esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Pointing Strategy and Model Establishment
### Disturbance Analysis and Descriptions
#### 2.3.4 Other Noise  
In addition to the aforementioned primary disturbance noises, the telescope pointing system is also subject to other noise influences: (1) actuator drive noise, which is the torque noise generated by the piezoelectric actuators in the telescope pointing mechanism [11], as illustrated in Figure 7a; (2) sensor noise. To achieve angle measurements at the nanoradian level, we utilized differential wavefront sensing (DWS) for angular measurements [23], with the DWS signal serving as the feedback control signal for the telescope pointing system. Figure 7b presents the DWS measurement noise [21].  
Based on the above analysis, the telescope pointing system is subject to a complex and numerous set of disturbances, and these disturbance parameters may change continuously with varying operational conditions. Consequently, relying on model parameter identification to eliminate or reduce the impact of multiple disturbances on the telescope pointing system may not be an optimal choice. Moreover, the disturbances affecting the system span different frequency bands, posing a significant challenge for controller design. Traditional anti-disturbance methods are primarily designed for single disturbance sources and are less effective in attenuating and suppressing disturbances across multiple frequency bands. In this paper, a high-performance controller is designed to ensure that the pointing stability of the system meets the requirements of the TianQin mission when compensating for changes in the breathing angle by the telescope pointing mechanism.
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
## 2 Preliminaries for Non-Binary Polar Codes  
### Notation and Conventions  
Throughout this paper, matrices and vectors are represented by upper-case and lower-case letters in bold, respectively. An \(N\)-tuple \(\mathbf{u}\) is denoted as \(\mathbf{u}=[u_{1},u_{2},\ldots,u_{N}]\). The notation \(u_{a}^{b}\) means the sub-vector \([u_{a},u_{a+1},\ldots,u_{b}]\) if \(b\geq a\) and a null vector otherwise. The calligraphic letter \(\mathcal{A}\) is used to denote sets, and the complement of set \(\mathcal{A}\) is \(\mathcal{A}^{c}\). The cardinality of \(\mathcal{A}\) is denoted as \(|\mathcal{A}|\). In this paper, we consider the Galois fields of characteristic 2, i.e., GF(2\({}^{q}\)), where \(q\) is a positive integer.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 1 Introduction
### Research Gap  
It is worth noting that the methods mentioned above may cause some unexpected issues. For example, undersampling techniques may ignore some valuable data, which could be vital for training a classifier. In contrast, oversampling algorithms may cause overfitting. Furthermore, for cost-sensitive learning techniques, it is not straightforward to determine the exact misclassification cost, and different misclassification costs might result in different induced outcomes. Moreover, Bagging and Boosting algorithms may exclude some valuable data while they propose sampling methods in every single iteration, and they may face an overfitting problem. Consequently, the classification results obtained by these methods are not stable.
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 5 Parameter Refinement Phase
### Weights and Biases  
Next, we describe how to refine weights and biases associated with the output layer in each iteration. For pattern \(i\), \(1\leq i\leq N\), by Equation (13), we want  
\[y_{1}^{(i)} =w_{1,1}(k+1)h_{1}^{(i)}(k+1)+\cdots+w_{1,J}(k+1)h_{J}^{(i)}(k+1)+ b_{1}(b+1)\] \[y_{2}^{(i)} =w_{2,1}(k+1)h_{1}^{(i)}(k+1)+\cdots+w_{2,J}(k+1)h_{J}^{(i)}(k+1)+ b_{2}(b+1)\] \[\cdots=\cdots\] \[y_{m}^{(i)} =w_{m,1}(k+1)h_{1}^{(i)}(k+1)+\cdots+w_{m,J}(k+1)h_{J}^{(i)}(k+1)+ b_{m}(b+1)\]  
where  
\[h_{j}^{(i)}(k+1)=\prod_{r=1}^{n}e^{-(\frac{v_{r}^{(i)}-v_{r,j}(k+1)}{w_{r,j}( k+1)})^{2}} \tag{49}\]  
for \(1\leq j\leq J\) and \(1\leq i\leq N\). Note that \(c_{r,j}(k+1)\) and \(v_{r,j}(k+1)\) are the newly updated values obtained in the first part of the current iteration. Let  
\[\mathbf{A}(k+1)=\left[\begin{array}{ccccc}h_{1}^{(1)}(k+1)&h_{1}^{(2)}(k+1) &\cdots&h_{1}^{(N)}(k+1)\\ h_{2}^{(1)}(k+1)&h_{2}^{(2)}(k+1)&\cdots&h_{2}^{(N)}(k+1)\\ \vdots&\vdots&\ddots&\vdots\\ h_{J}^{(1)}(k+1)&h_{J}^{(2)}(k+1)&\cdots&h_{J}^{(N)}(k+1)\\ 1&1&\cdots&1\end{array}\right] \tag{50}\]  
and  
\[\mathbf{W}(k+1)=\left[\begin{array}{ccccc}w_{1,1}(k+1)&w_{1,2}(k+1)&\cdots &w_{1,J}(k+1)&b_{1}(k+1)\\ w_{2,1}(k+1)&w_{2,2}(k+1)&\cdots&w_{2,J}(k+1)&b_{2}(k+1)\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ w_{m,1}(k+1)&w_{m,2}(k+1)&\cdots&w_{m,J}(k+1)&b_{m}(k+1)\end{array}\right]^{T} \tag{51}\]  
and \(\mathbf{Y}\) be the same matrix as shown in Equation (18). As before, we'd like to minimize  
\[\left\|\mathbf{A}(k+1)\mathbf{W}(k+1)-\mathbf{Y}\right\|^{2}+\lambda\parallel \mathbf{W}(k+1)\parallel^{2} \tag{52}\]  
The solution is \(\bigl{\{}\mathbf{A}^{T}(k+1)\mathbf{A}(k+1)+\lambda\mathbf{I}\bigr{\}}^{-1} \mathbf{A}^{T}(k+1)\mathbf{Y}\) which provides the new values of the weights and biases, i.e., \(w_{f,j}(k+1)\) and \(b_{f}(k+1)\), \(1\leq j\leq J\), \(1\leq f\leq m\).
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 5 Conclusions  
The paper has proposed a collision avoidance control method for the autonomous navigation of a USV in complex and real environments. In previous research, the CAS was divided into path planning and a control system. However, in practical applications, this structure will cause a delay of collision avoidance due to information asynchrony, which is very serious in the problem of real-time collision avoidance. Therefore, it is very necessary to consider the integration of path planning and the control system into one system. To solve the problem, the theory of FCS-MPC is introduced into the CAS of the USV. The proposed CAS has some advantages such as a concise form, rapidity and easy handling of system constraints. In addition, the finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV, which is more in accordance with the engineering requirements. The simulation experiments verify the computational speed and security of the proposed CAS in complex real scenarios with multiple dynamic obstacles.  
In the application prospects, this research would provide more reliable CAS for the automatic sailing of a USV. In the next step, the practicability of CAS can be further increased for a real ship experiment. First, the environmental model directly adopts the actual shape of the obstacles, without assuming an ideal shape. Second, considering that the sea environment is complicated and changeable, it is very necessary to consider collision avoidance under the sea environmental interference. Thirdly, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) should be followed by CAS.  
**Author Contributions:** The work presented here was performed in collaboration among all authors. [PERSON] designed, analyzed and wrote the paper. [PERSON] guided the full text. [PERSON] conceived of the idea. [PERSON] and [PERSON] analyzed the data. All authors have contributed to and given approval of the manuscript.  
**Funding:** This research was funded by the Nature Science Foundation of China (Grant Number 51609033), the Nature Science Foundation of Liaoning Province of China (Grant Number 2015020022) and the Fundamental Research Funds for the Central Universities (Grant Numbers 3132014321, 3132016312 and 3132017133).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: Evaluation results in a real environment. (**a**) Speed of the USV; (**b**) course of the USV; (**c**) thruster speed; (**d**) command propulsion angle; (**e**) computation time; (**f**) distance between the USV and the collision area.
###### Abstract  
As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional-integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section.  
cyber attack; wind turbine generator; artificial intelligence; mitigation; converter Article
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 2 Materials and Methods  
### MicroPET, Reconstruction, and Post-Filtering  
The PET images were obtained using a microPET scanner (Inveon, Siemens Medical Solutions, Malvern, PA, USA) with 20 \(\times\) 20 lutetium oxyorthosilicate (LSO) crystals (each measuring 1.5 \(\times\) 1.5 \(\times\) 10 mm\({}^{3}\)). The system comprised 64 detector blocks arranged in four contiguous rings with a diameter of 16.1 cm, a transaxial field of view (FOV) of 10 cm, and an axial FOV of 12.7 cm. Acquired data were sorted into 3D sinograms or directly into 2D sinograms. The 3D sinograms were then rebinned using FORE algorithm. The acquired 3D sinograms were reconstructed in 2D using the FBP and OSEM2D algorithms and in 3D using the OSEM3D and 3 DRP algorithms.  
PET data were reconstructed using FBP, 2 and 3D OSEM (OSEM2D and OSEM3D, respectively), and the 3 DRP algorithm. In the FBP and OSEM reconstruction methods, a projection filter was fixed to a Ramp filter (cutoff at Nyquist frequency). A Hann filter (projection filter) was used only in the 3 DRP reconstruction method. In the OSEM reconstruction methods, subsets were fixed to 16, and iteration numbers were 4, 6, and 8. The projection cutoff (0.5) was equally applied to all reconstruction methods. We measured the average reconstruction time per frame. The reconstructed images were filtered using a Gaussian smooth 3D filter. The three Gaussian smooth 3D filters were set on a full width at half maximum (FWHM) sizes of 1, 2, and 4 mm. A total 36 images were generated as FBP, OSEM2D_I4, OSEM2D_I6, OSEM2D_I8, OSEM3D_I4, OSEM3D_I6, OSEM3D_I8, 3 DRP_non, and 3 DRP_Hann (multiplied by the four Gaussian filters).
## 4 Experiments and Analysis of Results
### Evaluation Metrics  
The performance of the MGMI-CEHCN and baseline models was evaluated using precision and mean reciprocal rank as the two evaluation metrics.  
P@K is a metric for evaluating the accuracy of a recommender system and measures the number of correctly recommended items in the top K items in the recommendation ranking list as a percentage of the total sample size. The formula is calculated as  
\[P@K=\frac{n_{hit}}{n} \tag{26}\]  
where \(n_{hit}\) denotes the number of correctly recommended items in the top K items in the recommendation ranking list and n is the total sample size.  
MRR@K is a metric used to evaluate the accuracy of a recommender system, which considers the ranking of the recommended items in the sample. A larger value of this metric indicates that the correctly recommended item was placed higher in the ranked list. The formula is calculated as  
\[MRR@K=\frac{1}{n}\sum_{i\in M}\frac{1}{rank_{i}} \tag{27}\]  
where M denotes the sample set of correctly recommended items among the first K recommended items; \(rank_{i}\) represents the rank of item i in the recommended list.  
In this work, we report results for K = 10, 20.
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 8 Application Case Study: A Medium-Voltage PCSS Breaker
### Cascaded SiC JFET Switch Topology  
Figure 17a presents the circuit design of a 6 kV high-voltage (HV) switch, assembled through a series arrangement of eight 1.2 kV SiC JFET switches from UnitedSiC, UJ3N1200035K3S [94]. Seven avalanche diodes, \(D_{1}\) to \(D_{7}\), are strategically placed between the gates of consecutive JFETs to safeguard the transistors against overvoltage incidents. Furthermore, capacitors \(C_{1}\) to \(C_{8}\) are integrated between the gates of adjacent transistors, aimed at ensuring dynamic voltage balance across the drain and source terminals of the transistors. The circuit also includes two resistor branches connected between the gates of two JFETs that are sequentially separated by another JFET, with an offset of one JFET position between the two branches. These resistors are crucial for maintaining static voltage  
Figure 16: Idealized voltage and current timing diagram for different elements of the circuit breaker. (Colors serve to visually differentiate the elements of the schematic).  
Figure 15: Conceptual breaker diagram with source and load. (Colors serve to visually differentiate the elements of the schematic).  
balance across the drain and source terminals of each JFET device, enhancing the overall stability and reliability of the HV switch. Comprehensive details on the design process for each component of the proposed SSCB are documented in references [88,95].  
The steady-state voltage balancing of the cascaded JFETs, across a broad spectrum of blocking voltages ranging from 500 V to 6 kV, has been assessed by measuring the drain-to-source voltage (\(V_{ds}\)) on each device. This evaluation involved comparing the experimental outcomes with the ideal scenario, which is depicted by a dashed line in Figure 17b. The comparison reveals that there is a uniform distribution of the bus voltage across the JFETs, with only slight deviations observed. Figure 17c illustrates the dynamic voltage balancing of the proposed cascaded switch during the turn-off transition at 6 kV, with a resistive load of 400 \(\Omega\) connected in series. The waveforms showcased indicate uniform voltage distributions across the drain-to-source terminals of the JFETs throughout the switching transitions.
# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
## 3 The Improved GCS-YOLO Object Detection Model
### Introduction of CBAM  
Convolution operations may accumulate redundant background information, leading to a decrease in the target attention and affecting detection accuracy. To enhance the model's focus on key regions, we introduce the Convolutional Block Attention Module (CBAM) at the feature output layer of the backbone network. As shown in Figure 8, CBAM is an attention mechanism that combines spatial and channel attention to aggregate local information from feature maps. The channel attention module and the spatial attention module are two independent sub-modules of CBAM. This dual-module structure cleverly considers both channel and spatial dimensions, which aids the network in obtaining precise location and detailed information about the target region.  
The channel attention module performs global max-pooling and global average-pooling on the input feature map and then feeds the pooled results into a Multi-Layer Perceptron (MLP). The outputs are summed and passed through a Sigmoid activation function to generate the channel attention weights. The MLP and weighted summation help the model adaptively determine the contribution of each pooling method.  
The spatial attention module pools the input feature map to generate two feature maps, which are then concatenated into a single feature map via a \(7\times 7\) convolution and processed through a Sigmoid activation function to produce the final spatial attention weights.  
The feature map is processed by the channel attention weights and spatial attention weights, removing irrelevant information through pixel-wise processing, which enables the model to focus more on the detection target information.
## 3 Methodology
### Data Pre-Processing
#### 3.3.1 Data Denoising  
To enhance the signal quality and minimize the impact of noise sources, a denoising filter is applied to the raw multi-channel time-series data. This study employs a sixth-order zero-phase Butterworth infinite impulse response (IIR) bandpass filter. For the accelerometer and gyroscope measurements, cutoff frequencies of 20 Hz and 1 Hz are set for the low-pass and high-pass filters, respectively. To accommodate the wider frequency range of EMG signals, a band ranging from 10 to 400 Hz is utilized.
# Blasting of Unstable Rock Elements on Steep Slopes
## 4 Discussion
The proposed innovative PF formula provides a simple and robust tool for optimizing blasting designs, allowing engineers to tailor interventions based on site-specific conditions and operational goals. By integrating real-world validations, the method offers actionable guidelines that can streamline blasting designs while ensuring safety and cost-effectiveness. Through an in-depth analysis of various case studies, this study aimed to establish control parameters, particularly the powder factor (PF), for optimizing blasting schemes on rocky slopes. The PF serves as a synthetic parameter indicative of blasting efficiency, influenced by factors such as rock type, explosive properties, blast design, and desired fragmentation, eventually taking into account the stress or load level, as rock breakage by blasting is directly proportional to the exposed rock's stress level and pre-blasting conditions [28]; on this topic, comparison with other approaches could be applied for cross-validation, namely, after fragmentation determination by image analysis and after constraints are selected, when they are used in optimization control of the performance [29]. While traditional blasting formulas exist for tunnel and open-air bench blasting, specific criteria for blasting single elements on slopes are lacking. The proposed formula for PF calculation incorporates parameters such as hole spacing, rock and explosive characteristics, desired fragmentation, and material displacement. Real-world case analyses revealed a correlation between calculated and real PF values, with exceptions attributed to anomalous conditions. Normalized PF values showed an increasing trend with the ratio of spacing to drilling diameter. Despite the effectiveness of explosives in slope stabilization, challenges persist, including the variability of rock properties and the need for precise blasting designs to prevent adverse effects such as fly rocks and overbreaking at the rear residual surfaces. Safety  
Figure 8: Normalized powder factor (PF) vs. ratio spacing/borehole drilling diameter. measures, such as risk assessments, comprehensive staff training, and protection of in place surfaces, are essential to mitigate risks associated with blasting operations at civil and mining sites, also combined with protective countermeasures [30,31]. The considered experiments and engineering judgments made on available data can allow one to observe that even if explosives offer a valuable tool for improving safety conditions on rocky slopes, their use requires meticulous planning, adherence to safety protocols, and consideration of site-specific factors to ensure optimal outcomes while minimizing risks and environmental impacts; additional features such as the distribution of rock fragments can be considered for comprehensive results [32,33].
## 3 Methods
### Vegetation Index Calculation Method  
In the present study, empirical statistical regression models for the assessment of the crop LAI, FVC, and chlorophyll were developed utilizing a range of vegetation indices. Table 7 shows the vegetation indices selected for this analysis encompass the RVI [44], the NDVI [45], the Green NDVI (GNDVI) [46], the Green Chlorophyll Vegetation Index (GCVI) [31], EVI2 [47], the Modified Soil-Adjusted Vegetation Index (MSAVI) [48], and the Modified Triangular Vegetation Index 2 (MTVI2) [49].  
A total of 315 in-situ measurements of the LAI, FVC, and chlorophyll (T850 nm/T720 nm) were conducted across corn, rice, and sunflower fields situated in the Inner Mongolia Autonomous Region. The dataset was apportioned, with 80% of the measurements allocated for model development and the remaining 20% reserved for validation purposes. The LAI estimation based on the RVI exhibited a root-mean-square error (RMSE) of 0.73 (determination (R\({}^{2}\)) = 0.84, Table 8). In comparison, the RMSE for LAI estimations based on other vegetation indices (VIs) varied from 1.33 to 2.48, contingent upon the VI utilized (R\({}^{2}\) = 0.32-0.67, Figure 4). The FVC estimation derived from MTVI2 demonstrated an RMSE of 6.9% (R\({}^{2}\) = 0.81, Table 8), whereas the RMSE for other VI-based FVC estimations spanned from 12.1% to 32.4%, dependent on the VI in question (R\({}^{2}\) = 0.38-0.78, Figure 4). Furthermore, the chlorophyll (T850 nm/T720 nm) estimation predic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**V Vegetation Index** & **Equation** \\ \hline Ratio Vegetation Index (RVI) & \(\frac{NIR}{Rad}\) \\ Normalized Difference Vegetation Index & \(\frac{NIR-Rad}{NIR+Rad}\) \\ Green NDVI (GNDVI) & \(\frac{NIR-Green}{NIR+Green}\) \\ Green Chlorophyll Vegetation Index (GCVI) & \(\frac{NIR}{NIR+Green}\) \\ Enhanced Vegetation Index 2 (EVI2) & \(\frac{2S(NIR-Rad)}{NIR+2 Rad+1}\) \\ Modified Soil-Adjusted Vegetation Index (MSAVI) & \(\frac{2 NIR+1-\sqrt{(2 NIR+1)^{2}-8(NIR-Rad)}}{NIR+2 Rad+1}\) \\ Modified Triangular Vegetation Index 2 (MTVI2) & \(\frac{1.5[1.2(NIR-Green)-2.5(Rad-Green)]}{\sqrt{(2 NIR+1)^{2}-\left(6 NIR-5 \sqrt{Rad}\right)-0.5}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Vegetation indices used in this research (NIR, red, and green correspond to Jilin-1 GP01/02 PMS band 4, band 3, and band 2, respectively).  
Figure 3: Spectral response functions of Jilin-1 GP01/02 PMS and Landsat TM in visible to near-infrared bands.  
RMSE of 0.15 (R\({}^{2}\) = 0.85, Table 8), and the RMSE for chlorophyll estimations based on other VIs ranged from 0.23 to 0.67, with variation attributed to the VI selected (R\({}^{2}\) = 0.45-0.73, Figure 4).