esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Although CWT and CNN have been applied to the MI field in previous work, our network still has an advantage in accuracy compared with models using similar CWT-CNN technology. The results indicate that the CWT-CNN approach has the highest (84.1%) average classification accuracy when compared with the CSP-CNN (79.9%), FFT-CNN (79.6%), and STFT-CNN (81.9%) methods, as shown in Table 4. Prior research indicates that CSP, being a linear analysis method, may overlook short-term signal changes and lack precision when capturing signal details [31]. Moreover, FFT struggles to capture the local characteristics of MI-EEG signals effectively [32]. Additionally, due to the fixed window size of STFT, both overall and local features may not be adequately represented. Contrastingly, CWT offers a balance between global and local features by decomposing the signal and offering time-varying windows with a high temporal resolution [33]. Despite these promising results, several limitations are acknowledged, including the small dataset size and hyperparameter complexity. As shown in Figure 5, SVM exhibited the highest performance, achieving a prediction accuracy of 84.1%, followed by Softmax (80.6%), and lastly decision tree (75.3%). EEG signals are often affected by considerable noise and interference, yet CNN can effectively extract relevant data through convolution and pooling layers. These layers enable the abstraction of feature representations from the raw data, which are highly discriminative for classification tasks. In contrast, decision trees may be limited to utilizing original features or a restricted set of manual features, leading to comparatively weaker expressive capabilities [34]. Moreover, the size and quality of the dataset significantly impact the model performance. The 2008 BCI Competition IV Data Set 2B is relatively small, and the Softmax classifier is susceptible to overfitting when handling smaller datasets, particularly those with a higher complexity such as EEG. Overfitting results in a model that performs well on training data, but fails to generalize effectively to new data. Future research directions will involve addressing these limitations through data augmentation techniques and systematic hyperparameter tuning to improve model generalization and performance optimization.
# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
###### Abstract  
Due to the low quality of existing Chinese herbal medicine datasets and the lack of recognition algorithms for herbal images, automatic classification of Chinese herbal medicine is ineffective. In this paper, we constructed a comprehensive dataset comprising 4485 images across 20 categories of Chinese herbal medicine. This dataset captures the morphological diversity of Chinese herbal medicine while reducing inter-class variations and closely mimics real-world complexity. Considering the subtle differences among the data, we proposed a multi-scale feature extraction architecture called MSPyraNet. This architecture is composed of multiple FACNBlock units, which are designed explicitly for herbal medicine characteristics. FACNBlock utilizes a multi-scale representation module, using convolutions and atrous convolutions of varying sizes to generate and fuse multi-scale feature maps. Experimental results show that MSPyraNet improves accuracy by more than 4.72% and 4.54% compared to existing SOTA models on two datasets. Ablation studies validate the effectiveness of the multi-scale representation module. Furthermore, we discovered that MSPyraNet achieves a notable improvement in classifying Chinese herbal medicines that are morphologically similar but belong to different categories. Briefly, this study provides a dataset and methodological reference for future research on Chinese herbal medicine classification.  
Keywords:image recognition; deep learning; convolutional neural networks; Chinese herbal medicine +
Footnote †: journal: Applied sciences
## 3 Results
### Prebreakdown Streamers
#### 3.2.2 Propagation Length and Velocity  
Figure 6 indicates that the propagation length of streamers varies considerably across pure oil and nanofluids. It is obvious that streamers in Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanofluids keep shorter than that in pure oil during the whole development process. Moreover, it should be noted that the presence of nanoparticles extends the time to reach the maximum length, leading to a decrease of streamer velocity. The value of average velocity is calculated by dividing maximum length by arrival time. The streamers in pure oil develop fastest with a velocity of 1.40 km/s, followed by that in Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\) nanofluids, with velocities of 0.90 km/s, 0.73 km/s and 0.72 km/s, respectively. The values are in accordance with the propagation velocity of 2 nd streamers in mineral transformer oil [21; 22]. This indicates that the addition of TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\) nanoparticles dramatically affects the propagation velocity of streamers, which is lowered into around one half of that in pure oil.  
Figure 5: Prebreakdown streamers in pure oil and nanofluids. (a: pure oil, b: Fe\({}_{3}\)O\({}_{4}\) nanofluid, c: TiO\({}_{2}\) nanofluid and d: Al\({}_{2}\)O\({}_{3}\) nanofluid).  
Figure 6: Prebreakdown streamer propagation length in pure oil and nanofluids versus propagation times.
## 5 Discussion
### Influence on Fault Interruption Time  
Although \(L_{FCL}\) is involved in the EFCLC, it can still be bypassed by the thyristor after the main breaker operates. Thus, the fault current extinguishment speed cannot be delayed due to large inductance. As mentioned in Section 5.5, the fault current level is restricted by a large \(R_{FCL}\) and \(L_{FCL}\), and the interruption time can be reduced at a low fault current level at the beginning of fault extinguishment. According to the expression of the attenuation coefficient during the fault interruption expressed in Equation (37), the large \(R_{FCL}\) can accelerate extinguishment speed. In summary, the interruption time is reduced when the values of \(R_{FCL}\) and \(L_{FCL}\) are high. This result agrees with the simulation result demonstrated in Figure 22b.  
Figure 22: Characteristic curves of absorbed energy and interruption time under variables (**a**) \(R_{FCL}\) and (**b**) \(L_{FCL}\).
###### Abstract  
Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests' complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of mangrove distribution, and technical difficulties in high-resolution image processing. To address these challenges, we present two main contributions. (1) Using multi-source high-resolution satellite imagery from China's new generation of Earth observation satellites, we constructed the Mangrove Semantic Segmentation Dataset of Beihai, Guangxi (MSSDBG); (2) We propose a novel Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for precise mangrove segmentation. This network integrates Cascade Pyramid Fusion Modules, a Multi-scale Selective Kernel Attention Module, and an Auxiliary Edge Neck to process the unique characteristics of mangrove remote sensing images, particularly addressing issues of scale variation, complex backgrounds, and boundary accuracy. The experimental results demonstrate that our approach achieved a mean Intersection over Union (mIoU) of 94.54% and a mean Pixel Accuracy (mPA) of 97.14% on the MSSDBG dataset, significantly outperforming existing methods. This study provides valuable tools and methods for monitoring and protecting mangrove ecosystems, contributing to the preservation of these critical coastal environments.  
Keywords:remote sensing image; mangrove monitoring; semantic segmentation; Feature Pyramid Network (FPN); attention mechanism +
Footnote †: journal: Applied sciences
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 3 Imaging in BAV
### Key Hemodynamic Parameters  
Recent studies have highlighted additional hemodynamic parameters that provide valuable insights into BAV-related aortopathy:Flow Eccentricity and Helicity: BAV is associated with altered blood flow patterns, including increased flow eccentricity and helicity in the ascending aorta. These parameters are distinct from those observed in healthy individuals and are indicative of the abnormal flow dynamics caused by the bicuspid valve structure and phenotype [48; 49; 50], Figure 3.  
Wall Shear Stress (WSS): WSS has emerged as a critical biomarker in the understanding and management of BAV-associated aortopathy. WSS refers to the tangential force exerted by blood flow on the endothelial surface of the vessel wall. Abnormal WSS patterns have been implicated in the development and progression of aortic aneurysms and other vascular complications in BAV patients. Studies have shown that BAV leads to altered flow dynamics within the aorta, resulting in regions of increased or decreased WSS. These abnormal WSS patterns can contribute to the heterogeneous nature of aortopathy observed in BAV patients. For instance, regions of high WSS are associated with the development of aortic dilation and aneurysm formation, while low WSS can lead to areas of stagnation and potential thrombus formation [37; 38; 39; 40]. Elevated WSS is commonly observed in BAV patients, particularly along the greater curvature of the ascending aorta. This increased stress is linked to the development of aortic dilation and wall thinning, making it a critical marker for disease progression [49; 51].  
Clinical Utility of WSS: [PERSON] et al. (2019) highlighted the potential of using WSS as a non-invasive imaging biomarker for risk stratification in BAV patients. By creating individualized WSS 'heat maps', clinicians can identify areas of the aorta at higher risk of pathological changes, guiding the timing and extent of surgical interventions [16]. This approach allows for personalized management strategies, optimizing patient outcomes.  
Forward and Reverse Flow: BAV patients exhibit significant alterations in forward and reverse flow dynamics. Studies using advanced 4D-flow MRI techniques have shown reduced forward flow and increased reverse flow in the ascending aorta, which are associated with aortic dilation and may predict the need for surgical intervention [48; 50].  
Energy Loss and Vorticity: Parameters such as energy loss, vorticity, and relative pressure provide further insights into BAV hemodynamics. Increased vorticity and energy loss have been linked to inefficient flow patterns and higher mechanical stress on the aortic wall, contributing to disease progression [49; 51].  
Figure 3: Aortic valve phenotype impact in disturbed flow direction pattern. Created in BioRender.com.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Environmental Monitoring
#### 3.2.4 Example  
Consider a scenario where the quality of our environment is at stake. A forest area, a crucial part of our ecosystem, must be monitored for air quality, temperature, humidity, and vegetation health. The environment is dynamic, with potential obstacles like trees and varying terrain. A swarm of drones will be deployed to collect data and analyze environmental conditions, highlighting the significance of the task. There are the following constraints:  
* Area Coverage: The entire forest area must be covered without significant overlaps or gaps.
* Obstacle Avoidance: Drones must navigate around trees, branches, and other obstacles.
* Battery Life: Each drone has limited battery life, requiring efficient path planning.
* Data Reliability: Data must be accurate and representative of the monitored parameters.  
The objective is to maximize the coverage and accuracy of environmental data collection while minimizing energy consumption and ensuring robustness. The objective function \(F\) can be formulated as follows.  
\[F=\alpha\left(\frac{\sum_{i=1}^{N}C_{i}}{A}\right)+\beta\left(\frac{\sum_{i=1} ^{N}(Q_{i}+T_{i}+H_{i}+V_{i})}{N}\right)-\gamma\left(\frac{\sum_{i=1}^{N}E_{i} }{N}\right)-\delta\left(\frac{\sum_{i=1}^{N}O_{i}}{N}\right) \tag{23}\]  
where all variables were annotated in Table 3.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 2 Data and Methods
### Comparisons of CALIOP Cloud with MODIS Cloud  
To obtain the spatially consistent data between CALIOP and MODIS, a spatial collocation was made between the two satellites using the method introduced by [17]; that is, when performing a comparison between MODIS and CALIOP, the MODIS pixels on CALIPSO orbit tracks were chosen, and CALIOP observations within each MODIS 1 \(\times\) 1 km\({}^{2}\) pixel were considered. The overpass time of Aqua and CALIPSO is around 13:30 China Standard Time (CST = UTC + 8) during daytime. In total, 5,519,850 CALIOP profiles in 623 orbit tracks were collocated with 839,950 MODIS pixels during the period 2013-2016. Because of the sparse spatial sampling from CALIPSO [15; 16], when calculating the regional distribution of cloud fraction, SZA and COT, we considered all MODIS and CALIOP pixels in each 2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) box to ensure more data were included.  
In order to reveal the difference between MODIS and CALIOP cloud detection, four ratios were put forward to present the difference between MODIS and CALIOP. The ratio of the total number of cloud pixels observed by MODIS but not by CALIOP to all cloud-free pixels observed by CALIOP was labeled as r_MODcloud_CALfree. The ratio of the total number of cloud pixels observed by CALIOP but not by MODIS to all cloud-free pixels observed by MODIS was labeled as r_CALcloud_MODfree. The ratio of cloud pixel numbers observed by MODIS but not by CALIOP to all MODIS cloud pixels was labeled as r_MODalone. The ratio of cloud pixel numbers observed by CALIOP but not by MODIS to all CALIOP cloud pixels was labeled as r_CALalone.
## 2 Mechanistic Foundations of Skeletal Muscle Aging
### Key Regulators of Regeneration and Their Age-Related Decline  
Effective muscle regeneration requires coordinated activity of several pathways:  
* Wnt and Notch signaling regulate satellite cell fate decisions.
* Hypoxia-inducible factor 1-alpha (HIF-1\(\alpha\)) and Vascular Endothelial Growth Factor (VEGF) control hypoxia adaptation and angiogenesis.
* mTOR and AKT promote anabolic signaling and protein synthesis.
* SIRT1, an NAD\({}^{+}\)-dependent deacetylase, modulates mitochondrial biogenesis, inflammation, and autophagy.  
In aging, dysregulation of these networks contributes to impaired regeneration. For example, chronic activation of mTOR1 in aged muscle promotes anabolic resistance, while declining SIRT1 activity exacerbates oxidative stress and inflammation [7; 79; 80; 81; 82; 83; 84; 85; 86; 87].
## 6 Use Case: Cyber and Physical Attacks against a Water Critical Infrastructure
### Network Reconnaissance  
After being connected to the target infrastructure, the attacker performs passive actions through PING scans (Internet Control Message Protocol - ICMP packets sent to an IP or a range of IP addresses to see which ones respond and are alive); or Port scans to discover services running on a network or target machine. Once scanned, the attacker can plan an active attack against identified vulnerable services.  
The objective of this attack is to identify the hosts composing the network. For this purpose, the attacker, knowing that the network address is 192.168.66.0/24, he/she executes a PING command from the attacker machine running Kali Linux. In just a few seconds, the attacker can retrieve the list of IP address of all active hosts in the LAN (i.e., 192.168.66.1, 192.168.66.2, 192.168.66.5, 192.168.66.6, 192.168.66.7, 192.168.66.180).  
Once the attacker has confirmed that there are active hosts in the network, he/she may perform a port scanning using Nmap to identify open ports and services running in each host. After the reconnaissance of the network and hosts, the attacker prepares an active attack to read and/or modify information from any of the tables from the system's database.  
Suricata generates logs related to a potential policy violation in DHCP request packets. After the execution of the PING requests, Suricata generated logs entitled \"ICMP3 Packetfound\" provide an identification of the IP source and destination involved. Although the IDS is unable to classify this log under a predefined category, the priority of this log has a level 3, which requires a deeper analysis from the SCADA administrator. Examples of the Suricata messages received by the XL-SIEM are provided as follows:  
* 04/03/2020-08:16:14.515429 [**] [1:400006:0] ICMP3 Packet found [**] [Classification: (null)] [Priority: 3] ICMP 192.168.66.5:8 \(\longrightarrow\) 192.168.66.6:8
* 04/03/2020-08:19:15.046731 [**] [1:202297:3:1] ET POLICY Possible Kali Linux hostname in DHCP Request Packet [**] [Classification: Potential Corporate Privacy Violation] [Priority: 1] UDP 192.168.66.5:68 \(\longrightarrow\) 192.168.66.6:67 These logs are sent to the CLAP to be processed and displayed in the platform's dashboard with the following format: <**Signature; Date; Sensor; Source; Destination; Risk> Examples of the security events coming from Suricata and displayed by CLAP are given bellow:
* PING test; 2020-04-03 08:16:14; xlsiem-server; 192.168.66.5:8; 192.168.66.6:8; 0
* ET POLICY Possible Kali Linux hostname in DHCP Request Packet; 2020-04-03 08:19:15; xlsiem-server; 192.168.66.5:68; 192.168.66.6:67; 0  
Each detected event has been assigned a severity of cero, meaning that the risk level is very low. All ICMP logs are displayed in the CLAP dashboard as PING test events, indicating the time at which they were detected, the sensor responsible for the detection, the IP source and destinations involved, the asset value and the risk level associated with each event.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 3 Results
### Artificial Neural Network  
BP neural network is used to build OMC and MDD prediction models. ANN is a customized network model, since, on the one hand, the higher number of hidden layers in ANN is beneficial to the network to solve complex nonlinear problems, but too many hidden layers may lead to the overfitting of the model. On the other hand, a higher number of neurons is beneficial to increase the expressive ability and learning ability of the network. However, an unreasonable number of neurons may also lead to overfitting or underfitting. Therefore, it is important to determine the appropriate number of hidden layers and neurons. According to existing research, ANNs with one hidden layer have been able to meet most of the requirements [41; 42; 43; 44; 45]. Therefore, in this paper, an ANN with one hidden layer is used to study the effect of the number of neurons on the prediction model, and the results are shown in Table 3.  
As shown in Figure 7, in the OMC prediction model, when the number of neurons increases, the RMSE of the training set tends to decrease and then increase, and then decrease and then increase again. And the test set tends to decrease and then increase. Therefore, a neuron number of 7 is the best OMC prediction model. In the MDD prediction model, both  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Number of Neurons**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-5}  & & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{5}{*}{OMC} & 4 & 1.7850 & 0.8616 & 1.8375 & 0.8536 \\  & 5 & 1.5441 & 0.8964 & 1.7155 & 0.8724 \\ \cline{1-1}  & 6 & 1.5916 & 0.8899 & 1.6621 & 0.8802 \\ \cline{1-1}  & 7 & 1.5790 & 0.8917 & 1.6302 & 0.8848 \\ \cline{1-1}  & 8 & 1.5961 & 0.8893 & 1.7834 & 0.8621 \\ \hline \multirow{5}{*}{MDD} & 4 & 0.5828 & 0.8262 & 0.6237 & 0.8208 \\ \cline{1-1}  & 5 & 0.5675 & 0.8351 & 0.6179 & 0.8241 \\ \cline{1-1}  & 6 & 0.5625 & 0.8381 & 0.5949 & 0.8370 \\ \cline{1-1}  & 7 & 0.5249 & 0.8590 & 0.5727 & 0.8490 \\ \cline{1-1}  & 8 & 0.5574 & 0.8410 & 0.6100 & 0.8286 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different neuronal performance results of ANN.  
Figure 6: Regression plots between actual and predicted values with SVM (Gaussian kernel function); (**a**) OMC; (**b**) MDD.  
As shown in Table 4 and Figure 9, in the best prediction model for OMC, RMSE = 1.3605% and \(R^{2}\) = 0.9198. In the best prediction model for MDD, RMSE = 0.5664 kN/m\({}^{3}\) and \(R^{2}\)= 0.8523. Through the results, it is shown that RF has an excellent performance in predicting OMC, with an \(R^{2}\) of more than 0.90, while for MDD, although the \(R^{2}\) reaches about 0.85, there is still a gap of about 6% in comparison with OMC.
## 2 Materials and Methods
### MOE-Dock Analysis  
The binding analysis was carried out using MOE-Dock. The binding capacities in comparison to each other were calculated. The structure of KpSSB was used for docking analysis. Any water molecules present in this crystal structure were removed using MOE. To ensure accuracy, a 3D protonation step followed by energy minimization was applied to add hydrogen atoms to the protein structure. The highest-ranked conformations were generated. The binding modes were predicted and visualized directly through the MOE-Dock tool.