esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Target Recognition Based on Multi-Decision
## 4 Underwater Target-Recognition Framework Based on CNN  
CNN has proven its powerful capability in many fields, such as pattern recognition, computer vision, nature language processing, and wireless physical layer [1; 7; 8; 10]. It is composed of input layer, convolutional layer, activation function, pooling layer, and fully connected layer. The input layer can handle multi-dimensional data; similar to other neural networks, because of the use of gradient descent learning, the input features of CNN need to be standardized. The convolutional layer is used for feature extraction. The activation layer strengthens the expressive ability of the neural network. The pooling layer compresses the input features and extracts the main features [33; 34; 35]. The fully connected layer connects all the features and sends the output value to the classifier.  
From the LOFAR spectrum of the measured underwater acoustic signal which is extracted through multi-step judgment, we design a convolutional neural network structure according to its characteristics. The specific network parameters can be seen in Table 1. For this CNN network structure, it refers some ideas of the inception module which uses different sizes of convolution kernels and weighs the characteristics of the global and local information distribution. This network structure selects different convolution kernels and pooling kernels for preliminary feature extraction. The output of each sub-layer is cascaded and passes through several convolutional layers and pooling layers. Finally, the flatten layer extends the feature map into vectors and the network completes the classification by the dense layer. Convolution and pooling performed in parallel in the network obtain features of different information scales. The network has strong feature extraction capabilities for the positional relationship of line spectrum on different frequency points in the LOFAR spectrum.  
The network parameters of CNN have been marked in Table 1. \((p*q)*r\) means the size of the convolution kernel is \((p*q)\), \(r\) means the number of channels. \(stride=m*n\) means the step size is \(m*n\). Conv and MaxPool are convolution layer and max pooling layer, respectively. CNN training and optimization hyperparameters are shown in Table 2.  
Adam was originally proposed by [PERSON] of OpenAI and [PERSON] of the University of Toronto [36]. It is a first-order optimization algorithm that can replace the traditional stochastic gradient descent process. It can iteratively update neural network weights based on training data.
## 4 Discussion
Hence, ML models that only focus on a single type of cardiovascular incident lack practicability in providing diagnosis aids. As an advantage of this study, patients without a known history of cardiovascular disease were exclusively considered and investigated. It also demonstrated that the performance of the ML models was enhanced slightly after including non-catheterization features. Overall, the proposed models in our work achieved highly accurate predictions: 88.17%, sensitivity: 89.72%, and AUROC: 91.68% for predicting various cardiovascular incidents (including CABG, PCI, VS, stroke, and MI) with accuracy: 89.21%, sensitivity: 90.20%, and AUROC: 94.16% for predicting PCI treatments; and with accuracy: 93.86%, sensitivity: 77.57%, and AUROC: 96.47% for predicting CABG treatments. As a novelty of this study, cardiovascular treatment (PCI or CABG) was the predicted target. By predicting the likelihood of a treatment received, patients' cardiovascular risks were also indirectly estimated. The approaches can efficiently differentiate CVD patients under high risk and assist in timely treatment planning (PCI or CABG). Compared with traditional strategies for the CVD risk assessment (e.g., the Framingham Risk Score), ML could improve the accuracy of CVD risk prediction [33]. For 10-year cardiovascular predictions, the neural networks algorithm obtained a sensitivity of 67.5% and a specificity of 70.7% and performed better than the established algorithm based on American College of Cardiology guidelines [33]. AutoPrognosis, an ensemble of three ML pipelines, maintains higher accuracy (AUROC: 0.774) compared to the baseline Framingham score (AUROC: 0.724) on 5-year CVD risk prediction for patients without diabetes [34]. However, the level of CVD risk is still hard to be evaluated accurately. In fact, CVD has been long believed as a multifactorial disease, and its risk factors tend to interact with each other for each individual. Thus, the interactive effects within patients' medical history, vital signs, laboratory measurements, and physical examination records should not be ignored. Additionally, long-term predictions were unavoidably exposed to unseen factors that should be eliminated for accurate predictions. In the future research plan, feature selection will be performed based on the provided importance of each feature from the developed ML models in this study. With these selected features, new ML models will be explored to provide a better risk evaluation for survival analysis on patients without any adverse cardiovascular symptoms. Even though there is room for future improvement on the proposed method, this novel research is the first ML application on the DukeCath dataset, and it demonstrated the feasibility of using the ML models to classify high-risk CVD patients accurately.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 3 Experiment and Validation
### Bajjusi Yangtze River Bridge in Chongqing, China  
To further prove the practicability of the proposed method, another experiment was conducted on Baijusi Yangtze River Bridge, a two-tower cable-stayed steel truss bridge in Chongqing, China. Two cables, #15 and #17, were chosen for the test. Other experimental settings were the same as the first experiment. The results are presented in Table 5, where frequency and cable force were obtained by using the proposed smartphone-based method and compared to sensor-based dynamic measurements. The maximum error for frequency identification was 0.82% (under the fourth working condition), and the corresponding error for cable force estimation was 1.64%.  
It is worth noting that while using the vision-based method for cable force determination, observable displacements of cables are critical to the identification process. The cables fail to present considerable displacements when the cable length is not long enough, ambient excitations are low or vibration mitigation measures are applied. As a result, the proposed method will fail to accurately retrieve cable displacements. External excitations can be applied until the cable vibrations are easily identifiable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Working Condition**} & \multirow{2}{*}{**Sty Cake**} & \multirow{2}{*}{**Light Condition**} & \multirow{2}{*}{**Video Resolution**} & \multirow{2}{*}{**Frame Rate (PFS)**} & \multirow{2}{*}{**Duration (\(\alpha\))**} & \multicolumn{2}{c}{**The Third-Order Vibration**} & \multicolumn{1}{c}{**The Third-Order Vibration**} & \multirow{2}{*}{**Frequency Error \(F_{e}\)**} & \multirow{2}{*}{**Cake Force Error \(F_{e}\)**} \\
## 4 Numerical and Experimental Results
### Vertical Fibonacci Sequence: Confined and Surface Modes
The typical features of confined and surface waves in photonic Fibonacci stub structures can provide added value  
Figure 9: (**a**) Magnification of the FS modes around the central frequency \(f_{c}=53\) MHz from Figure 7. (**b–d**) The LDOS (in arbitrary units) as a function of the space position \(x\) at the central frequency \(f_{c}\) for the 12 th generation (mode 6) where the range on the horizontal axis is adapted for comparison to 11 th and 10 th FS generations. The insets of (**c**,**d**) correspond to the LDOS associated with modes 7 and 8 for the 11 th and 10 th generations at \(f_{c}\), respectively. for technology applications and offer many solutions to various problems with respect to periodic or defected structures. Moreover, the two configurations of FS proposed here can produce multiple photonic bandgaps and different resonant peaks/dips which can be utilized as a high precision selective/rejective filter to the incident wave. Finally, it is worth mentioning that the edge modes in a Fibonacci photonic quasicrystal [73] are found quantitatively equivalent to those of the Harper model [72], showing their topological origin [74]. Figure 10: Same as in Figure 7 but for a FS with vanishing magnetic field on the top surface of the structure and vanishing electric field on the bottom surface. Here, the discrete modes are obtained from the minima of the transmission spectra displayed in Figure 6. The labels \(1^{\prime}\) and \(2^{\prime}\) correspond to the surface modes.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 4 Data Processing  
This section is dedicated to the introduction of our computational models that are developed for extracting values of Land Surface Temperature (LST), PM10, and PM2.5 from the combination of remote sensing images and telemetry data collected from IoT stations. The models are designed to take advantage of the data sources available and provide a way to extract the relevant environmental variables of interest in an automated and efficient manner. The telemetry data from IoT stations provide ground-level measurements of various environmental parameters, while remote sensing images provide a broader view of the area of interest and help to fill in any gaps in the ground-level data. The computational models leverage both types of data and use them to generate the desired outputs. The results of these models can be used in various applications, such as real-time monitoring, alert notifications, and visualization. By using these models, we can achieve a better understanding of the environmental conditions in the study area and make informed decisions about how best to manage it.
## 2 System Architecture
### Counter-Assisted Phase-Locked Loop (CAPLL)  
The coarse frequency locking is implemented using a counter-assisted digital PLL (CAPLL) structure, comprising a counter, differentiator, comparator, integrator, digital loop filter, and a moving average filter (MAF). The output frequency of the DCO is converted into phase information through the counter and then differentiated to obtain frequency information. These frequency data are compared with the FCW, and the comparison result is processed by the integrator and digital loop filter to adjust the DCO output frequency.  
To minimize area, the counter, differentiator, and comparator used in the CAPLL are shared with the AFC. Unlike conventional counter-assisted PLLs that use a time-to-digital converter (TDC) and dithering blocks, the third loop ensures a final lock, thus eliminating the need for high phase comparison resolution and low \(K_{dco}\). This allows for the removal of the TDC, TDC gain matching circuit, dithering DSM, and SYNC circuits, reducing the area. However, low phase comparison resolution can cause instability in switching from the second to the third loop due to large variations in digital control bits. To address this, a moving average filter (MAF) is implemented. The MAF takes the digital control bits from the digital loop filter of the second loop as input and averages the previous 64 digital control codes during the locking process. Upon lock completion, a multiplexer (MUX) applies the MAF output to the DCO digital control bits, ensuring a stable loop transition. The CAPLL loop manages the initial frequency locking of the PLL and coarsely adjusts the output frequency of the DCO within a smaller range compared to the AFC loop, using 4 bits out of the total 15 control bits.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 2 Design of Adaptive High-Order Sliding Mode Trajectory Tracking Controller
### Active Disturbance Rejection Control and Linear Quadratic Regulator Controller Design
Firstly, the feedback coefficient matrix is obtained by selecting several discrete points and linearizing. Secondly, the deviation control quantity is obtained by combining the feedback matrix and the deviation state quantity obtained by the all-state sensor. Finally, the actual control quantity is obtained by combining the deviation and nominal control quantity.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 3 Results and Discussion
### Maps and Statistics
The local amount of inundation can also help to identify areas for storage of IEW, like reservoirs or permanent wetlands for use of water in later dry periods, this way supporting sustainable water management.
# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 2 Materials and Methods
### One-Class Techniques
#### 2.3.4 Non-Convex Boundary over Projections  
The Non-Convex Boundary over Projections (NCBoP) algorithm innovatively utilizes non-convex hull calculations to model the shape of the target dataset [41]. This approach addresses limitations observed in the conventional convex hull method applied to random projections [42]. The fundamental principle of NCBoP involves approximating dataset boundaries in \(\mathbb{R}^{n}\) by employing non-convex hulls across \(\pi\) random projections onto 2D planes. Subsequently, non-convex limits are determined on these planes, thereby reducing the computational complexity associated with calculating non-convex limits across \(\mathbb{R}^{n}\).  
The NCBoP algorithm computes a non-convex polygon for each \(\pi\) 2D random projection by initially selecting a starting point, determined as the lowest \(y\) coordinate among all points projected onto the \(p_{i}\) plane. Subsequently, it identifies the K-nearest points and arranges them based on the polar angle, retaining only the furthest point from the starting point. This process is repeated for each 2D random projection.  
Figure 5: Example of Gaussian Model for one-class task in \(\mathbb{R}^{1}\).  
Figure 6: An example of a K-means algorithm applied to anomaly detection in 2D.  
After computing the initial points, a stack structure is established to accommodate the next third point. Subsequently, it evaluates whether the subsequent point in the list turns left (added to the stack) or right (removes the top point from the stack). This iterative process continues until it returns to the starting point. Once the training process concludes, all points are situated within the non-convex polygon generated by the algorithm.  
Upon completion of the training phase, the criterion for identifying whether a new test point is anomalous is as follows: if the data fall outside of at least one of the \(t\) projected hulls, it is deemed anomalous. This determination is illustrated in Figure 7.
## 4 Case Study
### Comparison and Discussion  
With the continuous advancement of network technology, the construction industry is undergoing digital transformation. The choice of network platform for the construction industry has garnered heightened significance, as leveraging network technology can significantly enhance operational efficiency and reduce required costs. However, traditional construction industry Internet platform selection approaches cannot handle quantitative and qualitative information simultaneously, nor can they simultaneously handle the fuzzy semantic information that experts provide, often relying on a single arithmetic model. Moreover, expert-provided data containing both clear and incomplete information cause serious difficulties for the construction industry Internet platforms. To address these limitations, this study combined FAHP and WASP approaches. By integrating these approaches, this study aims to address the construction industry Internet platform selection problem within an incomplete information environment. This method applied the FAHP approach to calculate the relative weight of different criteria and then used the two arithmetic models of the WASPAS approach to improve the accuracy of the calculation results. Subsequently, the data-filling method was used to process the expert-provided incomplete and unclear information. In Section 4, a case study focusing on the construction industry Internet platform selection was used to illustrate and prove the effectiveness of the proposed approach. The calculation results were also compared with the WSM approach [37], WPM approach [35], FAHP approach [43], and a typical WASPAS approach [35]. The calculation results of the four different calculation approaches are shown in Table 11. Considering the differences in factors among the four different research approaches (as shown in Table 12), several advantages of the proposed approach are explained below.
## 2 Materials and Methods
### Zeta Potential  
The surface charge/zeta potential of the particles was measured at pH, and biofoculant dosage values were determined by RSM during the experimental design. The procedure for flocculation activity was followed with minor changes. Kaolin clay suspension was prepared, and 50 mL was aliquoted into 250 mL Erlenmeyer flasks. The pH of each suspension was adjusted using 1 M of NaOH or 1 M of HCl to the values predetermined by CCD. 1.5 mL of 1% (\(w/v\)) of CaCl\({}_{2}\) was added to the suspensions, excluding those tested without CaCl\({}_{2}\). Predetermined dosages of biofiloculants were added to their respective flasks with a mixture of kaolin suspension and CaCl\({}_{2}\). After that, the flasks were gently swirled, and the contents were poured into 50 mL measuring cylinders and left to settle for 8 min. After settling, the top layer of the supernatant was carefully withdrawn for zeta potential measurements using a Zetasizer Nano ZS (Malvern Panalytical Ltd., Malvern, UK). The measurements made were for:  
1. Kaolin clay before pH adjustments at various pHs,
2. Kaolin clay suspension with CaCl\({}_{2}\) at various pHs, and
3. Kaolin clay suspension with CaCl\({}_{2}\) and biofiloculants at various pHs and dosages.  
Additionally, a loopful of the suspension was quickly recovered after addition into measuring cylinders and fixed onto slides for visual observations of floc sizes and formation under an electron Olympus CX21 FS1 microscope.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 5 Future Research Directions
### Training and Sensing All at Edge  
A previous survey on edge computing [2] summarized six levels in the development of edge intelligence, ranging from level-1 cloud-edge co-inference to level-6 both training and inference on edge devices. We agree on this point and envision that ITS sensing with edge computing will follow a similar path of development. At present, most edge computing applications in ITS are level-1 to level-3, where the training happens on the cloud andmodels are deployed to the edge devices with or without compression/optimization. Sometimes the sensing function is done collaboratively by edge and cloud. Since federated sensing needs to be conceived in the future, with huge benefits from consistent data input to update the general model, it is reasonable to require an extension from federated sensing and for each device to update a customized sensing model online at the edge. Compared to a general model, all at-edge training and sensing is more flexible and intelligent. However, it does not mean that centralized learning from distributed devices is not useful; even in the era of level-5 or level-6, we expect that there will be models updating on single devices and aggregated learning to some extent for optimal sensing performances.
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 2 Calculating the Accurate Active Factors  
In the proposed novel direct torque control (NDTC) scheme in [2], two active vectors are applied in one control period, which can improve the steady-state performance of the control system effectively. Additionally, the fixed sector division criterion is proposed for NDTC (FS-DTC), which can classify the complexity of the control system [3], as shown in Figure 1. It is also shown in Figure 1 that the proposed discrete duty ratio determination method in this paper is used to replace the traditional duty ratio determination method in FS-DTC.  
Figure 1: Schematic diagram of the discrete duty ratio determination method for novel direct torque control based on the fixed sector division criterion (FS-DTC) for a permanent magnet synchronous motor (PMSM).  
As the main errors can be compensated by the applied active vector in SV-DTC [2], the applied two active vectors in FS-DTC are classified as master vector and slave vector [3]. Consequently, the active angle of the master vector and the slave active vector in FS-DTC are defined as \(\theta_{\text{sm}}\) and \(\theta_{\text{ss}}\), respectively, as shown in Figure 2.  
Therefore, the active factors of torque error and flux error supplied by the master vector in Figure 1 can be calculated as:  
\[\mu_{\text{Tm}}=\sin\theta_{\text{sm}} \tag{1}\]  
\[\mu_{\text{Fm}}=\cos\theta_{\text{sm}} \tag{2}\]  
Consequently, the active factors of torque error and flux error supplied by the slave vector can be calculated as:  
\[\mu_{\text{Ts}}=\sin\theta_{\text{ss}} \tag{3}\]  
\[\mu_{\text{Fs}}=\cos\theta_{\text{ss}} \tag{4}\]  
The relationships among the active vectors and the variables of the PMSM can be described as  
\[\left[\begin{array}{c}r_{\text{T}}\\ r_{\text{F}}\end{array}\right]=\left[\begin{array}{cc}\mu_{\text{Tm}}&\mu_{ \text{Ts}}\\ \mu_{\text{Fm}}&\mu_{\text{Fs}}\end{array}\right]\left[\begin{array}{c}d_{ \text{m}}\\ d_{\text{s}}\end{array}\right] \tag{5}\]  
where \(d_{\text{m}}\) and \(d_{\text{s}}\) are the duty ratio values of the master vector and the slave vector, respectively. \(r_{\text{T}}\) and \(r_{\text{F}}\) are the torque error rate and the flux error rate, respectively, which can be obtained by  
\[\left[\begin{array}{c}r_{\text{T}}\\ r_{\text{F}}\end{array}\right]=\left[\begin{array}{cc}\frac{1}{c_{\text{T}}} &\left[\begin{array}{c}e_{\text{T}}\\ \frac{1}{c_{\text{F}}}\end{array}\right]\left[\begin{array}{c}e_{\text{F}} \end{array}\right]\end{array} \tag{6}\]  
where \(C_{\text{T}}\) and \(C_{\text{F}}\) are the max compensations of torque error and flux error supplied by the active vector, respectively.  
From the aforementioned analyses, it can be observed that Equation (5) is the binary system of linear Equations, which will inevitably increase the computation burden of the control system.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 2 Related Studies
### Problem with Existing Schemes
The idea is to **exploit the trust update dynamics**--the node behaves well (\"on\") just long enough to raise its trust score above a threshold; then, when sufficiently trusted, it behaves maliciously (\"off\") for a period (e.g., dropping packets or injecting false data), then switches back to good behavior before its trust drops too low. Researchers [9] found that without special handling, such on-off attackers can continually exploit the system; they proposed a scheme to discriminate between \"disguised malicious behaviors\" and genuine temporary errors.