esa-sceva/satcom-chunk-collection huggingface dataset:
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 3 Results and Discussion
### Application of Long-Term Maintenance Decision-Making Optimization Method
#### 3.3.1 Decision-Making Model Parameters  
In 2010, AC overlay was paved onto PCC pavement at Sunan Shuofang Airport when the _PCI_ was 100. Table 4 presents the structural and traffic conditions of the eight regions.  
The maintenance period from 2013 to 2017 was divided into five stages at intervals of 1 year (_K_ = 5). According to the maintenance history, crack filling, fog seal, and hot-mix recycled AC were available, corresponding to \(x_{Rn,i=2}\), \(x_{Rn,i=3}\), and \(x_{Rn,i=5}\), respectively. Considering both the maintenance benefit and inflation influence, preventive maintenance must be implemented as soon as possible, and therefore, the maintenance decision was made at the beginning of each stage. According to the previous maintenance history, preventive maintenance was completed quickly; thus, the maintenance time was set as 0 to simplify calculations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region Code** & **Region of Airport** & \(h_{e}\) **(cm)** & _ESAL (Vday)_ \\ \hline \(1^{h}/2^{h}/3^{h}/4^{h}\) & Terminal runway & 36 & 39 \\ \(5^{h}/6^{h}/7^{h}/8^{h}\) & Middle runway & 39 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural and traffic conditions of Sunan Shuofang Airport.  
The annual average inflation rates in China for each year from 2013 to 2017 were 2.35, 1.98, 1.52, 2.07, and 1.47%, respectively. According to the preventive maintenance costs [41,42], the cost in each stage can be calculated and the \(\Delta c_{k=5,ni}\) is as shown in Table 5. Considering the pavement performance requirement and the annual maintenance budget limit, the \(PCI_{\text{min}}\) is 90 and \(C_{\text{max},k}\) is $64,000.
## 3 The key Technology of the Multi-DOF Automatic Image Acquisition System
### The Calculation of Camera Position
#### 3.1.1 Camera Position Calculating Method by Mathematical Modeling
After repeating many times, the mapping relationship between multiple groups of \(x_{0}\), \(y_{0}\), \(h_{0}\) and \(\Delta x\), \(\Delta y\), \(\Delta z\), \(\alpha\), \(\beta\) is obtained, as shown in Table 1. Since \(\Delta x\), \(\Delta y\), \(\Delta z\), \(\alpha\), \(\beta\) are all known parameters,  
Figure 5: The model of the relationship between camera angle and the motion of the object to be photographed: (**a**) the position of the camera installed at the initial moment and the position of the object to be photographed; (**b**) the position of the camera in the focusing state; (**c**) the position after the \(x\)-, \(y\)-, \(z\)-axes movement; (**d**) the rotation axis of the camera controls the camera optical axis to aim at the shooting object. a number of constraint equations about \(x_{0}\), \(y_{0}\), \(h_{0}\) can be established by introducing Equation (2), and the initial parameters \(x_{0}\), \(y_{0}\), and \(h_{0}\) can be calculated by solving the equations. By solving several constraint equations and averaging the calculated results, the solution is \(x_{0}=0.5241\) mm, \(y_{0}=0.1761\) mm, \(h_{0}=121.4259\) mm. To verify the effect of the modified equation, the above-calculated \(x_{0}\), \(y_{0}\), \(h_{0}\) were substituted into Equation (2), then 15 groups of test points were collected. \(\Delta z\), \(\alpha\), and \(\beta\) were calculated according to Equations (1) and (2) and compared with the actual measured value; the results are shown in Figure 6. We evaluated the accuracy of the calculated value by error. The smaller the error, the closer the calculated value was to the measured value, and the more obvious the effect of the equation's correction. It can be seen from the figure that the error values after correction were less than those before correction, indicating that the error correction had a certain effect. However, the calculated \(\Delta z\), \(\alpha\), and \(\beta\) still had some deviation from the measured values.
## 3 Theoretical Foundations  
An artificial neural network (ANN) comprises numerous interconnected layers, each composed of multiple neurons. There are two primary types of ANNs: feed-forward and recurrent. The distinction lies in their connectivity patterns. In recurrent neural networks (RNNs), neurons may be connected to other neurons within the same layer or to neurons in layers that are neither previous nor subsequent to it. Conversely, in feed-forward neural networks (FNNs), neurons in a given layer are only connected to neurons in the subsequent layer via synaptic weights (\(w\)) [39].  
In the feed-forward process, the input to a neuron \(i\) in the \(l\)-th layer is determined by the product of the results of the activation function \(\varphi(\cdot)\) (Equation (1)), derived from the preceding layer (\(l-1\)), and the weight vector \(\mathbf{w}\) of layer \(l\), which includes the bias weight \(b_{l}\). The output of the neuron corresponds to a spatial transformation of \(s_{i}^{l}\) by the activation function (Equation (2)) [40].  
\[s_{i}^{l}=\sum_{h}w_{hi}^{l}\varphi_{h}^{(l-1)}(s_{h}^{(l-1)}), \tag{1}\]  
\[\varphi^{(l)}(s_{i}^{l})=\varphi^{(l)}(\mathbf{w}^{l},s_{i}^{l}). \tag{2}\]  
The output \(j\) of the last layer in the ANN is \(z_{j}=\hat{f}(\mathbf{x},\mathbf{w})\) (Equation (3)); it depends on the parameters \(\mathbf{w}\) of all hidden layers and it estimates the transformation \(\{f:\mathbb{R}^{N}\rightarrow\mathbb{R}^{C}\}\), which partitions the input space \(\mathbb{R}^{N}\) into \(C\) classification regions (\(\mathbb{R}^{C}\)) [41] by using a linear combination \(\varphi_{h}(\mathbf{x})\).  
\[\hat{f}(\mathbf{x},\mathbf{w})=\sum_{h=1}w_{hi}^{l}\varphi_{h}^{(l-1)}( \mathbf{x}), \tag{3}\]  
with \(\mathbf{x}\) being the input vector (\(\mathbf{x}\in\mathbb{R}^{N}\)) and, when \(l=1\), \(s_{i}=\sum_{h}w_{hi}x_{h}\) (i.e., \(s_{i}\) is the \(i\)-_th_ neuron of the first hidden layer). \(w_{i}^{l}\) is the weight of the \(i\)-_th_ input that is connected from the \(h\) neuron of the \(l\)-_th_ hidden layer to the \((l-1)\)-_th_ hidden layer. \(\varphi_{h}^{l-1}\) is the \(h\)-_th_ output node on the \((l-1)\)-_th_ hidden layer, where \(L\) is the total number of layers in the ANN.
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
###### Abstract  
A growing pad is the key structure for the construction of water inrush grouting on the shaft working surface. Previous methods of calculating the bearing capacity have limitations due to a lack of understanding of the failure mode. To investigate the bearing capacity of a concrete grouting pad on the working surface of a shaft, this paper establishes a mechanical model for the punching shear failure of a grouting pad under symmetrical loading conditions. A unified solution for the bearing capacity is derived, and the influence of parameters is discussed. In addition, a new method for designing the plastic limit thickness is proposed based on this research. The results show that the reason for the grouting pads' punching shear failure resulted from the formation of peripheral grouting holes \"weak ring\" caused by the reduction of the bearing capacity. When the thickness of \(B_{0}\) remains constant, the bearing capacity \(q_{0}\) of the grouting pad is inversely proportional to the ratio of the diameter and the area of the bottom load. Therefore, following the method of \"dividing interval, and jumping holes\" during grouting construction is recommended. The greater the thickness of the grouting pad, the greater the bearing capacity \(q_{0}\) will be. When the grouting pad diameter is \(2r_{2}\) and the thickness \(B_{0}\) is constant, the bearing capacity \(q_{0}\) increases with the material tensile strength \(f_{1}\). When designing grouting pads, following the principles of \"large thickness, uniform strength theory, high strength materials\" will improve bearing performance. The findings have been implemented in the design of the grouting pad thickness for the Tianshan Shengli Tunnel shaft project, which can successfully solve the problem of frequent cracking caused by the weak bearing capacity of a grouting pad. The findings can provide a theoretical basis and reference for the design and construction of grouting pads in a highway tunnel shaft.  
grouting pad; punching shear failure; unified strength theory; bearing capacity; thickness design +
Footnote †: journal: Applied Sciences
###### Abstract  
As the scale and complexity of software for industrial equipment increases, the risk of defects being introduced into the software also increases. Over-the-air (OTA) technology eliminates software defects by regularly updating and maintaining the software equipment. However, downloading an upgraded file is time-consuming. The differential compression algorithm can rapidly complete OTA software upgrades. To solve the low-efficiency problem of suffix array sorting in the BsDiff algorithm, a fusion algorithm based on the suffix array induced sorting and BsDiff methods was proposed to reduce the time consumption in the OTA upgrade process. The execution of the difference algorithm in the cloud was simulated using bench experiments. Subsequently, the function and performance of the proposed algorithm were experimentally evaluated. The results showed that the optimized algorithm could save more than 35% of the time compared to the original algorithm, which improved the OTA upgrade efficiency to a certain extent.  
over-the-air technology; differential compression algorithm; upgrade efficiency; industrial internet
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Passive Microwave Sensors  
Passive microwave sensors measure space-source microwave radiation as reflected from the sea [3]. Passive microwave radiometers detect the microwave emissivity difference between water and oil. The microwave signal detected also changes with oil thickness, so radiometry can be used to gauge oil slick thickness. However, there are interferences, and signal differentials may be poor. The microwave radiometer suffers from low spatial resolution. Spatial resolution is typically in tensof metres. Currently, microwave is not being used for slick imaging. Research emphasis is now on employing multiple microwave wavelengths to gauge slick thickness. Slick thickness measurements will be covered later in this paper.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Digital Core Analysis
#### 3.3.4 Nuclear Magnetic Resonance (NMR)  
NMR analysis is a method that detects a synthetic electromagnetic signal, which is produced due to the spin relaxation of protons in a magnetic field [67]. NMR provides a fast and non-destructive method for characterizing complex porous media [68; 69; 70]. The attenuation of NMR signals and the measurement of total signal intensity can be used to determine pore size distribution, porosity, fluid distribution [71], and water phase movement in core samples [72], and stress loading methods, including static loading and cyclic loading, on rock permeability and pore structure [73]. In a recent study, [PERSON] et al. [54] combined core flow experiments and NMR techniques to diagnose the hydro-lock and water sensitivity damage quantitatively.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### Construction of the Micro-Capsule Networks
#### 2.2.3 Margin Loss of the Micro-Capsule Network Model  
For detecting multiple classes of epileptic EEG signals, margin loss values for each class are computed via dedicated margin loss functions, and the total loss value is derived through summation. After the trained model is obtained [34], the margin loss function \(L_{k}\) can be computed with Equation (2), which determines if an object of a particular class exists.  
\[L_{k}=p_{k}\max\left(0,m^{+}-\|v_{k}\|\right)^{2}+\lambda(1-p_{k})\max\left(0,\|v_{k}\|-m^{-}\right)^{2} \tag{2}\]  
where \(p_{k}\) is epileptic classification (binary or three classifications). \(\|v_{k}\|\) is the length of the vector \(k\). \(\lambda\) is used to adjust the error ratio of different classes, and the value of \(\lambda\) is set to 0.5. The hyperparameters of \(m^{+}\) and \(m^{-}\) are upper and lower boundaries set to 0.9 and 0.1, respectively.
## 1 Introduction  
Medical Imaging plays a key role in disease management, starting from baseline risk assessment, diagnosis, staging, therapy planning, therapy delivery, and follow-up. Each type of disease has led to the development of more advanced imaging methods and modalities to help clinicians address the specific challenges in analyzing the underlying disease mechanisms. Imaging data is one of the most important sources of evidence for clinical analysis and medical intervention as it accounts for about 90% of all healthcare data. Researchers have been actively pursuing the development of advanced image analysis algorithms, some of which are routinely used in clinical practice. These developments were driven by the need for a comprehensive quantification of structure and function across several imaging modalities such as Computed Tomography (CT), X-ray Radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Nuclear Medicine Imaging, and Digital Pathology [1].  
In the context of the availability of unprecedented data storage capacity and computational power, Deep learning has become the state-of-the-art machine learning technique, providing unprecedented performance at learning patterns in medical images and great promise for helping physicians during clinical decision-making processes. Previously reported deep learning-related studies cover various types of problems, e.g., classification, detection, segmentation, for different types of structures, e.g., landmarks, lesions, organs, in diverse anatomical application areas [2].  
The aim of this special issue is to present and highlight novel methods, architectures, techniques, and applications of deep learning in medical imaging. Papers both from theoretical and practical aspects were welcome, including ongoing research projects, experimental results, and recent developments related to, but not limited to, the following topics: image reconstruction; image enhancement; segmentation; registration; computer aided detection; landmark detection; image or view recognition; automated report generation; multi-task learning; transfer learning; generative learning; self-supervised learning; semi-supervised learning; weakly supervised learning; unsupervised learning; federated learning; privacy preserving learning; explainability and interpretability; robustness and out-of-distribution detection; and uncertainty quantification.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 3 Pattern-Reconfigurable Antennas
### Mechanically Pattern-Reconfigurable Antennas
Figure 5: Fabricated proposed antenna array based on magic cube origami for: (**a**) single antenna and cube (**b**) two antennas and (**c**) three antennas [95]. In Table 2, the flexible pattern-reconfigurable antennas are listed. The table presents the antenna types, reconfigurability method, number of states, the radiation performance and the materials that were used in these antennas.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 1 Introduction
The decision-making part has three sub-blocks: residual generation; windows characterization and energy feature extraction; fault classification using the SVM algorithm. Three main contributions in this research are listed as follows:  
1. Normal signal approximation for time-series normal signal using fuzzy orthonormal regressive technique. 2. Developing an adaptive cascade observer for signal estimation. 3. Improving the performance of the classification technique by generating the residual signals, extracting the features of energy from them, and applying to SVM for fault identification. The remainder of this manuscript is organized as follows. The second section outlines the datasets. The third section outlines the fuzzy orthonormal regressive signal approximation. The adaptive cascade observer with SVM for unknown signal classification is represented in Section 4. In the next section, verification of the adaptive cascade method with the SVM fault classifier is analyzed. In the last section, conclusions are explained.
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development
#### 3.3.4 Changes in the Concentration Profiles of Alcohols  
Previous studies showed that alcohols represent the major volatile components in aroma performance [16; 33]. As shown Figure 3, most of the alcohols were identified in the initial stage, i.e., S-0, and sharply decreased throughout the dehydration stage. Indeed, 1-octen-3-ol, an earthy odor compound, has the highest content (5.79 \(\mathrm{\SIUnitSymbolMicro g}\)/g) in sugarcane juice, followed by ethanol (5.23 \(\mathrm{\SIUnitSymbolMicro g}\)/g), and disappeared after 30 min. Meanwhile, green-scented 2-ethyl-1-hexanol was the only alcohol that existed in most of the samples except S-180, the final concentration was 1.79 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240. Waxy odor scented 1-octanol was the other alcohol detected in the final sugar product.
## 2 Materials and Methods
### Path Tracking and Control  
#### 2.4.1 Feedback PD Controller  
The moving platform is set to follow a desired trajectory over time. The platform changes its orientation by making a roll motion (\(\beta\)) over the \(Y\) axis. As previously stated, the pitch (\(\alpha\)) and yaw (\(\gamma\)) motions stay constant through all time. By changing the speed at which the platform changes its orientation, different thrust and water displacement will be produced. Since the caudal fin attached to the moving platform is the only propulsion system for the designed vehicle, attaining a precise control over limbs displacements will be important to achieve the desired motion. Higher flapping frequencies will demand greater control actions and will produce higher thrust levels. Figure 6 describes the block diagram of a closed-loop PD control system where the limb distance \(d_{i}\) is regulated based on the change of the desired platform's orientation \(\beta\) through time \(t\).  
The desired trajectory (orientation) that the platform is set to follow based on pitch-roll-yaw angles through time will be set as:  
\[f(\alpha,\beta,\gamma,t)=\left\{\begin{array}{rl}\alpha=0,&t\geq 0\\ \beta=30*\sin(2\pi ft),&t\geq 0\\ \gamma=0,&t\geq 0\end{array}\right.. \tag{35}\]  
The flapping frequencies \(f\) to which the caudal fin is set to oscillate will range from 0.5 to 5 Hz. The total amplitude of oscillation (by design) is set to \(\pm 30^{\lx@math@degree}\). Moreover, the control law for the implemented PD is defined as:  
\[F_{i}(t)=kp\cdot e_{i}(t)+kd\cdot\dot{e_{i}}(t)\ \,i=1,2,3; \tag{36}\]  
where:  
\[e_{i}(t)=d_{iD}(t)-d_{iR}(t), \tag{37}\]  
\[\dot{e_{i}}(t)=\frac{e_{i}(t)-e_{i}(t-1)}{\Delta t}. \tag{38}\]  
Figure 5: Plant’s block diagram. Forces and passive joints estimated by a controller and the inverse kinematics equation system, respectively, input the plant. Real position of limbs exits the plant.  
Figure 6: Block diagram for the implementation of the PD controller.  
The proportional and derivative gains for the PD controller are arbitrary defined to attain critical damping. Hence, the relation between \(kp\) and \(kd\) is established by the following relation:  
\[kd=2\sqrt{kp}. \tag{39}\]
## 4 Conclusions  
In this study, the radiopacity for hafnium ion-doped Bi\({}_{2}\)O\({}_{3}\) powders synthesized using the sol-gel process followed by calcination at various temperatures was performed. The results showed that the SGHB100 (i.e., Bi/Hf (90/10)) powder yields a significant increase of radiopacity over pristine Bi\({}_{2}\)O\({}_{3}\) (i.e., Bi/Hf (100/0)), indicating new dominant \(\beta\)-Bi\({}_{7.78}\)Hf\({}_{0.22}\)O\({}_{12.11}\) phase formation as well as possible structure densification by sol-gel process and 700 \({}^{\circ}\)C calcination for 1 h. After MTA-like cement hydration, the group containing radiopacifier of sol-gel Bi/Hf (90/10) exhibited the best radiopacity (6.36 \(\pm\) 0.34 mmAl), DTS (2.54 \(\pm\) 0.29 MPa), and cell viability (84.0 \(\pm\) 8.1%) (\(p\) < 0.05) when compared to that of Bi/Hf (100/0) powders. The Bi/Hf (90/10) powders offer MTA-like cement with high radiopacity, DTS, and acceptable biocompatibility reveal their potential usage as an alternative radiopacifier.  
Conceptualization, C.-K.L. and J.-C.Y.; methodology, T.-S.Y. and M.-S.C.; software; validation, A.K.; investigation, C.-J.H., A.B., A.K. and A.Z.; writing--original draft preparation, T.-S.Y. and J.-C.Y.; writing--review and editing. C.-Y.C. and C.-K.L. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Taipei Medical University Hospital for financially supporting this work under grant no. 110 TMU-TMUH-16 and partially supported by MOST 109-2221-E-038-014.  
Not applicable.  
Data availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Detection of Smoking in Indoor Environment Using Machine Learning
## 3 Results
### Data Characteristics
By extracting CO2, TVOC, PM 2.5, and PM 10 for each data set and comparing the graphs of smoking, cooking, and heating, it was confirmed that each data set has distinct characteristics. We used five methods of smoking detect algorithms. K-means as an unsupervised method and PFCM as a fuzzy method, linear SVM, Non-linear SVM, SVM RBF, KNN, and MLP as supervised models, based upon the selection cheat sheet. As shown in Figure 13, it is necessary to decide that SVM RBF, and other machine learning methods such as KNN and MLP can be applied to other places than to decide it as a good model simply due to high accuracy and recall. For example, in a system that detects smoker, it is even more important to classify them as smoking only if it can be sure that they are smoking. In this case, precision is important and the linear SVM model with 99% precision is most effective. On the other hand, it would be effective to use SVM RBF, in a system that detects air quality due to smoking and warns people about it. This is because it is sensitive to the deterioration of air quality, so if air pollution occurs even a little, it is effectively classified. In future research, it is necessary to apply several algorithms derived from the above types. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline K-means & 0.245117 & 0.268174 & 0.740257 & 0.393714 \\ PFCM & 0.491750 & 0.493748 & 0.480058 & 0.481908 \\ Linear SVM & 0.839724 & 0.991550 & 0.518633 & 0.680990 \\ MLP & 0.910538 & 0.956022 & 0.765015 & 0.849918 \\ KNN & 0.913537 & 0.989159 & 0.747396 & 0.851141 \\ SVM RBF & 0.931726 & 0.989533 & 0.802139 & 0.886026 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of smoking detection algorithms. Figure 13: Summary of performance comparison of cigarette detection algorithm. Receiver Operating Characteristic (ROC) curves are effective in evaluating the model on a binary target. Since clustering is unsupervised learning, so it was excluded as a case which no target could be assessed in terms of sensitivity and 1-specificity. ROC curves for the SVM linear, SVM RBF and MLP methods with binary targets are presented as shown in Figure 14. The SVM linear and SVM RBF methods have a wide Area under the Curve (AUC), and are shown to have good performance considering the correlation between sensitivity and specificity.