esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the aspects of network communication, research mainly focuses on the problems of network topology design [29], channel characterization [30], channel access control [31, 32], routing algorithm design [33], and lightweight communication protocols design, _etc._ These key technologies must be considered when building a complete BSN system. They are not only of great research value, but also of important practical value. Existing surveys have made detailed investigations on BSN architecture, signal processing, communication protocols, context awareness and QoS. Reference [12] mainly focused on propagation of information, communication protocols, QoS and security. Moreover, it gave some examples of medical monitoring based on BSN. In Reference [13] the authors provided a detailed research on sensor devices, physical layer, MAC layer and radio technologies, then made a classification on body sensor projects and summarized some open research issues on multi-aspects. In Reference [34], researchers presented an overview of BSN in enabling pervasive healthcare and assistive environments, listing many applications of sensor devices and communication protocols. The authors also analyzed current obstacles and future open issues from an overall angle. Reference [35] placed emphasis on network communication, discussing the issues of physical layer, MAC layer, network layer and routing protocols in detail. Different from the above papers, we set forth our views from the  
Figure 1: Architecture of a BSN. Figure 2: Main research areas in BSNs. perspective of system design, with a detailed explanation of the research status of three aspects: sensor, data fusion, and network communication. In each section, we summarize the issues which need to be considered in design phase, listing some typical related cases. We also present the trends of development and technical challenges. In addition, we list some key research achievements of BSNs in various fields, such as medicine, social welfare, sports, and man-machine interfaces. Moreover, we have provided a detailed analysis of trends and challenges of BSNs from a systemic perspective. The remainder of this paper is organized as follows: Section 2 describes the various types of sensors and hotspots in BSNs. Section 3 explores the development of data fusion. Section 4 discusses BSN network topology and communication between layers. In Section 5, we introduce the typical applications in various fields and the system design problems to be considered in BSNs. Section 6 briefly concludes this paper.
# Vertical GaN MOSFET Power Devices
## 2 GaN Power Devices
In a vertical GaN device, the epitaxial layer is grown homoepitaxially on the native GaN substrate, eliminating issues related to lattice or thermal expansion coefficient mismatches. The performance of the vertical GaN device remains unaffected by defect densities and manufacturing challenges such as wafer bow, warp, cracking, etc. Consequently, vertical GaN devices offer high reliability, yield, and, ultimately, reduced costs compared to lateral devices. Despite the significant potential of vertical GaN in dominating power devices, the current landscape still heavily relies on lateral device structures due to the complexities involved in preparing vertical devices and the high cost of GaN substrates [50]. A notable breakthrough in GaN devices is the introduction of GaN on SiC. GaN on SiC is a device that combines the advantages of both materials. The remarkable thermal conductivity of SiC, outperforming GaN alone, facilitates efficient heat dissipation, fostering enhanced device performance and durability. These devices are characterized by high power density and high-frequency response, attributes that lend themselves perfectly to applications in high-frequency electronics like radars and satellite communications. The symbiotic relationship between GaN and SiC, marked by minimal lattice mismatch and thermal stress, ensures devices that are reliable over extended operational timelines. Thisintegration is known to reduce parasitic capacitances and resistances, paving the way for quicker device switching and diminished energy losses [51; 52]. Despite the obvious advantages, SiC substrates tend to be more expensive compared to alternatives like Si or sapphire, which can escalate the overall device cost. The nuanced processes involved in the epitaxial growth of GaN on SiC lead to defects, which could compromise device efficiency. The demand for large-diameter SiC wafers often outstrips the supply, potentially hindering widespread commercial adoption and mass production [53; 54]. A major issue with these devices is their compromised off-state performance, characterized by increased leakage and reduced \(V_{BD}\). Studies suggest that these defects can intensify leakage by acting as electron-hole pair generators under strong electric fields. As illustrated in some research, when exposed to high electric fields, these defects create holes that can accumulate at the boundary between the dielectric and the semiconductor. This accumulation can then intensify the field in the oxide, leading to a potential device failure. Interestingly, in comparisons using trench MOSFETs, devices on bulk GaN outperform those on alternative materials [55; 56]. GaN on SiC in vertical devices is a tapestry of clear advantages punctuated by challenges.
## 3 Controller Design and Stability Analysis  
In this section, by combining a Nussbaum function, command filter, barrier Lyapunov function, RBFNN and auxiliary system with the backstepping method, a nonlinear adaptive safety control scheme for the trajectory tracking system of the ACV is designed. A schematic diagram of the control system of the ACV is shown in Figure 2.
DL models are composed of memory cells, gates, different layers (convolution, pooling, and fully connected), and activation functions. Data imbalance is considered to be the main problem in datasets. If the data are not balanced, the model will be biased towards a specific class. In [14], the authors did not specify any technique for balancing the dataset, even though the dataset is imbalanced. In our work, we have used the Proximity Weighted Random Affine Shadow Sampling (ProWRAS) balancing technique. This technique oversamples the minority class samples to make them equal to the majority class. In [15], the authors proposed the Synthetic Minority OverSampling Technique (SMOTE) for data balancing. However, SMOTE may not be the best choice as it often leads to overfitting and bridging. The authors in [16] have proposed a hybrid model of CNN and LSTM for CVD prediction. However, CNN can lead to vanishing gradient problems and LSTM is a computationally expensive model. To address this problem, we have proposed different DL models. Table 2 highlights the research gaps identified from the literature. In our proposed work, we have utilized DL models that would accurately predict CVD. This is because DL models have proven to be useful in the healthcare field for predicting various diseases such as diabetes, heart attacks, lung cancer, and so on. The abbreviations are mentioned in Table 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposed Methodology** & **Limitations** & **Proposed Solution** \\ \hline The authors have not proposed any technique for data balancing [14]. & The imbalanced dataset will not provide efficient results. & Proposed ProWRAS oversampling technique to balance the dataset. \\ \hline Proposed SMOTE for data balancing [15]. & SMOTE does not deal with borderline instances. & ProWRAS deals with borderline instances by giving higher weight to those close to the borderline. \\ \hline The authors have proposed a hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) for CVD prediction [16]. & CNN and LSTM are not good choices as CNN leads to vanishing gradient problems and LSTM is a computationally expensive model. & The proposed Dense Belief Network (DB-Net) can be used to overcome the vanishing gradient problem. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Identified research gaps.
# Improving CNN Fish Detection and Classification with Tracking
## 2 Material
### Post-Processing  
During the initial two stages of our pipeline (i.e., detection and tracking), we assigned a unique identifier and species label to each individual. The tracking module inherits the class assigned to the detected bounding box by the CNN as long as the _time frame_ lasts. Consequently, upon the completion of the video processing, we obtain a comprehensive list encompassing all tracked objects in each frame. For each individual at any given time, this list included its respective IDs, bounding box coordinates, associated class, and bounding box type (obtained from either the model's detection or the tracking module). Nevertheless, it is crucial to acknowledge that this list may contain multiple IDs with approximate bounding box distances and sizes, potentially impacting the tracking score. To address this matter, we perform a post-processing step to eliminate potentially erroneous IDs from the list. If two bounding boxes exhibit a significant overlap exceeding a selected IoU threshold, they are highly likely to correspond to the same object. This is independent of whether both bounding boxes are from the detector and/or tracker. In such instances, we retained the object associated with the detection bounding box type. Additionally, to refine the class information of each object, we employed a frequency analysis to determine the most representative class. The class that appears most frequently for one individual was selected as the most representative one. Subsequently, any objects with minority classes were reclassified to align with the most representative class. By conducting these post-processing steps, we ensured the accuracy and consistency of the tracked objects' identities, bounding box coordinates, and class information throughout the analysis.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 3 Methods
### A Method of Optimizing Energy Consumption  
At a constant temperature, the nitrogen adsorption capacity depends on the adsorption pressure. The higher the pressure, the greater the adsorption capacity. The capacity affects the time for which a given bed can adsorb nitrogen from a given air stream--transfer time. This is crucial, because only during the nitrogen adsorption, the oxygen can be transferred into the oxygen tank. The total efficiency of oxygen separation depends on the ratio of this transfer time to the time of the entire generator operation cycle. Too short a transfer time reduces the stream of separated oxygen, but too long causes a decrease in oxygen purity. The transfer time should be carefully selected to take full advantage of the adsorption capacity at a given pressure. During the experiment, the optimal transfer time, depending on the process pressure, was determined experimentally.  
The second factor influencing the energy consumption of oxygen separation in the installation is the minimization of energy losses related to the operation of auxiliary devices as well as measuring and control equipment. This is especially important in the case of low-capacity devices. To reduce these losses, the device should operate with maximum yield, which does not deteriorate the purity of the separated oxygen.  
During the research, it was found that the purity of oxygen obtained during the adsorption process is very sensitive to the difference between the adsorption pressure and the pressure in the oxygen tank. The correlation between these two pressures has not been discussed in the literature, so it was decided to investigate it more closely. The authors found experimentally that too large a difference between the operating pressure of the adsorption bed and the product pressure in the oxygen tank causes the effect of excessive purging and a decrease in the purity of the separated oxygen (see Section 4.2).
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 4 Experimental Results and Discussion
### Experimental Results  
Figure 9 shows the results of plain fabric defect detection; it can be seen that the position and shape of fabric image defects have been successfully detected. Furthermore, nine twill fabric defects were tested, and the defect detection results are shown in Figure 10. The fabric defects were well detected; it reveals that the defection method can detect the twill fabric defects.  
Figure 7: The optimum parameters about various fabric types.  
Figure 8: The accuracy bar chart of various fabric types’ detection under the number of different parameters \(\lambda\).  
Star-patterned defects, such as linear defects and blob-shaped defects, are also successfully segmented using the proposed method, as shown in Figure 11. The results highlight the utility of our technique of accurate defect detection and segmentation. Our method can defect the box-patterned fabric defect, which includes broken ends, thick bars, and thin bars, as shown in Figure 12.  
Figure 10: Twill fabric defect of detection results.  
Figure 9: Plain fabric defect of detection results.  
The Figure 13 shows a representative set of different types of defects in dot-patterned fabric obtained from a group of images. In addition, even in a complicated background with a pattern, striped fabric, and statistical-textures, our method also can achieve an outperform the results of others, as shown in Figures 14 and 15. It can be seen that the proposed method can detect a variety of fabric samples with different defect types, shapes and textured backgrounds.  
Figure 11: Star-patterned fabric defect of detection results.  
Figure 14: Striped fabric defect of detection results.  
Figure 12: Box-patterned fabric defect of detection results.  
Figure 13: Dot-patterned fabric defect of detection results.  
We further provide the computational time complexity of the proposed method, which is shown in Figure 16. We fit the curve. From the trend of the curve, the time complexity is \(O\big{(}N^{2}\big{)}\), where \(N\) is the size of image.
## 1 Introduction
Many studies have proved the significance of the ensemble model over a single and conventional model [47; 48; 49; 42]. Random forest (RF) and extreme gradient boosting (XGBoost) are some of the models based on an algorithm of ensemble methods. From the aforementioned literature, it can be observed that EO data can act as an alternative to traditional data acquisition and machine learning algorithms are more advanced than traditional supervised and unsupervised classifiers. To test their applicability for mountainous terrain, we have implemented GEE and ELMs for LC mapping of mountainous terrain in Nepal, i.e., the Kaski district. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoringthe decadal changes of LC using ELMs for mountainous terrain. The main objectives of the current research are:  
1. To acquire the median composite imagery and their three derivative indices for the years 2010 and 2020 using GEE;
2. To map and evaluate the accuracies of the LC changes using RF and XGBoost ELMs;
3. To compare the accuracy and identify the best performing method for mapping LC;
4. To quantify and validate the LC changes in Kaski District, Nepal. The applied methodology is a case study for the hilly region of Nepal. The methodology can be an essential adoption in developing countries like Nepal to utilize big EO data and overcome the computational challenges. In addition, the quantification of LC and changes over time would be helpful for decision-makers to understand current environmental dynamics. Figure 1 depicts the workflow adopted for the LC classification and change detection in this study.
# Surface Quality Evolution Model and Consistency Control Method of Large Shaft Multi-Pass Grinding
## 5 Case Study
### Results and Discussion  
The standard deviations of surface roughness and glossiness of measuring points are used to describe the surface quality consistency as shown in Figure 14, and it can be seen that the surface quality consistency gradually becomes better during the grinding process. The standard deviations of surface roughness and glossiness of measuring points are 0.651 \(\upmu\)m and 26.2 GU for the first grinding pass, and 0.060 \(\upmu\)m and 4.1 GU for the last grinding pass, respectively.  
The dimensional errors of rough grinding passes are shown in Figure 15. It can be seen that the dimensional errors are still within the tolerance range after the adjustment of the actual grinding depth, which proves the effectiveness of the compensation method. It is also proved that the consistency control method of surface quality will not make the dimensional error exceed the tolerance range.  
Figure 14: The standard deviations of surface roughness and glossiness of measuring points.  
Figure 15: The dimensional errors of rough grinding passes.  
The shaft is continued to be machined with rough grinding process parameters, without compensation and the consistency control method. The standard deviations of surface roughness and glossiness of measuring points become 0.628 \(\upmu\)m and 23.1 GU. Then, the shaft is machined again with process parameters in Table 4. During rough grinding, semi-finish grinding, and finish grinding, only the dimensional error is compensated, while the consistency of surface quality is not controlled. Finally, enough spark-out grinding passes are performed until the standard deviations of surface roughness and glossiness are close to the standard deviations of the last grinding pass in Figure 14. After eight spark-out grinding passes, the standard deviations of surface roughness and glossiness are 0.055 \(\upmu\)m and 4.1 GU.  
The standard deviations of surface roughness and glossiness are shown in Figure 16. It can be seen that without the consistency control method, it requires five more spark-out grinding passes to achieve high consistency. This shows that the consistency control method of surface quality can obtain a higher consistency of surface quality with reducing the grinding passes, resulting in high grinding efficiency.
## 4 Experimental Results
### Performance on Disturbance Defect Dataset
#### 4.5.1 Recognition Results on the Disturbance Defect Dataset
According to the t-SNE maps, it is observed that the original MRFFN could roughly differentiate the majority classes of pitted surface and patches defects. However, the majority classes of crazing, inclusion, rolled-in scale, and scratches were thoroughly mixed with each other. Thus, it can be clearly explained why the original MRFFN yielded the worst performance on the disturbance defect dataset. On the other hand, when the disturbance defect dataset retrained the proposed MRFFN, six clusters could be found clearly in Figures 7-9. Therefore, these results indicate that MRFFN shows its robustness when the models are retrained by the disturbance defect dataset. Furthermore, the experiments were conducted to evaluate the robustness of the MRFFN by comparing it with the other conventional pre-trained DL models, including AlexNet, VGG16, and ResNet-18. According to Table 4, it can be seen that the recognition accuracies of the MRFFN were 98.04% to 85.65%, while the other conventional DL models were 96.45% to 67.24%, 97.73% to 53.35%, and 97.60% to 73.02%, respectively. Obviously, the proposed method outperformed the other DL methods and retained an excellent performance for the variance 0.3 task. For instance, the recognition accuracy gaps of the DL-methods between each variance task were 4.79% to 5.79%, 5.49% to 6.86%, and 13.29% to 33.89% accordingly, while the proposed MRFFN yielded lower accuracy gaps (2.41%, 1.84%, and 8.14%) on the Gaussian white noise tasks. On a similar note, the proposed method had a lower sensitivity to the salt and pepper noise and motion blur compared with the conventional DL methods, which retained an excellent performance of 87.09% and 96.40% accuracies on the density 0.3 and length 20 tasks. To sum up, these results indicate that MRFFN was able to learn more representative features from the limited data, meanwhile suppressing the redundant noise from the raw data. Figure 7: Feature visualization via t-SNE on Gaussian white noise (Variance 0.3). The feature vectors are extracted from the activations of the fully connected layer. (**a**) The original level 0 model. (**b**) The retrained level 0 model. (**c**) The original level 1 model. (**d**) The retrained level 1 model. Figure 8: Feature visualization via t-SNE on salt and pepper noise (Density 0.3). The feature vectors are extracted from the activations of the fully connected layer. (**a**) The original level 0 model. (**b**) The retrained level 0 model. (**c**) The original level 1 model, (**d**) The retrained level 1 model. Figure 9: Feature visualization via t-SNE on motion blur (Motion length 0.3). The feature vectors are extracted from the activations of the fully connected layer. (**a**) The original level 0 model. (**b**) The retrained level 0 model. (**c**) The original level 1 model. (**d**) The retrained level 1 model.
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 3 Methodology  
### Framework  
To address redundant exploration and low efficiency in unknown environments, we propose a two-layer control architecture, as shown in Figure 1, with the fundamental concepts established in Section 2.1. This architecture enhances multi-robot collaborative exploration efficiency through coordinated centralized task decision-making and distributed navigation and mapping.  
The upper layer employs V-R-MATD3, a novel goal decision-making method combining Voronoi partitioning, Recurrent Neural Networks (specifically the GRU), MATD3, and curiosity-driven intrinsic rewards to efficiently select target points for each robot. Voronoi partitioning is incorporated to divide the exploration space among robots, enhancing multi-robot cooperative exploration efficiency. The GRU is introduced to effectively handle the sequential, time-dependent data of robot states and actions, capturing temporal correlations and improving performance. The lower layer utilizes a customized RL simulation environment [1] based on ROS [41] and Gazebo [42] for high-precision navigation and mapping. We enhance Cartographer-based multi-robot joint mapping and implement environment randomization tools. Robots navigate and map according to upper-layer decisions while collecting global exploration rates and robot states as training data.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 4 Scenario and Problem Statement
However, LoRaWAN and NB-Fi sensors operate in the Class A and DRX modes, respectively, and all of the transmissions are acknowledged. Sigfox sensors transmit each frame three times and choose the interval \(T_{U}\) between frame repetitions as a random value uniformly distributed within the window \([T_{start};T_{end}]\). For the described scenarios, we state the problem to compare NB-Fi, Sigfox, and LoRaWAN networks in terms of PLR, PER, and average delay and to determine which technology is the most reliable in each scenario.