esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of the Tip Leakage Vortex Characteristics in a Semi-Open Centrifugal Pump
## 3 Results and Discussions  
### Numerical Results Validation  
The comparison between experimental data and numerical results with the 1.0 mm tip clearance was conducted, as shown in Figure 6, to verify the accuracy of the numerical simulation method in this study. A good agreement between the numerical and the experiment results was obtained. The maximum and minimum relative errors of the head between experimental data and numerical results were 5.6% at 1.2 \(Q_{d}\) and 0.3% at 0.4 \(Q_{d}\). Under the design condition, the relative errors of head and efficiency were 4.2% and 4.0%. Consequently, the grids and numerical methods were verified to guarantee the accuracy and reliability of results and the internal flow field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tip Clearance/mm** & **Inlet Pipe/10\({}^{6}\)** & **Impeller/10\({}^{6}\)** & **Vaneless Diffuser/10\({}^{6}\)** & **Total/10\({}^{6}\)** \\ \hline
0 & 0.45 & 4.52 & 0.45 & 5.42 \\
1 & 0.59 & 6.10 & 0.62 & 7.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elements number in the computational domain.  
Figure 4: Mesh analysis for NTC (without tip clearance) and TC (tip clearance). (**a**) NTC, (**b**) TC.  
Figure 5: Computational domain grids of a centrifugal pump. (**a**) Impeller, (**b**) Leading edge of the blade, (**c**) Trailing edge of the blade.  
The head and efficiency of the centrifugal pump with and without tip clearance are shown in Figure 7. Regardless of the presence of tip clearance, the changing trend of the external characteristic curve was the same. However, the formation of leakage vortex in the tip clearance led to a considerable decrease in head and efficiency, which, respectively, decreased by 18.7% and 14.4% under the design condition. The decline became increasingly serious with the flow rate. The design condition was selected for a detailed analysis.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 3 System Model and Analysis
### Underwater Light Intensity for a Normal Light Beam  
As highlighted, an airborne light beam can penetrate the water surface and propagate within it. [PERSON] et al. [32] have investigated the underwater coverage area of a normal light beam of the water interface. Table 1 summarizes the notation used. Figure 1 depicts a scenario with a flat water surface and a uniform light source situated at point S, emitting light at an angle of \(\theta\). This light source is placed at a height of \(a_{d}\) meters above the water surface. To evaluate the light coverage area on the water surface, we also consider an underwater sensor located at a depth of \(w_{d}\) meters, i.e., at point J. Specifically, the light intensity at point J with respect to the light source is as follows:  
\[I=\frac{360}{\theta}\cdot\tau\cdot\frac{P}{4\pi\left(\frac{a_{d}}{cos\theta_{i }}\right)^{2}}\cdot e^{\frac{-\log{\theta_{d}}}{cos\theta_{i}}} \tag{1}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(P\) & Power of the light source \\ \(\theta\) & Beam angle of the light source for a flat surface \\ \(\theta_{i}\) & Incident angle of water surface for a flat surface \\ \(\theta_{r}\) & Refraction angle for a flat surface \\ \(a_{d}\) & Distance of light source from water surface for a flat surface \\ \(w_{d}\) & Depth of the sensor from water surface for a flat surface \\ \(\eta\) & Reflectance of light \\ \(\tau\) & Transmittance of light \\ S & Airborne transmitter position \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the notation used.  
According to Equation (1), the coverage area both on the water's surface and underwater is circular, with intensity decreasing as the distance from the center point increases, whether at the surface or at a specific depth. Figure 2 illustrates the intensity distribution for a normal beam in the \(x\)-\(y\) plane. It can be inferred that the light intensity received by an underwater node is uniform along the circumference of a circle. This uniformity is due to the equal distance of all points on the perimeter from the center of the coverage area at a given depth.  
When an underwater node receives an airborne VLC transmission for a known depth, by measuring the intensity the node can measure the proximity from its transmission center. The location lies on the circumference of a circle as it is equidistant from the center.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
###### Abstract  
In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-level information and fault recording documents and outputs a complete diagnosis and tracking report. According to the timeliness of three types of information transmission, the system is divided into three subsystems: real-time processing system, quasi-real-time processing system and batch processing system. The complete work is realized through the cooperation between them. While a real-time processing system completes fault diagnosis of elements, it also screens out incorrectly operating protections and circuit breakers and judges the loss of accident-level information. Quasi-real-time system outputs reasons for incorrect actions of protections and circuit breakers under the premise of considering partial warning-level information missing. The batch processing system corrects diagnosis results of the real-time processing system and outputs fault details, including fault phases, types, times and locations of faulty elements. The simulation results and test show that the system can meet actual engineering requirements in terms of execution efficiency and fault diagnosis and tracking effect. It can be used as a reference for self-healing and maintenance of power grids and has a preferable application value.  
fault diagnosis; fault tracking; partial information missing; multi-data sources +
Footnote †: journal: Applied Sciences
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### Performance Comparison Criteria  
This study uses R\({}^{2}\), MAE, RMSE, and (Global Performance Index) G\({}_{\rm p}\)[26, 42, 43, 44] as evaluation metrics to assess the performance of different models. The specific calculation methods and formulas for R\({}^{2}\), MAE, and RMSE can be found in reference [45]. The specific calculation formula for G\({}_{\rm p}\)[26] is as follows:  
\[\rm G_{p}=\sum_{j=1}^{3}\alpha_{j}(T_{j}-M_{j}) \tag{14}\]  
where T\({}_{j}\) is the normalized value of the three evaluation metrics (R\({}^{2}\), MAE, RMSE), and M\({}_{j}\) is the median value of the corresponding evaluation metric. When T\({}_{j}\) corresponds to R\({}^{2}\), \(\alpha_{j}\) is 1; for other cases, \(\alpha_{j}\) is \(-1\).  
The higher the R\({}^{2}\) value, the better the model's fit to the data, indicating better performance. The lower the RMSE and MAE, the better the model's performance. The higher the G\({}_{\rm p}\) value, the better the overall performance of the model. G\({}_{\rm p}\)-Rank ranks the models based on their G\({}_{\rm p}\) values, from the highest to the lowest.
## 4 Experimental Results and Discussion
### Vision Capabilities Simulations
#### 4.2.1 Recognition in Indoor Controlled Environment  
The first recognition experiments have been carried out in a closed-space laboratory in conjunction with the first spraying tests. The environment and recognition results are summarized later in Section 4.3.1. In line with the numeric analysis reported in Table 4, during the laboratory simulations the work team noticed a grape cluster detection success rate clearly in favor of the **608@VOC+WA-WGISD** network, which was taken as a reference network for all subsequent experiments.
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 3 Data and Methods
### The Improved Yolov5_ESGW Model
#### 3.3.2 Shuffle Attention  
The attention mechanism is a method that simulates human vision and cognitive systems and has wide applications in the field of deep learning. It can selectively focus on important feature information related to the target and is often used to improve the detection performance and generalization ability of the model. This study mainly integrates the Shuffle Attention [24] mechanism into the appropriate position of the recognition model. It mainly disrupts the order of input data, so the model needs to reconsider the order of data when processing data, thereby improving the model's feature expression ability. As shown in Figure 5, Shuffle Attention mainly includes the following parts.  
First, the input feature \(X\in R^{c\times h\times w}\) is divided into \(g\) groups along the channel dimension, denoted as \(X=[X_{1},\cdot\cdot\cdot,X_{k},\cdot\cdot\cdot,X_{g}],X_{k}\in R^{c/g\times h \times w}\). In order to extract important feature information of the target, the feature information of each group is divided into two branches \(X_{k1},X_{k2}\in R^{c/2g\times h\times w}\). Among them, \(X_{k1}\) is used to extract the channel attention features of the target, and \(X_{k2}\) is used to extract the spatial attention features of the target.  
Figure 4: Proposed C3 Ghost module structure.  
Figure 5: Shuffle Attention structure diagram.  
Secondly, the input feature \(X_{k1}\), \(X_{k2}\) is used to extract the target feature information using channel attention and spatial attention mechanisms, respectively. The channel attention mechanism in Sa-net is similar to the SE attention module. In order to reduce the amount of calculation, Sa-net uses a combination of global averaging pooling (GAP), scale, and Sigmoid to extract important feature information.  
\[s=F_{gp}(X_{k1})=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}X_{k1}(i,j) \tag{1}\]  
\[X_{k1}^{\prime}=\sigma(F_{c}(s))\cdot X_{k1}=\sigma(W_{1}s+b_{1})\cdot X_{k1} \tag{2}\]  
where \(s\) is channel-wise statistics, and \(W_{1}\) and \(b_{1}\) are parameters utilized for scaling and shifting \(s\).  
The spatial attention mechanism uses Group Norm (\(GN\)) and \(F_{c}(\cdot)\) to obtain spatial feature information. The specific expression is as follows:  
\[X_{k2}^{\prime}=\sigma(W_{2}\cdot GN(X_{k2})+b_{2})\cdot X_{k2} \tag{3}\]  
where \(W_{2},b_{2}\in R^{c/2g\times 1\times 1}\).  
Finally, the \(X_{k1}^{\prime}\) and \(X_{k2}^{\prime}\) obtained from the two branches are combined and concatenated to achieve aggregation of all feature information. In addition, the channel shuffle operation is used to reorganize the information within the group and realize the circulation of information between different groups.
## 3 Applications  
The synthesis process and the control described above led to several applications. Three of them are presented here and constitute firstly the creation of a tool for motor disease rehabilitation and gesture learning, secondly the possibility to create flexible bowed string synthesizers enabling to choose their playability a priori, and finally the conception of an event-driven interactive sound synthesis tool in virtual audiovisual environments.
# Supervoxels-Based Histon as a New Alzheimer's Disease Imaging Biomarker
## 2 Background
### Superpixel Segmentation and SLIC  
A superpixel can be defined as a perceptually uniform region in the image (see [35]). The concept behind the idea of superpixels is the fact that the construction of an image using pixels is simply a technological limitation associated with the image capturing device, not a real property of the source of that image. A superpixel segmentation produces a set of small spectrally-constrained areas in the image, an over-segmentation, which can be used for the estimation of local scale image features. Superpixels capture redundancy in the image and significantly reduce the complexity of the subsequent image processing operations. Since their introduction, superpixels have been successfully used in applications such as pre-processing steps in image segmentation [36; 37], depth estimation [38], tracking [39] or skeletonization [40]. Multiple techniques have been developed to generate superpixels. In this work, we will use SLIC. Simple Linear Iterative Clustering (SLIC, [41]) is a spatially constrained revision of the k-means algorithm. It begins by sampling a number of regularly-spaced positions as centres of the clusters, followed by a k-means clustering procedure. SLIC redefines the k-means distance \(D_{w}\) by adding a second, colour-based component so the clustering distance between two different pixels is weighted by the colour distance and space distance, defined as:  
\[D_{w}=\sqrt{\left(\frac{d_{c}}{m}\right)^{2}+\left(\frac{d_{s}}{In}\right)^{2}}, \tag{1}\]  
\[d_{c}=\sqrt{\sum_{s_{i}\in S}\left(I\left(x_{1},y_{1},s_{i}\right)-I\left(x_{2 },y_{2},s_{i}\right)\right)^{2}}\qquad d_{s}=\sqrt{\left(x_{1}-x_{2}\right)^{2 }+\left(y_{1}-y_{2}\right)^{2}}, \tag{2}\]  
where both the colour and spatial distance between the pixels \(I\left(x_{1},y_{1},s_{i}\right)\) and \(I\left(x_{2},y_{2},s_{i}\right)\) in the spectral band \(s_{i}\) are represented by \(d_{c}\) and \(d_{s}\), respectively. Related to the spatial distance, the parameter \(In\) is the sampling interval of the cluster's centroids. Associated with the color distance, the parameter \(m\) controls the compactness of the superpixels, as the greater the value of \(m\), the more the spatial proximity is emphasized. The spatial distance restrictions ensure superpixel homogeneity.  
SLIC produces an even superpixel distribution on the image that adheres to the object's limits as well as or better than the superpixels generated by others methods (see [42]). The SLIC generation algorithm can be easily modified to work on different spatial or spectral domains. Specifically, the extension of these ideas to a volumetric image is called \"supervoxel\".
## 2 Materials and Methods
### Irrigation Scheduling  
Measuring the soil moisture is important for optimizing irrigation. The mass balance method was used to calculate the irrigation amounts. The mass balance method, sometimes referred to as scientific irrigation scheduling, is an irrigation schedule determined by calculating how much water is needed based on accurate soil moisture readings and estimates of the  
Figure 4: Hardware (**a**) and software (**b**) of automatic irrigation system (AIS). Each data collector controls two pots (A and B).  
soil properties. Equation (1) can be used to calculate the amount of irrigation needed. The following equation utilizes terms commonly used in soil hydrology studies:  
\[\text{Irr}=\text{A}\times\text{H}\times\text{SD}\times\text{FC}\times\Delta \text{SM}, \tag{1}\]  
In which Irr (m\({}^{3}\)) is the irrigation amount, A (m\({}^{2}\)) is the irrigation area, H (m) is the irrigation depth, SD (g/cm\({}^{3}\)) is the soil bulk density, FC (%) is the field water capacity and \(\Delta\text{SM}\) (%) is the difference between the upper and lower limit of the soil moisture threshold.
## 2 Materials and Methods  
### Raw Materials  
In this study, limestone and sandstone for clinker production were sourced from Nanjing Lishui Tianshan Cement Co., Ltd. (Nanjing, China), while bituminous coal and iron tailings were obtained from Zhejiang Xindu Cement Co., Ltd. (Jiaxing, China). Additionally, analytically pure dihydrate gypsum (CaSO\({}_{4}\):2H\({}_{2}\)O, \(\geq\)99.0%) was from Xilong Chemical Co., Ltd.(Shenzhen, China). The chemical compositions of the milled raw materials were analyzed by X-ray fluorescence (XRF; Spectro, Bruker, Billerica, MA, USA), with the results summarized in Table 1. Mineralogical phases were identified through X-ray diffraction (XRD, Rigaku-2500, Rigaku, Japan) using Cu K\(\alpha\) radiation, and the corresponding diffractograms are shown in Figure 1.  
The biomass alternative fuels, including rice straw (DG), corn cob (YMX), pine sawdust (JM), and sycamore leaves (SY), were sourced from Xuzhou Suining Shangyi New Material Co., Ltd. (Suning, China). while bituminous coal (C) was obtained from Zhejiang Xindu Cement Co., Ltd. (Jiaxing, China). Fuel analysis was conducted according to the Chinese national standard GB/T 28731-2012 [36] to determine the moisture, ash content, volatile  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Materials** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **SO\({}_{3}\)** & **MgO** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **Cl\({}^{-}\)** & **LoI** \\ \hline Limestone & 6.77 & 2.37 & 0.74 & 49.16 & 0.05 & 0.61 & 0.40 & 0.05 & 0.02 & 39.57 \\ Sandstone & 74.23 & 11.81 & 4.63 & 1.27 & 0.16 & 0.54 & 1.82 & 0.13 & 0.03 & 4.18 \\ Iron tailings & 44.99 & 3.91 & 22.53 & 14.80 & 0.25 & 2.99 & 0.45 & 0.15 & 0.04 & 8.49 \\ Gypsum & - & - & - & 45.75 & 30.18 & - & - & - & - & 20.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw materials (wt%).  
Figure 1: XRD diagram of raw materials: (**a**) limestone, (**b**) sandstone, (**c**) iron tailings, (**d**) gypsum.  
matter, and fixed carbon. Total sulfur content was determined in accordance with the Chinese national standard GB/T 28732-2012 [37], while the calorific value was assessed based on GB/T 30727-2014 [38]. The results are presented in Table 2. The XRF component analysis of the aforementioned fuel ash was conducted using the melting method, with the results presented in Table 3.  
The analytically pure anhydrous potassium carbonate (K\({}_{2}\)CO\({}_{3}\), \(\geq\)99.8%) and analytically pure anhydrous sodium carbonate (Na\({}_{2}\)CO\({}_{3}\), \(\geq\)99.8%), used in this study were sourced from Sinopharm Chemical Reagent Co., Ltd. (Ningbo, China).
## 3 Performance of NS RA-Modified Asphalt
### BBR Test  
The low temperature performance of NS RA was evaluated by a bending beam rheometer (BBR) test. The creep stiffness change rate, m, at 60 s, and the creep stiffness S, are the most important two parameters of the BBR test. A larger m-value and a lower S-value correspond to a better stress relaxation ability and deformation ability, respectively. That is, a larger m-value and a lower S-value correspond to a better low-temperature crack resistance of bitumen binder. A maximum S-value of 300 MPa and a minimum m-value of 0.3 f are set by the Superpave binder specifications. NS RA-modified asphalts for Esso AH-70 base asphalt before and after aging and at three different temperatures (\(-10\)\({}^{\circ}\)C, \(-16\)\({}^{\circ}\)C and \(-22\)\({}^{\circ}\)C) were used in the BBR test. According to Superpave specifications, BBR tests were conducted by using a BBR 9728-V30 from the Cannon Company. The test results are shown in Figure 4.  
Figure 4 shows that whether original asphalt or aging asphalt, the m-value decreased, and the S-value increased with a decrease in temperature, from \(-10\)\({}^{\circ}\)C to \(22\)\({}^{\circ}\)C for NS RA-modified asphalts. According to the Superpave specifications, for modified asphalt with different NS RA content (2%-8% by weight), the low-temperature performance grade was \(-16\)\({}^{\circ}\)C. At the same time, the content of NS RA has a great influence on the m-value decrease and the S-value. In detail, whether it was the original asphalt or aging asphalt, the m-value obviously decreased and the S-value increased with an increase of NS RA content. Under the same conditions, the m-value of the NS RA-modified asphalt was lower than that of the base asphalt, and the S-value of the NS RA-modified asphalt was higher than that of the base asphalt. That is, in comparison with the base asphalt, the low-temperature performance of NS RA-modified asphalt slightly declined, and its low-temperature performance becomes weaker with an increase in NS RA content. Therefore, considering the low-temperature cracking resistance, RA-modified asphalt was not appropriate for extremely low-temperature areas.  
Figure 4: The bending beam rheometer test results of NS RA modified asphalt.
## 3 Other Applications
### Environmental Monitoring Other Than Rainfall Estimation
#### 3.1.5 Dynamics of Rainfall and Wind  
Based on a dense CML network, the spatial distribution of rainfall with high spatial and temporal resolution can be theoretically reconstructed [126; 127]. According to the analysis of the spatiotemporal evolution of the rainfall field, the dynamics of precipitation, and thereby the dynamics of wind, can be retrieved [128]. Using a stochastic space-time model based on a rainfall advection and a nonlinear extended Kalman filter, [PERSON] et al. [129] attempted to obtain rainfall dynamics information from CML observations. Subsequently, the wind dynamics can be obtained by calculating the speed and direction of rain cloud movement.
## 2 Methods
### Experimental Setup
#### 2.3.1 Data Preprocessing
As observed in Figure 3, certain data points (the second green data point) exhibit localized deviations from the overall trend. These deviations primarily originate from experimental uncertainties in the original dataset, such as sensor accuracy, material property variations, and environmental influences. These uncertainties propagate through the stiffness degradation model parameter fitting and the damage data conversion process, gradually accumulating during numerical optimization, which ultimately results in localized data point shifts. However, from an overall perspective, the damage evolution and stiffness degradation curves under different loading levels exhibit expected trends, which are largely consistent with the findings of previous studies. Therefore, despite the presence of localized measurement errors in individual data points, they do not affect the overall degradation trend or compromise the conclusions of this study. The purpose of \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Loading Level**} & \multicolumn{5}{c}{**Percent of Life (\(u\)/\(N\))**} \\ \cline{2-7}  & **0** & **0.145** & **0.250** & **0.315** & **0.750** & **1** \\ \hline
20\% & 1 & 0.9887 & 0.9847 & 0.9821 & 0.9729 & 0.9568 \\
40\% & 1 & 0.9829 & 0.9800 & 0.9782 & 0.9675 & 0.9526 \\
60\% & 1 & 0.9831 & 0.9795 & 0.9779 & 0.9649 & 0.9488 \\
80\% & 1 & 0.9830 & 0.9891 & 0.9767 & 0.9628 & 0.9470 \\
100\% & 1 & 0.9826 & 0.9788 & 0.9771 & 0.9616 & 0.9421 \\ \hline \end{tabular}
\end{table} 
Table 2: Blade stiffness under different fatigue life ratios. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Loading Level**} \\ \cline{2-5}  & **20\%** & **40\%** & **60\%** & **80\%** & **100\%** \\ \hline \(a\) & 0.42466 & 0.42265 & 0.45750 & 0.57364 & 0.45208 \\ \(b\) & 0.11875 & 0.08192 & 0.11092 & 0.21731 & 0.12594 \\ \(E_{f}\) & 0.9568 & 0.9526 & 0.9488 & 0.9470 & 0.9421 \\ \hline \end{tabular}
\end{table} 
Table 3: Fitted parameters of the stiffness degradation equation under five loading levels. selecting [PERSON]'s model is to fit the experimental data, generating additional data points to expand the dataset. On the basis of the experimental results, five original datasets--\(E_{20\%}(n)\), \(E_{40\%}(n)\), \(E_{60\%(n)}\), \(E_{80\%}(n)\), and \(E_{100\%}(n)\)--were constructed.
## 3 Experiment and Results  
The improved semi-supervised seizure prediction model proposed in this study was implemented and tested using the TensorFlow framework. Training was performed using an NVIDIA GeForce RTX 4090 graphics processing unit (GPU) with 64 GB of memory. The proposed prediction model was trained in two steps: first, unsupervised feature model training, where the feature learner trained through unsupervised learning was combined with a classification network to form a classification (prediction) model for supervised training.