esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Conventional Machine Learning Experimental Results
#### 5.2.2 Eclipse Data Set Experimental Results  
The Eclipse data set experimental results of the classification algorithms are presented in Table 8. According to the experiments, no significant increase or decrease was seen in the Naive Bayes algorithms. ACC values of the remaining algorithms increased. As seen in Table 8, the average eACC value of five base classifiers is 70.74%, and the average ACC is 76.54%. The Diff value that shows the difference between two primary metrics indicates a positive increase of 5.80%. Similarly, the average AUC increased from 0.701 to 0.815 and the average FM increased from 0.727 to 0.761.  
Table 8 clearly shows that SVM ensembles classifier has a higher ACC compared to the other classifiers. It has a better ACC in 79.93% among five algorithms.
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics  
To further illustrate the advantages of the glider for underwater acoustics, in this section, the ambient noises collected at different depths are analyzed first. The underwater passive surveillance  
Figure 4: The spectrograms of the ambient noise (**a**), roll motor noise (**b**) and the pump motor noise (**c**), respectively. The ambient noise is relatively flat. The noise of the roll motor is a broadband pulse signal, and the pump motor is a multitone signal.  
Figure 5: The frequency spectra of the roll motor noise and the pump motor noise compared with the noise of no-motor time, respectively. The pump motor noise has the highest noise level, and the roll motor noise level is 10-20 dB higher than that of the no-motor time. The noise at no-motor time is quite closed to the ambient noise at sea state 3 [22].  
ability of the Petrel II glider is then studied from two aspects, the target detection ability and the target azimuth estimation ability, using the data from the sea trial in August 2018.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems  
Since the FD-UW channel is divided into different classes, a physical layer for the UWAC channel is needed to ensure adequate operation in various UW environmental classes [15; 16]. The physical layer can be used to demonstrate theoretical acoustic wave propagation concepts, impulse response measurements, and signal limitations in the underwater environment. The efficient operation of FD-UWAC using the physical layer necessitates a variable quality of service (QoS) as well as limited bandwidth systems. Many challenges confront FD physical layer-based systems, including synchronization and delays, self-loop and SI, noise, attenuation, multipath effects, and Doppler spread. Researchers created FD physical layer-based systems to address these problems, which are divided into three categories: OFDM based, digital modulation-based, and spread-spectrum-based.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 2 Materials and Methods
### Point Cloud Semantic Segmentation Model
#### 2.3.3 Loss Function Design  
RandLA-Net employs a cross-entropy loss function to constrain the model's learning characteristics using the prediction errors of individual samples. The composition of categories in road intersection scenarios is diverse, and the sample count of each category within the point cloud dataset fed into the network is imbalanced. Thus, using the original loss function may lead the model to learn the sample ratio and other prior information in the training set, giving it bias towards categories with a higher number of samples during prediction.  
To ensure that the samples in each category contribute relatively equally to the loss function, a weighted cross-entropy loss function [31] is established, assigning weight coefficients to each category according to the size of the sample pool:  
\[\mathrm{r_{i}}=\frac{\mathrm{n_{i}}}{\sum_{j=1}^{n_{i}\mathrm{n_{j}}}\mathrm{ n_{j}}} \tag{11}\]  
\[\mathrm{w_{i}}=\exp(-\mathrm{r_{i}}) \tag{12}\]  
where \(\mathrm{n_{i}}\) is the number of points in the ith category, \(\mathrm{r_{i}}\) is the proportion of the ith category in the total number of point clouds across all categories, \(\mathrm{w_{i}}\) is the weight coefficient for the ith category, and \(\mathrm{n_{class}}\) is the number of categories on which the model is trained.  
The expression for the weighted cross-entropy loss function \(\mathrm{L_{w}}\) is as follows:  
\[\mathrm{L_{w}}=-\sum_{i=1}^{n_{class}}\mathrm{w_{i}y_{i}}\mathrm{ln}(\hat{y}_ {i}) \tag{13}\]  
where \(y_{i}\) is the one-hot vector encoding of the ith category, and \(\hat{y}_{i}\) is the predicted probability of the model for the ith category.
## 4 Experimental Results and Analysis
### Comparison of Model Performance Across Different Datasets  
To further validate the superior generalization capability of the proposed model, we conducted training and evaluation on various datasets. Specifically, we performed comparative experiments on the LSOD-YOLOv8 model using a fire detection dataset and a waste classification dataset. Subsequently, considering that the coco dataset contains a large number of small target objects, our LSOD-YOLOv8 model was used to train the coco dataset, further demonstrating the versatility of our model in the field of small-target detection. During the experiment, each dataset was split into training, validation, and test sets in a 7:1:2 ratio. All experiments were run on a machine equipped with 32 GB of RAM,  
Figure 14: Comparison of detection performance of LSOD-YOLOv8n model at different blur levels.  
Figure 13: Comparison of detection performance of YOLOv8n model at different blur levels.  
a 13 th Gen Intel(R) Core (TM) i5-13490F 2.50 GHz processor, and an NVIDIA GeForce RTX 4070 GPU. The comparative results are shown in Table 5.  
As illustrated in Table 5, LSOD-YOLOv8 consistently outperforms YOLOv8n across most metrics. For cigarette detection, LSOD-YOLOv8 demonstrates higher precision and improved mAP50 and mAP50-95, highlighting its superior accuracy and robustness. In fire detection, LSOD-YOLOv8 significantly improves in precision and mAP, showing enhanced capability to reduce false positives. For waste detection, both models perform exceptionally well, but LSOD-YOLOv8 marginally surpasses YOLOv8n across all metrics, particularly in mAP50-95. In the coco dataset detection task, the LSOD-YOLOv8 model shows its excellent detection performance in all metrics, especially in mAP50, which is 4.3% higher. These results underscore the model's ability to achieve performance gains across different datasets. Overall, our LSOD-YOLOv8 model not only boosts detection accuracy but also exhibits strong precision and generalization, making it a highly effective solution for diverse detection tasks.
# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
## 4 Conclusions  
A predictive phase current control scheme is proposed in this paper. Current regulation is realized through PWM control to ensure a fixed switching frequency. A linear phase voltage model is introduced to fit the relationship between the voltage and the current slope in a short period. The parameters of the model are dynamically identified using the historical voltage and current information in the previous control cycle, where the voltage changes from zero to positive or negative DC-link voltage, and then the average voltage required in the next PWM cycle is predicted according to the model. Experimental results are provided to compare the performance of the proposed control scheme with the conventional hysteresis current control. The main conclusions are as follows:  
1. Given the same sampling rate, the current and torque ripples using the proposed PCC are significantly lower than those using hysteresis control. The current RMSE using the proposed method is 32.6% lower on average and 48.1% lower at most, and the torque RMSE using the proposed method is 50.9% lower on average and 62.96% lower at most.
2. The execution time of the proposed control scheme is computationally efficient, so the proposed PCC can be implemented in a low-cost MCU.
3. The proposed PCC is easy to implement and does not need to know the motor characteristics in advance. It is insensitive to the characteristic changes caused by factors such as motor aging.  
In summary, the proposed PCC is relatively suitable for tracking a given current profile. In future work, the proposed control scheme could be modified to be suitable for power converters with reduced current sensors.  
Conceptualization, S.S. and Y.F.; methodology, H.W.; software, M.L.; validation, S.S. and M.L.; formal analysis, Y.Z.; investigation, M.L.; resources, Y.Z.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, Y.F.; visualization, S.S. and Y.F.; supervision, H.W.; project administration, H.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hunan Provincial Natural Science Foundation of China (2021J)[30185].  
Figure 16: (**a**) Current RMSE and (**b**) torque RMSE for a 1.25 Nm torque reference.
## 4 Experimental Procedure and Analysis of Results
### Analysis of Results
#### 4.2.1 Results of LSTM-GARCH-Family Models
Therefore, the prediction performances of combined models were effectively improved. Figure 8 shows the percentage reductions of MAE, RMSE, and MAPE of the better performing combined models compared with the LSTM model, and it was found that the LSTM-GP combined model had the most significant reduction of evaluation metrics, indicating that the LSTM-GP combined model has the best prediction performance.
## 2 Theoretical Analysis of the Edge Effect
### Finite Element Analysis of the Contact Pressure  
To further understand the variation of the contact pressure, finite element analysis of the contact pressure under different overhang ratios has been done. Figure 3a shows the simulated displacement result when \(\tau=0.5\). Half of the model was built to simplify the simulation model. The bonnet tool was simplified, just leaving the spherical part. The bonnet radius is 80 mm, and the workpiece material is BK7. In the simulation process, the bonnet tool was fixed by constraining all DOFs of the fringe area, and the workpiece was defined to move upward with 1.25 mm to deliver a 28 mm diameter contact spot (The size is calculated when the spot is in the central zone.) In addition, the inflated pressure loaded on the inner surface of the bonnet tool is 0.25 MPa. The overhang ratio \(\tau\) varies from 0 to 0.5 in the simulation, with an increment of 0.1. Other detailed simulation parameters had been reported in our previous publications [23; 26].  
When \(\tau=0.5\), the finite element model and corresponding displacement deformation result are presented in Figure 3a. Figure 3b shows the contact pressure distribution shapes under this different \(\tau\), and their corresponding sectional profiles have also been extracted, as shown in Figure 3c. The sharp increase of the contact pressure turns out at the edge zone, and its peak value becomes more significant with the rise of \(\tau\).
# A Primer on CFD-DEM for Polymer-Filled Suspensions
## 2 Single-Phase Fluid Flows
### Numerical Procedure and Meshes  
The governing equations for a single fluid phase are solved using the finite-volume method together with the SIMPLE algorithm (a procedure for the calculation of pressure and velocity, by guessing and correcting pressure and velocity fields) [36]. For the case of power law fluid, we have to use an iterative procedure to update the viscosity and stress components.  
The velocity gradient is computed using a second-order least-squares method, and the diffusive term in the momentum balance is discretized using second-order linear interpolation. The advective terms in the momentum and constitutive equations are discretized using the second-order linear-upwind scheme, which follows a component-wise and deferred correction approach to improve numerical stability. The time derivatives are discretized using the bounded implicit second-order Crank-Nicolson scheme. Therefore, the overall accuracy of the numerical method is equal to two for both spatial and temporal discretizations.  
The numerical torque calculations were assessed using the forces _postProcessing_ utility [37] available in OpenFOAM by comparison with the analytical profiles. This tool computes the forces and torques by integrating the stress tensor effect in a given list of patches (sets of boundary faces). For moment/torque calculations, the center and axis of rotation need to be defined.  
As shown in Figure 2, a O-block mesh was used to discretize the geometrical domain represented in Figure 1. Three different mesh refinement levels were created using the _blockMesh_ application, being defined as M1: 15\(\times\) 30 \(\times\) 4, M2: 30\(\times\) 60 \(\times\) 8 and M3: 60\(\times\) 120 \(\times\) 16, where M#: \(R\times\theta\times H\) represents the spatial discretizations on the radial, azimuthal and longitudinal directions, respectively. The accuracies of the numerical torque \(\mathcal{T}\) calculations were accessed through the comparison with the analytical results given by Equations (8) or (9) via the application of [PERSON]'s extrapolation [38] to the limit.  
For the numerical calculations the following parameters were used: the cylinder has a height of \(H=0.5\,\mathrm{mm}\) and a radius of \(r=15\,\mathrm{mm}\). The angular velocity was imposed on the top plane using the _rotatingWallVelocity_ boundary condition available on the OpenFOAM CFD framework. Simulations were done for shear rates between 0 and 100 s\({}^{-1}\). The Newtonian fluid has a dynamic viscosity of \(\eta=0.001\) Pa.s and density of \(\rho=1000\) kg/m\({}^{3}\). For the power law fluid, the flow consistency index \(k\) adopted the same value of the Newtonian viscosity and the power law exponent \(n\) was varied between 0 and 1.
## 4 Data Processing and Features
### Machine Learning
#### 5.1.1 Supervised Learning  
Supervised learning trains the machine learning algorithm using input samples and labels. The model approximates the function \(y=f(x)\) as closely as feasible. Using a training dataset with proper labels to instruct the algorithm is supervised learning. Supervised learning algorithms are classified:  
1. **Regression:** is determined by the variable that represents the output. If the output variable is continuous, then the job in question is referred to as a regression task. Predicting the price of a home and the price of a stock are both instances of regression problems.
2. **Classification:** Classification tasks use categorical variables such as color and form. Most machine learning applications employ supervised learning. Supervised learning methods include logistic regression, linear regression, SVMs, and random forests.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 6 Conclusions  
In the present paper, we explored techniques for hiding information in audio containers using a direct spread spectrum. We considered various spreading sequences (chip codes) and studied their influence on the error rate in recovered messages. We also investigated the distortion of the audio container in terms of MSE and PSNR.  
The results clearly demonstrate the promising characteristics of this direction. Precisely, in each segment of the container, we managed to hide a part of the information message reliably. At the same time, the BER, MSE, and PSNR values are within an acceptable range. The best characteristics were shown by Walsh-Hadamard chip codes, which showed the lowest error rate (with comparable container distortions).  
At the same time, it should be noted that there exist objectively conflicting factors that negatively affect one another. For example, by increasing the volume of hidden messages, we inevitably increase the BER and distort the container. By increasing the power of the chip codes, it is possible to reduce the BER. However, this distorts the container even more. This is consistent with known results in steganography based on direct spectrum expansion. For example, in [41; 53], it is shown that by increasing the power of chip codes, it is really possible to reduce BER, but the distortion of the covers becomes very significant. We managed to present the availability of compromise solutions. This is especially true for Walsh-Hadamard chip codes, for which the range of possible solutions is much wider thanfor other spreading sequences. Nevertheless, it should be noted that when using traditional data hiding techniques, the volume of hidden messages cannot be large. The search for new ways of hiding information is promising, e.g., based on the adaptive formation of spreading sequences [56], advanced techniques for addressing chip codes [58], etc.  
Conceptualization and methodology, A.K.; software and validation, A.O. and T.G.; writing--review and editing, O.P. and R.Z.; visualization, K.W. All authors have read and agreed to the published version of the manuscript.  
The research work reported in this paper was in part supported by the National Centre for Research and Development, Poland, under project no. POIR.04.01.04-00-0048/20.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Model Development
### Robot-to-Camera Kinematic Model  
For a stereo camera system, this camera frame is located at the center of the stereo baseline, as shown in Figure 6. The coordinates of a point in space, measured in the base frame, can then be expressed in the camera frame as:  
\[P^{C}=T_{E}^{C}T_{0}^{6}\,P^{O} \tag{21}\]  
Equation (21) describes how a given 3D point in the base frame \(P^{O}=[X^{O},\ Y^{O},Z^{O}]\) is mapped to the camera frame \(P^{C}=[X^{C},\ Y^{C},Z^{C}]\) using transformation:  
\[\text{Robot manipulator mapping }\mathcal{H}\colon\ P^{O}=\left[X^{O},\ Y^{O},Z^{O} \right]^{T}\rightarrow\ P^{C}=\left[X^{C},\ Y^{C},Z^{C}\right]^{T} \tag{22}\]  
The mapping function \(\mathcal{H}\) is nonlinear and depends on the current joint angle of robot \(q=[q_{i}|i\ e\ 1,2,3,4,5,6]\), robot geometric parameter \(P_{a\_robot}=[a_{i},\ a_{i},\ a_{i}\ |\ i\ e\ 1,2,3,4,5,6]\), and constant transformation matrix \(T_{E}^{C}\).
## 3 System Architecture
### Overall Architecture
#### 3.1.3 Computing Resource Management Gateway  
The computing resource management gateway accesses and manages computing clusters. It connects to computing clusters and regularly monitors their resource status. After integrating the status information, the gateway announces it to other network nodes according to a specific strategy and maintains the integrated information locally.
## 1 Introduction
### Contribution and Paper Organization  
In this article, we have summarized epileptic seizure detection techniques in the literature that have relied on EEG signals. We have provided an overview of the required processing factors involved in epileptic seizure detection, which includes data acquisition, pre-processing, decomposing, feature extraction, feature selection, and classification. The purpose of this overview was to provide the epilepsy research community with guidelines for the different methods that have been used for epileptic seizure detection, which may also aid in selecting the most appropriate technique for their research. Furthermore, this study focused on developing an efficient, automated epileptic seizure detection technique using EEG signals that could be easily implemented via hardware or a mobile application. In this approach, we used filter design to accommodate our technique, applied an enhanced decomposition method, and developed a new combined feature for classification and analysis. The finite impulse response (FIR) filter was designed for pre-processing; the Butterworth filter was applied and then followed by a fast Fourier transform (FFT) as the final step of filtering. Then, we applied an enhanced wavelet analysis decomposition technique using an appropriate filter for each decomposed level based on the study of each decomposed signal. The next step was feature extraction, and several features were utilized for further EEG signal processing, one of which was a new combined feature, crest range. After that, a seizure detection classification was performed using the most used machine-learning algorithms, such as neural networks (NNs), K-nearest neighbors (KNN), and multiclass Support-vector machines (SVMs). The average accuracy of 98.61% was achieved by the proposed method when KNN was applied. Finally, we developed and tested a mobile application that monitored the epilepsy classification process and provided notifications based on the results. Our research would add value to existing and future research in the medical field in regard to epilepsy seizure monitoring. Furthermore, it will have an impact on the Human-Computer Interaction field, especially in the field of Brain-Computer Interaction (BCI).  
This paper is structured as follows: General structure and material summary of epilepsy and EEG signals are provided in Section 2. Section 3 describes the literature review. The proposed method is explained in Section 4. Section 5 provides a variety of techniques utilized in epileptic seizure detection. Section 6 details the results and experiments. Mobileapplication development is presented in Section 6. Lastly, the conclusion of this work is provided in Section 7.
## 4 Results and Discussion
### Effect of Droplet Diameter  
The purpose of the following simulation is to explore the behaviour of von Mises and S33 stress propagation when varying the droplet diameter from 0.5 mm up to 5 mm. The measuring point is taken on the top surface of the LEP for different droplet sizes. Increasing the diameter enables the simulation of droplet impacts on wind turbine blades under various environmental conditions and extreme precipitation events. [PERSON] et al. [5] state that droplet size measures typically range between 0.5 mm and 5 mm in diameter and that 0.5 mm to 3 mm are the most common. Droplets greater than 3 mm can be seen during extreme weather conditions.  
The von Mises stress curves over time are displayed in Figure 9, illustrating the propagation of von Mises stress through the model's thickness. The first peak value corresponds to the initial impact of the droplet on the LEP, representing the maximum stress experienced by the blade during the contact process. Figure 9 shows the von Mises stress evolution as the droplet diameter increases from 0.5 mm to 4.5 mm. The von Mises stress rises with increasing droplet diameter until it reaches 2.5 mm, after which it begins to decrease with a further increase in diameter. A droplet size of 2.5 mm impacts the surface with the highest von Mises stress. Since von Mises stress is a combination of principal stresses and shear stresses, one of its components causes the stress to reduce.  
Figure 8: S33 Peak Stress for different impact velocities (Theoretical values vs. Numerical values).