esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison of Control Effect between BN Control and GBN Control  
### Simulation Results  
An optimal Bingham number (BN) control method was proposed assuming post-yield linear damping [26]. However, we assumed that post-yield quadratic damping more accurately predicts field-off damping force, as shown in Section 2. In highspeed impacts, the relationship between impact velocity and field-off damping force becomes nonlinear because the flow in the gap transitions from the laminar to the turbulent state. In order to compare the controllability between the linear BN control method and nonlinear GBN control method, Figure 10 compares these two field-off damping force models. The quadratic damping parameters are given in Equation (15), and the two linear damping parameters are \(c_{l1}=712.1\) Ns/m and \(c_{l2}=306.2\) Ns/m, respectively. The field-off damping force predicted by linear damping model 1 is higher than the actual one, while the one predicted by linear damping model 2 is lower than the actual one. For simplification, we named these two BN control strategies based on linear damping models as BN1 and BN2.  
Figure 11 shows the dynamic response of shock mitigation system under the BN control and GBN control. The initial drop velocity is 4.3 m/s. Under the GBN control, payload comes to a rest after it utilizes the total available stroke, i.e., a soft landing can be achieved.  
Figure 11: Dynamic response of shock mitigation system under different control algorithms.  
Figure 10: Comparison of linear and nonlinear field-off damping force characteristics of MREA.  
Under the BN1 control, the controllable yield force is suboptimal (Figure 11d), i.e., the shock mitigation system underestimates the damping force, which implies that the payload cannot come to a rest after using the available stroke, leading to an end-stop impact. The payload reaches its available stroke with a remaining velocity of 1.67 m/s. The maximum end-stop deceleration reaches 22.7 g, which may result in payload damage. In the BN2 control, the controllable yield force is super optimal, i.e., the shock mitigation system overestimates damping force. The payload deceleration under the BN2 control is higher than that under the GBN control, which may increase the probability of payload damage. The payload comes to a rest at the stroke of 0.09 m, i.e., a soft landing cannot be achieved under the BN2 control using the entire available stroke.  
The GBN control can achieve a soft landing with an optimal load-stroke profile. The controllable yield force of the linear BN control would cause either an end-stop impact or incomplete exploitation of the available stroke due to damping model inaccuracies.
## 3 Analysis of Dynamic Performance and Energy Consumption
### Analysis of Dynamic Mechanical Properties in the First Impact
The sandstone recrystallizes at 400 \({}^{\circ}\)C and 600 \({}^{\circ}\)C, the micro-cracks increase, the sandstone exhibits obvious plastic characteristics, and the peak strain increases. In addition, under high temperatures of 800 \({}^{\circ}\)C and 1000 \({}^{\circ}\)C, the sandstone is seriously embrittlel, the mineral cohesion is reduced, and the strength and strain greatly decrease. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen No.** & **Temperature/\({}^{\circ}\)C** & **Impact No.** & **Impact Velocity/m \(\cdot\) s\({}^{-1}\)** & **Peak Stress/MPa** & **Peak Strain** & **Average Strain** \\ \hline \multirow{3}{*}{7-1} & & 1 & 7.81 & 56.86 & 0.00635 & 45.64 \\  & 25 & 2 & 8.23 & 57.53 & 0.00486 & 50.97 \\  & 3 & 8.05 & 41.63 & 0.00545 & 47.57 \\ \hline \multirow{3}{*}{8-5} & & 1 & 7.90 & 35.45 & 0.00335 & 70.95 \\  & 200 & 2 & 7.84 & 38.98 & 0.00497 & 60.93 \\  & 3 & 7.58 & 37.21 & 0.00684 & 58.19 \\ \hline \multirow{3}{*}{6-3} & & 1 & 7.78 & 34.24 & 0.00648 & 51.23 \\  & 400 & 2 & 7.99 & 29.29 & 0.00178 & 53.29 \\  & & 3 & 7.87 & 20.17 & 0.00018 & 57.37 \\ \hline \multirow{3}{*}{17-2} & & 1 & 8.23 & 29.76 & 0.00733 & 67.31 \\  & 2 & 7.90 & 27.90 & 0.00129 & 67.42 \\ \hline \multirow{3}{*}{23-2} & & 1 & 8.47 & 14.92 & 0.00055 & 30.95 \\  & 2 & 8.08 & 10.89 & 0.00291 & 59.73 \\ \hline \multirow{3}{*}{4-4} & & 1000 & 1 & 7.84 & 8.66 & 0.00080 & 38.24 \\  & 2 & 8.05 & 6.56 & 0.00024 & 22.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cyclic impact test conditions and results of sandstone after different temperatures.
## 3 Results
### Ablation Experiment and Comparison with Traditional Model  
The Kappa coefficient, FOM, Accuracy, F1 and Hamming Loss are used to verify the simulation results of the proposed model, and compared with the results of the other five models: Degraded I (the RF-CA model without neighborhood factors); Degraded II (using the CA model based on a random seed selection algorithm); a (using the neighborhood time feature data extracted by the LSTM model); b (using neighborhood density data as neighborhood driving factors); c (using extended neighborhood enrichment data as neighborhood driving factors) (Table 4). Among them, the model represented by the letters a, b and c is to further verify the superiority of using CNN-LSTM model to extract neighborhood spatio-temporal features. The neighborhood density data are the area proportion of various types of land in the 3 x 3 Moore neighborhood of the central cell. The extended neighborhood enrichment data are the ratio of the land density in the 9 x 9 Moore neighborhood of the central cell to the 3 x 3 Moore neighborhood. Both neighborhood information is obtained from the single-phase land use/cover model in 2015. As shown in Table 4, the proposed model performs better than other models in simulating the LUCC from 2015 to 2020 (Kappa = 0.9684, FOM = 0.1744, Accuracy = 0.9829, F1 = 0.9641, Hamming, = 0.0171) and from 2010 to 2020 (Kappa = 0.9599, FOM = 0.4662, Accuracy = 0.9785, F1 = 0.8113, Hamming, = 0.0214). More specifically, the the proposed model surpasses the RF-CA model (Degraded I) by 1.56% and 18.88% of FOM for 2015-2020 and 2010-2020, indicating that using CNN-LSTM to extract neighborhood spatiotemporal features in historical land use/cover patterns as driving factors helps to improve the simulation accuracy of traditional RF-CA models. The FOM of the proposed model are 11.60% and 24.79% higher than Degraded II for 2015-2020 and 2010-2020. It indicates that the CA model based on HQSSIA has better performance in spatial simulation accuracy than the CA model based on random seed selection mechanism. The accuracy of the proposed model is better than that of a model, b model, and c model. Therefore, compared with the traditional neighborhood feature extraction methods, CNN-LSTM model can better learn the neighborhood interaction in the process of complex multi-purpose land change. As shown in Table 3, there are fewer cells in Chongqing's from 2015 to 2020 state change, accounting for about 1.24% of the total. Furthermore, the seed selection process in Degraded I is completely random, and the probability of correctly selecting the changed seed points decreases as the number of land changes in the two periods decreases. The high-quality seed mechanism uses the existing LUCC suitability probability data and constraints to initially screen the change location, which can improve the hit rate and accuracy of the seed selection process, thereby reducing the running time of the CA model. The results show that the running time of the CA model based on the random seed selection algorithm
## 2 Data and Methods
### Analysis and Processing of SWOT-Derived SSH Data
#### 2.3.2. Noise Filtering  
As illustrated in Section 3, noise greatly affects the computation of the velocities derived from the pseudo-SWOT data. We therefore investigate filtering procedures for noise removal. The geometry of the SWOT data prevents us from using classical Fourier and convolution-based low-pass filters [33]. Fourier-based filters impose circularity constraints, which cannot be fulfilled; the masks associated with convolution-based filters should be significantly smaller than the width of the SWOT swath, which greatly limits low-pass filtering capabilities. We then considered a Partial Derivative Equation (PDE)-based formulation, such that the low-pass filtering results from an iterated Laplacian diffusion:  
\[\partial_{t}a\left(t,y,x\right)-\triangle a\left(t,y,x\right)=0\iff\frac{ \partial a}{\partial t}=\frac{\partial^{2}a}{\partial y^{2}}+\frac{\partial^{ 2}a}{\partial x^{2}}. \tag{5}\]  
Figure 2: WMOP relative vorticity normalized by f on 23 January 2009 (**left**) and 3 February 2009 (**right**). Black boxes indicate the two regions studied in Section 3: box 1, pass 15 (**left**) and box 2, pass 168 (**right**).  
As the Green's function for the heat equation is a Gaussian kernel, the implementation of this PDE-based diffusion is equivalent to a Gaussian convolution and results in an isotropic filtering, that is to say that the filtering acts equally in all directions [34]. Using a four-neighbourhood discretization of the Laplacian operator, we can deal with missing data (e.g., nadir) or land (e.g., island) pixels. The Laplacian operator comes to compute a local mean over the four neighbours of a given pixel. Withdrawing land pixels and missing data from the computation of this local mean, we can iterate the Laplacian diffusion to reach the expected filtering level for all pixels. Each iteration of the Laplacian diffusion can be regarded as a low-pass filtering with a high cut-off frequency. The selection of the number of iterations of the Laplacian diffusion then allows us to reach lower cut-off frequencies. By contrast, the direct application of two-dimensional low-pass filters for cut-off frequencies in the range [30 km, 60 km] would result in filter supports in the range [60 km, 120 km], meaning that no filtering output could be computed for any pixel closer than 30 km (rest. 60 km) from the swath boundaries or a missing data or land pixel. Overall, the filtering level is set by the number of iterations of the Laplacian diffusion and the parameter lambda. This is shown in the following equation, which shows the implementation that we use:  
\[a^{k+1}=a^{k}-\lambda\triangle a^{k}. \tag{6}\]  
With this being an iterative method, in contrast to a traditional Gaussian filter, we can apply cut-off wavelengths greater than the width of a half-swath. In Appendix A, we apply the filter to white noise to show how different combinations of the filter's parameters (lambda and number of iterations) are associated with different cut-off wavelengths (\(\lambda_{c}\)s).
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 3 Research Methods
### The Network Structure of YOLOv7-Tuna
#### 3.1.1 ELAN-DS Module  
DySnakeConv (Dynamic Snake Convolution) is designed to process data with periodic and temporal characteristics [22]. DySnakeConv employs dynamic, deformable convolution kernels to learn feature representations of input data, enabling the network to better capture temporal information present in the input data and adapt to variations in features across different time points or periods. This facilitates more effective feature extraction of targets. Since the tuna dataset in this study is obtained by extracting frames from monitoring videos of tuna fishing, it possesses evident temporal characteristics. Therefore, DySnakeConv is suitable for the dataset under investigation in this paper. Within the backbone network, the incorporation of DySnakeConv is achieved by embedding it within the Efficient Layer Aggregation Network (ELAN), resulting in the ELAN-DS module.  
Figure 1: Architecture of YOLOv7-Tuna network.  
As depicted in Figure 2, ELAN-DS replaces the final 3 \(\times\) 3 convolution layer in ELAN with DySnakeConv, maintaining the same kernel size. Due to the deformable nature of DySnakeConv, which adjusts based on the shape and boundary information of the target, our proposed ELAN-DS can better adapt to the varying shapes of tuna during the fishing process.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 5 Conclusions  
We proposed a query-based multicascade instance segmentation method called QCIS-Net. The method is based on the Swin Transformer backbone network in the EFE module to extract high-quality feature maps for segmentation. Compared with traditional transformer architectures, our method improves detection efficiency and reduces parameter capacity. It can integrate and optimize detection and segmentation, which significantly improves segmentation quality. Compared with traditional detection and segmentation methods, our end-to-end detector does not require the tedious RPN an a priori box and complicated postprocessing process. With the learnable and adjustable feature of the query, we can obtain good performance through its self-learning ability. Experimental results show that, when using QCIS-Net for instance segmentation and object detection, the box detection AP reaches 77.1% and the IS detection AP reaches 73.3% in the NWPU-VHR-10 dataset. In the SAR dataset, the box detection AP reaches 79.1% and the IS detection AP reaches 75.2%. The method proposed in this paper can enhance the practical application efficiency of RS images.  
Conceptualization, E.C. and M.L.; methodology, M.C.; resources, Q.Z.; data curation, Q.Z.; writing--original draft, E.C.; writing--review and editing, M.L. and M.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. The authors declare no conflict of interest.
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
## 3 Result and Discussion  
### Curing Characteristics of NR/SBR-x Composites  
The cure characteristics of the NR/SBR-x composites are shown in Table 2. Both NR and SBR are non-polar unsaturated rubbers with similar curing characteristics and can be cured in the same cure system. Maximum torque (\(M_{H}\)), minimum torque (\(M_{L}\)), curing time (\(t_{90}\)), \(t_{s2}\) and \(CRI\) are important parameters [31]. \(M_{H}\) is the maximum torque achieved, and it can be used to indicate the shear modulus for a rubber composite. \(M_{L}\) is the lowest torque value, and it can be used as a measure of the rigidity and viscosity of an uncured rubber compound. \(\Delta M\) is usually used to estimate the crosslink density of a rubber composite. The time required to reach 90% of the maximum torque is termed \(t_{90}\), from which the cure time of the NR/SBR-x compounds is determined to prepare the NR/SBR-x composites. The time for the torque to reach the minimum torque plus 0.2 dN-m is termed \(t_{s2}\), which reflects the operational safety of the rubber compound. The cure rate index (\(CRI\)) indicates the rate of the cure process for NR/SBR-x composites, and it is calculated as follows [32]:  
\[CRI=\frac{100}{t_{90}-t_{s2}} \tag{2}\]From Table 1, it can be seen that the increase in SBR content in general leads to the decrease in both the \(M_{L}\) and \(M_{H}\) of the composites, indicating the decreased viscosity of uncured NR/SBR compounds and the reduced shear modulus of NR/SBR composites. It can also be observed that there is an overall trend which demonstrates that the increasing SBR content reduces the \(\Delta\)M values of NR/SBR composites slightly, even though the trend shows some irregularity in the order of \(\Delta\)M values as functions of SBR content. Such results indicate that the crosslink density is slightly lower for the NR/SBR composites with a larger SBR content. It is worth noting that the curing rate of the NR/SBR-x composites increases significantly with the increase in SBR content, which can be observed from the decrease in \(t_{90}\) and the increase in _CRI_. A higher vulcanization rate of the rubber compound means higher productivity, but the value of \(t_{s2}\) needs to be considered, as too short a \(t_{s2}\) may lead to scorching during production. Therefore, the vulcanization rate of the compound needs to be controlled within a reasonable range according to the actual situation.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 4 Results and Discussion
### Flexural Strength of Waste Fiber-Reinforced Concrete  
In general, the flexural strength of concrete is known to be very low, approximately less than 10%, compared to the compressive strength, and for this reason, the propagation of cracks under flexural load is very fast. So, controlling cracks due to such flexural load may play an important role in the durability life of concrete [45]. Reinforcement of concrete through fibers can increase the flexural capacity of concrete and reduce the generation of tensile cracks [46]. This experiment was conducted to evaluate the effect of improving the flexural strength of concrete through the addition of waste glass wool and waste mineral wool. The results of measuring the flexural strength of concrete according to the waste fiber wool addition rate are shown in Figures 13 and 14. As shown in Figure 13, flexural strength at 28 days for the reference concrete was 6.12 MPa and for the specimen with the addition rate of 0.5% of waste glass wool and mineral wool was 6.16 and 6.13 MPa, respectively, so it was confirmed that the improvement effect on the flexural strength was not significant at the addition rate of 0.5%. However, at the addition rate of 1.0% or more, it was found that  
Figure 11: Compressive strength test results at 28 days.  
the flexural strength was 6.42 MPa (waste glass wool) and 6.47 MPa (waste mineral wool), and when the addition rate was increased to 2.0%, the strength increase was 6.77 MPa (waste glass wool) and 6.76 MPa (waste mineral wool), about an 11% increase. Similarly, as shown in Figure 14, the strength of the reference concrete was 6.34 MPa at 90 days, and in concrete with 0.5% addition rate of waste glass wool and mineral wool, it was 6.52 and 6.67 MPa, respectively, and no significant improvement effect was confirmed. This means that the appropriate addition rate of the waste fiber wool for the flexural strength improvement effect is 0.5% or more, and at the addition rate of 1.0%, it is 7.09 MPa (waste glass wool) and 6.73 MPa (waste mineral wool), respectively, which shows a clear strength improvement effect compared to the reference concrete. In addition, when the addition rate is increased to 2.0%, it is 7.56 and 7.03 MPa, which have a strength improvement effect of 19 and 11%, respectively, compared to the reference concrete. In particular, waste glass wool was found to have the highest strength increase between 28 and 90 days at a 2.0% addition rate, and at the addition rate of 1.0% or more, it was confirmed that the effect of improving flexural strength was more pronounced than that of waste mineral wool. This effect of improving flexural strength is considered possible because fiber strands dispersed in concrete form a 3D network structure to support a part of flexural load and performed a role of a bridge until destruction by flexural load.
## 3 Methods
### EEG-Conformer  
The second model in this study is EEG-Conformer, proposed by [PERSON] [26], based on the concept of using CNN to learn the low-level temporal and spatial features in EEG signals combined with self-attention to learn the global temporal dependencies in EEG feature maps. EEG-Conformer could be described in detail as follows:  
1. [label=()]
2. Convolution module: The EEG signals after preprocessing and augmenting are learned through two 1D temporal and spatial convolution layers. The first temporal convolution layer learns the time dimension of input, and then the spatial convolution layer learns the spatial features. The spatial features are the information between the electrode channels. Then, the feature maps go through batch normalisation to avoid overfitting and the nonlinearity activation function ELU. The average pooling layer smooths the feature maps and rearranges to put all feature channels of each temporal point as a token into the self-attention module.
3. Self-attention module: The self-attention module has advantages in learning global features and long sequence data, which improve the CNN weaknesses. The self-attention module calculates as below: \[Attention(Q,K,V)=Softmax(\frac{QK^{T}}{\sqrt{k}})V,\] (4) where \(Q\) is a matrix of stacked temporal queries, \(K\) and \(V\) are temporal matrices that denote key and value, and \(k\) is the length of tokens. Multi-head attention (\(MHA\)) is applied to strengthen representation performance. The tokens are separated into the same shape \(h\) segments, described as follows: \[MHA(Q,K,V)=[head_{0},\dots,head_{h-1}],\] (5) \[head_{l}=Attention(Q_{l},K_{l},V_{l}),l=1,2,\dots,h,\] (6) where \(MHA\) is the multi-head attention \(Q_{l},K_{l},V_{l}\in\mathbb{R}^{m\times k/h}\), indicating the query, key, and value acquired by a linear transformation of a segment token in the \(l\)th head, respectively.
4. Classifier module: The features from the self-attention module go through two fully connected layers. The Softmax function is utilised to predict \(M\) labels. Cross-entropy loss is utilised as a cost function as follows: \[\mathcal{L}=-\frac{1}{N_{b}}\sum_{i=1}^{N_{b}}\sum_{c=1}^{M}y\ \log{(\hat{y})},\] (7) where \(N_{b}\) is the batch size, \(M\) is the number of target labels (\(M=4\)), \(y\) is the ground truth, and \(\hat{y}\) is the prediction.  
Figure 7 visualises the architecture of an EEG-Conformer architecture. The network starts with a shallow convolution module; temporal information of the input is extracted by temporal convolution, and then the spatial information is extracted by spatial convolution. After that, the global temporal dependencies in feature maps are learned by the self-attention module. Finally, the classifier module, including fully connected networks, produces the prediction.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 8 Conclusions  
This study proposes a cross-chain DNS root synchronization scheme. The goal was to solve trust and interoperability problems among heterogeneous blockchains in the consensus roots system. The system adopts a 1 + N master-subchain architecture. The master chain works as a relay. Each subchain corresponds to a regional root server. This design improves flexibility, scalability, and security. A staged atomic swap mechanism guarantees secure and independent participation. It also enables collaborative governance among root nodes. The system provides a practical blockchain-based solution for DNS infrastructure. It helps decentralize and strengthen global Internet governance. The current design assumes that root servers cooperate, but this assumption may not hold in adversarial or conflicting policy environments. Future work will aim to enhance fault tolerance in such cases. It will also explore privacy-preserving cross-chain protocols and support for hierarchical DNS models. Another key direction is to improve compatibility with existing Internet infrastructure.  
Conceptualization, L.Z.; methodology, L.Z. and S.H.; software, S.H.; validation, L.Z. and S.H.; formal analysis, Z.Z. and C.M.; investigation, Z.Z. and C.M.; resources, L.Z. and C.M.; writing--original draft preparation, L.Z. and S.H.; writing--review and editing, L.Z. and S.H.; visualization, Z.Z.; supervision, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science Research Project of the Hebei Education Department (BJK2024111), the Natural Science Foundation of Hebei Province (F2024207003), and the 2025 Hebei Province Foreign Intelligence Introduction Project: Realistic 3D Space Reconstruction System Based on Radar-Vision Integration.  
Not applicable.  
The data in this study are included in the article, which is visualized in the figures and tabulated in the table.  
The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.