esa-sceva/satcom-chunk-collection huggingface dataset:
# Holographic Lens Array for Solar Collector with Large Angle and Expanded Spectral Width
## 5 Conclusions  
This paper proposes a holographic lens array solar collector based on periodically arranged off-axis holographic lenses, which has both a large acceptance angle and a wide collection spectral width. Firstly, the holographic optical element has the advantages of being lightweight, flexible, and efficient. Secondly, the arrangement of off-axis collection brings more freedom to the design and can effectively reduce the area of solar cell, which is beneficial for improving the power to weight ratio and reducing costs. In addition, an HLASC with larger collection angles and spectral widths will also bring many benefits. On the one hand, a large acceptance angle contributes to the non-use of sun tracking equipment, ensuring that it is lightweight and portable while meeting the need for long-term collection. On the other hand, the spectral expansion method based on spectrum spatial allocation aids in improving energy collection and enhancing power supply efficiency. It is expected that the proposed design method for the HLASC could contribute to the development of compact and efficient solar collectors.  
Conceptualization, C.W. and J.L.; methodology, C.W., Y.X., H.X. and J.L.; software, C.W.; validation, C.W. and Y.X.; formal analysis, Y.X. and J.L.; investigation, C.W. and H.X.; resources, J.L.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, C.W.; visualization, C.W. and Y.X.; supervision, J.L.; project administration, C.W. and Y.X.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U22A2079 and 62035003; Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park, grant number Z211100004821012.  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 4 Results  
The GRU method described in the previous section was evaluated with respect to two key features: prediction performance and model scalability. In terms of prediction performance, this study compared the proposed algorithm results with those obtained using alternative methods, LSTM and ARIMA.
## 4 Enabling Technologies for FSO Communication
### Cooperative Relaying
#### Synchronous Transmission Scheme  
Synchronous transmission, a fundamental technique, has been utilized since the introduction of cooperative communication in radio systems. In this method, the source node transmits identical data on both the relay links and the direct link, enabling the destination node to combine the received signals synchronously, resulting in a stronger SNR. In this regard, all nodes involved in the cooperative FSO system transmit their signals in a coordinated manner, ensuring that the signals arrive at the destination simultaneously. This coordination requires precise timing synchronization among the source and relay nodes. This approach aims to enhance the reliability of the communication by providing multiple copies of the same signal. The destination node combines these received signals to improve signal quality and mitigate errors caused by AT and path loss. While cooperativecommunication has been extensively investigated in FSO systems, the redundancy inherent in synchronous transmission persists. Consequently, the FSO system must address the challenge of channel underutilization to achieve spatial diversity through cooperative communication [284].  
An asynchronous transmission scheme is designed to minimize the substantial link redundancy in cooperative FSO systems (typically seen in synchronous transmission). In contrast to synchronous transmission, the asynchronous scheme enables the source node to send different data over both the direct and relay links, facilitating more flexible and independent data transmission. To leverage the diversity gain from these links, which carry distinct data, a squared signal combination method can be utilized to merge the signals received from multiple links. By jointly processing the signals before and after the squaring operation, the data on each link can be accurately recovered. As a result, asynchronous transmission provides diversity gain for cooperative FSO systems, improves channel utilization, and ultimately increases the effective data rate compared to synchronous transmission [284].
###### Abstract  
Non-thermal plasma (NTP), defined as a partially ionized gas, is an emerging technology with several biomedical applications, including tissue regeneration. In particular, NTP treatment has been shown to activate endogenous biological processes to promote cell regrowth, differentiation, and proliferation in multiple cell types. However, the effects of this therapy on nervous system regeneration have not yet been established. Accordingly, the current study explored the effects of a nanosecond-pulsed dielectric barrier discharge plasma on neural regeneration. Following mechanical trauma in vitro, plasma was applied either directly to (1) astrocytes alone, (2) neurons alone, or (3) neurons or astrocytes in a non-contact co-culture. Remarkably, we identified NTP treatment intensities that accelerated both neurite regeneration and astrocyte regrowth. In astrocyte cultures alone, an exposure of 20-90 mJ accelerated astrocyte re-growth up to three days post-injury, while neurons required lower treatment intensities (\(\leq\)20 mJ) to achieve sub-lethal outgrowth. Following injury to neurons in non-contact co-culture with astrocytes, 20 mJ exposure of plasma to only neurons or astrocytes resulted in increased neurite regeneration at three days post-treatment compared to the untreated, but no enhancement was observed when both cell types were treated. At day seven, although regeneration further increased, NTP did not elicit a significant increase from the control. However, plasma exposure at higher intensities was found to be injurious, underscoring the need to optimize exposure levels. These results suggest that growth-promoting physiological responses may be elicited via properly calibrated NTP treatment to neurons and/or astrocytes. This could be exploited to accelerate neurite re-growth and modulate neuron-astrocyte interactions, thereby hastening nervous system regeneration.  
Keywords:plasma medicine; non-thermal plasma; dielectric barrier discharge; astrocytes; neural regeneration; axon; neurite outgrowth +
Footnote †: journal: Applied Soft. 2019, 9, 3747; doi:10.3390/app9183747
# An Effective Hybrid Routing Algorithm in WSN:
## 5 Experimental Results and Analysis
### Comparison of Convergence Characteristics of the Several Algorithms  
In the WSNs, due to the limited energy power of nodes and the dynamic network topology, it is critical that the routing protocol to calculate the optimal path with low cost. Therefore, the routing algorithms should have a high convergence speed and a high success rate of searching for the optimal solution.  
We conduct experiments that compare the convergence speed of the several algorithms including the classic ACO, LTAWSN, ACO-GA and ACOHCM. These four algorithms are run 100 times separately and Figure 2 reports the average number of iterations of each algorithm. In Figure 2, it is clearly to show that ACOHCM algorithm finds the optimal solution after 38 iterations on average, while classic ACO needs 198 iterations on average, 102 for LTAWSN, and 64 for ACO-GA. Therefore, it proves that ACOHCM algorithm is superior to the other three algorithms in terms of the convergence speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(E_{\mathit{elec}}\) & 50 mJ/bit & \(\epsilon_{\mathit{mp}}\) & 0.0013 (pJ/bit/m\({}^{4}\)) \\ \(\epsilon_{\mathit{fs}}\) & 10 (pJ/bit/m\({}^{2}\)) & \(d_{\mathit{max}}\) & 20 m \\ \(k\) & 100 KB & \(\gamma\) & 2.5 \\ \(C\) & 1000 J & \(Q\) & 100 \\ \(\alpha\) & 0.5 & \(\beta\) & 5 \\ \(\rho\) & 0.3 & \(iter_{\mathit{cnt}}\) & 100 \\ _threshold_ & 800 J & \(threshold_{\mathit{min}}\) & 300 J \\ \(\tau_{\mathit{max}}\) & 200 & \(\tau_{\mathit{min}}\) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters in the simulation.  
Figure 2: Convergent features of the different algorithms.  
The improvement of the convergence speed of the ACOMHC algorithm is because of the following two reasons: on one hand, it owes to the strategy of using the minimal hop count to guide the path searching direction of the next hop node selection in the hop count-classification topology. On another hand, it owes to the role of pheromone, that is, to increase the pheromone on the optimized path in a certain range, we can speed up the convergence.  
The average time for one iteration of the four algorithms and the success rate of searching for the optimal solution in 100 iterations of the four algorithms are recorded in Table 2. Table 2 shows that ACOHCM algorithm with hop count-classification topology is apparently superior to the other three in computing time. In order to test the success rate of searching for the optimal solution of the algorithms, each algorithm is tested 100 times respectively with 100 iterations in each test. As Table 2 shows that ACOHCM has a strong ability to search for the global optimal solution, and its success rate is higher than that of the other algorithms.  
The above simulation results have shown that ACOHCM can greatly improve the efficiency of routing computation and the success rate of searching for the global optimal solution, and verify that ACOHCM algorithm is reliable and effective.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 3 Strategies for Analyzing Image DNA Techniques
### Limitations of the Study  
Despite the promising results, this study has several limitations that should be addressed. First, while the COCO dataset is widely used in image-based deep learning research, its size and diversity may not be sufficient to fully represent real-world scenarios. As a result, the model's performance in highly variable or complex environments remains uncertain. Nevertheless, the dataset serves as a reasonable benchmark for performance comparisons, providing a foundational reference for evaluating different approaches.  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Image Name** & **Transformation Details** \\ \hline
1 & COCO\_val2014\_000000524333\_modified & Reduce image size \\
2 & COCO\_val2014\_000000393267\_modified & Adjust image colors \\
3 & COCO\_val2014\_000000393243\_modified & Add partial mosaic \\
4 & COCO\_val2014\_000000393226\_modified & Crop image border \\
5 & COCO\_val2014\_000000262175\_modified & Add caption to image \\
6 & COCO\_val2014\_000000262161\_modified & Rotate 90 degree \\
7 & COCO\_val2014\_000000262148\_modified & Stretch horizontally \\
8 & COCO\_val2014\_00000031115\_modified & Enhance image color \\
9 & COCO\_val2014\_00000013108\_modified & Rotate slightly and crop \\
10 & COCO\_val2014\_00000131089\_modified & Change image texture \\
11 & COCO\_val2014\_000000262197\_modified & Enlarge image size \\
12 & COCO\_val2014\_00000039327\_modified & Adjust color levels \\
13 & COCO\_val2014\_000000262200\_modified & Obscure face with mosaic \\
14 & COCO\_val2014\_000000013138\_modified & Crop image border \\
15 & COCO\_val2014\_000000393284\_modified & Add caption to image \\
16 & COCO\_val2014\_0000000074\_modified & Convert black and white \\
17 & COCO\_val2014\_00000131152\_modified & Rotate \(-90\) degrees \\
18 & COCO\_val2014\_00000524373\_modified & Stretch vertically \\
19 & COCO\_val2014\_00000262229\_modified & Blur face \\
20 & COCO\_val2014\_00000262235\_modified & Overlay with pen strokes \\ \hline \end{tabular}
\end{table}
Table 2: The 20 transformations of COCO images in our experiments.  
Additionally, although various editing methods were applied to the original images, the study did not incorporate manually augmented datasets. More extensive manual augmentation could have further enriched the dataset, enhancing the robustness of the model. Moreover, future research could benefit from employing advanced data augmentation techniques to improve dataset diversity and generalization. Finally, while this study primarily focused on autoencoders and classification-based deep learning models pretrained on ImageNet, it did not evaluate models that directly learn transformation methods. Exploring such models could provide further insights into optimizing image DNA-based techniques.
## 5 Reactor Systems and Process Parameters
### Ionic strength and competing ions  
In natural water matrices, variousinorganic ions such as chloride (Cl\({}^{-}\)),sulfate (SO\({}_{4}^{2}\)), and phosphate (PO\({}_{4}^{3-}\)) can influence the photocatalytic reactions. These ions may compete with pollutants for adsorption sites on the photocatalyst or scavenge ROS, thereby reducing degradation efficiency. However, some studies have reported that certain anions can promote the photocatalytic activity under specific conditions [159]. For example, research demonstrated that chloride and sulfate ions enhanced the generation of hydrated electrons, leading to improved degradation of perfluorocarbylates in a vacuum ultraviolet/hydrogen system. Chloride ions can have dual effects on photocatalytic degradation. At lower concentrations, Cl\({}^{-}\) may enhance degradation by forming reactive chlorine species. Conversely, at higher concentrations, Cl\({}^{-}\) can inhibit degradation by scavenging hydroxyl radicals (\(\bullet\)OH) and competing for active sites on the photocatalyst. For instance, a study demonstrated that chloride ions at a 0.5 mM concentration increased the degradation of methylene blue, while higher concentrations led to inhibition [105,160]. Sulfate ions generally exhibit an inhibitory effect on the photocatalytic processes by adsorbing onto the photocatalyst surface and blocking active sites. Research indicates that sulfate anions had the most significant inhibitory effect on the degradation of methylene blue, likely due to their strong adsorption affinity [160]. Phosphate ions can also inhibit the photocatalytic activity by adsorbing onto the photocatalyst surface, thereby reducing the availability of active sites for pollutant degradation. A study observed that phosphate ions significantly decreased the generation of photogenerated holes and hydroxyl radicals, leading to reduced photocatalytic efficiency [161].  
To enhance the efficiency of photocatalytic processes, it is essential to optimize various operational parameters. For example, several studies reported optimal catalyst dosages ranging from 0.5 to 2.0 g/L for TiO\({}_{2}\)-based systems, with irradiation times typically between 60 and 180 min under visible or UV light sources [97,98]. Reaction temperatures are often maintained between 20 and 35 \({}^{\circ}\)C to simulate ambient conditions [104].  
Tailoring these factors to the specific photocatalyst and pollutants involved can lead to more effective and sustainable environmental remediation strategies [51,149,162]. A comprehensive understanding of these interactions is essential for optimizing photocatalytic systems for environmental applications.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 3 Materials and Methods
### Feature Importance and the Selection Process
The sixth stage standardizes the extracted features across the dataset using z-score normalization (7):  
\[\hat{\gamma_{k}}=\frac{i_{k}-\mu_{k}}{\sigma_{k}} \tag{7}\]  
with the mean and standard deviation calculated as (8):  
\[\mu_{k}=\frac{1}{N}{\sum_{i=1}^{N}}_{j,k},\ \sigma_{j}=\sqrt{\frac{1}{N}\sum_{i =1}^{N}}\ \Big{(}_{j,k}-\mu_{k}\Big{)}^{2} \tag{8}\]  
\[X^{(i)}e\ R^{C*T} \tag{1}\]0 Schematic overview of EEG data processing and hybrid model training for ADHD detection. At the seventh stage, the dataset is split into training and validation subsets (9):  
\[X^{(i)}e\ R^{C*T} \tag{1}\]1  
where \(y^{(i)}\in\{0,1\}\) is the class label (control or ADHD). Finally, in the eighth stage, the standardized features are formatted into 3D tensors and input into the CNN-GRU-LSTM hybrid model (10):  
\[X^{(i)}e\ R^{C*T} \tag{1}\]2  
The model performs spatial feature extraction via convolutional layers and temporal modeling using GRU and LSTM units and produces the final prediction through a dense sigmoid-activated output (11):  
\[X^{(i)}e\ R^{C*T} \tag{1}\]3  
where \(h\) is the learned representation, and \(\sigma\) is the sigmoid function. A thorough pipeline that preserves essential temporal and spatial characteristics was used to ensure high-quality input from EEG for deep learning. The CNN-GRU-LSTM model, a hybrid model that accurately captures complex brain dynamics, improves ADHD classification accuracy and interpretability.
# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 2 Materials and Methods  
### Study Area  
SWC is composed of the Guangxi Zhuang Autonomous Region, Yunnan Province, Guizhou Province, Sichuan Province, and Chongqing Municipality (Figure 1a,b). The terrain in the region is complex, including the Hengduan Mountains, Yunnan-Guizhou Plateau, Sichuan Basin, and Guangxi Basin, and karst landforms are widely distributed [19]. The climate of SWC is mainly dominated by the subtropical monsoon and is also influenced by alpine and tropical monsoons [22]. The vegetation distribution in SWC has obvious regional geographical differences. Grassland is the dominant vegetation in the high-altitude areas in west Sichuan, shrubland and forest are mainly distributed in the middle altitude areas, and low-altitude areas such as the Sichuan and Guangxi Basins are suitable for farming.  
The vegetation map of SWC shown in Figure 1 was obtained from the _Land Cover Atlas of the People's Republic of China_ (1:1,000,000) [23; 24]. The vegetation distribution and the percentage of each type can be seen in Figure 1a,c. Here, the main vegetation types were considered in SWC, including evergreen broadleaf forest (12.58%), deciduous broadleaf forest (2.19%), evergreen needleleaf forest (24.82%), broadleaf and needleleaf mixed forest (1.09%), evergreen broadleaf shrubland (8.29%), deciduous broadleaf shrubland (6.83%), grassland (14.84%), and cropland (22.03%).
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 3 Design
### Periodical Polling for Network Packet Processing  
NetAP develops a periodic polling technique in the vHost thread to maximize the utilization of network and CPU resources. The modified vHost thread in NetAP periodically polls the shared queue instead of waiting for a vIRQ from the front-end device driver. At each polling interval, the vHost thread is scheduled, and it processes packets sent from the guest OS. After the vHost thread completes packet processing, it sleeps until the next polling interval. During this sleep time, the guest OS generates packets, and they are buffered into the shared queue. At the next interval, the vHost thread wakes up and processes the buffered packets in the shared queue.  
The main difference between NetAP and the original VirtIO is that NetAP frequently monitors the shared queue and proactively handles the buffered packets before a vIRQ is generated from the front-end driver. In original VirtIO, the front-end driver produces packets and inserts them into the shared queue. When the number of buffered packets in the queue is more than a certain threshold, a vIRQ is delivered to the vHost thread to wake up the thread. However, NetAP processes the buffered packets at an appropriate rate with periodic polling before the number of packets exceeds the threshold. Therefore, the front-end driver does not generate vIRQs. This mechanism can prevent expensive VM-Exit and VM-Entry operations and, therefore, improve network performance with efficient CPU use.
## 5 Discussion and Conclusions  
This research clarifies that the RST rule induction approach offers a unique and viable posterior classification approach to extract the patterns of how the electrofacies where constructed from the unique description of well log responses reflecting minerals and lithofacies from the logged interval by using the non-hierarchical k-means clustering algorithm. Electrofacies have a very significant bearing on reservoir parameter calculations like lithology, which is also shown in the experiment portion. RST provides efficient algorithms for finding hidden patterns in well log datasets and generates interpretable and understandable rules to classify the electrofacies with higher accuracy. The rules can explain the contributions of the well log attribute values in interpreting and classifying the electrofacies classes, which makes this module a unique whitebox electrofacies prediction module. It is also shown that, by analyzing the rules, valuable information on how the features contribute to interpreting the lithology classes can be extracted. A comparison study was also provided on the same datasets by employing some other renowned methods that have been used in this field, and we found that RST provides slightly better accuracy in classifying the electrofacies classes. Furthermore, RST provides a whitebox approach to classification, whereas deep learning, SVM or Random Forest provide only a blackbox prediction approach. However, despite RST being independent in its numerous achievements, to its tribute, in the future, for better accuracy, we will be working on multilayer RST to improve the prediction accuracy, by using larger datasets with more samples and datasets from several wells.  
Conceptualization, T.M.H.; methodology, T.M.H. and J.W.; software, T.M.H.; validation, T.M.H., J.W. and M.H.; formal analysis, J.W.; investigation, J.W. and I.A.A.; resources, M.H.; data curation, M.H.; writing--original draft preparation, T.M.H.; writing--review and editing, T.M.H. and J.W.; supervision, J.W. and I.A.A. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Petroleum Research Fund (PRF), Cost Center 0153 AB-A33, under the leadership of Dr. [PERSON].  
The authors declare no conflict of interest.  
Figure 7: Random Forest Classifier scores for electrofacies classification.