esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
In this paper, we proposed the data forwarding method in joint consideration of VoI of packets and energy consumption, with passive mobility of sensors in UWSNs. We explicitly take both VoI and energy consumption into account in its reward function, thus reducing the energy consumption as well as enhancing the timeliness of data forwarding in UWSNs. In our method, the \(Q\)-value of the same sensor node can be different along the time, thus avoiding the same node acting as a relay node until the depletion of its battery. Meanwhile, packets with larger value of information have higher priority to be transmitted so as to realize better timeliness. Although the packet delivery ratio of our method is relatively lower, our proposed method achieves much higher timeliness and consumes less energy than DBR and QELAR in the circumstance of dynamical topology change due to the passive mobility of sensor nodes. Given that the timeliness and energy consumption were more significant than the delivery ratio in our scenario, our method enhances the performance of UWSNs. In our scenario, the sink nodes are stationary and the performance of data collection may be different if the sink nodes are moving on the surface of the detection region. As a future work, we will study how the movement  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PDR0** & **DBR** & **QELAR** & **Our Method** \\ \hline PDR(10) & 79.47\% & 70.25\% & 66.36\% \\ PDR(50) & 93.12\% & 76.86\% & 71.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Packet Delivery Ratio.  
Figure 7: Residual energy of sensor nodes (50 sensor nodes).  
of sink nodes can influence the data collection of UWSNs. Additionally, recent studies of harvesting ambient energy of UWSNs has drawn large attention. For instance, the kinetic energy of underwater currents can be harvested to prolong the lifetime of UWSNs. Therefore, we intend to carry out the research of energy harvesting-aware data forwarding in UWSNs with passive mobility in the future.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] visualized the simulation results; [PERSON] wrote the paper.  
This work was supported by the National Natural Science Foundation of China (61371119).  
The authors declare no conflict of interest.
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 3 Materials and Methods
### Network Architecture
As the features undergo progressive refinement in the first two stages, the feature map size is significantly reduced, leading to lower memory access overhead in the subsequent stages and enhancing actual computational efficiency. Therefore, in the third stage, increasing the number of FasterNet Blocks to eight enables more effective extraction of critical features from finger vein images while preserving recognition efficiency and strengthening the model's representational capacity. In the fourth stage, the features will already have been sufficiently extracted and processed in the previous stages. Therefore, further increasing the number of modules would not significantly improve the model's performance; instead, it could increase the risk of overfitting. Reducing the number of modules helps avoid excessively complex feature representations while maintaining sufficient model capacity to process global information. This approach ensures stability in the final stage and mitigates potential negative impacts on the model's generalization ability. Hence, only two FasterNet Blocks are used in this stage to generate the final output features suitable for the recognition task. To further improve recognition accuracy and mitigate the effects of finger position variations, uneven illumination, and noise from image acquisition devices, while enhancing the model's multi-scale representation ability and recognition accuracy, we incorporated the Multi-Scale Convolutional Attention (MSCA) module in the third stage. By introducing a multi-scale attention mechanism into the convolution layers, the model assigns appropriate weights to features at different scales. Since the first two stages mainly extract low-level features such as textures and edges, using a complex attention mechanism at these stages would introduce redundant information, wasting computational resources and potentially  
Figure 1: Overall architecture of FMFVNet. The network consists of four feature extraction stages, with spatial downsampling and channel expansion performed through convolutions. reducing the efficiency of attention computation. Although the fourth stage has a lower computational load, the feature map size is too small, meaning that using MSCA would not provide significant multi-scale information gain, making the additional computational cost unjustifiable. Therefore, MSCA is introduced exclusively in the third stage, ensuring that multi-scale attention is fully utilized while keeping computational resources under control, ultimately enhancing feature extraction efficiency.
## 2 Analysis of the Pore-Structure Characteristics of the Filter Layer
### Ideal Ellipsoidal-Particle Filter Layer
#### 2.2.2 Theoretical Minimum Pore Radius  
Under ideal conditions, the particles in the filter layer that form the minimum effective pore radius are arranged as shown in Figure 4. The long-axis length of the filter particles OA is represented by \(a\); the short-axis radius OB is represented by \(b\). The effective pore radius of the filter in this particle arrangement is denoted as AD = \(r_{\min}\).  
Through geometric derivation, \(r_{\min}\) can be expressed by Equation (5), as follows:  
\[r_{\min}=\sqrt{a^{2}+\frac{1}{3}b^{2}}-a \tag{5}\]
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude
#### 3.4.4 Magnitude Error with Time  
In order to explore the performance of the DCRNN in in-depth real-time earthquake estimation, we analyze the change in its magnitude estimation results over time in detail (Figure 9). The results show that the DCRNN has a prominent phenomenon of \"overestimation for small earthquakes and underestimation for large earthquakes\" in the first 3 s when predicting earthquakes of different magnitudes. However, by the third second, the underestimation of large earthquakes has been significantly improved, and the average value of the errors for the whole magnitude range has been gradually reduced. By the 5 th second, the overestimation of small earthquakes has also been effectively improved. After  
Figure 8: Error variation with depth at 1–20 s (all events in gray; events greater than magnitude 6 in orange).  
Figure 7: Error variation with SNR at 1–20 s (all events in gray; events larger than magnitude 6 in orange).  
6 s, the overall mean error stays near 0, which indicates that the magnitude estimation performance of the DCRNN continues to improve with time.
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 3 Domain Chinese Long Text Classification Model
### BERT-Based Text Vectorization Coding  
As an important component of modern NLP tasks, pre-trained word embedding (e.g., word2 vec and GloVe) can substantially improve the performance of NLP tasks compared to embedding learned from scratch. For each word in a vocabulary, these context-free models produce only a single representation of the word embedding, even though the meaning of these words may vary across scenarios. In contrast, contextual models, such as  
Figure 2: Flow of domain Chinese long text classification method.  
OpenAI GPT, ELMo, and BERT [32], can generate different representations for the same word in different contexts based on the context (i.e., surrounding words in a sentence). These contextual models usually contain more hidden layers and improve model performance by training on a large amount of unlabeled data and fine-tuning with only a small amount of labeled data when applied to domain-specific tasks. BERT was proposed as an alternative to Word2 Vec, and its accuracy was substantially refreshed in 11 directions in the NLP domain, such as text classification, question answering, and language. BERT is massively pre-trained to give words an initialized vector rich in semantic information. The word vector is trained using BERT and used as a node for GCN to avoid the problem of having only one vector representation of a word, thus learning a dynamic word vector.  
The longest text data can reach several thousand words, while the maximum length of BERT input is limited to 512, which also includes CLS markers. Therefore, special processing is required for BERT representation of long texts. General processing can directly truncate the long text and take only the beginning or the end part, which may lead to the omission of key information. Using the sliding window approach, different parts of a long text are intercepted by sliding window, and then all the sentence representations are summed and averaged as the final BERT vector.  
In addition to making the BERT output as GCN nodes, referring to the predictive interpolation practice of BertGCN, the text treats the document representation of BERT as a separate output, calculates the loss together with the final output of GCN, and backpropagates. Due to the large number of parameters of GCN, the model is prone to overfitting or difficult to converge after fusion with BERT; to solve this problem, the vectors of the last four layers of BERT are spliced to prevent the model from using only the last layer of BERT output with too many parameters calculated. Then, we can obtain the output of the vectors of BERT layers, and this expression is shown in Equation (1):  
\[h_{bert}=\text{Re}LU(W[h_{bert,-1};h_{bert,-2};h_{bert,-3};h_{bert,-4}]+b) \tag{1}\]  
where ; represents the splicing; \(h_{bert,-1}\), \(h_{bert,-2}\), \(h_{bert,-3}\), and \(h_{bert,-4}\) denote the vectors obtained from the last four layers of BERT encoding; \(W\) is the trainable weight matrix; and \(b\) is the bias term.
# Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures
###### Abstract  
The coupling beam damper is a fundamental energy dissipation component in coupling shear wall structures that directly influences the performance of the shear wall. Here, we proposed a two-fold design method that can give better energy dissipation performance and hysteretic behavior to coupling beam dampers. First, we devised four in-plane yielding coupling beam dampers that have different opening types but the same amount of total materials. Then the geometry parameters of each opening type were optimized to yield the maximum hysteretic energy. The search for the optimal parameter set was realized by implementing the Kriging surrogate model which iterates randomly selected input shape parameters and the corresponding hysteretic energy calculated by the infinite element method. By comparing the maximum hysteretic energy in all four opening types, one type that had the highest hysteresis energy was selected as the optimized design. This optimized damper has the advantages of having a simple geometry and a high dissipation energy performance. The proposed method also provided a new framework for the design of in-plane coupling beam dampers.  
coupling beam dampers; hysteretic behavior; carrying capacity; Kriging surrogate model +
Footnote †: journal: Applied Physics Letters
## 4 Discussion
### Uncertainties and Limitations  
Based on MODIS and GLDAS data, this study analyzed the dynamics of GPP and WUE in different aridity gradients and the responses to climate change across China from 2001 to 2021. Although data such as MODIS have been validated to meet the needs of most studies, the use of remote sensing data inevitably introduces some potential uncertainties and limits the results of final spatiotemporal analyses [63]. At the same time, we have analyzed the variations of GPP and WUE in China for only 21 years since 2001 owing to the limited available data, and the understanding to support both responses to climate change is still inadequate. Furthermore, since the main objective of this study was to investigate the effects of climate change on GPP and WUE in different arid-humid zones, we extracted annual GPP and WUE values for each vegetation type using land use/land cover data from the initial year (2001) in order to exclude the effects of LUCC as much as possible [64; 65]. In real situations, however, it is difficult to completely separate the effects of climate change and LUCC on vegetation GPP and WUE; LUCC inevitably has an impact on the optimization and validation of the results [66]. In the next study, the interactions of LUCC-related processes with vegetation GPP and WUE under climate regulation need more research.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 5 Discussion  
This study employs a Bayesian-optimized explainable ML approach to model DO dynamics in the Mississippi River. The findings showcase the potential of ML methods for improving water quality predictions and addressing model interpretability challenges.  
The practical significance of this study lies in its ability to enhance water resource management through reliable DO forecasting. The BO-SVR model achieves a CD = 0.97, demonstrating high accuracy in predicting DO levels. This makes it a valuable tool for decision-makers to monitor and manage aquatic ecosystems. The inclusion of SHAP analysis enhances the interpretability of the model, fostering trust in its applications. This is particularly important for policy-making and public communication, where transparency is critical. Furthermore, the robustness of the model, confirmed through sensitivity analysis, indicates its potential for real-world applications.  
This study exhibits several limitations that should be acknowledged. First, this study relies on data from a single monitoring station over eight years (2016-2023). This dataset provides wide temporal coverage; however, the findings may not be generalized to other locations with different environmental and hydrological conditions. This is a common issue of data-driven approaches. Second, this study considers only a limited set of physicochemical parameters, excluding other factors like nutrient concentrations and biological indicators. This may restrict the model from capturing the full complexity of DO dynamics.  
To address these limitations, future work should expand the dataset to include multiple monitoring locations with varying environmental characteristics. This would enhance the model's generalizability and enable its application across diverse river systems. Including additional parameters, e.g., nutrient concentrations and biological indicators, would improve the model's ability to capture a broader spectrum of DO dynamics. Validating the model under extreme events is another priority. By incorporating data from periods of floods, droughts, or extreme temperatures, future studies can evaluate the model's performance in non-stationary and stress conditions. This would ensure its reliability for long-term planning and emergency responses. Furthermore, transfer learning techniques can be explored to adapt the model to new sites with limited data. This approach could enhance its applicability in data-scarce regions, addressing a common challenge in water quality modeling.
## 4 Experimental Results
### Comparative Study  
In this scenario, the comparative investigation of the metaheuristic-based LSTM network and the existing models is detailed in Table 7 and Figure 14. [PERSON] et al. [16] integrated a Bi-LSTM network and a CNN for household EECP. Initially, the discriminative feature values were extracted from the IHEPC dataset using a CNN model, then the EECP was accomplished with the Bi-LSTM network. Extensive experimentation showed that the presented Bi-LSTM and the CNN model obtained an MAPE of 21.28, an MAE of 0.18, an RMSE of 0.22, and an MSE of 0.05 for the IHEPC dataset. [PERSON] et al. [17] implemented an ensemble-based deep learning model to predict household energy consumption. In the resulting phase, the presented model performance was tested on the IHEPC dataset by means of MAPE, RMSE, MAE, and MSE. The presented ensemble-based deep learning model obtained an MAPE of 0.78, an MAE of 0.31, an RMSE of 0.35, and an MSE of 0.21 on the IHEPC dataset. [PERSON] et al. [32] combined CNN with GRUs for an effective household EECP. Experimental evaluations showed that the presented model attained MAE values of 0.33 and 0.24, RMSE values of 0.47 and 0.31, and MSE values of 0.22 and 0.09 for both the IHEPC and AEP datasets.  
Similarly, [PERSON] et al. [33] integrated a Bi-LSTM network with a dilated CNN for predicting power consumption in the local energy system. Experimental evaluation showed that the presented model achieved an MAPE of 0.86, an MAE of 0.66, an RMSE of 0.74, and an MSE of 0.54 on the IHEPC dataset. [PERSON] et al. [34] combined a multilayer bidirectional GRU with a CNN for household electricity consumption prediction. The experimental investigation showed that the presented model achieved MAE values of 0.29  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Dataset** & **MAPE** & **MAE** & **RMSE** & **MSE** \\ \hline Bi-LSTM with CNN [16] & IHEPC & 21.28 & 0.18 & 0.22 & 0.05 \\ Ensemble-based deep learning model [17] & IHEPC & 0.78 & 0.31 & 0.35 & 0.21 \\ CNN with GRU model [32] & IHEPC & - & 0.33 & 0.47 & 0.22 \\  & AEP & - & 0.24 & 0.31 & 0.09 \\ Bi-LSTM with dilated CNN [33] & IHEPC & 0.86 & 0.66 & 0.74 & 0.54 \\ Multilayer bidirectional GRU with CNN [34] & IHEPC & - & 0.29 & 0.42 & 0.18 \\  & AEP & - & 0.23 & 0.29 & 0.10 \\  & IHEPC & 0.05 & 0.04 & 0.16 & 0.04 \\ Metaheuristic based on the LSTM network & AEP & 0.09 & 0.07 & 0.13 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical comparison of the proposed model with the existing models for the AEP and IHEPC datasets.  
Figure 14: Comparison of the proposed model with the existing models.  
and 0.23, RMSE values of 0.42 and 0.29, and MSE values of 0.18 and 0.10 for the IHEPC and AEP datasets. As compared to the prior models, the metaheuristic based on the LSTM network achieved a good performance in EECP and also obtained a minimum error value for the IHEPC and AEP datasets. Hence, the obtained experimental results show that the metaheuristic based on the LSTM network significantly handles long and short time series data sequences to achieve better EECP with low computational complexity.
# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 2 Convolutional Neural Network and Problem Statement  
The convolutional neural networks (CNNs) are a category of neural networks which are very effective in image recognition, classification, and so on [25]. The CNN is one of the deep learning approaches that is composed of multiple convolutional and nonlinearity layers with optional pooling, followed by fully-connected layers, as shown in Figure 1.  
Let \(I\) be an image (\(m\times n\times c\)) to be input, where \(m\), \(n\), and \(c\) are the height, width, and channel numbers of the image, respectively. In the convolutional layers, \(I\) is convolved by 2D \(k\) kernels and mapped by a nonlinearity function, called rectified linear units (ReLU), to build \(k\) feature-maps (\(F\)). The feature-map output of the \(l\)th layer is connected to the input of the (\(l+1\))th convolution and pooling layer. The final feature-maps are connected to a fully-connected layer. The last layer of fully-connected layer produces the class probability output (\(P_{class}\)). A cross-entropy function is then used to compute an objective loss. All of the weighting parameters of the network can be trained using the backpropagation algorithm.  
Changes on remote-sensed images can be detected by analyzing two registered images over the same geographical area. For change detection, CNN could be employed to learn changed image characteristics and detect changed areas on remote-sensed imagery. However, the difference image (DI) or the feature fusion (FF) is widely used as an input feature of CNN, as shown in Figure 2. The DI is extracted by image subtraction or log ratio. Then, the FF is constructed by concatenating the two images. Note that these approaches are sensitive to noise as direct pixel-wise comparison features; thus, the traditional CNNs with DI or FF features could be weak to distorted data. In practice, distorted images and data are common in the remote sensing field. This distortion can be caused by not only radiometric but also geometric and viewing angle factors. In general, a geometric distortion is generated when satellites or acirrafts acquire images. In addition, image registration is required to align two images, even over the same geographical area, in a pre-processing stage. However, it is almost impossible to perfectly achieve distortion correction through automated methods. In addition, a viewing angle difference in acquisition is another challenging issue in registration and change detection. This problem cannot be resolved without precise 3-D building models, complicated algorithms, and manual intervention. For robust change detection, a robust and stable classification model is required that resolves all of the problems described above.  
Figure 1: Traditional convolutional neural network (CNN) architecture.
## 3 System Design
### Synthetic Data Generation for Users and Resources
#### 3.3.4 Synthetic Dataset Generation  
In order to simulate a realistic operational environment, synthetic datasets are generated for both users and mining licenses. This approach is commonly used in blockchain performance testing to ensure control over input variance and reproducibility [33].  
1. User Dataset: A script is developed to programmatically generate 50 user profiles, each associated with randomized yet structured attributes. These included roles such as Regulator, CompanyOfficial, and FieldEngineer, along with geographic affiliations.
2. Resource Dataset: A sample of mining license data is obtained from Australian government datasets of current mining licenses. Each license is identified by a unique Tenement Number (TNO) and accompanied by relevant metadata.  
Attribute values are carefully balanced to simulate diversity, ensuring a mixture of users who qualify for access under different policy conditions. This was achieved by using stratified random sampling across predefined attribute categories (e.g., role, clearance level, organization), ensuring that each combination occurred with sufficient frequency to meaningfully trigger policy-based access decisions during simulation. Generated users are stored in a structured JSON file (generated_users.json), and mining licenses are organized similarly in mining_licences_sample.json.