esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 5 Discussion of the Results
This is, of course, not the case for small time interval annotated measurements, where LSTM models perform well. Furthermore, the LSTM model requires significantly more time to train concerning stranded-NN model strands. From the authors experimentation with close-to-real-time detections, the authors also faced one case in Scenario I where the LSTM (60) model presented 4% better accuracy results than the best close-to-real-time stranded-NN (160) model. Nevertheless, this case has been considered as an over-fitting one, due to the threshold-based data annotation. Scenario II experimentation has proven that, for all cases, the LSTM presents significantly less accuracy than the close-to-real-time stranded-NN models. The authors have also experimented by deploying their MLP LSTM and stranded-NN implementations in an Industrial Shields PLC data collector [47]. That is, an industrial automation component and concentrator device, capable of real-time online training and detection. This PLC component includes a 64 bit, 1.5 GHz Broadcom BCM2711, Quad-core Cortex-A72 (ARM v8) SoC with 4 GB of RAM. Table 12 presents the training execution time results of the three algorithms using a fixed-size dataset of 7,439,584 temperature samples as input. Detection times have not been taken into account since the classification processes per sample are fast enough for all algorithms, close to \(8.14\times 10^{-4}\) s for the LSTM model, \(3.19\times 10^{-6}\) s for the MLP model, and \(3.23\times 10^{-4}\) s for the stranded-NN model. The training performance results from Table 12 show that the LSTM (16) model requires at least 26 times more training time, and the MLP (100) 44 times more training time, than the best selected real-time stranded-NN model (80). In terms of accuracy, the stranded-NN model significantly outperforms the MLP model by 21.3% in terms of accuracy and slightly outperforms the LSTM (16) model by 2.6%. That is, the stranded-NN model can perform real-time detections adequately at the edge industrial concentrators, as well as re-train its model in significantly less time, in the presence of new annotated data streams. Regarding close-to-real-time industrial maintenance processes, the LSTM (60) model presents 5.1% better accuracy results than the best close-to-real-time stranded-NN (160) model. However, LSTM (60) training times with the industrial edge concentrator devices are 20 times longer than with the stranded-NN (160) model; that is, the training effort is significantly more.
## 3 Tests and Results
### Summary of Tests and Results  
From the two cases above, the configurability of the backplane was demonstrated, and the following parameters were confirmed:  
* _Number of configurable pins._ A maximum of 20 out of 50 pins on each mission board were made configurable. This number of pins allows the payload developers to assign any kind of serial digital interface to link over the bus.
* _Transmission bit errors._ The integrity of the data received over the configurable backplane was confirmed in two different instances. First, data were transmitted at baud rates up to 4 Mbps at room temperature. Second, the test was conducted in a vacuum environment at temperatures ranging from \(-10\)\({}^{\circ}\)C to \(+70\)\({}^{\circ}\)C for 12 h at a 1 Mbps baud rate. No bit errors were detected during either test, and the findings demonstrated the reliability and integrity of the backplane.
* _Transmission delay._ A maximum transmission delay of 20 ns over four CPLDs in the backplane was observed, which is acceptable for simple serial communications such as UART and SPI.
* _Power consumption._ The maximum power consumed by the backplane with four CPLDs was 18.5 mW or 28.0 mWh of energy per cycle in ISS orbit. The power drawn by the backplane is insignificant to the overall power consumption of a satellite.
* _Development time._ From the experience of satellite development, a simple routing error in a hardwired backplane would take at least three weeks and additional costs to fix. However, in the case of a configurable backplane, a simple change in the CPLD code could resolve the error within hours without incurring costs.
## 2 Modeling and Formulation
### Failure Handling  
As explained before, when the \(BS\) receives recharging requests from the nodes, it can determine the failure by analyzing these requests and verifying which node sends several abnormal recharging requests, even if it has been recharged shortly. This node is considered having a problem (i.e., either failure or damage). The \(BS\) prioritizes such a node and decides which one needs to send Air-R to replace it depending on \(PN_{\max}\). Air-R is selected based on the current location for Air-R. Specifically, the \(BS\) selects the Air-R with the closest current location to the sector containing the failed nodes. The continuous synchronized information exchange with the \(BS\) allows the \(BS\) to remain informed of the current location of the Air-R. When the Air-R moves at a certain distance on its path, to replace the failed node with a new fully functional one at the same location of the failed one, the \(BS\) deletes the replaced nodes from the node set \(N\). Therefore, recharging no longer takes place for the failed node when it has already been replaced. The node replacement process can be performed in three steps: (i) The \(BS\) analyzes the recharging request and determines if failure occurs; if failure is determined, then it starts the next step; (ii) The Air-R is arranged in replacement \(RT\) to replace all failed or damaged nodes with a new fully charged one to fulfill its working duty; (iii) Recharging no longer occurs for these failed nodes until the batteries are drained. The application of the proposed SBR technique can guarantee R-WSN sustainability, and fast expectations and detection of the failed nodes in the sectors before this failure become complicated or unsolvable.  
The Air-R can determine whether it can replace the additional node after completing the first mission \(RT\) (replacing sector failure nodes that are already planned before the Air-R starts \(RT\)). The \(BS\) has the global information of all the failed nodes and all the Air-R traveling distances, and thus the movement energy of Air-R (because it always forwards the NRs to the closest Air-R). When the Air-R is moving toward a failed node, the Air-R can still receive replacement requests from the \(BS\). The Air-R effort movement can contribute to fast replacement by applying the new field-dividing mechanism. Figure 2 illustrates the flowchart for the replacement process from the beginning, when the sensors are deployed until discovering the failure.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 3 Analysis of Coupling Factors
### Coupling Relationship Between Electromagnetic and Temperature Field  
The following Equation (1) shows [PERSON]'s equations in the electromagnetic field [16]:  
\[\left\{\begin{array}{l}\
abla\times H=J+\partial D/\partial t\\ \
abla\times E=-\partial B/\partial t\\ \
abla\cdot D=\rho\\ \
abla\cdot B=0\end{array}\right.. \tag{1}\]  
Equation (1), in turn, represents [PERSON]'s loop law, [PERSON]'s law of electromagnetic induction, [PERSON]'s law of electric current, and [PERSON]'s law of magnetic flux.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Value** \\ \hline Stator Diameter & 310 mm \\ Stator Bore & 240 mm \\ Stator length & 69 mm \\ Rotor inner diameter & 200 mm \\ Air gap length & 0.9 mm \\ Rotor length & 71 mm \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the in-wheel motor (IWM).  
Figure 1: The detailed structure of the in-wheel motor (IWM).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Value** \\ \hline Rated power & 15 kW \\ Phase number & 3 \\ Rated line voltage & 330 V \\ Winding form & Y \\ Rated torque & 143.25 Nm \\ Rated speed & 1000 r/min \\ Pole number & 8 \\ \hline \end{tabular}
\end{table}
Table 2: Basic parameters of IWM.  
The heat conduction equation of the temperature field is shown as [17]  
\[\rho_{m}c\frac{\partial T}{\partial t}-k\
abla^{2}T=Q. \tag{2}\]  
The above equation expresses that the heat required for heating is balanced with the incoming heat and the heat generated by the heat source itself.  
Equations (1) and (2) show that the temperature of the IWM is affected by parameters such as heat source, heat conduction coefficient, material density, etc. The heat source is produced by the Joule heat and magnetocaloric effect converted from the loss caused by the electromagnetic field. At the same time, the change in temperature affects the material properties of the motor components, such as conductivity, permeability, and other parameters. Further, it will affect the magnetic field vector, flux density vector, and other electromagnetic parameters of the IWM, and then affect the electromagnetic characteristics of the IWM. During the operation of the IWM, the electromagnetic field and temperature field always interact with each other. Figure 2 shows the coupling relationship between the electromagnetic field and temperature field.
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 5 Experimental Analysis  
### Experimental System Setup  
Experimental studies are carried out on a 100-W PMSM drive system to validate effectiveness and feasibility of the discrete duty ratio determination method for FS-DTC (FS-DDTC). The experimental hardware setup is illustrated in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{5}{c}{**Value**} \\ \hline \(\sigma_{p}(^{\circ})\) & 0 & 5 & 10 & 15 & 20 & 25 \\ \(k_{\mathrm{ms}}\) & 0 & 0.09 & 0.2 & 0.33 & 0.5 & 0.71 \\ \hline \(\sigma_{p}(^{\circ})\) & 30 & 35 & 40 & 45 & 50 & 55 \\ \(k_{\mathrm{ms}}\) & 1 & 1.4 & 2 & 3 & 5 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Duty ratio scale switching table.  
Figure 6: Experimental setup of control system.  
The parameters of the PMSM are given as follows: \(R_{\rm s}\)= 0.76 \(\Omega\); \(L_{\rm s}\) = 0.00182 H; the number of pole pairs \(p\) = 4. The DC voltage is 36 V. The experiments are implemented in a TMS320F28335 DSP control system with a sampling period of 100 \(\upmu\)s.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 3 Methodology  
In traditional VI-SLAM systems [1; 2; 28], visual data from cameras are combined with inertial measurements from an IMU to estimate the pose of the system. The visual data are typically used for feature tracking and map construction, while the IMU provides motion estimates between frames. These systems usually consist of a frontend, where visual features are extracted and tracked, and a backend, which optimizes the pose graph and map using both visual and inertial constraints. However, in dynamic environments, the presence of moving objects can corrupt feature correspondences and lead to errors in pose estimation, reducing the system's robustness. Our proposed E\({}^{2}\)-VINS extends this traditional pipeline by introducing a dynamicity-aware mechanism that assigns adaptive weights to dynamic features, improving the system's performance in challenging dynamic environments.  
The overall pipeline of the E\({}^{2}\)-VINS is illustrated in Figure 1. After data preprocessing, during the initialization of E\({}^{2}\)-VINS, the system performs visual-based motion structure recovery and aligns it with inertial measurements. Gyroscope measurements are corrected through bias estimation during visual-inertial alignment. The system then uses the corrected measurements to assist in motion compensation, achieving spatiotemporal alignment of events. The aligned events are used to generate event-based dynamicity metrics, which assess the dynamicity of each pixel. Based on these metrics, dynamicity weights are assigned to the visual residuals of different pixels. Subsequently, E\({}^{2}\)-VINS jointly and alternately optimizes camera poses, map points, and dynamicity weights, adaptively filtering dynamic features to enhance the system's robustness in dynamic environments.  
This section begins with a brief review of the working principle of the event camera in Section 3.1. Then, the preprocessing and visual-inertial alignment of the system are introduced in Section 3.2. Next, we discuss the computation of event-based dynamicity metrics in Section 3.3. Finally, Section 3.4 covers the adaptive assignment of visual residual weights and the joint alternative optimization framework.
## 3 Materials and Methods
### RFAC Architecture
#### 3.2.2 RFAC Module  
The primary objective of this section is to enhance the segmentation performance of kidney organs by understanding the connections between different organs in abdominal CT images. The model initially extracts concatenated multi-scale information features; however, further extraction of information from these features is required. The obtained features are divided into spatial position dimensions and channel dimensions of the feature map. In typical scenarios, convolutional neural networks need to stack multiple convolutions to capture dependencies across a global scope. However, this approach increases the network depth and model complexity, and it is less efficient for capturing dependencies between spatially distant positions. Each channel dimension corresponds to a semantic information class, mapping to a class of image features in the original image. All feature classes share the same weights in the channel dimension, expressing them with equal importance in the model. However, we aim to assign higher importance to the image features corresponding to kidney organs. Using identical channel weights does not favor highlighting the importance of critical features.  
To address the above two problems, the residual full-attention convolution RFAC module (Figure 5) is designed in this paper to highlight important semantic features in both the spatial and channel dimensions. The result is shown in the dashed box in Figure 5. The input of RFAC needs to go through a \(1\times 1\) convolution to map the output feature channel to the specified dimension, then go through a \(1\times 1\) convolution and a \(3\times 3\) convolution and obtain the sum of the two features, after that, go through the full-attention network to re-calibrate its spatial and channel features and use a residual path to fuse the original features with the full-attention features to obtain the output features of the RFAC module. The \(3\times 3\) convolution extracts the nonlinear features, and the residual path ensures that the model does not have the problem of gradient vanishing. The above process can be summarized as  
\[Y_{RFAC}(\mathbf{X})=Y_{FLA}(\mathbf{W_{R}}\mathbf{X}+\mathbf{W_{C}}\mathbf{X})+\mathbf{X}, \tag{3}\]  
where \(Y_{RFAC}(\mathbf{X})\) denotes a multilevel full-attention convolution operation, and \(\mathbf{X}\) denotes an input feature. \(\mathbf{W_{R}}\) is a \(1\times 1\) convolution matrix used to linearly map the original input, which is equivalent to a residual path. \(\mathbf{W_{C}}\) is a \(3\times 3\) convolution matrix used to perform feature extraction on the input features, and \(Y_{FLA}\) denotes a full-attention operation.
###### Abstract  
The purpose of multi-modal visual information fusion is to integrate the data of multi-sensors to generate an image with higher quality, more information, and greater clarity so that it contains more complementary information and fewer redundant features. Infrared sensors detect thermal radiation emitted by objects, which is related to their temperature, whereas visible light sensors generate images by capturing the light that interacts with objects, including reflection, diffusion, and transmission. However, due to the different principles of infrared and visible light sensors, there is a large similarity difference between the generated infrared and visible images, which makes it difficult to extract complementary information. Existing methods generally use simple splicing or addition methods to fuse features at the fusion layer without considering the intrinsic features of different modal images and the interaction of features between different scales. Moreover, only correlation is considered. On the contrary, the image fusion task needs to pay more attention to their complementarity. For this reason, we introduce a cross-scale differential features attention generative adversarial fusion network, namely DAGANFuse. In the generator, we designed a cross-modal differential features attention module to fuse the intrinsic content of different modal images. We proposed a parallel path calculation of differential features and fusion features for attention weights and performed parallel spatial and channel attention weight calculations on the two paths. In the discriminator, a dual discriminator was used to maintain the information balance between different modalities and avoid common problems such as information blurring and loss of texture details. Experimental results show that our DAGANFuse has state-of-the-art (SOTA) performance and is superior to existing methods in terms of fusion performance.  
image fusion; differential feature; attention mechanism; generative adversarial +
Footnote †: journal: Applied Sciences  
1  
[PERSON], [PERSON], W. DAGANFuse: Infrared and Visible Image Fusion Based on Differential Features Attention Generative Adversarial Networks 15 4560, [[https://doi.org/10.3390/app15084560](https://doi.org/10.3390/app15084560)]([https://doi.org/10.3390/app15084560](https://doi.org/10.3390/app15084560))
## 2 Islanding Detection Principles and Methods
### IDM Method Classification
Thus, the additional signal is injected only when the passive component is inefficient. Good examples of hybridization between passive and active methods (see Figure 5) are voltage unbalance and frequency setpoint [28], Sandia frequency shift and reactive power shift [29], voltage and active power shift [30], etc. Based on the previously published review papers [3; 6; 7; 10] in which detailed analysis and comparison of different IDMs are performed, the conclusion is that passive methods gain great attention, and there is a plethora of research papers that deal with improvements of passive methods. Passive methods can be further classified (see Figure 5) into direct passive methods, signal processing (SP), and computational intelligence (CI) passive methods. Direct passive methods simply check the change in variable signal without changing and processing the signal itself. They show a rather high NDZ, and to improve the accuracy,researchers start processing the input signal and extract the appropriate features that make up the essence of the SP passive methods. It is the passive method form with the capability for hidden feature extraction of any signal, which is an added advantage. Commonly used SP methods are Fourier transform [32], Stockwell transform [33], wavelet transform [34], Hilbert-Huang transform [35], matrix pencil method [36], etc. Although the accuracy of islanding operation detection is improved by the SP approaches, the detection based on the extraction signal feature can be hard in more complicated network structures. Therefore, the next step is to add computational intelligence (CI) methods already proven in data classification. Instead of checking whether some values are below or above the prespecified threshold, the signal is sent to the intelligent classifier, trained to detect islanding operation. Before an intelligent classifier is applied, the signal can be processed and appropriate features extracted. In that case, it can be said that signal processing methods and computational intelligence-based methods are hybridized (see Figure 5). Some of the computational intelligence methods used in islanding detection are artificial neural network (ANN) [37], fuzzy logic [38], adaptive neuro-fuzzy inference system (ANFIS) [39], decision tree [40], probabilistic neural network (PNN) [41], support vector machine (SVM) [42], and deep learning [43].
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Data Preprocessing  
In the data preprocessing stage, several essential steps were employed to ensure the suitability of the data for subsequent analysis. This subsection outlines the preprocessing techniques applied, including mask preprocessing, image resizing, the CLAHE method, and image augmentation. These preprocessing steps are crucial for enhancing the quality of the data, optimizing the performance of the segmentation models, and improving the accuracy of the results.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 4 Results and Discussion
### Comparison of Experiments Results among Different Algorithms  
To evaluate the performance of the proposed algorithm in this paper, a comparison was conducted with the U-Net, Deeplabv3+, and PspNet algorithms. The experimental results are presented in Table 5. It is evident that the DHRECA-Net outperformed the other three algorithms in all three metrics. In comparison to the highest performing PspNet algorithm, the DHRECA-Net demonstrated improvements of 4.28% in the MIOU, 2.76% in the MPA, and 2.78% in the F1-score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MIOU/\%** & **MPA/\%** & **Precision/\%** & **Recall/\%** & **F1-Score\%** \\ \hline BCE loss & 84.38 & 90.55 & 91.94 & 90.55 & 91.07 \\ Combined loss & **88.37** & **94.55** & **93.17** & **94.55** & **93.85** \\ \hline \hline \end{tabular}  
* The best results are in bold.  
\end{table}
Table 4: Comparison of the training results of the supervised networks with different loss functions.  
Figure 9: The network model’s recognition performance was compared before and after improvements.  
Figure 10 indicates that the U-Net, Deeplabv3+, and PspNet segmentation algorithms exhibited deficiencies in detecting multiple small-scale landslide areas, while the proposed DHRECA-Net in this study demonstrated comprehensive detection of all landslide areas in the images. Moreover, when segmenting old landslides, the three aforementioned algorithms displayed inaccuracies and segmentation errors, which suggests challenges in accurately discerning between background and landslide features. In contrast, the proposed algorithm in this paper demonstrated an improvement in the accuracy of segmenting old landslides. Consequently, these results underscore the superiority and enhance the efficiency of the proposed DHRECA-Net over the three comparative algorithms.
# Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin
## 2 Mathematical Model of Marine Steam Power System Equipment
### Mathematical Model of Deaerator  
According to the structure composition and working principle of the deaerator, in order to establish the mathematical model of the deaerator, the deaerator is divided into a deaerator area and a water tank area, and its mathematical model is established [15].  
(1) Mass conservation equation:  
\[\frac{d(\rho_{d}V_{d})}{dt}=q_{t}+q_{o}+q_{i}-q_{e} \tag{13}\]  
where:  
\(V_{d}\)--The volume of water in deaerator, m\({}^{3}\);  
\(\rho_{d}\)--The density of water in deaerator, kg/m\({}^{3}\);  
\(q_{t}\)--The mass flow of steam entering deaerator, kg/s;  
\(q_{o}\)--The mass flow of condensate entering deaerator, kg/s;  
\(q_{i}\)--The mass flow of water entering deaerator, kg/s;  
\(q_{e}\)--The mass flow of outlet feed water, kg/s.  
(2) Energy conservation equation:  
\[\frac{d(\rho_{d}V_{d}h_{d})}{dt}=q_{t}h_{t}+q_{o}h_{o}+q_{a}h_{a}-q_{e}h_{e} \tag{14}\]  
where:  
\(h_{d}\)--The enthalpy of water in deaerator, kJ/kg;  
\(h_{t}\)--The enthalpy of steam entering deaerator, kJ/kg;  
\(h_{o}\)--The enthalpy of condensate entering deaerator, kJ/kg;  
\(h_{i}\)--The enthalpy of feed water entering deaerator, kJ/kg;  
\(h_{e}\)--The enthalpy of outlet feed water, kJ/kg.