esa-sceva/satcom-chunk-collection huggingface dataset:
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 5 Conclusions  
In this study, a new finger vein recognition model based on a capsule network of vision transformers was proposed. We combined the advantages of a capsule network in processing the underlying vision with the advantages of a transformer in processing the relationship between visual elements and objects, thus solving the problem that capsule networks lack the ability to encode long-range dependencies in images and cannot selectively focus on important image feature information when it is used for image classification. Thus, a better classification effect of the finger vein was achieved. At the same time, we used dynamic routing mechanisms to solve the problem of poor model training effect caused by the small amount of finger vein data. It is worth noting that the ViT-Cap model we first proposed was used to solve the task of finger vein recognition, which provided a completely new reference model for finger vein recognition. ViT-Cap showed a significant advantage over other CNNs in that it was more effective in processing smaller categories of data. Different from the traditional CNN, ViT-Cap endowed the model with a new expression of the relationship between local features and overall features, which provided a new idea for the direction of computer vision.  
In the future, we will study how to clarify the correlation between intra-patch and inter-patch and conduct experiments in more open-finger vein datasets. In view of the problem that direct resizing in the process of model training may affect the performance of the model, we will seek a specific model or method to achieve lossless resizing of images.  
Conceptualization, Y.L. and H.L.; methodology, Y.L.; software, Y.L., Y.W. and C.Z.; validation, H.L. and R.G.; formal analysis, Y.L.; investigation, Y.W., H.L. and C.Z.; resources, Y.L.; data curation, R.G.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and H.L.; visualization, Y.W.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research is sponsored by the Key R&D Project of Jilin Provincial Science and Technology Development Plan in 2020 (No. 20200401103 GX).  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 Finite Element Model Analysis
### Design and Modeling of Seismic Isolation Bearings  
In order to examine the seismic mitigation effect of the ring spring-friction pendulum bearing, in this paper, three types of seismic isolation structures were established. The three seismic isolation structures were, respectively, set up with friction pendulum, annular spring, and annular spring-friction pendulum bearings. According to the results in Section 2, the vertical stiffness and horizontal stiffness of the bearing were different when the bearing parameters were different. The size of the bearing model in this section was based on the design of the ring spring and friction pendulum in Section 2, the angle of the friction surface of the friction pendulum was 3\({}^{\circ}\), and the friction coefficient was 0.04 [32]. The seismic bearings were arranged equally spaced at the bottom of the structure. The upper top surface of the bearing was connected with the bottom plate of the structure using Tie constraints, and there was no relative sliding between the bearing and the structure. The lower surfaces of the bearings were fixed. The front and rear bearings in each group of seismic bearings were separated by 7 m. The arrangement of the three bearings is shown in Figure 11. It should be emphasized that the bearing dimensions in the previous section do not necessarily achieve the best seismic isolation effect. The calculation should be repeated to get the best bearing parameters in practical application. According to the formula of the horizontal and vertical stiffness of the bearing, the equivalent horizontal stiffness of the isolated structure was calculated as 1.92 kN/mm, the horizontal self-oscillation period of the structure was 7.28 s, the equivalent vertical stiffness was 79.2 kN/mm, and the vertical self-oscillation period of the structure was 1.96 s. Based on the soil survey conducted by the Japan Railway Institute of Technology (JTRI) in the surroundings of the Dakai station, the yard where the Dakai station is located can be judged as a Class II yard. According to the Chinese Standard (GB 50011-2010) [33], the characteristic period of a Class II yard in the first design earthquake grouping is 0.35 s, so the design of the seismic isolation bearing is rational [23; 34].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Elastic Modulus (GPa)** & **Density (kg/m\({}^{3}\))** & **Poisson’s Ratio** & \
\begin{tabular}{c} **Peak** \\ **Compressive Strength (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Peak Tensile** \\ **Strength (MPa)** \\ \end{tabular} \\ \hline Concrete for the roof & 30 & 12,750 & 0.2 & 29.6 & 2.95 \\ Wall and baseplate concrete & 30 & 2500 & 0.2 & 29.6 & 2.95 \\ Mid-pillars & 35 & 2500 & 0.2 & 38.5 & 3.22 \\ Rebar & 200 & 7800 & 0.33 & 240 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concrete and rebar material parameters.
## 4 Security Attacks
### Data Layer
#### 4.1.1 Transaction Malleability Attack  
In a transaction malleability attack [18; 27; 29; 31; 42], the adversary exploits a design flaw where certain non-essential parts of a transaction can be modified without changing its underlying intent. This manipulation results in a different transaction ID, leading the sender to believe that the original transaction has failed or not been confirmed on the network. Consequently, the sender may inadvertently reissue the same transaction, effectively causing the funds to be transferred twice.  
Figure 2 illustrates the process of a transaction malleability attack, which targets the integrity of transaction identifiers in a blockchain network. The attack begins with a sender node (top left) that initiates a legitimate transaction, generating an original transaction ID. Meanwhile, an attacker node (top right) intercepts this transaction as it is being broadcast to the network. The attacker slightly alters the transaction's structure, such as by modifying a non-critical field, which changes its ID while keeping the core transaction valid. This results in an altered transaction ID, shown in the right branch. Both the original and the modified versions may reach the blockchain network at the same time, causing confusion over which transaction is valid. If the network accepts the altered version and discards the original, the sender may believe their payment did not go through and issue a second transaction. This leads to a double-spending scenario, where the same funds are spent more than once. The figure clearly shows how a small change introduced by an attacker at the transaction level can mislead the network and compromise the integrity of the ledger.  
If the network confirms the modified transaction before the original, the sender ends up paying twice the intended amount. Historically, this vulnerability played a significant role in the collapse of Mt. Gox in 2014, a major Bitcoin exchange that controlled approximately 70% of the market at the time. Although the exact impact of malleability on the Mt. Gox incident is subject to debate, it is widely cited as a contributing factor. Modern blockchain implementations, such as the upgraded Bitcoin protocols with Segregated Witness, have largely mitigated this risk, though the process can still introduce delays in transaction confirmations.
## 5 Conclusions  
The paper focuses on the inertial sensor-based two gimbal mobile satcom antenna. In order to obtain high quality communications, the MSA should point to the specific satellite when the carrier is moving. The dynamic model of MSA is established based on traditional Newton-Euler method and the corresponding control block diagram is built. In this paper, the non-singular fast terminal sliding mode control is adopted and developed to increase the line of sight stabilization accuracy. Meanwhile, the features of existence and convergence in finite time are proved. Then a neural network is employed to approximate the nonlinear item in the system. In addition, a novel BNN is proposed and used in this paper. A brief study of B-spline basis and B-spline function is also carried out, then the computational function used to obtain the arbitrary point on the curve is derived. The B-spline function is reformed to enhance its adaptive capacity. To validate the effectiveness of the proposed NFTSM and BNN, simulations and experiments are conducted. Results of different methods, including PID, NFTSM, NFTSM-RBF, NFTSM-BSNN, are compared in this paper. It is shown that the proposed method has better decoupling effects and disturbance rejecting ability than the others. Because the B-spline curve has an excellent ability called \"local control\", it can be used to approximate arbitrarily shaped curves. According to the analysis in this paper, we can conclude that the proposed BNN is good at nonlinear approximation owing to its local features. The robustness of the system can also be improved by applying this method.  
**Acknowledgments:** This work was supported in part by the Natural Science Foundation of China under Grant 61233005 and National 973 Project under Grant 2014 CB744200. The author would like to thank the reviewers whose critical analysis and suggestions brought insight into the final editing of this paper.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived the study idea and designed the experiments. [PERSON] and [PERSON] developed the simulation platform. [PERSON] and [PERSON] performed the experiments. [PERSON] drafted the manuscript. All authors read and approved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
###### Abstract
Shorter wavelengths exhibit greater phase fluctuations than longer wavelengths, and the degree of distortion increases with temperature. This study lays a theoretical foundation and provides experimental evidence for optical transmission in atmospheric turbulence.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation
### Neural-Network-Based Method  
The artificial neural network (ANN) structure has been mathematically proven to be a universal function approximator, and these networks possess the ability to intelligently \"learn\" functions through an automated training process. The fundamental form of an ANN is referred to as a multilayer feedforward network. [PERSON] et al. [28] developed an adaptive method based on a modified counter propagation network (MCPN), which represents a hybrid three-layer network comprising two components. The input hidden layer transformation constitutes a self-organizing feature map (SOFM) that identifies and categorizes patterns in the input data. The hidden- output layer transformation involves an enhanced version of the Grossberg linear layer, which calculates a specific precipitation rate for each input pattern. The nodes in the hidden layer are typically organized in a square matrix configuration. Initially, the SOFM parameters undergo training via an \"unsupervised\" self-organizing clustering procedure. Subsequently, the parameters of the hidden output layer are trained using a \"supervised\" process based on a simple recursive gradient search.  
To utilize the MCPN model effectively, it is necessary to specify the input-output data and determine the number of nodes. In the PERSIANN algorithm, the network structure comprises six normalized input variables derived from GEO IR and 225 nodes arrangedin each matrix within the hidden and output layers [28]. An adaptive procedure is employed for the recursive updating of network parameters when ground-based data become available. [PERSON] et al. [123] enhanced the PERSIANN algorithm by incorporating PMW data to ensure regular updates of network parameters. To address a limitation of PERSIANN--which relies on local pixel information for rainfall estimation--[PERSON] et al. [29] developed the PERSIANN-CCS algorithm. The algorithm encompasses a segmentation step applied to GEO IR cloud images, the extraction of cloud features, the classification of cloud patches, and the calibration of Tb and rainfall relationships. Additionally, this algorithm can be utilized for obtaining the PERSIANN-CCS-CDR dataset. PERSIANN-CCS precipitation is employed by the IMERG algorithm.  
In comparison to a conventional ANN, a deep neural network (DNN) is characterized by an increased number of hidden layers, which enable it to provide enhanced classification and regression capabilities [124; 125]. [PERSON] et al. [126] developed a DNN-based approach for estimating rain and snow rates at high latitudes. Their method leverages the precipitation estimates derived from spaceborne radars to train using PMW and IR Tb data along with reanalysis environmental variables. Moreover, DNNs were trained based on various scenarios of combined channels, such as combinations of 6.2 \(\upmu\)m, 7.3 \(\upmu\)m, 10.8 \(\upmu\)m, and 12 \(\upmu\)m and of 0.6 \(\upmu\)m and 10.8 \(\upmu\)m.
## Materials and Methods
### Antibacterial Activity  
Antibacterial activity of CPD/PU and C\({}_{60}\)/PU composite films was tested according to International Standard ISO 22196 (Plastics--Measurement of antibacterial activity on plastic surfaces) [32]. Following species of bacteria were used: _Klebsiella pneumoniae ATCC_3883 (_K. pneumoniae_), _Proteus mirabilis ATCC_14153 (_P. mirabilis_), _Salmonela enterica ATCC_13076 (_S. enterica_), _Enterococcus faecalis ATCC_29212 (_E. faecalis_), _Enterococcus epidermidis ATCC_12228 (_E. epidermidis_), _Shigella flexneri ATCC_12022 (_S. flexneri_), _Pseudomonas aeruginosa ATCC_27853 (_P. aeruginosa_), _Aspergillus niger ATCC_16404 (_A. niger_).  
All tests were performed in triplicate, and adequate controls for untreated samples were used (three to measure viable cells immediately and three to measure viable cells after incubation for 24 h). All tested materials--CPD/PU and C\({}_{60}\)/PU composite films--were prepared as flat squares, 50 mm \(\times\) 50 mm, sterilized by UV lamp at 258 nm for 30 min. Untreated specimens were used for control of bacterial viability, and other material specimens were incubated under blue light at 470 nm for 1 h. Specimens were inoculated with 0.4 mL bacterial suspension of 2.9-15.2 \(\times\) 10\({}^{7}\) cell/mL. Test inoculums were covered with films (40 mm \(\times\) 40 mm), and Petri dishes with test specimens were incubated for 24 h at 35 \({}^{\circ}\)C, humidity 90%. Bacteria were recovered with 10 mL of SCDLP, and 10 \(\mu\)L (different dilutions) were plated on LB agar and incubated for 24 h at 35 \({}^{\circ}\)C, after which bacterial colonies were counted. The distance from the blue lamp and samples was 50 cm to provide homogeneous irradiation of the samples.  
N--number of viable bacteria recovered for test specimen  
\[\text{N}=(100\times\text{C}\times\text{D}\times\text{V})/\text{A} \tag{1}\]  
C--average plate count, D--dilution factor for the plate counted, V--volume (mL) of SCDLP added, A--surface area (mm\({}^{2}\)) of the cover film.  
R--antibacterial activity of tested material  
\[\text{R}=(\text{Ut}-\text{U}_{0})-(\text{A}_{\text{t}}-\text{U}_{0})=\text{U}_ {\text{t}}-\text{A}_{\text{t}} \tag{2}\]  
U\({}_{0}\)--average of log 10 of the number of bacteria from nonirradiated sample in 0 h, U--average log 10 of the number of bacteria from nonirradiated sample after 24 h, A--average log 10 of the irradiated sample after 24 h.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 2 Generalized Hilbert Transform (GHT)  
GHT extends HT (Appendix A) by introducing the order parameter in the time-frequency domain to enhance the noise robustness and resolution of HT, which make GHT easier for new applications than HT.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 4 Experimental Results
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{\(k\) = 5} & \multicolumn{3}{c}{\(k\) = 10} & \multicolumn{3}{c}{\(k\) = 15} \\ \cline{3-10}  & & **Y** & **U** & **V** & **Y** & **U** & **V** & **Y** & **U** & **V** \\ \hline \multirow{9}{*}{Solid} & Basketball & 1.95\% & 1.96\% & 2.60\% & \(-\)0.43\% & \(-\)0.49\% & \(-\)0.54\% & \(-\)0.53\% & \(-\)0.54\% & \(-\)0.69\% \\  & Dancer & 1.98\% & 1.97\% & 2.72\% & \(-\)0.42\% & \(-\)0.54\% & \(-\)0.58\% & \(-\)0.53\% & \(-\)0.67\% & \(-\)0.71\% \\  & Longdress & 3.05\% & 4.18\% & 4.02\% & \(-\)0.93\% & \(-\)1.13\% & \(-\)1.09\% & \(-\)1.10\% & \(-\)1.31\% & \(-\)1.29\% \\  & Loot & 4.77\% & 6.22\% & 6.19\% & \(-\)1.24\% & \(-\)0.92\% & \(-\)0.99\% & \(-\)1.51\% & \(-\)1.18\% & \(-\)1.29\% \\  & Redandblack & 3.44\% & 3.64\% & 4.21\% & \(-\)1.18\% & \(-\)1.38\% & \(-\)1.37\% & \(-\)1.27\% & \(-\)1.42\% & \(-\)1.46\% \\  & Soldier & 1.61\% & 2.31\% & 2.26\% & \(-\)1.30\% & \(-\)0.92\% & \(-\)0.88\% & \(-\)1.63\% & \(-\)1.16\% & \(-\)1.18\% \\  & Thaidancer\_v & 11.17\% & 16.41\% & 16.82\% & \(-\)3.12\% & \(-\)4.39\% & \(-\)4.55\% & \(-\)3.11\% & \(-\)4.37\% & \(-\)4.54\% \\  & Queen & 4.02\% & 4.58\% & 5.08\% & \(-\)0.36\% & \(-\)0.51\% & \(-\)0.53\% & \(-\)0.73\% & \(-\)0.84\% & \(-\)0.80\% \\  & Facade & 1.34\% & 1.93\% & 2.18\% & \(-\)0.44\% & \(-\)0.60\% & \(-\)0.58\% & \(-\)0.35\% & \(-\)0.57\% & \(-\)0.52\% \\ \hline \multirow{9}{*}{Dense} & Longdress\_v & 10.61\% & 14.00\% & 13.10\% & 8.30\% & 10.87\% & 10.16\% & 8.30\% & 10.87\% & 10.16\% \\  & Loot\_v & 17.12\% & 20.64\% & 19.98\% & 12.63\% & 13.77\% & 13.48\% & 12.63\% & 13.77\% & 13.48\% \\ \cline{1-1}  & Redandblack\_v & 9.47\% & 12.05\% & 10.73\% & 7.01\% & 9.05\% & 7.93\% & 7.02\% & 9.05\% & 7.94\% \\ \cline{1-1}  & Soldier\_v & 12.59\% & 16.56\% & 16.06\% & 9.74\% & 11.92\% & 11.49\% & 9.74\% & 11.92\% & 11.49\% \\ \cline{1-1}  & Boxer\_v & 7.95\% & 8.99\% & 8.91\% & 5.34\% & 5.34\% & 5.16\% & 5.35\% & 5.35\% & 5.18\% \\ \hline \multicolumn{2}{c}{**Average Results**} & 6.50\% & 8.25\% & 8.20\% & 2.40\% & 2.86\% & 2.65\% & 2.31\% & 2.78\% & 2.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparisons between the texture-guided graph connectivity mask and the fixed parameter \(k\) = 5, 10, and 15 on point cloud attribute lossy compression. optimization parameter compared to all tests with fixed optimization parameter values. For example, when \(\theta\) is set to 1, significant coding performance reduction is introduced, with an average BD-BR loss of 42.42%, 65.47%, and 64.53% on the Y, U, and V components, particularly evident in MPEG dense point clouds. These findings affirm the advantages of our proposed sparsity-related optimization parameter in the graph construction process and point cloud attribute compression.
# Regeneration Performance of Activated Carbon for Desulfurization
## Results and Discussion
### SO2 Adsorption
#### 3.2.2 Effect of Flue Gas Flow Rate on SO\({}_{2}\) Adsorption  
The influence of gas flow rate on adsorption performance was studied by analysis of the desulfurization efficiency at flow rates of 1, 2, 3, and 4 L/min. As seen in Figure 5, the average SO\({}_{2}\) adsorption rates of AC at the gas flow rate of 1 L/min, 2 L/min, 3 L/min and 4 L/min were 86.2%, 75.9%, 62.3%, and 57.4% in ten minutes, respectively. The slower the gas flow rate, the higher is the adsorption efficiency. It is evident that the faster the gas flow rate, the lower the retention time of the SO\({}_{2}\) in the AC of the U-shaped tube, thus the adsorption efficiency decreases. In addition, as the gas flow rate decreases, the adsorption efficiency gradually becomes improved. The reason for this phenomenon is that the lower the flue gas rate, the bigger is the resistance [27]. So for the other experiments, the flue gas flow rate is selected at a slow flow rate of 1.65 L/min as a fixed condition.  
Figure 4: Effect of change of carbon dosage on SO\({}_{2}\) adsorption behavior (operated at 60 \({}^{\circ}\)C, for 2, 4, 6, 8, 10 g samples, 2000 ppm SO\({}_{2}\), 5% O\({}_{2}\), N\({}_{2}\) to balance and 1.65 L/min total flow rate).  
Figure 5: Effect of flue gas flow rate on SO\({}_{2}\) adsorption (operated at 60 \({}^{\circ}\)C, 4 g sample, 2000 ppm SO\({}_{2}\), 5% O\({}_{2}\), N\({}_{2}\) to balance and 1, 2, 3, 4 L/min total flow rate).
## 4 Results
### Results Comparison
The same results are obtained in the following tests, similar to what was observed in the time measurement. In the second test, as shown in Figure 24b, with the use of [PERSON]'s algorithm, it is noted that nodes 2, 5, and 7 have the same number of hops. The other nodes show similar values, but node 3 performs only one jump, which is explained by the higher voltage levels in nodes 3 and 4. In the third test (as shown in Figure 24c), a graph similar to the graph without the algorithm is observed when applying the [PERSON] algorithm. The hops decrease as the  
Figure 23: Comparison of results without using algorithms, the [PERSON] algorithm, and the [PERSON] algorithm. (**a**) Test 1: Time for a frame to reach the controller with nodes with a similar battery level. (**b**) Test 2: Time for a frame to reach the controller with nodes three and four having higher battery levels. (**c**) Test 3: Time for a frame to reach the controller with nodes seven and eight having higher battery levels. (**d**) Time comparison for each test. nodes closest to the controller are considered. Furthermore, it is highlighted that node 7 makes a single hop in all three cases. Figure 24d compares the sum of jumps obtained in each test. It can be seen that in all tests, a smaller number of jumps is achieved when applying both algorithms. It is also relevant to mention that, in the second test, when applying the Kruskal algorithm, a decrease in the number of hops is observed, even though the sending time remained constant when no algorithm was used. In summary, based on the results evidenced in each of the tests and even though both algorithms generate a reduction in sending times and the number of hops, it can be stated that [PERSON]'s algorithm presents better results. This is because both the frame sending time per node and the number of hops decreases significantly with their application.