esa-sceva/satcom-chunk-collection huggingface dataset:
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Experimental Setting  
In the simulation, the water to be monitored in the simulation is a three-dimensional volume of 6 km \(\times\) 4 km \(\times\) 4 km. The initial deployment in the volume is composed of eight clusters, that is, the network contains eight clusters, and each cluster is responsible for monitoring the three-dimensional volume of 3 km \(\times\) 2 km \(\times\) 2 km. In each cluster there is a glider as an advanced node and common sensor nodes to carry out environmental monitoring tasks in their respective volumes. The initial energy, transmission power, receive power and communication distance of these common sensor nodes are all the same. The performance of the sensor carried on the underwater glider is not different from that of the common sensor nodes. The transmission power and receive power of the data are also consistent in every node. For source nodes, the data packet generation rate and sending rate follow the independent Poisson distribution process and the rate is \(\lambda\) (packets/s).  
The parameters used in the experiment refer to the Aqua-sent OFDM MODEM [28] and \"Petrel\" glider [11] parameters. The following simulation is done using Aqua-sim [29], which is an underwater network simulation software based on NS-2. The simulation parameters are listed in Table 1.
# Quantitative Scale Analysis of the Channel Bar in a Braided River and Its Internal Architecture
## 3 Results
### Study on the Architecture Elements of Channel Bar
With reference to previous research results, and according to the square value of \(R\) (the closer to 1 indicates the better fitting effect), the fitting function method is optimized. Through fitting regression, the length and width of the channel bar, and the length and width of the braided river, demonstrate a good e-index function relationship (Figures 5 and 6), with determination coefficients of above 0.75. The fitting function of the width and length of single braided river is as follows:  
\[W_{h}=0.664L_{h}-15.433\quad R^{2}=0.8074 \tag{1}\]  
The fitting function of the width and length of the sand body of single channel bar is as follows:  
\[W_{b}=0.2776L_{b}-2.0084\quad R^{2}=0.825 \tag{2}\]  
In the above formulas, \(L_{h}\) is the length of a single braided river sand body (unit: m). \(W_{h}\) is the width of the single braided river sand body (unit: m). \(L_{b}\) is the length of the single channel bar (unit: m). \(W_{b}\) is the width of the single channel bar (unit: m). Combined with the sedimentary characteristics and the width of braided river and channel bar, the correlation between the width of the river sand body and the width of the channel bar is relatively good. Figure 7 shows a positive linear relationship, with a decision coefficient of 0.9598. It means that the larger the development scale of the channel bar, the wider the braided river. Figure 4: Channel bar on the beach shore of the braided river under the action of tides. Its function relationship is as follows:  
\[W_{h}=0.2802W_{b}+0.3242\qquad R^{2}=0.8703 \tag{3}\]  
Combined with the above formulas of beach shore sedimentation, the scale of the single braided river and single channel bar in the dense well pattern area of the Nanwu area are calculated. The minimum length of the channel bar in PI3 layer in the south five area is 600 m, and the maximum channel bar length reaches 1000 m. The minimum width of the channel bar is about 400 m, and the maximum width is up to 500 m. The width of the single braided channel shows relatively narrow distribution, whose overall distribution is between 90-140 m (Figure 8). Generally, through the statistical analysis of sand body configuration parameters, it is found that there is a good positive correlation between the length and width of the core beach and the width and thickness, and the correlation coefficient is usually more than 0.8. There is also a positive correlation between channel length and width, and the correlation  
Figure 5: Curve of the length and width of the braided river sand body. Figure 6: Length and width relationship curve of the channel bar sand body. Figure 7: Relationship curve between the width of the sand body of single braided river and the width of the channel bar. coefficient is above 0.5, and the correlation between width and thickness is good. The width-to-thickness ratio and length-to-width ratio have similar change rules with the core beach body.
## 2 Results
### Metabolic Pathways of VOCs  
Based on the literature, there are several biosynthetic pathways leading to the diverse range of plant VOCs. The three main metabolic pathways involved in the formation of the identified VOCs in this study are summarized in Figure 3. The metabolic pathways included fatty acid pathway (for secondary volatile metabolites, e.g., GLVs), MEP pathway (for terpenes), and glucosinolate pathway (for ITCs/nitriles). The GLVs are known to be produced from C18 unsaturated fatty acids including linoleic acid or linolenic acid through the lipoxygenase (LOX) pathway [1]. Our results showed that green leafy vegetables (e.g., watercress, rocket, Brussels sprouts, broccoli, kai lan, choy sum, and pak choi) generated more GLVs, indicating that the LOX pathway may behave differently between these green leafy vegetables and others. Monoterpenes are synthesized mainly by the MEP pathway in plastids [1], and our results showed that Brussels sprouts and broccoli may generate more monoterpenes than other vegetables through this pathway. ITCs and nitriles are hydrolysis products of GLSs in plants, which are metabolized from their corresponding precursor amino acids such as alanine, leucine and methionine [27]. Methionine is also the precursor of numerous sulphide compounds including dimethyl trisulfide and dimethyl disulphide [28]. In our study, radish, cherry radish, cabbage, watercress and Brussels sprouts emitted higher levels of volatile ITCs, and broccoli, Chinese cabbage and cauliflower emitted higher levels of nitriles and sulphur compounds.  
Figure 3: Main metabolic pathways of VOCs and their distribution in 12 Brassicaceae vegetables.
## 3 Proposed Method
### Frequency Domain Similarity Gated Selection Vision Transformer  
In the field of image restoration, noise interactions among unrelated features can have a negative impact on image restoration. The conventional Transformer employs global computation of self-attention to handle all token features, which is not an ideal operation for image restoration. The emergence of the sparse Transformer [42] effectively addresses this issue. It selects the top-k contributing scores with the aim of retaining the most significant elements and removing unnecessary parts. This is achieved by computing the similarity between all queries and keys in terms of pixel-wise similarity and masking out irrelevant elements with lower attention weights in the transposed attention matrix. This dynamic selection transforms the attention in the Transformer from dense to sparse, effectively mitigating the impact of unrelated features. The outputs of the standard Transformer and sparse Transformer are generally represented as follows:  
\[\text{SelfAtt}(Q,K,V)=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{1}\]  
\[\text{SpaAtt}(Q,K,V)=\text{softmax}(\text{TopK}(\frac{QK^{T}}{\sqrt{d_{k}}}))V \tag{2}\]  
where \(\text{SelfAtt}(Q,K,V)\) represents the output representation of the standard Transformer, \(Q\) is a matrix composed of a set of query sequences, \(K\) is a matrix composed of a set of key sequences, \(V\) is a matrix composed of a set of value sequences, and \(d_{k}\) is the dimension of the Query and Key vectors. \(\text{SpaAtt}(Q,K,V)\) represents the output representation of the sparse Transformer. TopK denotes the learnable top-\(k\) selection operator.  
The sparse Transformer provides a valuable insight by discarding irrelevant features during the similarity calculation, thereby reducing computational complexity while increas  
Figure 1: The overall architecture of the network generator proposed for image enhancement in this paper is depicted. It mainly comprises MDFN, FSGS-ViT, and EB, along with MKSM and AGMM. The detailed structures of MDFN, EB, and AGMM are presented beside the overall architecture.  
ing the utilization of useful features. Inspired by this idea, we have designed a novel block specifically for multi-task learning called the Frequency Domain Similarity-Gated Selection Vision Transformer block (FSGS-ViT). In our FSGS-ViT, we incorporate two branches, an upper branch containing the Frequency Domain Adaptive Attention Block (FAB), and a lower branch consisting of a Gate Selection Block (GSB). This design is guided by the notion of enhancing the utilization of essential features while efficiently managing computational resources, and the specific structures of both components are illustrated in Figure 2.
## 2 Materials and Methods
### X-ray Diffraction (XRD) Measurements  
Although single-crystal X-ray diffraction (XRD) measurements were performed and the molecular arrangement of F\({}_{8}\)CuPc has been revealed in [21], the atomic coordinates are not available. Therefore, XRD data was collected for a single crystal of F\({}_{8}\)CuPc, using an automated Rigaku Rapid system with the monochromated Cu-K\(\alpha\) radiation (\(\lambda\) = 1.54187 A). The structure was solved using a direct method, using SIR2004 [22] and refined by a full-matrix least-squares technique with SHELXL-2014/7 [23] with anisotropic and isotropic thermal parameters for non-hydrogen and hydrogen atoms, respectively. The crystallographic data has been deposited at the Cambridge Crystallographic Data Centre (CCDC) as CCDC-1531473. Crystal data for F\({}_{8}\)CuPc: Triclinic, \(P\overline{1}\), \(a\) = 3.62740(10) A, \(b\) = 12.7357(4) A, \(c\) = 13.4538(4) A, \(\alpha\) = 95.079(9)\({}^{\circ}\), \(\beta\) = 90.529(6)\({}^{\circ}\), \(\gamma\) = 96.441(6)\({}^{\circ}\), and \(V\) = 615.07(3) A\({}^{3}\), \(Z\) = 1, \(F(000)\) = 357, \(d_{\rm cal}\) = 1.944 g cm\({}^{-3}\), \(\mu\) (CuK\(\alpha\)) = 2.231 mm\({}^{-1}\) (\(\lambda\) = 1.54187 A), \(R_{1}\) = 0.0879, wR\({}_{2}\) = 0.2008, GoF = 0.999.
## 3 Related Works
By combining VPLS with Q-in-Q tunneling, it becomes possible to create a virtual network that spans multiple sites and service provider networks [10]. Moreover, the combination of VPLS and Q-in-Q tunneling provides flexibility in network design and enables the integration of diverse network environments. Implementing and managing VPLS with Q-in-Q tunneling can be complex, requiring coordination between different network devices and service providers. Compatibility issues may arise, as the effectiveness of Q-in-Q tunneling can vary across different network equipment and vendors [40]. Therefore, our objective is to propose an innovative H-VPLS architecture utilizing Q-in-Q tunneling, specifically on high-speed and commodity routers, to meet the additional requirements posed by emerging VPLS applications.
# A Review of Deep Learning Applications for Railway Safety
## 4 Railway Infra Safety
### Rail Geometry  
Recent studies have utilized deep learning to analyze vibration data to evaluate railway track quality. [PERSON] et al. [65] proposed a method to evaluate the quality of the rail track based on vehicle-body vibration. CNN and LSTM structures were integrated to process vehicle-body accelerations and predict vertical vehicle-body vibration. Such vehicle-body vibration prediction is beneficial for locating potential track geometry defects with lower costs than existing methods, such as using track inspection vehicles.  
[PERSON] et al. [66] further proposed a deep learning-based model applying attention structure and gated current unit (GRU) structure. CNN and GRU learn shape features and sequential features, respectively, and the attention structure receives the vertical, horizontal vibration, and train speed of the train as inputs, outputting the degree of vertical rail irregularity.
## 4 Taxonomy of the Existing Multipath Routing Protocols
### Concurrent Multipath Routing  
Thus far, the first group of multipath routing protocols, which are designed to fulfill the primary motivation of utilizing multipath routing, _i.e._, fault-tolerant routing, were discussed. Ongoing research on multipath routing approach tries to cope with the resource limitations of low-cost sensor nodes through concurrent data forwarding over multiple paths. This section introduces some of the recent research in this area.
# Edge Federated Optimization for Heterogeneous Data
## 5 Experimental Results
### Performance Evaluation
#### 5.2.6 Comparative Analysis  
As mentioned in [22], while FedAvg has demonstrated empirical success in heterogeneous settings, it does not fully address the underlying challenges when learning over data from non-IID data distributions (i.e., statistical heterogeneity). Accordingly, to tackle heterogeneity in federated networks with the FedAvg algorithm, this work applies the layer freezing technique and the unstructured pruning method to effectively strike the right balance between model complexity and classification performance, compared with the performance of the FedAvg with the optimization strategies proposed in Section 5.1.  
For the FedProx algorithm, this series of research results strongly demonstrates the significant advantages of structured pruning in improving the overall performance of the FedProx model, particularly in enhancing the model's capability to recognize specific categories. By selectively removing entire channels or filters, structured pruning systematically reduces the complexity of the model, showcasing better adaptability to heterogeneous datasets while effectively improving computational efficiency. This holds particular significance for resource-constrained application environments. Therefore, in this study, structured pruning technology is identified as a superior model optimization method. In summary, structured pruning successfully enhances the model's performance on heterogeneous datasets, confirming its feasibility in maintaining model accuracy and opening new possibilities for resource-efficient optimization.  
Figure 12: The F1 score with respect to the number of epochs on the ST dataset using the FedProx algorithm at a pruning rate of 10%.  
By combining the FedProx algorithm, layer freezing technique, and structured pruning method, this study successfully demonstrates the practical effectiveness of these methods in optimizing deep learning models in a federated learning environment. Importantly, federated learning aims to enhance the overall network performance rather than focusing solely on the accuracy of individual nodes. This discovery points to new research directions and methodological pathways for future exploration and application in the field of federated learning. Table 4 summarizes the classification performance of the FedAvg and FedProx algorithms with optimized operations.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 5 Conclusions  
This paper proposes an efficient person search method that employs spatio-temporal features in surveillance videos. The spatial features of persons are extracted from each frame, avoiding the influence of complex surveillance background. GRU is then utilized to process the temporal relationship of spatial features between adjacent frames, followed by an average pooling layer to generate spatio-temporal features. Compared with spatial features, the spatio-temporal features are more representative for surveillance videos. To ensure search efficiency, LSH is used to organize massive spatio-temporal features and calculate similarity. We also constructed a surveillance video dataset to evaluate the proposed method, and the experimental results demonstrate that the proposed method improves the search accuracy while ensuring search efficiency.  
In future work, we aim to apply this method to more video surveillance scenarios, such as the P-DESTRE dataset [36]. Therefore, we will explore more video frames to organize the temporal relationship of spatial features and increase the distinction of spatio-temporal features between different persons.  
Conceptualization and methodology, D.F. and J.Y.; formal analysis, D.F., Y.W. and L.Z.; writing--original draft preparation, D.F.; writing--review and editing, J.Y. and H.X.; supervision, J.Y. and H.X.; project administration, D.F. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, China (No. 61603171), the Natural Science Foundation of Shandong Province, China (No. ZR2021 MF091), and the Open Project of the Key Laboratory of System Control and Information Processing, Ministry of Education, China (No. Scip202011).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors are grateful to the Editor-in-Chief, Editor, and anonymous reviewers for their valuable reviews.  
The authors declare no conflict of interest.
# A Novel Approach for Acute Mental Stress Mitigation Through Adapted Binaural Beats: A Pilot Study
## 4 Discussion
This result makes us assume that the task complexity was comparable, ensuring a reliable comparison between CBBs and ABBs. It is worth noting that the \(f_{BB}\) for this study (i.e., 4.0-8.0 Hz) was chosen to elicit relaxation [11] and not an improvement in cognitive performance, which would rather require a stimulation in the beta range, as performed in refs. [14,21]. Therefore, an absence of improvement in the task's performance may be linked to this factor. An in-depth analysis of the average stress index is shown in Figure 7A. The pairwise comparison shows that all the participants showed lower average index values with the ABB stimulation, except one. The advantage of ABBs over CBBs is supported by the reduction in the average heart rate shown in Figure 7B. Interestingly, the only participant who showed an increase in heart rate during ABB stimulation was the same who exhibited a rise in the average stress index. This suggests that the adaptation algorithm may have failed for this subject, not achieving optimal stimulation throughout the task. It is worth mentioning that the consistency in the direction of the differences between subjects may lead to statistical significance despite a small median difference between the distributions. In addition to average heart rate, we investigated an additional feature commonly considered to assess parasympathetic predominance: the RMSSD. The results do not indicate a significant increase in this temporal metric, which is associated with lower acute mental stress [50]. However, the difference between the medians is in favor of ABBs, and the result of the statistical test (\(p=0.068\)) is slightly above the level of significance, suggesting a possible effect on this index as well. Interestingly, the participants who showed worsening in the average stress index and heart rate also exhibited a lower RMSSD, thus supporting the failure of adaptation in these cases. Altogether, this work emphasizes the necessity of personalized treatments [51], which in this case are meant to mitigate acute mental stress. From a psychological perspective, stress response varies significantly among individuals, being influenced by personal [52,53]and situational factors [53, 54]. By combining the above with the outcomes of this study, it is clear that there is no one-size-fits-all approach for BBs stimulation, justifying the need for closed-loop treatments, which takes into account the individual's specific characteristics.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### Land Cover Type Data  
To distinguish vegetation types in our study area (Figure 1), we utilized the 2019 MODIS Land Cover Type (MCD12Q1) Version 6.1 data product based on the Annual International Geosphere-Biosphere Programme (IGBP) classification system. We obtained the map from [[https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)]([https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)) (accessed on 14 July 2023) and the classification system from [[https://climatedaguide.ucardu/climate-data/ceres-igbp-land-classification](https://climatedaguide.ucardu/climate-data/ceres-igbp-land-classification)]([https://climatedaguide.ucardu/climate-data/ceres-igbp-land-classification](https://climatedaguide.ucardu/climate-data/ceres-igbp-land-classification)) (accessed on 25 July 2024). The spatial resolution of this dataset is 5600 m. The dataset is created by classifying MODIS Terra and Aqua reflectance data through supervised methods. After classification, additional processing integrates prior knowledge and supplementary information to enhance the accuracy of specific class distinctions. The specific classification scheme is delineated in Table 1.  
Figure 1: The Yangtze River Basin. The dark gray lines represent the primary and secondary water systems of the Yangtze River. The base map depicts the land cover type based on data processed from 2019 MCD12Q1 v006. Forest is a collective term encompassing evergreen coniferous forests, evergreen broad-leaved forests, deciduous broad-leaved forests, and mixed forests.
# FEM Analysis of Superplastic-Forming Process to Manufacture a Hemispherical Shell
## 4 Results and Discussion  
### Numerical Results and Comparison Between 2D and 3D Analyses  
Once the 3D and 2D FEM analyses of the superplastic-forming process to manufacture a hemispherical shell were performed, the distribution of the thicknesses in the manufactured shell was achieved. Figures 5 and 6 show some sequences involved in the simulations.  
Figure 5: Selected steps of the 3D FEM simulation of the hemispherical-shell-forming process. (**a**) starting forming; (**b**) first shaping; (**c**) subsequent formation; (**d**) final shape.  
The obtained results are shown in Figure 7, where the thickness of the manufactured hemispherical shell is reported versus the distance from the axis of symmetry of the hemispherical shell itself. The results highlight how the thickness distribution is non-uniform and increases from the pole (where there is the y-axis in the graph) to the outer edge of the manufactured shell. The pole area of the hemispherical shell represents a critical zone as the sheet thins by approximately 66%. Conversely, at the edge, the thinning is contained and amounts to about 13%. Those percentage variations were measured as the difference between the thickness of the manufactured hemispherical shell at the pole or edge and that of the initial blank.  
Figure 6: Selected steps of the 2D FEM simulation of the hemispherical-shell-forming process. (**a**) plane disc and die; (**b**) start forming of the disc; (**c**) subsequent formation; (**d**) final shape.  
The same graph of Figure 7 shows that the results obtained through 3D and 2D analyses are substantially overlapping, and the maximum difference is approximately 2%. Therefore, this result allows us to consider the two adopted schemes (3D analysis and 2D analysis) to simulate the superplastic-forming process interchangeably. The numerical-experimental validation is addressed below.