esa-sceva/satcom-chunk-collection huggingface dataset:
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 2 USV Thruster Fault Feature
### Analysis of Thruster Fault Features
#### 2.2.2 Consumed Current and Rotation Speed  
When blade breakage or the entanglement of floating objects with an underwater thruster happens, changes in torque occur due to the difference in the diameter or the  
Figure 3: Vibration generated by mass unbalance.  
Figure 2: Materials for reproducing various fault conditions. Clockwise from top left: thin rope, thick rope, net, 3 cm breakage blade, 2 cm breakage blade and 1 cm breakage blade.  
variation in the thruster blade area which pushes the fluid. A general equation representing the torque of the thruster is presented in Equation (3) [28].  
\[Q=\rho K_{Q}n^{2}D^{5} \tag{3}\]  
where \(Q\) denotes the torque of the thruster, \(\rho\) represents the fluid density, \(K_{Q}\) is the torque coefficient, \(n\) is the propeller revolutions per second and \(D\) is the diameter of the propeller. Equation (4) represents a commonly known expression for the electrical torque of a DC motor [29].  
\[Q=K_{t}I-J\frac{dn}{dt} \tag{4}\]  
where \(K_{t}\) indicates an electrical torque constant, \(I\) is the current and \(J\) is the moment of inertia. Assuming that there is no sudden change in the rotational speed of the thruster, Equation (4) can be simplified as follows:  
\[Q=K_{t}I \tag{5}\]  
Based on Equation (3), a torque equation for the thruster, and Equation (5), a simplified torque equation for a DC motor, the current equation can be expressed as follows:  
\[I=\ \frac{\rho K_{Q}n^{2}D^{5}}{K_{t}} \tag{6}\]  
Assuming that the density, torque coefficient and electrical torque constant in Equation (6) are all constant, the consumed current \(I\) is proportional to the square of the rotational speed \(n\) and the fifth power of the diameter \(D\) of the thruster. Therefore, because the diameter and the area where the thruster comes into contact with the fluid change according to the fault condition, it was conjectured that the consumed current and rotational speed would change; thus, these two variables were added to the fault features.
## 5 Experiments
### Analysis of the Energy-Saving Characteristics  
In order to analyze the energy-saving characteristics of the VSP-SMEHSS proposed in this paper, further experiments were carried out to compare the energy consumptions of the EHSS and the VSP-SMEHSS. The EHSS consisted of a pressure-compensated pump, a servo valve, and a hydraulic cylinder. The output pressure of the pump was adjusted to 15 MPa. The supply pressure, the pressures of the two hydraulic chambers, and the load force of the VSP-SMEHSS and the EHSS are shown in Figures 16 and 17, respectively.  
losses in the VSP-SMEHSS were reduced. Figure 19 shows the total energy consumptions of the VSP-SMEHSS and the EHSS during one work cycle. Compared with that of the EHSS system, the energy consumption of the VSP-SMEHSS can be reduced by 44.6%.
## 1 Introduction
Through the uniaxial compression test, splitting test, and triaxial compression test, it is concluded that the Poisson's ratio of the materials increases with the increase in porosity, and the compressive strength, tensile strength, elastic modulus, cohesion, and internal friction angle decrease with the increase in porosity. Many academics have provided analytical solutions to compute the equivalent modulus of porous materials and other mechanical indicators during the creation and research process of porous materials. The calculating approach is thus only suited for evaluating a small number of random models with substantial uncertainty. As a result, the goal of this study is to discover the variation law of porous materials under various influencing circumstances and to derive an appropriate formula for predicting the mechanical characteristics of various porous materials. It allows researchers to grasp the mechanical characteristics of porous materials in a more efficient manner. The SBFEM utilized in this study offers a built-in advantage when it comes to solving random complicated models. With the advent of contemporary computers capable of simulating a huge number of specimens in recent years, the Monte Carlo approach now avoids not only the challenge of a high number of specimens but also the dimensionality of the study problem. The higher the number, the more precise the result. The more advantageous scaled boundary finite element approach is utilized in this research to compute a material with a large number of pores. This research employs a simpler and more practical Monte Carlo approach to investigate the variation law of mechanical characteristics of porous materials. In summary, this study combines the benefits of the two techniques, calculates the random model of a large number of pore materials, examines the change law of their mechanical properties with connected influencing variables, and also provides the fitting formula for related mechanical characteristics.
## 2 Analysis of Previous Studies
### Maritime Traffic Safety Assessment  
Various approaches have been investigated to identify and assess risks related to maritime traffic safety, such as Formal Safety Assessment (FSA), the IALA Waterway Risk Assessment Program (IWRAP), Ports And Waterway Safety Assessment (PAWSA), and the Event Sequence (ES) model. When revising agreements, regulations, and laws with regulatory characteristics, the cost-benefit of obtaining them, administrative burdens, and incurred costs should be extensively considered. In addition, it was confirmed that it is important to quantify data such as the number of marine accidents to obtain a quantitative data assessment in maritime risk assessment.  
The FSA approach was formally adopted as a guideline at the 74 th IMO MSC meeting in 2001, and is still in use today. The IMO defines FSA as an organizational and systematic safety assessment method that uses risk and cost-benefit assessments to improve maritime safety, including human life, the marine environment, and property protection [14].  
IWRAP, which was developed by the Technical University of Denmark's ISESO Project, calculates the risk of ship collisions and grounding incidents. This program analyzes the risk to waterways using a quantitative risk assessment that is based on factors such as the type and size of vessels as well as vessel navigation data for specific water areas [15].  
PAWSA, an assessment model developed in the United States to ensure maritime traffic safety in waterways, uses the Analytical Hierarchy Process (AHP) developed by Dr. [PERSON] of George Washington University and [PERSON] of Virginia Commonwealth University [16; 17].  
The ES model is used to quantitatively evaluate the degree of burden imposed on ship operators, and is also utilized in the MTSA Scheme in Korea [18; 19].  
The Potential Assessment of Risk (PARK) model, which is a Korean model under development, measures the maritime traffic risk based on quantitative data that distinguishes ship type, tonnage, length, width, boarding experience, license possession, duties, etc., using regression analysis methods [20].  
[PERSON] et al. investigated the feasibility of automatically setting the optimal route for ships without human intervention using ML techniques to ensure safety during navigation [21].  
[PERSON] et al. identified the major thematic clusters in risk-influencing factors. They proposed a model for synthesizing and illustrating the relationships among environmental factors, ship-related risk-influencing factors, and accident scenarios in Arctic shipping [22].
## 3 Results and Discussion
### Feature Extraction and Selection
#### 3.2.4 Average Differential Value  
Average differential value (ADV): The average differential value was a method that could comprehensively reflect the total information of the sensor's dynamic response process and directly reflected the mainstream information of the gas sensor's response to gas.  
\[ADV=\frac{1}{T-1}\sum\
olimits_{i=1}^{T-1}\frac{x_{i+1}-x_{i}}{\Delta t} \tag{6}\]  
In the formula, \(x_{i}\) is the response value of the sensor to the ith second of a sample; \(\Delta t\) is the time interval between two adjacent sampling points, 1 s; and \(T\) is the acquisition time of a sample by the sensor.
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 2 Materials and Methods
### Data Extraction  
Table 1 summarizes the data items that are further extracted from the 368 selected studies. This review focuses on diverse aspects, including the general information, ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability. A detailed description of the extracted information from those aspects is as follows:  
1. **General Information**: An overview of the origin of the selected studies, i.e., the conference proceedings or journals in which they are published and their publication years, is provided;  
Figure 1: Paper search and refinement process.  
* **ECG Database**: The publication information, ECG signal information, and demographic information are analyzed for popular ECG databases employed for arrhythmia classification;
* **Preprocessing**: Two types of commonly used preprocessing techniques, i.e., denoising to remove artifacts and data augmentation to deal with imbalanced datasets, are summarized;
* **DL Methodology**: The DL algorithms from all the selected studies are investigated and summarized. The information about the types of DL models, optimization techniques, and classification categories for arrhythmia is presented;
* **Evaluation Paradigm**: The data-driven ECG diagnosis can be categorized into intra- and inter-patient paradigms depending on how the training and testing ECG data from patients are organized;
* **Performance Metric**: In addition to the widely used performance metrics such as overall accuracy, other metrics such as sensitivity (Sen), positive predictivity (Ppv), false positive rate (FPR) and \(F_{1}\) score of the selected studies is discussed;
* **Code Availability**: Detailed information about studies that publish their code and the source of the code is listed.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model
### Memoryless Nonlinear PA Models  
As illustrated in the system model in Figure 3, the complex baseband information signal can be written as \(s(t)=r_{s}(t)\exp(j\phi_{s}(t))\). Similarly, the complex baseband energy signal can be written as \(e(t)=r_{e}(t)\exp(j\phi_{e}(t))\). It follows that the total complex baseband input signal, which is obtained by the superposition of the information signal and energy signal, is given by \(x(t)=s(t)+e(t)=r(t)\exp(j\phi(t))\). The output signal \(y(t)=r_{o}(t)\exp(j\phi_{o}(t))\) is the complex baseband output signal of the nonlinear PA. For the memoryless PA, the output signal \(y(t)\) can be regarded as a function of the envelope of the input signal \(r(t)\), i.e.,  
\[y(t)=g(r(t))\exp(j(\phi(t)+f(r(t)))) \tag{3}\]  
where \(g(r(t))\) and \(f(r(t))\) are the magnitude response and phase response of \(r(t)\), respectively. The magnitude response and phase response are commonly referred to as AM-AM and AM-PM characteristics. Because the time variable \(t\) is not important in a memoryless PA model, it will be neglected in the remainder of this paper for clarity.
## 7 Conclusions and Future Work
### Applying Reactive Discovery for Point-to-Point Routes  
Process closed-loop control applications require peer-to-peer sessions between sensors and actuators. In those applications, sensor data periodically streams from sensors to the actuators without needing to involve the gateway or central controller. The RPL used in the D-MSR routing layer, is not recommended to be used for a peer-to-peer traffic mode. That is because, when sensor and actuator need to communicate, the sensor data are restricted to travel in the \"up\" direction toward a common ancestor and is then forwarded \"down\" toward the actuator. This scheme may also result in traffic congestion near the gateway. We intend to use a source-initiated reactive extension of the RPL protocol called P2P-RPL [26] in the D-MSR network layer. P2P-RPL enables the field devices to discover the shorter routes to one or more field devices on demand and addresses the point-to-point traffic model requirements without the mentioned drawbacks.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 4 Network Traffic Anomaly Detection Scheme Based on LNN-RLSTM Neural Network
### Automatic Labeling of Data  
It should be emphasized that there are two sources of training sets for the identification neural network.  
**Source 1:** Abnormal dataset automatically marked by machine.  
**Source 2:** Manually selected anomalous datasets.  
Although the initial labeling data only identify these anomalies from a mathematical statistical point of view, when these anomalies are substituted into the labeling neural network for training, the labeling neural network will automatically learn relevant data features other than numerical values. This means that, compared with traditional statistical models, the identification neural network will have the ability to detect anomalies in terms of data features. In addition, with the input of a limited number of manually selected \"special case\" anomalies, the labeling neural network will have the ability to accurately label each period. Eventually, the labeling neural network can not only identify anomalous  
Figure 2: On the phenomenon of clustering of anomalies in isolated forest.  
data features, but also learn the expertise of business operators from a business perspective, thus, achieving the characteristics of scientific identification.  
Since [PERSON] [40], [PERSON] [41] et al. developed a software that allows for manual tagging, and this paper will focus on the principle of automatic machine tagging, which divides into two initial tagging steps.  
**Step 1:** Initial labeling of anomalous isolated points.  
**Step 2:** Initial labeling of anomalous cycle points.  
After filtering by the statistical anomaly labeling algorithm, the raw data will be automatically labeled. Then, these labels plus the manual input data will train the labeling neural network well. This labeling neural network will replace the statistical initial labeling method after reaching a certain training amount, i.e., the later data labeling will be done automatically by the labeling neural network.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 4 Results and Discussion  
In this section, we use two simulated InSAR datasets and a realistic dataset to verify the performance of the proposed TSPA-based Bayesian filtering MBPU methods. The second group of  
Figure 1: Simple flowchart of two-dimensional Bayesian filtering phase unwrapping (PU) method.  
Figure 2: Schematic representation of two-stage programming approach (TSPA)-based Bayesian filtering multi-baseline phase unwrapping (MBPU) method framework.  
simulation data is used to analyze the noise robustness of different methods. To quantitatively evaluate the performance, the root mean-square error (RMSE) is used to evaluate the results (as Equation (23)):  
\[RMSE=\sqrt{E\big{[}\|\psi-\psi_{true}\|^{2}\big{]}} \tag{23}\]  
where \(\psi\) is the unwrapped phase collected from the different MBPU methods, \(\psi_{true}\) is the reference unwrapped phase, and \(\|\cdot\|^{2}\) is the quadratic norm. The high-precision ICESat data points are used to verify the accuracy of the realistic dataset.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 3 Methodology
### Accuracy Assessment  
The performance of icing detection algorithms was evaluated using five-fold cross-validation based on a 2 \(\times\) 2 contingency table, helping to quantify the detection probability of models [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] (Table 5). Based on the components of the contingency table, four performance metrics were calculated: the probability of detection (POD; calculated as H/(H + M) \(\times\) 100%); the probability of false detection (POFD; calculated as F/(N + F) \(\times\) 100%); overall accuracy (OA; calculated as (N + H)/(N + F+ M + H) \(\times\) 100%); and total skill statistics (TSS; calculated as (H \(\times\) N\(-\)F \(\times\) M)/[(H + M) \(\times\) (F + N)]). In addition to the performance indices, the standard error (E), which is the standard deviation divided by the total fold number, was also used to compare model stability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**FIT Algorithm**} & \multicolumn{6}{c}{**KMA Algorithm**} \\ \hline
**Cloud** & **COT** & **Icing** & **IR1** & **Visible** & **SWIR-IR1** & **IR1-IR2** & **Icing** \\
**Phase** & **COT** & **Max** & **IR1** & **Visible** & **SWIR-IR1** & **IR1-IR2** & **Mask** \\ \hline Clear & – & No icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(\geq\) 37\% & BTD \(\geq\) 10 K & BTD \(<\) 1 K & Icing \\ \hline Water & All & No icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(\geq\) 37\% & BTD \(\geq\) 10 K & BTD \(\geq\) 1 K & No icing \\ \hline SLW & \(\tau\)\(>\)1.0 & Icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(\geq\) 37\% & BTD \(<\) 10 K & – & No icing \\ \hline SLW & \(\tau\)\(\leq\) 1.0 & No icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & 4.5\% \(\leq\) Albedo \(<\) & – & – & No icing \\ \hline Mixed & \(\tau\)\(>\)1.0 & Icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(<\) 4.5\% & BTD \(\leq\) –2.5 K & BTD \(<\) 1 K & Icing \\ \hline Mixed & \(\tau\)\(\leq\) 1.0 & No icing & \begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(<\) 4.5\% & BTD \(\leq\) –2.5 K & BTD \(\geq\) 1 K & No icing \\ \hline Ice & \(\tau\)\(\leq\) 6.0 & No icing & \
\begin{tabular}{c} 243 K \(\leq\) Tb \\ \(\leq\) 272 K \\ \end{tabular} & Albedo \(<\) 4.5\% & BTD \(>\) –2.5 K & – & No icing \\ \hline Ice & \(\tau\)\(>\) 6.0 & Unknown &
\begin{tabular}{c} Tb \(<\) 243 K or \\ Tb \(>\) 272 K \\ \end{tabular} & – & – & – & No icing \\ \hline \hline \end{tabular}
\end{table}
Table 4: Existing rule-based icing detection approaches (i.e., flight icing threat (FIT) and Korea meteorological administration (KMA) algorithms to produce icing mask).
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 1 Introduction
[PERSON] [30] used a hopping point search algorithm to extend the hopping points on sub-nodes and improve the efficiency of the A* algorithm. [PERSON] [31] proposed a bidirectional A* algorithm based on third-order Bessel curve trajectory optimization, which solved the problem of multiple folds and large corners in the search path. [PERSON] [32] defined a new evaluation sub-function to obtain a new DWA evaluation function, which solved the problem of falling into evaluation function failure when the mobile robot encountered an obstacle. The planning algorithms chosen in this paper are the improved A* algorithm and the Open_Planner algorithm. Both the traditional A* algorithm and the Open_Planner algorithm can be applied to the path-planning problem. However, the disadvantages of the traditional algorithm, the A* algorithm, are reduced efficiency due to the presence of a large number of non-essential nodes, unsmooth routes around corners and difficulty in dealing with unknown obstacles when searching in complex environments. Open_Planner requires a global environment to establish the best locally planned paths. To address these issues, we fuse the global-path-planning algorithm with the local-path-planning algorithm. To improve the planning efficiency of the A* algorithm, we extract key points based on obstacles and map boundaries in the A* algorithm. The slowest part of the A* algorithm is finding the node with the smallest F value in the open list, and repeatedly searching large lists slows down the process. Therefore, we use a binomial heap to optimize the way the A* algorithm stores the list, placing the smallest points at the top of the heap to improve the algorithm's search efficiency. Unsmooth paths around corners can also have an effect on vehicle operation; thus, we make the paths smoother using the minimum snap method. Afterwards, the key points of the A* algorithm are used as intermediate target points for the Open_Planner algorithm and the two algorithms are fused. With the planning of the A-OP fusion algorithm, the vehicle has an optimal path and, at the same time, achieves the safety effect of obstacle avoidance. In summary, we aim to realize a driverless sweeper vehicle which can both drive with the global optimal planning route and reach the destination safely by avoiding the obstacles in real time. In this paper, based on the traditional path-planning algorithm,the key points of the algorithm are selected and the node search structure is improved to enhance the smoothness of the path, after which the global-planning algorithm and the local-path-planning algorithm are fused by key points and the algorithm is fused. The contribution of the work in this paper is shown in the following aspects:  
* A new route-planning method by fusing improved A* and Open_Planner;
* Feature point search strategy for planning paths;
* Path smoothing strategy based on minimum snap.