esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Deformation Validation by FEM  
In the FEM simulation, a thermal load of \(-\)220 \({}^{\circ}\)C (from 250 to 30 \({}^{\circ}\)C) was applied for calculating the curvature or warpage of the PCB during the reflow cooling process. Figure 9 shows the curvature distribution along the edge line a-d on the PCB assembly from the simulation's result. Note that the valley appearing at the position of 140 mm is caused by the free end effect of the three sockets at the front. It is shown that the curvature at the same location of the strain gauge is 205 \(\times\) 10\({}^{-6}\) mm\({}^{-1}\), which fairly equals the value of 207 \(\times\) 10\({}^{-6}\) mm\({}^{-1}\) measured from strain gauge during the reflow cooling process. Furthermore, the full-field out-of-plane deformation in Figure 10 from the FEM simulation shows a cylindrical bending of the PCB assembly at room temperature after the solder reflow. With regard to the out-of-plane deformation of the PCB along line a-d, the out-of-plane deformation measured from shadow moire is quite close to that from the FEM simulation, as shown in Figure 11. Hence, the FEM simulation for calculating the curvature or warpage of the PCB assembly is proved to be valid.  
Figure 8: (**a**) Out-of-plane deformation of the bare PCB and PCB with DIMM sockets (after the reflow process) measured from shadow moiré, and (**b**) thermally-induced out-of-plane deformation of the PCB with DIMM sockets.  
Figure 11: Comparison of out-of-plane deformation of the PCB assembly between shadow moiré experiment and FEM simulation.  
Figure 9: Curvature distributions along edge line a-d on the PCB assembly at room temperature after the solder reflow process from the FEM simulation, and one curvature data from the strain gauge measurement.  
Figure 10: Full-field out-of-plane deformation (\(W\)) for the PCB assembly at room temperature after the solder reflow, obtained from the FEM simulation (\(\Delta T=-220\)\({}^{\circ}\)C).
## 3 Results
### Datasets and Evaluation Metrics  
The data quality is important for deep-learning based methods [6]. The commonly used open-source datasets for single image rain removal mainly include Rain100H [32], Rain100L [32], Rain800 [33], and Rain14000 [14]. Compared with other datasets, the Rain14000 dataset fully considers the distribution of rain streaks. The inability of composite images to reflect the characteristics of real images has been compensated from multiple angles. Therefore, the Rain14000 dataset was selected to train and compare the rain removal network designed in this paper and all the comparisons.  
Rain14000 dataset is a new open-source dataset published in 2017, which contains a total of 14,000 images. The training set of 12,600 images is a total of 14 synthetic rain images with different raindrop densities and different tilts corresponding to 900 real clear images, respectively. Correspondingly, the test set consisted of 1400 images, or 14 synthetic rain images of varying degrees of clarity corresponding to 100 real images. An example of the Rain14000 dataset is shown in Figure 5, which shows rain-free images and 14 corresponding synthetic rain images.  
For synthetic image, peak signal-to-noise ratio (PSNR) and structure similarity were selected as evaluation metrics to assess the performance quantitatively, in which a larger value indicates a better-quality image. For real-world rainy images, visual performance is a criterion to validate the robustness and generalization of our method. Besides, two quantitative indicators, image information entropy and image contrast, were additionally selected to evaluate the experimental results. Information entropy is the overall characteristic of the information source in the average sense, and the unit is bits/pixel. There is only one information entropy for the particular source. Image information entropy was used to measure the average amount of information carried by each image [34].  
Figure 5: Example of Rain14000.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 4 Result and Discussion
### Performance in G2V Mode  
In G2V mode, the charging process takes place from the grid to the EV batteries using grid- and battery-side bidirectional converters. In this mode, it is expected that the grid current and voltage should be sinusoidal and should achieve a unity power factor. Additionally, the battery-side voltage or current should be constant. Figure 9 shows that the input grid current, \(I_{\mathit{inf}}\), is in phase with the input grid voltage, \(V_{g}\). Simultaneously, Figures 10 and 11 show that the power factor is near to unity, or in other terms, the obtained active and reactive powers are approximately rated and zero, respectively, whereas, in Figure 12, the THD present in \(I_{\mathit{inf}}\) is within the IEEE standard 519, which shows the effectiveness of the PFC control and utilization of high switching frequency with the SiC devices in the totem pole converter for rectification process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Passive Components** & **Value** \\ \hline \(L_{\mathit{inf}}\) & 5 mH \\ \(C_{\mathit{DC}}\) & 8200 μF \\ \(L_{\mathit{bat}}\) & 5 mH \\ \(C_{\mathit{bat}}\) & 20,000 μF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed passive components for BDCI.  
Figure 8: Grid-side bidirectional totem pole inverter control in V2G mode.  
Here, the power loss for the totem pole PFC is estimated using (12) and there components are discussed in Appendix A [48].  
\[P_{L,PFC}=P_{L,ind}+P_{L,co}+P_{L,sw}+P_{L,cond} \tag{12}\]  
where \(P_{L,PFC},P_{L,ind}\), and \(P_{L,co}\) are the power loss in PFC, interfacing inductor, and DC-link capacitor, respectively, whereas \(P_{L,sw}\) and \(P_{L,cond}\) are the switching and conduction loss in the SiC switch, respectively. Since the interfacing inductor loss is low, it will be neglected for all current level. The power loss of the switching device for the totem pole PFC is calculated using an online tool, i.e., UnitedSiC FET-Jet Calculator [49], as mentioned in [50]. Additionally, the DC-link capacitor loss is calculated using (A2) and, altogether, other losses  
Figure 11: Performance curve of the active and reactive power in G2V mode.  
Figure 12: THD in input current during G2V mode.  
Figure 9: Dynamic performance of the input voltage and current in G2V mode.  
are presented in Table 3. In Figure 13, the simulation-based efficiency is presented on the basis of the input and output power of the totem pole PFC rectifier, which matches with the online calculated measure. Furthermore, as an output of the rectification process, a DC-link voltage is obtained for the battery-side bidirectional DC-DC converter, where \(\Delta V_{DC-link}\) is also achieved within the expected limit, as shown in Figure 14.  
In Figure 14, the performance of \(V_{DC-link}\) in rectification mode is presented from \(t\) = 0 s to \(t\) = 2 s. Furthermore, the obtained \(V_{DC-link}\) is provided to the BSBD converter to charge the EV batteries using buck mode control. The charging
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 4 Experimental Results
### Global Localization  
The experiments to test the global localization algorithm consisted of extracting LIDAR readings from the aerial platform for different time instants and inputting them for computation. These instants include moments before take-off and during flight and landing. To illustrate this process, Figure 11 shows two alignment examples at take-off.  
On the one hand, the initial alignment of the total station 3D map and the LIDAR data readouts are shown, taking into account the assumptions introduced in Section 3.1. On the other hand, both point clouds aligned with the algorithm results are shown.  
During the experimentation, the execution of the global localization algorithms was carried out on a laptop with a 4-core Intel Core i7-8564U CPU and 8 GB of RAM. The execution and convergence time of the algorithm varied between 12 and 25 s for each resulting transform. This duration is not a problem since the first iteration is performed before take-off. The rest of them can be performed during the flight and update the transform at convenience, as explained in Section 3.1.  
The performance evaluation of the global localization algorithm is carried out by studying the mean error between matches after each ICP step. All tested cases converge to a valid solution. Tables 1 and 2 show the metrics obtained for Algodonales and Alora, respectively. As explained in Section 3, the ICP1 step is not always executed, so it is denoted in the tables as \"*\" when there is no data. The so called \"Proportional Correspondence\" metrics (Prop. Corr.) shows the number of correspondences in that ICP stage divided by the size of the LIDAR point cloud at that instant in time. The MSE metrics represents the mean square error of the correspondences after applying the transform obtained at that stage.  
Additionally, during these instants, the position given by the total station--which has the same reference system as the 3D map--was obtained. This position serves as a ground truth to check the output of the global localization algorithm, since the output of this algorithm should correspond with the reading of the total station. Again, since the total station does not provide orientation, only the transitional part is considered. Tables 3 and 4 show the obtained results.  
The results are considered valid since the algorithm is able to converge correctly in the proposed realistic cases. The advantage of the global localization system is that, if run before take-off, the operator can visually validate the obtained result and proceed with the inspection if there is no problem. Tables 3 and 4 show how the final 3D error is between 0.2 and 0.64 m for the tested cases, the z-axis (vertical) being the most affected. The results are considered good since the uncertainty of the point clouds and of the algorithm itself must be taken into account. While the total station error is in the order of millimeters (always proportional to the distance), the LIDAR points have an error of \(\pm\)1.5-5 cm (both errors according to the manufacturers), which may influence the result.
## 2 General Properties of Method
### Far-Field Approximation  
The model for detecting an explosion sound occurring at a distance of \(d_{k}\) using a sensor array consisting of microphones placed at intervals of \(d_{m}\) is illustrated in Figure 2, where \(d_{k}\): the distance of the source to the origin, \(d_{i}\): the distance vector of the source to \(m_{i}\), and \(d_{m}\): distance vector between \(m_{1}\) and \(m_{2}\) and observe that \(\overset{\rightarrow}{d}_{1}=\overset{\rightarrow}{d}_{2}+\overset{ \rightarrow}{d}_{m}\).  
Figure 1: The azimuth (\(\theta\)) and elevation (\(\varphi\)) angles of a vector defined in Cartesian space.  
Figure 2: Direction-of-arrival (DoA) estimation and microphone array structure.  
In the case where \(d_{k}\gg d_{m}\), the sound wave is assumed to propagate as a linear beam (\(\theta_{1}=\theta_{2}\)). Therefore, with \(v_{s}\) being the speed of sound, the distance difference \((\Delta d)\) and the DoA that form the time delay between the microphones (\(\tau_{12}\)) are given:  
\[\Delta d=v_{s}\times\tau_{12} \tag{3}\]  
\[\theta_{1}=\text{acos}\bigg{(}\frac{v_{s}\times\tau_{12}}{d_{m}}\bigg{)} \tag{4}\]  
When the DoA is calculated with two microphones, two symmetrical angle values are obtained along the direction of the microphones. To avoid this, a third microphone is placed perpendicular to the direction of the two microphones, as shown in Figure 3a. The result obtained with the other two microphones also produces two angle values. However, the intersection of the two solutions gives the correct angle of arrival. If the elevation angle is also to be calculated, a fourth microphone is placed perpendicular to the plane formed by the three microphones, as shown in Figure 3b, and both the lateral and elevation angles are detected, but the distance cannot be estimated [28].  
In one study, the origin was considered to be the midpoint of the microphones shown in Figure 2, the DoA was calculated relative to the origin, and the half scale of the right triangle obtained in Figure 2 was used to model it [29]. According to this model, the DoA formula is given by [PERSON] and [PERSON] as follows:  
\[\theta=90-\text{acos}\left(\sqrt{1-\bigg{(}\frac{v_{s}\times\tau_{12}}{d_{m}} \bigg{)}^{2}}\right) \tag{5}\]
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 4 Numerical Modelling
### Mesh Generation  
Mesh is a very important aspect of numerical simulation and one of the most time-consuming processes. The quality of the mesh directly affects the accuracy and efficiency of CFD simulations. The fully structured overset mesh was applied in the present study, which can be divided into two parts before the overset: background mesh and overset mesh. The two parts were both generated as trimmed-type topology. For wave problems,  
Figure 5: The computational domain and boundary conditions.  
the number of meshes of the free surface is the key factor for the accuracy of the numerical simulation. In the mesh division of free surface, 10-20 layers of meshes in one wave height and 30-50 layers of meshes in one wavelength are needed. The mesh size ratio of element mesh in wave region in \(x\), \(y\), and \(z\) directions was 2:2:1. In order to ensure the accuracy of the overset mesh in the interpolation calculation, the \(y+\) value of wall grid was between 30 and 300. The background meshes were also encrypted in the region where the overset mesh pitches and sinks, which occupied a large part of the number of meshes and increased the calculation time. Figure 6 shows the mesh division in the wave condition.  
For the computations in calm water, only a certain degree of mesh refinement was required in the Kelvin wave region behind the stern to capture the Kelvin wave system. For the computations in waves, further refinement of the meshes near the free surface occurred in order to better simulate the incoming regular waves. More than 50 cells per wave length and 15 cells per wave height were used. Prism layers were applied on the hull surface, making the y+ range from 100 to 300 according to the ITTC recommendation [48]. Figure 7 shows the horizontal section of the mesh in calm water and waves. The mesh size of the monohull and pentamaran surface was 0.1 m. The mesh size of the free surface in the \(z\) direction was 0.1 m. The total mesh number in calm water and waves is listed in Table 6.  
The time step size was 0.01 s, and the convective courant number was 1.25. At the beginning of the calculation, the flow field was relatively rough, and a small time step was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulations** & **Background** & **Overset** & **Total** \\ \hline Calm water & 2,697,325 & 681,023 & 3,378,348 \\ Regular waves & 5,901,263 & 681,713 & 6,582,976 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mesh number in calm water and waves.  
Figure 6: The background mesh and overset mesh of the pentamaran in the wave condition.  
Figure 7: Horizontal section of the mesh: (**a**) calm water condition; (**b**) wave condition.  
used to control the range of the courant number between 0.5 and 1. When the calculation was relatively stable, the time step size and the courant number could be increased to save the calculation time.
## 5 Quality of Experience (QoE) Assessment
### Objective QoE Assessment  
Objective QoE assessments are video-centric models which analyze the performance of QoE directly bases on the video quality by comparing the presented video's distortions to the original version. This method is known as video quality evaluation (VQA) [88]. The VQA metrics of 360 videos are developed on the basis of the 2D video metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Various research [89; 90; 91; 92] apply Viewport PSNR (V-PSNR) and Spherical Peak Signal-to-Noise Ratio (S-PSNR) [93] based on PSNR modified by sphere-to-plane mappings for 360\({}^{\circ}\) video streaming. [PERSON] et al. [94] presented weighted-to-spherically-uniform PSNR (WS-PSNR), which computes PSNR on each pixel of the projected picture before multiplying each pixel with a weight that represents the sphere-plane relationship. Next, adaptations of SSIM, Spherical SSIM (S-SSIM) [95] and Weighted to Spherically Uniform SSIM (WS-SSIM) [96] adjust the structural similarity to compensate for the geometrical distortion using a weighting function. Content Preference PSNR (CP-PSNR) and Content Preference SSIM (CP-SSIM) in [97] showed adaptation to viewport direction and content saliency with a predictive model to predict future viewing direction.  
In the above works, the measurement of VQA metrics needs to refer to the reference videos which are the original 360-degree videos without distortion. To overcome this restriction, QoE evaluation models without reference videos have been created [98; 99; 100] by assessing metrics derived from the properties of degraded videos or network statistics such as bandwidth, packet loss, and latency.  
[PERSON], [PERSON] [101] divided each 360\({}^{\circ}\) video into several patches with equally spaced pixels on the sphere and measure the QoE with extended objective metrics of 2D video using Voronoi patches. VI-PSNR, VI-SSIM, and VI-VMAF are the quality measures that arise from this process. VI-VMAF has the strongest association with the MOSs of total QoE, according to their user survey. [PERSON], [PERSON] [102] further develop weighted variations of their measures named Visual Attention (VA) maps [31]. VI-VA-metrics, such as VI-VA-PSNR and VI-VA-VMAF are the resultant metrics. According to the findings, VI-VA-VMAF and VI-VA-MS-SSIM are the two quality indicators that have the strongest impact on the overall QoE.  
However, in most real-world video streaming settings, objective QoE measurement is problematic because it ignores the perception of the users such as immersiveness and cybersickness. Moreover, these widely used 360\({}^{\circ}\) video quality indicators have been demonstrated to have a low impact on user satisfaction [103]. Thus, the assessment of QoE in 360-degree material using 2D video theory and methodology requires additional specialized 360-video QoE study.
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 2 Materials and Methods
### Mobile Application  
The previously presented automatic meter reading pipeline was deployed as an Android application running on a mobile device aiming to support the reading process of digital meters and save the data analysed. The application was developed using Android Studio and Kotlin as the main programming language. The models presented in the  
Figure 4: Example of the text boxes returned by the text detection module (**left column**) and selection of important information (**right column**).  
previous section were converted to the ONNX format for mobile deployment, except the screen detection module, which uses the TensorFlow Object Detection API that is also available in Android. To reduce the model size and speed up inference, dynamic quantization was performed.  
The mobile application was designed for operators using a Samsung Tab Active3 (SM-T575), as required by the company. It is equipped with an 8-inch screen, an Octa-core (4 \(\times\) 2.7 GHz Mongoose M3 and 4 \(\times\) 1.7 GHz Cortex-A55) central processing unit and a 13 Megapixel back camera with f/1.9 and autofocus, being capable of acquiring pictures with a resolution of 1920 \(\times\) 1080 pixels.  
The application runs in two stages: (i) first, it automatically detects the screen of the device in real-time for each preview frame and, (ii) in the acquired image, the complete pipeline is applied. The user is first guided to center the meter inside a square (Figure 5). While in preview, each frame is analyzed to detect the equipment screen. An image is automatically acquired when the majority of 20 consecutive frames or when the majority of all frames acquired in the span of 1250 milliseconds (ms) detect a screen with a score of at least 70%. In case this process fails to automatically acquire the image, the user can always use the capture button located at the bottom part of the screen to manually acquire an image.  
All the modules, screen detection, text detection and recognition, are run in the acquired image, and in case all of them return a result, the final reading is returned as depicted in Figure 5. Otherwise, the user is prompted to another image acquisition stating one of two errors: that the screen was not detected properly or any of the other models had no result. In case the system is never able to obtain a proper reading, the user can always cancel the process and manually write the readings, and the last image acquired is saved into the application.
###### Abstract  
Given that rotating machinery is one of the most commonly used types of mechanical equipment in industrial applications, the identification of its health status is crucial for the safe operation of the entire system. Traditional equipment health status identification mainly relies on conventional single-modal data, such as vibration or acoustic modalities, which often have limitations and false alarm issues when dealing with real-world operating conditions and complex environments. However, with the increasing automation of coal mining equipment, the monitoring of multimodal data related to equipment operation has become more prevalent. Existing multimodal health status identification methods are still imperfect in extracting features, with poor complementarity and consistency among modalities. To address these issues, this paper proposes a multimodal joint representation learning and residual neural network-based method for rotating machinery health status identification. First, vibration, acoustic, and image modal information is comprehensively utilized, which is extracted using a Gramian Angular Field (GAF), Mel-Frequency Cepstral Coefficients (MFCCs), and a Faster Region-based Convolutional Neural Network (RCNN), respectively, to construct a feature set. Second, an orthogonal projection combined with a Transformer is used to enhance the target modality, while a modality attention mechanism is introduced to take into consideration the interaction between different modalities, enabling multimodal fusion. Finally, the fused features are input into a residual neural network (ResNet) for health status identification. Experiments conducted on a gearbox test platform validate the proposed method, and the results demonstrate that it significantly improves the accuracy and reliability of rotating machinery health state identification.  
m +
Footnote †: journal: Applied Sciences  
Article  
A Health Status Identification Method for Rotating Machinery Based on Multimodal Joint Representation Learning and a Residual Neural Network. _Appl. Sci._**2025**, 15, 4049. [[https://doi.org/10.3390/appr15074049](https://doi.org/10.3390/appr15074049)]([https://doi.org/10.3390/appr15074049](https://doi.org/10.3390/appr15074049))  
Copyright: (c) 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion  
A series of algorithm analyses will be taken to evaluate the performance of the MSAMT in this part. First, the test functions and parameters are provided, and the behavior of the MSAMT is studied in depth. Then, the MSAMT is contrasted with six recently proposed SAEAs, including HSAOA [28], DSP-SAEA [27], BiS-SAHA [25], FSAPSO [15], GL-SADE [21], and LSADE [22], on fourteen test functions and a practical engineering problem. Finally, the computational complexity of MSAMT is analyzed.