esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 4 Simulation Results
### Quantitative Performance Analysis and Discussion
Furthermore, it is important to connect the theoretical convergence to a \"small neighborhood,\" as proven in Theorem 1, with its practical size. The size of the final error bound, given by \(\lim_{k\to\infty}\|\mathbf{e}(t+1,k)\|=P/(1-\rho)h_{1}\), is directly proportional to the term \(h_{1}\), which encapsulates the magnitude of disturbances, including the sensor noise \(w_{i}(t,k)\). This theoretical relationship implies that higher noise levels will lead to a larger steady-state voltage deviation. Our existing quantitative analysis provides a concrete example of this. The robustness test reported in Table 3 was conducted with a low-level white Gaussian noise, resulting in a practical error fluctuation of approximately \(\pm\)0.15 V. This value represents the practical size of the convergence neighborhood under typical, low-noise conditions. While the size of this neighborhood would increase with higher noise levels, the adaptive mechanism of the DAILC is designed to actively suppress these disturbances, ensuring the deviation remains within a small and acceptable range, thereby validating the practical robustness of our approach. Beyond the PI controller, it is crucial to position our DAILC method relative to other advanced control strategies. Approaches like feedback linearization [17] and finite-time control [21] offer high performance but fundamentally rely on accurate system models. In real-world microgrids with high penetration of diverse and plug-and-play renewable sources, obtaining precise models is often impractical. Our DAILC method circumvents this challenge by being entirely data-driven, thus offering greater robustness to parameter uncertainties and system heterogeneity. Techniques using neural networks [26] or deep reinforcement learning [27] also handle nonlinearities well. However, they typically require extensive offline training datasets, significant computational resources for the training process, and their \"black-box\" nature can make stability verification challenging. In contrast, our DAILC is an online adaptive method that is computationally lighter and comes with a formal proof of convergence, making it more suitable for real-time implementation with lower-cost hardware. In conclusion, the DAILC method strikes a balance between performance, robustness, and implementation simplicity. It avoids the rigid model dependency of classical methods and the heavy computational/data requirements of complex AI techniques, positioning it as a practical and effective solution for modern, heterogeneous AC microgrids.
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 1 Introduction
In order to overcome this drawback, in this paper, we combine the KFC with the HS algorithm to help the KFC perform better. Although the modifications of HS have shown a better performance than the classical HS, the performances still need improvement. In this paper, we proposed a new differential harmony search algorithm (DHS), and applied it to kernel fuzzy clustering. A comparison of the proposed method with other methods has been carried out. Finally, the proposed method is applied to the water diversion scheduling assessment in East Lake, which is a new study in the East Lake Ecological Water Network Project. The water diversion scheduling tries to divert water from the Yangtze River to the East Lake Network, aiming to improve water quality in the sub-lakes. Using a hydrodynamic simulation model and a water quality model, the quality of the lakes at the end of the water diversion can be simulated. In order to obtain a better improvement of the water quality and reduce the economic cost, the water diversion scheme must be carefully developed. The diversion scheduling in the East Lake Network is a multi-objective problem, however, multi-objective evolutionary algorithms cannot be adopted because the water quality simulation is time-consuming. Thus, we made some typical schemes in the feasible range, and obtain the scheme results by the water quality model. The purpose of the kernel clustering with differential harmony search method is to classify the results in order to find out the schemes which perform better than others. This paper is organized as follows: Section 2 presents an overview of the harmony search algorithm and kernel fuzzy clustering; Section 3 describes the modification and the combination of kernel fuzzy clustering and the differential harmony search algorithm; Section 4 discusses the computational results; and Section 5 provides the summary of this paper.
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 2 Materials Furthermore, Methods
### Definition of the Coordinate System  
Figure 3 depicts the coordinate system defined for imaging device calibration. The origin of the world coordinate system is defined as being in the center of the detector, and the X-ray source is aligned with the z-axis of the system. This means that the x and y of the centers of the X-ray source and detector are the same and have different heights (z coordinate), a choice that avoids computational complexity and burden. The CT system rotates in the y-axis direction about the rotational center, which is commonly referred to as the isocenter.  
Figure 1: Calibration phantom model (**A**) was designed using CAD software, and the phantom (**B**) was built with a 3D printer, with lead-bearing fiducial markers to establish precise locations.  
Figure 2: Two-dimensional drawings of the top and bottom layers of the phantom. The brackets represent the 3D coordinates of the center of each ball bearing, in mm. (**A**) is the lower layer with 2 mm diameter ball bearings, and (**B**) is the upper layer with 4 mm ball bearings.  
Equation (1) defines the position of the upper and lower layers in the calibration phantom, position of the X-ray source, center of the X-ray detector, and isocenter in the world coordinate system. These represent the 3D information regarding the imaging device. The center of the X-ray detector is the origin of the world coordinate system.  
\[\begin{split} P^{u}&=(P^{u}_{x},P^{u}_{y},P^{u}_{z}) :\text{Upper of Phantom}\\ P^{l}&=(P^{l}_{x},P^{l}_{y},P^{l}_{z}):\text{Lower of Phantom}\\ S&=(S_{x},S_{y},S_{z}):\text{Position of X-ray Source}\\ D&=(D_{x},D_{y},D_{z})=(0,0,0):\text{Center of X-ray Detector}\\ I&=(I_{x},I_{y},I_{z}):\text{Isocenter}\end{split} \tag{1}\]  
Equation (2) presents the coordinates of the projected point on the upper and lower layers of the phantom, which give the 2D information in the X-ray image.  
\[\begin{split} P^{\prime u}&=(P^{\prime u}_{x},P^{ \prime u}_{y},P^{\prime u}_{z})\\ P^{\prime l}&=(P^{\prime l}_{x},P^{l}_{y},P^{l}_{z}) \end{split} \tag{2}\]  
where, \(P^{\prime u}_{z}=P^{\prime l}_{z}=D_{z}=0\)
## 4 Conclusions  
Compressor aerodynamic parameters and the blade fillet have shown strong influences on the corner separation and also on the cascade performance for a certain range of incidence angles. Based on the previous work, the slot configuration on the reference PVD cascade can decrease the total pressure loss and blockage in the passage and then improve the cascade performance. The slot configuration, combined with the aerodynamic parameters and fillet which may influence the corner separation, have been studied to determine how cascade performance would be affected by these parameters. The following are a few findings of this research work:  
1. High pitch-chord ratio blades are inclined to stall with the increase of the loading for each blade. With the slot configuration, the overall performance of the high pitch-chord ratio cascade is almost the same with reference to low pitch-chord ratio by reducing the corner separation in the cascade. Therefore, a slot can be used to decrease the number of blades to improve the thrust-weight ratio and to avoid large separation caused by the high loading of the blade.
2. High aspect ratio blades are also inclined to stall. For the low aspect ratio, flow becomes three-dimensional almost over the entire blade span and the influence of the corner separation plays a more important role in the flow passage. For all of the aspect ratios studied, slot configuration still has a positive effect on the corner separation and the overall performance of the cascade has been improved.
3. For the PVD cascade investigated in this study, flow near the endwall has shown some improvement, while a radical shift of the results is also evident. For the blade with fillet more studies are required to be carried out to understand the relationship between blade fillet, slot configuration, and the corner separation.  
This work is supported by the National Natural Science Foundation of China (No. 51676007, No. 51376001, No. 51420105008, No. 51136003), and the National Basic Research Program of China (2014 CB046405). [PERSON] was supported by the China Scholarship Council (CSC). Theauthors would like to thank Whittle Laboratory and Rolls-Royce Plc for providing their experimental results.  
This paper is a result of the collaboration of all authors. All authors have previous experience on flow control and numerical simulation that have been shared in order to reach the results discussed in the paper. [PERSON] and [PERSON] meditated this paper. [PERSON] has done the validation of the turbulence model and grid independence. [PERSON] has supervised the research work. All authors discussed the results and implications and commented on the manuscript at all stages.  
The authors declare no conflict of interest.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Visual-Based Modality
#### 3.1.4 Location-Based  
In a recent paper, [PERSON] et al. [11] demonstrated an application prototype that constitutes a continuation of the research proposal development called Mergin'Mode [115]. Their demonstration used the _QR code recognition_ method to locate a user in the real world and serve virtual objects presented in an MR environment. In addition, QR codes contain information regarding the user's orientation and which content should be delivered from several predefined virtual worlds created to promote cultural heritage. Each observation point (the stations where the QR codes are located) exposed specific interactions in selecting a virtual agent to obtain information. In [116], [PERSON] et al. used the image registration method to determine the user's position and serve virtual content. The technique they describe needs a sufficient number of calibrated reference images stored in a database and requires a continuous internet connection. However, this method can provide the user's position accurately, thus improving the interaction experience.
## 5 Results and Discussions
### Impact on Cache Hit Ratio  
Figure 8 shows that as the cache size increases from 10 MB to 500 MB, the cache hit ratio also increases. For smaller cache sizes such as 10 MB, the node has limited storage for caching the content; thus, the performance of all the approaches are enhanced slightly. CEE follows a strategy of caching non-popular content; hence it achieved a smaller hit ratio of 10% while Random Capacity \(p\) = 0.9 achieved a 26% cache hit ratio, which is 16% better than CEE. The better performance of Random Capacity \(p\) = 0.9 is its capability of caching the popular contents with a probability of 0.5. As the cache size increases from 10 MB to 210 MB, the storage capacity of the node increases; hence the overall performance of all the approaches also increases. In Figure 8 it can be observed that, at cache size of 210 MB, the cache hit ration of CE, \(p\) = 0.9, \(p\) = 0.5 and \(SOCC\) increases to 35%, 45%, 53% and 61%, respectively. At a cache size of 500 MB, \(SOCC\) outperformed other schemes by achieving the highest hit ratio of 65% while CEE, \(p\) = 0.9, \(p\) = 0.5 achieved hit ratios of 25%, 15% and 6%, respectively.  
Figure 7: Average latency vs. Cache size.
## 4 Case Study
### Working Mode Identification and Parameter Selection  
#### 4.1.1 Working Mode  
Because different working modes have a great influence on system parameters, it was necessary to store the state parameters according to the working modes and to establish prediction models according to different modes. The working mode of FOSS usually has a judgment mark (by three-way valve), so an algorithm was not needed to achieve pattern recognition in this study. The system had two working modes: a diesel oil mode and a heavy oil mode. Because the system only worked in diesel oil mode for a short time, this mode had little impact on system performance. In the whole lifecycle of the system the heavy oil mode was dominant, and the diesel oil mode was equivalent to the local fluctuation of the system, so only the data from the heavy oil mode were used to produce the health-condition prediction.
# YOLOv4-MN3 for PCB Surface Defect Detection
## 5 Conclusions  
In order to improve the performance of PCB surface defect detection, a DL-based detector, YOLOv4-MN3, was proposed. YOLOv4-MN3 replaces the CSPDarknet53 backbone network of YOLOv4 with a lightweight one, MobileNetV3, and the original activation function in the neck/prediction network was optimized. To verify the efficiency and the effectiveness the proposed detector, a customized dataset was built using a specially designed image-acquisition device. Experimental results on the customized dataset showed that YOLOv4-MN3 achieved the highest detection accuracy, with 98.64% \(mAP\) and 97.83% \(F_{1}\), the fastest detection speed, at 56.98 _FPS_, and the lowest _Madds_ as compared with the SOTA models. Generalization experiments of YOLOv4-MN3 indicated that it can adapt to different categories of surface defect and handle the difficult problem of defect morphology diversity, which has promising application prospects.  
Although the detector proposed in this paper achieved good results, there are some problems to explore. Firstly, PCB surfaces' backgrounds, defect categories and morphology diversity, in real industry, are complex and diverse. Thus, it is necessary to update PCB surface defect samples continuously and facilitate the training of practicability-oriented DL-based models. Secondly, YOLOv4-MN3, as a supervised learning method, requires a large amount of manually labeled samples, and exploring semi-supervised or unsupervised mechanism for this problem is a worthy attempt.  
Conceptualization, X.L. and S.L.; methodology, X.L. and S.L.; software, X.L. and D.L.; validation, X.L. and Y.L.; formal analysis, X.L. and Z.Z.; investigation, X.L. and D.L.; re-sources, X.L. and D.L.; data curation, C.J. and Y.L.; writing--original draft preparation, X.L. and S.L.; writing--review and editing, X.L. and S.L.; visualization, X.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Natural Science Foundation of Guangdong, China with grant number 2021A1515012395.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors wish to thank the editor and reviewers for their suggestions.  
The authors declare no conflict of interest.
# A Multimodal Recurrent Model for Driver Distraction Detection
## 2 Materials and Methods
### Model Architecture
#### 2.4.1 Vision-Based Approach with a CNN  
The first phase involved training a CNN model for driver distraction detection using visual information, which served as a baseline for further improvements using LSTM networks and sensor data. The chosen architecture was _VGG16_[18], processing images in the dimensions \(224\times 224\times 3\). The input passes through five blocks of convolutional layers, each followed by max-pooling layers. The network ends with three fully connected layers, the last of which uses a sigmoid activation function, while the others use _ReLU_. _VGG16_, despite its high parameter count (\(\approx\)138 million), has shown effectiveness in previous research on driver distraction detection [7; 12; 13; 19].  
Figure 4 illustrates the _CNN_ model's architecture. After the convolutional layers, a _GlobalAveragePooling2D_ layer replaces the typical flattening layer to reduce parameters and improve training efficiency [20]. The model then employs two fully connected layers with _ReLU_ activation and dropout layers for regularization [21]. The final layer uses a _softmax_ activation function to output probabilities for five distraction classes.  
To counteract overfitting, data augmentation was applied using random translations, rotations, zooms, and contrast adjustments during training. This approach helps the model generalize better by introducing variations in the input images, addressing issues like differing driver seating positions [21].  
Given the large number of layers and weights in _VGG16_, transfer learning was employed. _VGG16_ weights pre-trained on the ImageNet dataset were used, with only the final convolutional layer fine-tuned for this task, resulting in a model with \(\approx\)15 million parameters, of which \(\approx\)2.7 million were trainable.
# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## 5 Conclusions  
Here, we proposed an MKSVR approach for FFA estimation during grain storage. To validate the performance of MKSVR, ANN, MLR, and SKSVR were also applied to the FFA estimation. The performance of models was assessed by MAE, RMSE, and MAPE. All of the experiments were conducted using real data. The experimental results showed that the proposed MKSVR model outperformed the other models when estimating FFA content in stored grain. The smallest mean value of MAE was 0.341 mg KOH/100 g, the smallest RMSE was 0.442 mg KOH/100 g, and the smallest MAPE was 2.026%. The proposed MKSVR method integrates linear and nonlinear relations between FFA content and storage factors, effectively improving the predictive power and robustness of the model. Specifically, it provides technical support for the precise control of the stored grain quality and also plays a certain role in the management of grain storage.  
In further study, there is a need to formulate a model that is more practical to suit the full range of grain types and qualities. This requires a larger number of samples and a greater number of independent variables in the analysis. We will collect more grain storage data, such as weather conditions, aeration, and humidity, and increase the number of input feature parameters of the model to improve its accuracy. We will analyze the influence of the collected characteristic parameters on fatty acid accumulation, then select characteristic parameters with a significant influence to retrain the model to try to improve the models.  
Conceptualization, Q.W. and W.W.; Data curation, Q.W. and T.L.; Funding acquisition, F.H.; Investigation, Q.W., F.H. and T.L.; Methodology, Q.W.; Software, Q.W.; Supervision, F.H., Z.W. and W.W.; Validation, F.H., Z.W. and W.W.; Writing--original draft, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the People's Republic of China (2017 YFD0401003-3).  
The authors would like to thank all of the grain reserve depots for providing the storage data for this study.  
The authors declare no conflict of interest.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 3 Trajectory Planning
### Joint Space Trajectory Planning  
The fifth-order polynomial for interpolation was used as shown in Equation (5) to plan the PM trajectory, ensure the smooth movement of each joint, and prevent the sudden change in acceleration.  
\[\left\{\begin{aligned} s(t)&=a_{0}+a_{1}t+a_{2}t^{2}+ a_{3}t^{3}+a_{4}t^{4}+a_{5}t^{5}\\ v(t)&=a_{1}+2a_{2}t+3a_{3}t^{2}+4a_{4}t^{3}+5a_{5}t^{4}\\ a(t)&=2a_{2}+6a_{3}t+12a_{4}t^{2}+20a_{5}t^{3}\end{aligned}\right. \tag{5}\]  
where \(s\), \(v\), and \(a\) represent the path length, velocity, and acceleration, respectively; \(a_{0},\ a_{1},\ \ldots,\ \ \text{and}\ a_{5}\) are the undetermined coefficients of the polynomial; \(t\) is the time.  
The following boundary constraints are specified to ensure the smooth operation of the PM. Equation (6) is the specified boundary constraint condition:  
\[\begin{aligned} s(0)=0,v(0)=0,a(0)=0\\ s\Big{(}t_{f}\Big{)}=s_{f},v\Big{(}t_{f}\Big{)}=0,a\Big{(}t_{f} \Big{)}=0\end{aligned} \tag{6}\]  
where \(t_{f}\) represents the terminal time of the trajectory and \(s_{f}\) is the total length of the trajectory planning path.  
Undetermined coefficients can be solved by substituting the above boundary conditions. The trajectory planning Equation (7) can be obtained as:  
\[\left\{\begin{aligned} s(t)&=\frac{10s_{f}}{t_{f}^{3}} t^{3}-\frac{15s_{f}}{t_{f}^{4}}t^{4}+\frac{6s_{f}}{t_{f}^{5}}t^{5}\\ v(t)&=\frac{30s_{f}}{t_{f}^{3}}t^{2}-\frac{60s_{f}}{t_{f}^ {4}}t^{3}+\frac{30s_{f}}{t_{f}^{5}}t^{4}\\ a(t)&=\frac{60s_{f}}{t_{f}^{3}}t-\frac{180s_{f}}{t_{f}^ {4}}t^{2}+\frac{120s_{f}}{t_{f}^{5}}t^{3}\end{aligned}\right. \tag{7}\]
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 2 Materials and Methods
### Test Methods  
The overlay welded parts were cut by water-jet cutting equipment, and the samples were characterized by different methods. The macro- and microstructure were studied by microscopic investigations after surface preparation. For the macrostructure analysis, a Zeiss Stereo Discovery V20 microscope (Zeiss, Gottingen, Germany) was used, while for microstructure characterization, we applied a Zeiss Axio Imager A1 optical microscope (Zeiss, Gottingen, Germany).  
The rough preparation of the samples was done using SiC grinding papers (STARCKE, Melle, Germany) of different grits (80, 180, 400, 600, 1000, 1200, and 2500), and polishing was performed with MD-Dac and MD-Chem polishing pastes (Struers ApS, Ballerup, Denmark). The etching was carried out with 6% nital and ammonium molybdate.  
For a detailed analysis of chemical composition, mainly the Cr and Ni content of austenite and \(\delta\)-ferrite, we used a HITACHI 3400 (Hitachi, Tokyo, Japan) for scanning electron microscopy (SEM), as well as energy-dispersive X-ray analysis (EDX).  
The hardness measurement was performed according to the ISO 15614-7 standard [21] at room temperature, using the Vickers method based on the ISO 6507-1 standard [22] with a KB 30-type hardness tester (KB Pruftechnik, Hochdorf-Assenheim, Germany). The load applied was F = 98.1 N (10 kp).  
For characterization of the corrosion susceptibility of the welded part, a salt spray test was performed using an Ascott CC1000 iP device (Ascott, Tamworth, UK) according to the ISO 9227 NSS standard [23]. The relevant testing parameters were as follows:  
* Test duration: 336 h (2 weeks).
* Test chamber temperature: 35 \({}^{\circ}\)C.
* Corrosive medium: 5% salt solution (deionized water + salt); the salt quality was Ascott Corro salt.
* pH of the solution: 7.05; pH of the runoff solution: 7.00; average runoff rate: 1.50 mL/h.