direction of the overall tipping of the structure. In addition, the premature destruction of the beam and plate will affect the integrity of the structure, which will lead to a relatively large back-slip phenomenon after the structure disintegrates at ground level.
## 1 Introduction
The authors in [27] investigated the average symbol error rate of M-ary PSK modulation over various fading channels, which is also subject to phase noise. In [22], the influence of thermal noise, shot noise, and background noise on the performance of P-i-N (PIN) diodes and APD-based FSO communication system was analyzed under weak turbulence conditions. However, to the best of our knowledge, the influence of various types of noises on the FSO communication system are rarely studied. Moreover, the study on the comparison of system performance under different turbulence conditions influenced by several types of noise has also seldom been investigated. Motivated by the above analysis, to fill this gap, the impacts of thermal noise, quantum noise, and background noise on the average BER and average capacity of an FSO communication system are investigated. Besides, a detailed comparison of the system performance under various turbulence conditions is discussed in this study. We also explore the improvements in system performance from the use of different modulation schemes. The remainder of this paper is organized as follows. In Section 2, the system and channel models are introduced in order to analyze the processes in the FSO communication system. Exact closed-form results for the average BER and the average channel capacity are derived in Section 3, taking into consideration thermal noise, quantum noise, and background noise. Simulation results and the discussion are presented in Section 4, followed by the conclusions in Section 5.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 6 Example Analysis
### Influence of Axial Tensile Stress  
When the shallow-tunnel burial depth of the slope section \(H=20\) m, rectangular tunnel height \(h=10\) m, span \(b=10\) m, bulk density of surrounding rock \(\gamma=20\) kN/m\({}^{3}\), cohesion of surrounding rock \(c_{0}=30\) kPa, axial tensile stress \(\sigma_{\rm t}=30\)-70 kPa, internal friction angle of surrounding rock \(\varphi=18^{\circ}\), slope surface inclination angle \(\delta=18^{\circ}\), ratio of horizontal support force to vertical support force of surrounding rock \(K=1.0\), nonlinear coefficients \(m=1.0\), \(1.1\), \(1.2\), \(1.4\), and order slope top load \(p=100\) kPa, the variations in pressure and damage patterns of the surrounding rock in shallow-buried tunnels are shown in Figures 10 and 11.  
As illustrated in Figure 10, with the increase in axial tensile stress, the surrounding rock pressure increases significantly. \(\sigma_{\rm t}\) changes from 30 kPa to 70 kPa when \(m=1.0\), and the value of the increase in the surrounding rock pressure reaches a minimum of 53 kPa; when \(m=1.4\), the value of the increase in the surrounding rock pressure reaches a maximum of 132 kPa. When \(m\) changes from 1.0 to 1.4, when \(\sigma_{\rm t}=30\) kPa, the value of the increase in the surrounding rock pressure reaches a minimum of 137 kPa; and when \(\sigma_{\rm t}=70\) kPa, indicating that the larger the axial tensile stress is affected by the nonlinear coefficient more significantly. As evidenced by Figure 11, it can be observed that with the increase in axial tensile stress, the damage surface has an obvious tendency to extend outward, and the area of the shear zone increases, which indicates that the plastic zone caused by the bias pressure will increase in the excavation process. It can be inferred that the axial tensile stress significantly affects both the magnitude of the surrounding rock pressure and the location of the failure surface. Therefore, numerical simulation methods can be employed to analyze the distribution patterns of stress and displacement within the surrounding rock. For weak surrounding rock, it is essential to implement engineering support measures in advance and to optimize the support scheme and construction sequence in a timely manner to prevent collapse caused by insufficient support force.  
Figure 11: Influence of axial tensile stress on failure mode of surrounding rock (\(m=1.2\)).  
Figure 10: Influence of axial tensile stress on surrounding rock pressure.
## 2 Materials and Methods
### Models' Development
#### 2.4.1 Artificial Neural Network (ANN)  
Artificial neural networks (ANNs) are powerful data modeling tools consisting of an input layer, hidden layers, and an output layer [25]. The nodes in the input layer receive the input information, which is processed by activation functions and passed to the nodes in the hidden layers. The information from the hidden layers is then sent to the output layer. Each node is connected to the others through corresponding weights and thresholds [26]. If the output of any individual node is above the specified threshold, then that node is activated and the data are sent to the next layer of the network. Otherwise, no data are passed on to the next layer of the network [27]. Many factors can affect the stability of an ANN model, including the model used, the modeling process, and the simulated training data. The optimal combination of neurons could not be determined in advance; therefore, the optimal structure of the network was determined by testing multiple models using correlation coefficient and root mean square error (RMSE) guidelines [28]. Artificial neural networks are widely applied in various research fields due to their strong learning ability and good predictive performance [29; 30; 31; 32]. In the field of ecology, [PERSON] et al. [14] used an artificial neural network model to predict the cadmium content in rice, and the prediction results were optimistic.  
In this study, the elastic backpropagation algorithm (RPROP) was used to train the ANN model, where the maximum step size of the neural network training was set to 100,000, and the partial derivative of the error function was specified as a stopping criterion threshold of 0.01. As a variant of backpropagation, the RPROP eliminates the influence of derivative changes on the weight step size and only considers the sign of the derivative, which enhances the efficiency and stability of the algorithm [33]. The ANN prediction model in this study was built using the neuralnet package in R Studio.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 2 Materials and Methods
### Study Population and Image Annotation  
Overall, 13,222 angiograms were retrospectively collected from 3275 patients who underwent X-ray coronary angiography performed at Fuwai Hospital, 11,900 of which were used for training a vessel segmentation model, and 1322 angiograms were used in the validation. Additionally, another 249 angiograms were used to evaluate the performance of AI-QCA, including 139 LAD, 36 LCX, and 74 RCA. Figure 2 shows the study flowchart. The vessel segmentation annotation was on a pixel-by-pixel level. All coronary arteries with a diameter of at least 1 mm were visually identified and marked by cardiological analysts on the original angiograms, and then a team of trained and certified technicians meticulously labeled each pixel based on the initial markings. The study was approved by the Institutional Review Board of Fuwai Hospital. All the angiograms were processed by information desensitization algorithms.  
Figure 1: The overall workflow of AI-QCA. The items in gray represent figures or results from the process, while the items in blue represent the methods utilized in the process. CAG = coronary angiography; CAT = coronary artery tree.  
Figure 2: Study flow chart. In total, 13,222 angiograms with 28,539 vessels were used for segmentation validation, and 249 angiograms were used for QCA validation. CAG = coronary angiography; QCA = quantitative coronary analysis; LAD = left anterior descending; LCX = left circumflex artery; RCA = right coronary artery.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Multi-Omics Visualization and Pathway Exploration
#### 2.2.4 ProteomicsDB: A Platform Dedicated to Large-Scale Protein Expression and Interaction Data  
Finally, ProteomicsDB shifts the focus to proteomics, offering large-scale data integration and visualization, much like MVP but with a more specific focus on protein expression. ProteomicsDB is a powerful platform designed for the integration, analysis, and visualization of proteomics data. The platform allows researchers to explore large-scale protein expression datasets across a wide range of tissues, organisms, and experimental conditions. ProteomicsDB enables users to visualize protein abundance, examine protein-protein interactions, and link proteomic data to biological functions and molecular pathways, making it a valuable resource for systems biology and functional genomics research [21]. The platform supports the exploration of protein expression profiles, providing insights into how proteins are regulated in different tissues and under various conditions. By connecting protein expression data with biological pathways, ProteomicsDB aids understand the molecular mechanisms underlying biological processes and disease states.  
ProteomicsDB offers users a range of visualization tools, including heatmaps, bar charts, and protein network maps, allowing for intuitive exploration of complex proteomics data. Researchers can also query specific proteins or pathways, making it easier to identify potential biomarkers or targets for therapeutic intervention. Being this platform specialized for proteomics data, users looking for broader multi-omics integration may need to supplement it with additional tools for transcriptomics, metabolomics, or genomics data. Additionally, the platform does not directly support clinical decision-making applications without additional layers of data integration. To bridge this gap, ProteomicsDB could be combined with complementary tools that enable network-based analysis, biomarker discovery, and the integration of transcriptomics or metabolomics data. By overlaying proteomicsdata with other omics layers and incorporating clinical patient data, ProteomicsDB could provide deeper insights into disease progression, therapeutic targets, and personalized treatment strategies.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 2 Related Works
### Heuristic Optimization Algorithms  
Heuristic algorithms are intuitively or empirically constructed algorithms that search for a feasible solution to each instance of an optimization problem at a limited cost (in terms of computational time and space). Since heuristic algorithms can usually find promising solutions in a reasonable amount of time when dealing with many practical NP-hard problems, they have become a research hotspot in recent years. Inspired by various phenomena, such as animal behavior and natural laws, researchers have proposed many novel and effective optimization algorithms and have proven their value in practical applications. For example, based on the gravitational search algorithm (GSA), which was inspired by the law of gravity and interactions between mass entities, [PERSON] et al. [26] proposed the tuning of a class of fuzzy control systems to obtain a reduced sensitivity. [PERSON] et al. [27] proposed an effective rule classifying method, namely the heuristic algorithm to reduce memory demand (HARD), for heterogeneous bit-split string matching architectures. Based on the gray wolf optimization (GWO) algorithm, which was inspired by the action of a gray wolf preying on its prey, [PERSON] et al. [28] proposed an RL-based control approach to train neural networks. [PERSON] et al. [29] proposed a hybrid metaheuristic optimization algorithm called the particle filter-particle swarm optimization (PF-PSO) algorithm, which can effectively optimize the position control of a family of integral-type servo systems. The above works have been proven to be successful in various applications and thus, are valuable for the further improvement of heuristic algorithms. In order to solve the multi-objective task scheduling problem of intelligent production lines, we proposed a hybrid algorithm called the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) [30] to combine the advantages of cloud computing and fog computing. Based on our previous research, we started trying to solve the routing problems in TSN transmission using heuristic algorithms.
## 4 Experiment and Evaluations
### Experimental Results
#### 4.4.2 Ablation Experiment
Comparative analyses with the initial model reveal that the enhanced model not only detects diseased regions more accurately but also effectively identifies subtle and complex disease features, showcasing exceptional generalization performance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Faster-** \\ **LTNet** \\ \end{tabular}  & \begin{tabular}{c} **CG** \\ **Attention Module** \\ \end{tabular}  & \begin{tabular}{c} **RMT** \\ **Spatial Prior Block** \\ \end{tabular}  & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **mAP50** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **mAP50.95** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular}  & \begin{tabular}{c} **FPS** \\ **(F/S)** \\ \end{tabular}  & \begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular}  & \begin{tabular}{c} **Size** \\ **(M)** \\ \end{tabular}  \\ \hline \ \begin{tabular}{c} **\(\checkmark\)** \\ \(\checkmark\)** \\ \(\checkmark\)** \\ \(\checkmark\)** \\ \end{tabular}  &
\begin{tabular}{c} 89.00 \\ 88.90 \\ 88.40 \\ 88.90 \\ 88. Figure 11: Effect diagram of tea disease detection before and after model enhancement. Column (**a**) is the original image of red leaf spot, tea white spot, tea coal disease, and tea brown blight from top to bottom. Columns (**b**,**c**) correspond to the effect of disease detection before and after improvement of the model, respectively.
## 4 Selection and Analysis of Resonant Compensation Network
### Selection of Resonant Compensation Networks
#### 4.1.2 High-Order Resonant Compensation Networks  
To further improve the performance of the system, researchers have proposed various high-order resonant compensation networks, including LCC-S, S-LCC, LCL-LCL, and LCC-LCC [26; 27], whose topologies are shown in Figure 10. The characteristics of these four high-order resonant compensation networks in terms of parameter design, output gain, output characteristics, and parameter design degrees of freedom are shown in Table 3. After comparing these four high-order resonant compensation networks, the LCC-S network was chosen to provide CV to the loads. Specifically, the parameter design of the LCC-S resonant compensation network includes a series inductor (\(L_{a}\)), parallel capacitor (\(C_{a}\)), and series capacitor (\(C_{s}\)). Compared with low-order resonant compensation networks, the system parameter design dimension was increased by incorporating additional inductors and capacitors, resulting in improved overall flexibility of the system under different operating conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topologies** & **Parameter Design** & **Output Gain** & **Output Characteristics** \\ \hline S-P & \(\omega_{0}=\frac{1}{\sqrt{(L_{p}-M/L_{s})C_{p}}}=\frac{1}{\sqrt{L_{s}C_{s}}}\) & \(\frac{L}{M}\) & CV \\ S-S & \(\omega_{0}=\frac{1}{\sqrt{L_{p}C_{p}}}=\frac{1}{\sqrt{L_{s}C_{s}}}\) & \(\frac{1}{\omega_{0}M}\) & CC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operating characteristics of S-P and S-S resonant compensation networks.  
Figure 9: Low-order resonant compensation networks. (**a**) S-P; (**b**) S-S.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 7 Conclusions  
In this work, the AE and 3-SgOut methods were applied for the detection of anomalies in DC motor body temperature. The anomaly dataset used in the study was created by using DC motor experimental equipment, which was designed specifically for this study. The performance of the methods was examined on this dataset and the results were analyzed by using accuracy, recall, and precision measures.  
Based on the results of the experimental study, it can be seen that the AE achieved remarkable performance results, which were 99.97%, 96.62%, and 100% for accuracy, recall, and precision, respectively, and it can be successfully used for anomaly detection in DC motor body temperature. Moreover, the proposed system can be preferred for anomaly detection in time series data from different sensor types in industrial systems when the performance results of the AE are taken into consideration.  
The results and the findings presented in this study show that AE-based methods can be a good solution to successfully detect the anomalies in time series data that will be included in future studies.  
Future research directions for this study can be grouped into two main approaches. The first one is to examine the performance of the proposed AE-based anomaly detection method on multi-sensor data. The second is to use deep learning methods in the AE architecture while working with multi-sensor data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Recall** & **Precision** \\ \hline AE & 99.97\% & 96.62\% & 100\% \\
3-SgOut & 99.39\% & 17.99\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results for AE and 3-SgOut.  
**Author Contributions:** Conceptualization, E.H.D. and E.Y.; methodology, E.H.D. and E.Y.; software, E.H.D. and E.Y.; validation, E.H.D. and E.Y.; formal analysis, E.H.D. and E.Y.; investigation, E.H.D. and E.Y.; resources, E.H.D. and E.Y.; data curation, E.H.D. and E.Y.; writing--original draft preparation, E.H.D. and E.Y.; writing--review and editing, E.H.D. and E.Y.; visualization, E.H.D. and E.Y.; supervision, E.Y.; project administration, E.Y.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under project number 118C157, within the scope of the 2244 Industrial Ph.D. Program. [PERSON] takes part in this project as a Ph.D. scholarship student.  
Not applicable.  
The data are available on request from the authors.  
We would like to thank TUBITAK for their support. The authors would also like to thank EMKO Elektronik A.S. for their support in this project.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effects of STL Addition on BT VMs
#### 3.2.3 PLS-DA Analysis
Additionally, 19 common volatiles were found between STL vs. BoS and BT vs. STL, such as 2-ethyl-1-hexanol, 1-phenyl-1,2-propanediol, 3,6,6-trimethyl-bicyclo[3.1.1]hept-2-ene, 3-carene, isophorone, linalool, 4-chloro-2-methyl-1-phenyl-3-buten-1-ol, linalool oxide (pyranoid), 2-methoxy-3-(2-methylpropyl)-pyrazine, 3-hexenyl ester, (Z)-butanoic acid, 1-dodecanol, \(n\)-valeric acid _cis_-3-hexenyl ester, formic acid dodecyl ester, nerol, 6-methyl-pentadecane, and \(\alpha\)-ionone. The contents of these VMs differed significantly between the STL and BT or BoS, and we used them as the key VMs on which the distinction of the glutinous rice aroma of STL was based. Furthermore, benzaldehyde and 1-hexanol were common substances between STL vs. BoS and BT vs. BoS; therefore, they were identified as the key VMs responsible for the improved BT aroma after the addition of STL. Finally, five unique substances were found to differ between the BT and BoS samples, namely, hexanal, _cis_-2-methyl-cyclopentanol, decane, 3,3,5-trimethyl-heptane, and (Z)-hexanoic acid-3-hexenyl ester. These compounds did not directly affect BT aroma with STL addition but were produced by indirect reactions and resulted in the detection of aroma differences between the BT and BoS.
# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 5 Results and Discussion
These findings suggest that: (i) The value of the learning rate and the optimizer choice can have a strong influence on each other, in other words, the performance of the built models highly depends on the combination of the learning rate and the optimizer; (ii) With the appropriate learning rate setting, Adam outperforms SGD not only in final generalization performance but also able to converge with a smaller number of epochs. In our previous work [17], we have explored using shallow machine learning techniques to build prediction models on the SNS24 clinical triage classification task. Four shallow machine learning algorithms were used in the previous work: linear SVM, RBF kernel SVM, Random Forest, and Multinomial Naive Bayes [17]. For the features, we used word n-grams with word counts and TF-IDF; we also used word embeddings built with BERT [25] and Flair [27] models. The experiments carried out in this work use the same dataset and train/val/test splits. The performances between the shallow machine learning techniques and neural networks are presented in Table 6.  
We can see that the transformers-based architecture achieves the highest score of all the experimented techniques, and was able to surpass the other two neural network architectures and the linear SVM algorithm, presenting the best among the four shallow machine learning techniques. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Machine Learning Techniques** & **Architecture/Algorithm** & **Testing Accuracy** \\ \hline Neural network & CNN-based architecture & 76.56\% \\  & RNN-based architecture & 75.88\% \\  & Transformers-based architecture & 80.57\% \\ \hline Shallow machine learning & Linear SVM & 77.96\% \\  & RBF SVM & 76.97\% \\  & Random Forest & 74.93\% \\  & Multinomial NB & 66.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the neural network and shallow machine learning techniques on the SNS24 clinical triage classification task. Figure 8: The validation accuracy comparison between SGD and Adam over 150 epochs using transformers-based architecture.