esa-sceva/satcom-chunk-collection huggingface dataset:
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 3 Methodology
### Taguchi Method  
The input layer of LSTM is a cluster analysis of the topology of input frequencies (128, 256, 512, and 1024 Hz) of SOM. There are many parameters in the LSTM model, such as the number of units in the hidden layer, the maximum number of iterations, and memory capacity, which should be adjusted to obtain the best performance. The number of units, \(C_{t}\), in the LSTM layer determines the complexity of the model. The value of \(f_{t}\) is how much memory capacity of the previous unit is retained. The parameter settings to be determined in this paper are the cell state (\(C_{t}\)) and memory capacity (\(f_{t}\)). The output layer is represented by the number of the type; Type 1 = normal, Type 2 = out-of-phase voltage, Type 3 = unbalanced voltage, and Type 4 = axis misalignment. The OED of the Taguchi method looks for the optimal parameter combination of the SOM dimension parameter \(m\) and the two parameters \(C_{t},f_{t}\) of LSTM within a limited number of experiments. SOM uses the dimension parameter \(m=[3,\ 4,\ 5]\) and the two parameters \(C_{t}=[100,\ 125,\ 150]\) and\(f_{t}=[0.4,\ 0.5,\ 0.6]\) of LSTM as 3 levels of their variables, so the 3 levels and 3 factors of the OED is used to construct the experiment's matrix as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exp.** & **1** & **2** & **3** \\ \hline
2 & 1 & 2 & 2 \\ \hline
3 & 1 & 3 & 3 \\ \hline
4 & 2 & 1 & 2 \\ \hline
5 & 2 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 3 levels and 3 factors of the OED.  
Figure 4: The selection of the frequency-feature window after the same-frequency-ratio axis correction.  
The 3-level, 3-factor experimental design of the OED is used to analyze the performance of SOM and LSTM in different parameter combinations. The optimal parameter combination is selected as the best parameter combination from SOM-LSTM to improve the processing capacity and resolution for the operational sound of the rotary motor.
# Coordination in Emergency Response System Design:
## 1 Introduction
With a population of over 10 million, Chengdu's urban road network comprises about 1300 nodes and over 2000 edges. The city is currently served by 62 ambulance stations and 20 fire stations. Clearly, this is a large-scale problem instance, and the proposed model is not amenable to solution with readily available optimization software. Therefore, we developed a genetic algorithm for solving the problem instances in the case study. The algorithm allows for the prioritization of one of the response times or the wait time in identifying the ambulance-fire truck pair that should be serving each possible emergency site. We find that the current performance of the response network in Chengdu can be improved by a reallocation of the response resources, rather than simply assigning the closest resource to each zone. We also develop and assess alternative scenarios to expand the current response network to reduce the response and wait times drastically. The remainder of this paper is organized as follows. An overview of the most relevant literature is provided in Section 2. We present the conceptual underpinnings of our mathematical model in Section 3. The two-resource maximal covering problem is formulated in Section 4, and an illustrative example is outlined Section 5. For solving large instances of the problem, Section 6 outlines the genetic algorithm we developed. Section 7 provides the application of the proposed methodology for designing a hazmat emergency response network in the city of Chengdu. The concluding remarks and future research directions are provided in Section 8.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 5 Application to the AGV Case  
This section applies the above strategies to logistics company A, uses the grid method to discretize the environmental information of its warehouse, sets the shelves and other facilities as obstacles, configures the environmental information according to their locations and sizes, and uses the improved ant colony algorithm to plan a shortest path from the starting point to the end point for the AGV formation. For the leader AGV, start point coordinates are (1.8, 48.5), and end point coordinates are (49.5, 0.5). For the follower  
Figure 6: Single leader and single follower.  
Figure 7: Single leader and double follower.  
AGV, start point coordinates are (1.5, 49.5), and end point coordinates are (49.5, 1.5). The simulation result of single leader formation is shown in Figure 8.  
As for the single leader-two-follower AGV shown in the Figure 9, the starting point positions of the leader AGV and follower AGV 1 are the same as above, and the starting coordinates of the follower AGV 2 are (0.5, 48.5).  
The simulation results describe that no matter singleleader-single follower or singleleader-two-follower, AGV formation can be maintained well and reach the end position smoothly without collision.
# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 2 Materials and Methods
### Effective Area  
The fuzz defect regions identified in the processed image are further analyzed using a learning approach based on neural networks. In this context, the neural network automatically detects potential fuzz defects and annotates them with rectangular bounding boxes. While these bounding boxes provide a convenient and intuitive representation of the defect region, they may not accurately reflect the true extent of the defect, particularly for irregularly or skewed-distributed defects.  
Specifically, when the fuzz defect is not aligned with the axes of the image, the area of the axis-aligned bounding box tends to overestimate the actual defect size. To address this issue and improve the accuracy of area-based severity calculation, the effective area \(\text{A}_{\text{eff}}\) is defined as the smallest area of the rotated bounding rectangle that tightly encloses the detected fuzz defect. This is achieved by rotating the image and recalculating the bounding rectangle until the minimum enclosing area is found.  
The effective area \(\text{A}_{\text{eff}}\) serves as a more precise geometric descriptor of the fuzz defect, allowing for a more reliable and robust assessment of its severity. By using \(\text{A}_{\text{eff}}\) instead of the standard axis-aligned bounding box area, the method reduces the impact of spatial misalignment and improves the consistency of quantitative defect evaluation across different orientations and shapes of fuzz regions.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### The Results of the Study of the Dynamic Characteristics and Discussion of the Results  
Figure 10 shows the dependence of the membrane displacement on the applied bias voltage.  
Figure 9: A parallel plate with thickness \(t\).  
At \(V(x)<V_{pull-down}\) the membrane is in a stable region. At \(V(x)>V_{pull-down}\) the membrane collapses on the fixed down electrode, which corresponds to the unstable part in the Figure 10. The calculated value of the pull-down voltage is 5 V.  
The dependence of the stiffness coefficient \(k\) of the elastic fastening elements of the membrane on the number of meanders \(N\) is shown in Figure 11. Figure 12 shows the dependence of the value of the pull-down voltage on the stiffness coefficient \(k\) of the elastic suspension elements.  
As can be seen from Equation (21), \(V_{pull-down}\) can be reduced in several ways. For example, when the distance between the membrane and fixed electrode is reduced, both the pull-down voltage and the ratio of the capacitances of the control variable capacitor are reduced, which greatly affects the ratio of the insertion loss and the isolation of the RF MEMS signal of the switch.  
Figure 11: Graph of dependence of the coefficient of elasticity on the number of meanders.  
Figure 12: The dependence of the pull-down voltage on the stiffness coefficient.  
Figure 10: Displacement of the membrane from the applied bias voltage.  
The increase in the area of the membrane entails an increase in its own losses, as well as an increase in residual stresses.  
Thus, there remains a third option, which involves reducing the stiffness coefficient of the elastic elements of the membrane. Mounting with geometry in the form of a meander allows us to flexibly solve the problem in different frequency ranges and for different pull-down voltage.  
Figure 13 shows the dependence of the \(t_{pull-down}\) from different switching voltage (\(V_{s}\)) values.  
Switching time analysis is critical to the design of RF MEMS switches, as achieving a short switching time from ON to OFF is a major challenge. It should be noted that the time of pull-down is slightly longer than the time of return to the initial state even in the case of a significantly  
Table 4 presents the calculated values of the coefficient of stiffness of elastic suspension elements, pull- and hold-down voltage, return voltage, switching time in the down and initial states.
###### Abstract  
One of the hottest topics in today's meteorological research is weather nowcasting, which is the weather forecast for a short time period such as one to six hours. Radar is an important data source used by operational meteorologists for issuing nowcasting warnings. With the main goal of helping meteorologists in analysing radar data for issuing nowcasting warnings, we propose _NowDeepN_, a supervised learning based regression model which uses an ensemble of _deep artificial neural networks_ for predicting the values for radar products at a certain time moment. The values predicted by _NowDeepN_ may be used by meteorologists in estimating the future development of potential severe phenomena and would replace the time consuming process of extrapolating the radar echoes. _NowDeepN_ is intended to be a proof of concept for the effectiveness of learning from radar data relevant patterns that would be useful for predicting future values for radar products based on their historical values. For assessing the performance of _NowDeepN_, a set of experiments on real radar data provided by the Romanian National Meteorological Administration is conducted. The impact of a _data cleaning_ step introduced for correcting the erroneous radar products' values is investigated both from the computational and meteorological perspectives. The experimental results also indicate the relevance of the features considered in the supervised learning task, highlighting that the radar products' values at a certain geographical location at a time moment may be predicted from the products' values from a neighboring area of that location at previous time moments. An overall _Normalized Root Mean Squared Error_ less than 4% was obtained for _NowDeepN_ on the cleaned radar data. Compared to similar related work from the nowcasting literature, _NowDeepN_ outperforms several approaches and this emphasizes the performance of our proposal.  
weather nowcasting; machine learning; deep neural networks; autoencoders; Principal Component Analysis
## 4 Experiments and Results
### Evaluation Metrics  
To evaluate the algorithm's performance, we calculated the Accuracy, Dice Similarity Coefficient (DSC), Sensitivity (Sen), and Intersection over Union (IoU) for pixelwise comparison of the cornea and corneal ulcer segmentation results with the ground truth data. In addition, the Mean Absolute Percentage Error (MAPE) was calculated to compare the  
Figure 6: Sample dataset for training and testing cornea segmentation models and corneal ulcer segmentation models.  
predicted size of the cornea to the actual cornea size in pixels, while the Mean Absolute Error (MAE) was calculated to compare the predicted percentage of the ulcerated cornea area to the percentage of the actual sizes. For TG grading results, Accuracy was calculated as the ratio of correct predictions for corneal ulcer severity grading to all predictions. The evaluation metrics were calculated as follows:  
\[DSC=\frac{2 TP}{2 TP+FP+FN} \tag{3}\]  
\[Sen=\frac{TP}{TP+FN} \tag{4}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{5}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|A_{Ci}-\hat{A}_{Ci}|}{A_{Ci}}\times 100 \tag{6}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|P_{i}-\hat{P}_{i}| \tag{7}\]  
\[Segmentation\ Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[Grading\ Accuracy=\frac{S}{S} \tag{9}\]  
where TP denotes the number of cornea/corneal-ulcer pixels which are classified correctly, TN denotes the number of non-cornea/non-corneal-ulcer pixels which are classified correctly, FP denotes the number of non-cornea/non-corneal-ulcer pixels which are classified as cornea/corneal-ulcer, FN denotes the number of cornea/corneal-ulcer pixels which are classified as non-cornea/non-corneal-ulcer, \(A_{Ci}\) and \(\hat{A}_{Ci}\) respectively denote the actual cornea size of sample \(i\) and the corresponding predicted cornea size, \(N\) denotes the number of samples in the experiment, \(P_{i}\) and \(\hat{P}_{i}\) respectively denote the actual percentage of ulcerated area on cornea for sample \(i\) and the corresponding predicted percentage, and \(\hat{S}\) and \(S\) respectively denote the number of correct severity grading results and the total number of samples.
## 3 The Proposed Co-Training Scheme
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Algorithms**} \\ \cline{2-6}  & \(Co(Vote^{SEC}_{soft})\) & \(Co(Vote(DT,LR))\) & \(Co(Vote(all))\) & \(Co(Vote(5\)NN,LR)\) & \(Self(Vote(all))\) \\ \hline bands & 0.716 \(\pm\) 0.083 & 0.652 \(\pm\) 0.079 & **0.764 \(\pm\) 0.063** & 0.716 \(\pm\) 0.052 & 0.69 \(\pm\) 0.06 \\ breast & **0.701 \(\pm\) 0.077** & 0.671 \(\pm\) 0.105 & 0.652 \(\pm\) 0.062 & 0.661 \(\pm\) 0.071 & 0.621 \(\pm\) 0.081 \\ bupa & 0.584 \(\pm\) 0.09 & 0.567 \(\pm\) 0.065 & **0.602 \(\pm\) 0.08** & **0.602 \(\pm\) 0.08** & 0.54 \(\pm\) 0.082 \\ chess & 0.99 \(\pm\) 0.004 & **0.992 \(\pm\) 0.004** & 0.985 \(\pm\) 0.007 & 0.981 \(\pm\) 0.006 & **0.992 \(\pm\) 0.005** \\ colic.ORIG & **0.835 \(\pm\) 0.044** & 0.817 \(\pm\) 0.029 & 0.722 \(\pm\) 0.079 & 0.813 \(\pm\) 0.026 & 0.762 \(\pm\) 0.097 \\ diabetes & 0.835 \(\pm\) 0.037 & 0.772 \(\pm\) 0.028 & **0.854 \(\pm\) 0.011** & 0.824 \(\pm\) 0.035 & 0.801 \(\pm\) 0.039 \\ h-statlog & 0.765 \(\pm\) 0.05 & 0.779 \(\pm\) 0.053 & **0.802 \(\pm\) 0.024** & 0.73 \(\pm\) 0.051 & 0.769 \(\pm\) 0.033 \\ kr-vs-kp & 0.99 \(\pm\) 0.004 & 0.991 \(\pm\) 0.004 & 0.985 \(\pm\) 0.007 & 0.979 \(\pm\) 0.006 & **0.992 \(\pm\) 0.005** \\ mammographic & 0.857 \(\pm\) 0.016 & **0.876 \(\pm\) 0.021** & 0.856 \(\pm\) 0.015 & 0.87 \(\pm\) 0.021 & 0.874 \(\pm\) 0.016 \\ monk-2 & 0.996 \(\pm\) 0.005 & **1 \(\pm\) 0** & **1 \(\pm\) 0** & 0.956 \(\pm\) 0.018 & 0.958 \(\pm\) 0.025 \\ pima & 0.655 \(\pm\) 0.041 & **0.681 \(\pm\) 0.031** & 0.68 \(\pm\) 0.02 & 0.647 \(\pm\) 0.041 & 0.651 \(\pm\) 0.041 \\ saheart & 0.784 \(\pm\) 0.057 & 0.764 \(\pm\) 0.033 & 0.772 \(\pm\) 0.019 & **0.791 \(\pm\) 0.028** & 0.767 \(\pm\) 0.046 \\ sick & 0.956 \(Adoption of more dedicated preprocessing stages oriented towards more specific problems should be applied, in order to boost the performance of the SSoftEC strategy and provide the Co-training scheme a more appropriate base learner [58; 59; 60]. However, in our generic experimental stage, which covers various applications, the proposed algorithm recorded a both robust and accurate enough performance, especially in the case of the F\({}_{1}\)-score metric which is critical for real problems with class distribution different from the optimal, under a computational inexpensive manner, in contrast with DEC strategies that employ a new classifier search per test instance. The smoothing of the decisions that are produced through the proposed soft-Voting ensemble seems to favor the exported decision profile, since a large number of decisions that were initially misclassified based on individual predictions were reverted towards the ground truth label. While at the same time, numerous cases where the two participants disagree over the binary label were not affected. This happens because a large correct confidence value combined with a smaller incorrect one remains untouched under such a voting scheme, according to Equation (3).
## 3 Results
### Results for Radiological Variables
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **PSB** & **PS** & **SB** & **CONTROL** \\ \hline \multirow{4}{*}{Von Kossa} & BH [mm] & 0.55 \(\pm\) 0.68 & 0.45 \(\pm\) 0.39 & 0.32 \(\pm\) 0.53 & 0.36 \(\pm\) 0.70 \\  & BA [\(\mu\)m\({}^{2}\)] & 6,282,673.45 \(\pm\) & 5,062,533.54 \(\pm\) & 1,692,143.97 \(\pm\) & 3,221,432.66 \(\pm\) \\  & 8,253,714.11 \({}^{***}\) *** & 3,969,879.62 \({}^{**}\) & 1,984,710.21 \({}^{*}\) & 3,969,747.89 *** \\  & \%BA & 16.04 \(\pm\) 8.45 \({}^{***}\) *** & 16.08 \(\pm\) 5.53 \({}^{**}\) & 13.05 \(\pm\) 11.63 \({}^{*}\) & 16.55 \(\pm\) 8.245 *** \\  & BP [\(\mu\)m] & 510,190.71 \(\pm\) & 184,974.83 \(\pm\) & 153,907.889 \(\pm\) & 208,103.83 \(\pm\) \\  & 424,927.16 \({}^{***}\) *** & 164,277.64 \({}^{**}\) & 194,164.45 \({}^{*}\) & 191,808.85 *** \\  & BA [\(\mu\)m] & 50,955.48 \(\pm\) 17,668.89 & 31,502.58 \(\pm\) 971.37 & 85,634.73 \(\pm\) & 43,814.77 \(\pm\) 5869.72 \\ Fluorescence & \%BA & 8.62 \(\pm\) 3.39 & 8.58 \(\pm\) 2.33 & 9.04 \(\pm\) 8.36 & 8.1 \(\pm\) 1.44 \\  & BP [\(\mu\)m] & 0.09 \(\pm\) 0.04 \({}^{*}\) \$ \$ & 0.17 \(\pm\) 0.06 \({}^{***}\) \$ & 0.55 \(\pm\) 0.54 \({}^{***}\) *** & 0.21 \(\pm\) 0.03 \({}^{***}\) \$ \\ \hline \end{tabular}
\end{table} 
Table 3: Histological results after 3 weeks (BH, bone height; BA, Bone area; BP, Bone perimeter. The statistically significant differences are shown in the same row by pairs of similar symbols (*
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Based Position Sensorless Methods
#### 3.1.3 Based on Intelligent Control Algorithm  
Intelligent control algorithms have outstanding performance in dealing with non-linearity [102; 103; 104]. For the position estimation of SRM, the advantage of this type of algorithm is that the nonlinear modeling of flux linkage and inductance is accurate, and the disadvantage is that the algorithm is difficult to design and needs to measure a large number of motor parameters.  
In [105; 106; 107], the fuzzy logic control algorithm replaces the traditional three-dimensional look-up table method and mathematical modeling method. This reduces the amount of pre-stored data. Figure 8 shows the rotor position estimation scheme based on the principle of the neural network. Complicated fuzzy rules and complex offline training limit its use. Figure 8a shows the block diagram of the neural network application, which takes the phase current and phase voltage as input, calculates the rotor position, and then outputs it. The input layer, hidden layer, and output layer constitute a functional neural network, as shown in Figure 8b. A neural network is trained based on the relationship between flux linkage and current position to form a nonlinear SRM mapping relationship [108; 109; 110; 111]. The established neural network model only needs to use the sampled current/voltage for rotor position estimation [112]. After that, the neural network is improved to improve the performance of rotor estimation, such as back-propagation neural network (BPNN), by adding a pretreatment section that refines the input layer to improve performance [113]. Although neural networks have outstanding advantages in terms of model accuracy, they all require a large number of actual measurement data samples to have sufficient accuracy.  
Figure 7: Phase inductance curve of a 12/8 three-phase SRM. (**a**) Balanced inductors. (**b**) unbalanced inductors.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 3 Classes of Algorithms
### Artificial Intelligence--Machine Learning Algorithms  
Artificial neural networks (ANNs) draw inspiration from biological neural networks. They are particularly adept at pattern recognition and have been used to detect anomalies in network traffic, predict future traffic for load balancing, and recognize complex patterns in system behavior that may indicate security breaches [17].  
The flexibility and adaptability of ANNs make them suitable for dynamic and evolving network environments where patterns of use and behavior can shift rapidly. [PERSON] et al. [17] used Mininet to generate a network where traffic with special characteristics was generated and logged. Based on patterns that simulated attack behaviors alternated with normal traffic, the experiment aimed to process the data traffic points via a developed artificial neural network (ANN) used as a detection model. The detection system had the capability to supervise the traffic on various layers like input and output, where specific attributes of the data flow were parsed. Lowering the time of the detection and increasing the accuracy has been improved with various techniques designated to enhance the learning process like Adam or RMSProp.  
Support Vector Machines (SVMs) are a set of supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. In the context of networking, SVMs have been employed for classifying network traffic, thereby enabling traffic prioritization and QoS differentiation, as well as for intrusion detection [18] by classifying behavior as normal or anomalous. [PERSON] et al. [19] used an SVM model that was trained with data composed of normal traffic mixed with malware data and had the target to classify if other traffic patterns are either malicious or normal by analyzing characteristics like byte counts or packet transmission rates. The simulation using the SVM model had the role to evaluate the accuracy in detecting malware affecting the network and used metrics like detection rate relative to false positive rate.