esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of DDoS Attacks in Software Defined Networking Using Entropy
## 6 Conclusions  
This paper focuses on protecting the controller as the core of a software-defined network by detecting DDoS attacks. In order to achieve the purpose of attack detection, this paper proposes a fusion entropy method. This method effectively uses the advantages of information entropy and log energy entropy to achieve complementarity. Fusion entropy has the advantages of rapid detection and obvious changes in entropy value, which can be used in attacks. The attack is detected in the fastest time. At the same time, the entropy value when the attack comes is about 91.25% lower than the entropy value in the normal scenario, which can effectively detect the attack. Compared with other attack detection methods, it has great advantages in protecting SDN controller from DDoS attack.  
In the future, detecting DDoS attacks in structures that connect or communicate between multiple controllers will be the subject of further research. In addition, since low-rate DDoS attacks are more difficult to detect because the difference between normal traffic and low-rate DDoS attacks is not obvious when they occur on the network, low-rate  
Figure 7: Comparison of different entropy variation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Decline Rate of Entropy** \\ \hline Average normalized entropy [27] & 89.90\% \\ Information entropy + trigger [35] & 82.14\% \\ Shannon entropy [38] & 74.75\% \\ Proposed method & 91.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other attack detection methods.  
DDoS attacks can also be the subject of further research in this paper. At the same time, to reduce the interference in attack, detection is also a key research issue in the future. It can be considered to combine entropy value with dimension reduction technology and machine learning method to achieve better attack detection.  
Conceptualization, C.F., S.C.; methodology, C.F., N.M.K.; software, C.F.; validation, C.F., Y.Z. and H.J.; formal analysis, C.F., Y.Z.; investigation, C.F.; resources, H.J., S.C.; data curation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, Y.Z.; visualization, C.F.; supervision, S.C., N.M.K.; project administration, S.C., C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hubei Province, China, under Grant No. 2021 CFA001.  
Not applicable.  
For the experimental data and results of this article, you can contact us at [EMAIL_ADDRESS].  
The authors declare no conflict of interest.
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 2 Methods
### Optimization and Purification Based on Geometric Constraints  
After line segments extracted by the multi-scale image space were projected to the original image, severe redundancy. Overlapping. and clutter problems of line segments occur. Many line segments are meaningless, so the entire set of line segments needs to be optimized and purified. As a result, the down-sampled image with low resolution can only extract a few segments, which brings extraction error. In this subsection, a line segment optimization and purification strategy is proposed, which optimizes line segment extraction results through line segment geometric constraint relationships. The optimized line segments have longer length and higher integrity, while alleviating the redundancy problem.  
Before optimization and purification, it is necessary to describe and determine constraint relationships between line segments. As shown in Figure 4, the geometric constraint relationships between line segments are defined as follows:Based on the geometric constraint relationships in Figure 4, we define horizontal, vertical, and angle thresholds to determine whether line segments need to be optimized and purified. We define \(l_{1}=\left\{u_{1}^{(1)},v_{1}^{(1)},u_{2}^{(1)},v_{2}^{(1)},length^{(1)}\right\}\) and \(l_{2}=\left\{u_{1}^{(2)},v_{1}^{(2)},u_{2}^{(2)},v_{2}^{(2)},length^{(2)}\right\}\), respectively, as reference and determined line segments that needed to be judged. The distance between midpoints of two segments is \(D_{1}=\frac{1}{2}\sqrt{\left(u_{1}^{(1)}+u_{2}^{(1)}-u_{1}^{(2)}-u_{2}^{(2)} \right)^{2}+\left(v_{1}^{(1)}+v_{2}^{(1)}-v_{1}^{(2)}-v_{2}^{(2)}\right)^{2}}\) and the average distance between the endpoints of two segments is \(D_{2}=\left(d_{1}+d_{2}+d_{3}+d_{4}\right)/4\). The angle between two lines is \(\theta\).
## 2 Materials and Methods
### Geo-Positional Transformer-CNN
#### 2.5.2 The 1D-CNN Branch  
The structure of the 1D-CNN branch, as illustrated in Figure 5, is designed to capture variations in environmental covariates at a small scale, thereby extracting local features closely related to SOM. In soil systems, factors such as terrain undulation and the topographic wetness index often exhibit spatial heterogeneity at the micro-scale [45], which influences the distribution of SOM. Through one-dimensional convolutional operations, this branch is capable of capturing high-frequency, localized variation patterns, thereby enhancing the model's ability to identify small-scale spatial distribution features of SOM and its spatial correlations with environmental covariates [46].  
Figure 4: The architecture of Multi-Head Attention consists of 16 Scaled Dot-Product Attention layers.  
The 1D-CNN branch consists of four convolutional blocks, one flatten layer and two fully connected layers. Each convolutional block includes a one-dimensional convolution module, a BatchNorm layer, and a ReLU activation function. The model parameters are detailed in Table 3. The 1D convolution modules are used to extract local features. The BatchNorm layers standardize the features to accelerate model convergence and reduce the risk of overfitting. The ReLU activation functions introduce nonlinearity, enhancing the model's representational capacity. The flatten layer is used to convert the multi-channel feature tensor output through the convolution layer into a one-dimensional vector, so that it can be input to the full connection layer for feature fusion and regression prediction. The fully connected layers are employed to integrate features and complete the mapping from the feature space to the output space.
## Materials and Methods
### Study Plot  
We manually selected the study plot (Figure 2a) using raster maps of biomass by tree species (Pine: Figure 2b and broadleaf: Figure 2c) from the National Forest Inventory for 2013 (MS-NFI, @ Natural Resources Institute Finland, Helsinki, Finland, 2015). We selected a visually homogeneous polygon in the AHS imagery where we had a Scots pine biomass fraction of at least 50%; no spruce was present in the study plot. Scots pine and silver birch can easily be visually distinguished from Figure 2a, as broadleaf trees have a considerably higher near infrared (NIR) reflectance than Scots pine, and hence look red in the false color figure. The study plot covered 1.2 hectares, and included approximately 35,690 AHS pixels. During field measurements in 2011, the plot had been classified as a young mesic pure Scots pine stand with 84% canopy cover and effective LAI = 2.7 (Figure 2a). The study site had not been subjected to any harvesting or other abrupt changes. Considering the slow growth of forests in this region, the measured LAI value can be used to characterize the plot with reasonable accuracy still in 2015.  
Figure 2: \(Cont.\)  
We then masked out all non-vegetative pixels, such as bare soil, in ENVI (Harris Geospatial Solutions, Broomfield, CO, USA) using a normalized difference vegetation index (NDVI) threshold of 0.8 (Table 3). This threshold successfully retained most of the vegetative pixels while removing only clearly non-vegetated ones. The number of pixels masked out was less than 8% in the nadir direction, and less than 1% for the off-nadir directions (Table 3).
## 3 Results
### Bactericidal Efficacy  
The bactericidal effects of PAW were tested against Gram-negative _E. coli_ and Gram-positive _S. aureus_ strains using four different contact times: 15, 30, 60 min and 24 h (Figure 3). Generally, by increasing contact time, higher bactericidal activity was observed. Specifically, 15 min contact time was able to reduce _E. coli_ concentrations by approximately 4 log when incubated with GD PAW plasma treated for 15 min but showed very little effect on _S. aureus_ (Figure 3A,B). _S. aureus_ was less susceptible, even after 30 min of contact time, whereas _E. coli_ was inactivated below the limit of detection (log 2 CFU/mL), when incubated with SD PAW 15 min, GD PAW 5, 10, and 15 min at 30 min contact time (Figure 3C,D). Increasing contact time to 60 min increased inactivation efficacy. _E. coli_ was reduced by 6 log by all PAW generated for longer than 10 min, whereas only GD PAW generated for 15 min was able to cause 6 log reduction for _S. aureus_ (Figure 3E,F), and only on prolonged contact. The 24 h contact time reduced bacteria below the detection limit (approximately 6 log reduction) for all PAW generation times (Figure 3G,H; SD (red lines) not distinguishable due to overlay of GD (blue lines)), whereas untreated control water (plasma activation time 0 min) showed no effect on microbial survival even after 24 h.  
Comparison of PAW generated with the different discharge modes, showed that overall GD PAW was more efficient than SD PAW in reducing microbial counts for _E. coli_ but showed no difference for _S. aureus_ at shorter contact times, and increased antimicrobial efficacy of GD10 and GD15 was observed with extended contact time only.  
In Figure 4, the bactericidal stability of PAW after 1 week is presented. For these experiments only 30 and 60 min contact times were investigated. After 1 week of storage, the bactericidal activity of PAW had decreased and none of the PAW samples were able to reduce _S. aureus_ to undetectable levels (Figure 4B,D). Only the solutions plasma treated for 15 min retained the functionality to cause 4 log and 6 log reductions for _E. coli_, respectively, after 30 min contact time. Prolonging contact time to 60 min did enhance antimicrobial efficacy, indicating that the bactericidal activity of the liquids was reduced but not completely lost after the storage time.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Hybrid Methods
For the fusion of low-frequency and high-frequency sub-bands, respectively, the fusion rules based on sparse representation and improved PCNN are used. In the end, inverse NSCT is employed to produce the fused image.
###### Abstract  
Ridership analysis at the local level has a pivotal role in sustainable urban construction and transportation planning. In practice, urban rail transit (URT) ridership is affected by complex factors that vary across the urban area. The aim of this study is to model and explore the factors that impact metro station ridership in Shenzhen, China from a local perspective. The direct demand model, which uses ordinary least squares (OLS) estimation, is the most widely used method of ridership modeling. However, OLS estimation assumes parametric stability. This study investigates the use of a direct demand model on the basis of geographically weighted regression (GWR) to model the local relationships between metro station ridership and potential influencing factors. Real-world Shenzhen Metro smart card data are used to test and verify the applicability and performance of the model. The results show that GWR performs better than OLS estimation in terms of both model fitting and spatial interpretation. The GWR model demonstrates a high level of interpretability regarding the spatial distribution and variation of each coefficient, and thus can provide insights for decision-makers into URT ridership and its complex factors from a local perspective.  
geographically weighted regression (GWR); metro ridership; influencing factors; spatial autocorrelation 2019
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
###### Abstract  
The need to reduce greenhouse gas emissions and guarantee a stable and reliable energy supply has resulted in an increase in the demand for sustainable energy storage solutions over the last decade. Rechargeable batteries with solid-state electrolytes (SSE) have become a focus area due to their potential for increased energy density, longer cycle life, and safety over conventional liquid electrolytic batteries. The superionic sodium conductor (NASICON) Na\({}_{3}\)Zr\({}_{5}\)Si2 PO\({}_{12}\) has gained a lot of attention among ESS because of its exceptional electrochemical properties, which make it a promising candidate for solid-state sodium-ion batteries. NASICON's open frame structure makes it possible to transport sodium ions efficiently even at room temperature, while its wide electrochemical window enables high-voltage operation and reduces side reactions, resulting in safer battery performance. Furthermore, NASICON is more compatible with sodium ions, can help with electrode interface issues, and is simple to process. The characteristics of NASICON make it a highly desirable and vital material for solid-state sodium-ion batteries. The aim of this study is to prepare and characterize ceramic membranes that contain Na\({}_{3,06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) and Na\({}_{3.18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\), and measure their stability in seawater batteries that serve as solid electrolytes. The surface analysis revealed that the Na\({}_{3,06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) powder has a specific surface area of 7.17 m\({}^{2}\) g\({}^{-1}\), which is more than the Na\({}_{3,18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) powder's 6.61 m\({}^{2}\) g\({}^{-1}\). During measurement, the NASICON samples showed ionic conductivities of 8.5 \(\times\) 10\({}^{-5}\) and 6.19 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\). Using platinum/carbon (Pt/C) as a catalyst and seawater as a source of cathodes with sodium ions (Na\({}^{+}\)), batteries were charged and discharged using different current values (50 and 100 \(\mu\)A) for testing. In an electrochemical cell, a battery with a NASICON membrane and Pt/C catalysts with 0.00033 g platinum content was used to assess reproducibility at a constant current of 2 h. After 100 h of operation, charging and discharging voltage efficiency was 71% (50/100 \(\mu\)A) and 83.5% (100 \(\mu\)A). The electric power level is observed to increase with the number of operating cycles.  
energy storage; NASICON membrane; solid-state electrolytes; seawater batteries ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction
For a vessel sailing in restricted waters with varying water depths, challenges in the yaw tracking control system lay in the inherent nonlinearity, unknown parameters and external disturbances. To overcome these difficulties, a back-stepping approach was adopted to design the nonlinear yaw tracking control system [2; 23]. However, the back-stepping method requires repeated differentiation of the virtual control function, which results in a complicated control law. Alternatively, the sliding mode technology was proposed to design the sliding mode yaw tracking control system [21; 24], while imperfections in switching devices and delays lead to the system suffering from chattering. Furthermore, up till now, very few researchers considered the yaw tracking control for a vessel sailing in restricted waters with varying water depths. Motivated by the above studies, a new high-gain extended state observer based adaptive sliding mode path following control (HGESO-ASMPFC) scheme is proposed in this essay, to obtain the straight-line path following for a vessel sailing in restricted waters with varying water depths. The high-gain extended state observer is adopted to design the guidance law, which can exactly estimate time-varying sideslip angles, and guarantee system stability and performance in spite of vessel-bank interaction and varying water depths. In the yaw tracking control system, the vessel's steering dynamics is described by Nomoto equations. The equations' parameters vary with water depths and vessel-bank distance. According to the Nomoto equations, an adaptive continuous proportional integral sliding mode control (PI SMC) is put forward to make the yaw converge to the guidance angle despite unknown parameters, model uncertainties, and external disturbances. The proposed yaw tracking control system can reduce chattering and relax assumptions in [25], such as the existences of first derivatives of disturbances and reference input. In the end, the closed-loop system is proven to be global uniform asymptotic stable (GUAS), the path following error converges to zero asymptotically. Simulation experiments are carried out to validate the proposed path following control scheme and demonstrate its superiority.
## 3 Results
### Experimental Results Analysis  
Table 4 and Figure 3 presents five optimal models determined by Bayesian optimization of the hyperparameters in five different experiments, and their performance evaluations. These results indicate the performance of each model. The RMSE and R\({}^{2}\) values for both the training and test datasets show how well the models generalize in relation to unseen dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Lambda**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{1}{c}{**MAE**} \\ \cline{5-10}  & & & **Train** & **Test** & **Train** & **Test** & **Test** & **Test** \\ \hline I (10-fold CV) & 8-110-104-1 & 0.0001 & 1.0324 & 1.1201 & 0.9914 & 0.9892 & 1.2546 & 0.7214 \\ \hline II (5-fold CV) & 8-93-120-1 & 0.0002 & 1.0690 & 1.0976 & 0.9907 & 0.9896 & 1.2046 & 0.7101 \\ \hline III (10-fold CV) & 8-298-1 & \(1.0816\times 10^{-7}\) & 1.1544 & 1.3222 & 0.9892 & 0.9849 & 1.7483 & 0.8560 \\ \hline IV (10-fold CV) & 8-100-1 & 0.0001 & 1.2083 & 1.3529 & 0.9882 & 0.9842 & 1.8305 & 0.8897 \\ \hline V (10-fold CV) & 8-30-1 & \(2.367\times 10^{-5}\) & 1.5505 & 1.6299 & 0.9805 & 0.9771 & 2.6565 & 1.1553 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal models were determined through Bayesian optimization of the hyperparameters in the five experiments.  
Figure 3: Comparison of the training loss of the best five models from the five experimental runs.  
All models perform well, as shown by the relatively low RMSE values for both the training and test datasets. The R\({}^{2}\) values, which represent the proportion of variance explained, are consistently high, indicating that the models capture a significant portion of the variability in the data.  
Model II is characterized by the lowest RMSE in the test dataset (1.0976), indicating a higher predictive accuracy compared to the other models. The performance of the model is shown in Figure 4 for more clarification. Model V has shown the highest RMSE on the test dataset (1.6299), indicating that it is less effective when generalizing to new data.  
The differences in architecture (number of hidden layers and nodes) between the models do not consistently correlate with performance. Models I, III, IV, and V show similar performance values despite their different architectures. The lambda values for regularization vary, indicating the influence of regularization strength on model performance.  
Of the top models from each experiment, Model II proves to be the most promising, as it has the lowest RMSE for the test set and high R\({}^{2}\) values. It appears to be the best-performing model among the options offered and shows superior predictive accuracy for the new unseen test set.
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
## 4 Results and Discussion
### Wavelet Transform Decomposition  
Mallat wavelet transform was applied to decompose and analyze the deflection curves under temperature load and vehicle load, which provided insights into the temporal variations and magnitudes of deflections in structural elements, such as arches, subjected to different loading conditions.  
The deflection time-history curve under temperature load using Mallat wavelet transform decomposition is illustrated in Figure 6, which reveals critical information regarding the deflections in the upper and lower arches due to temperature variations. Specifically, the maximum deflection in the upper arch, caused by temperature action, occurred approximately at 16:00, with a magnitude of approximately 10 mm. Conversely, the maximum deflection in the lower arch was observed at around 6:00, reaching a magnitude of about 8 mm. These findings highlight the diurnal variations in temperature-induced deflections and their impact on different parts of the arch structure.  
Similarly, the deflection time-history curve under vehicle load by Mallat wavelet transform decomposition is shown in Figure 7. However, the deflection curves exhibit a shorter duration compared to those influenced by temperature variations, which is expected since vehicular loads are typically transient and localized in nature. The peak deflection in the lower arch, concentrated at approximately 5 mm, underscores the impact of vehicular traffic on structural deflection. The concentration of this peak deflection indicates the specific times or conditions under which vehicles contribute most significantly to arch deflection.  
The Mallat wavelet transform decomposition technology has proven to be a valuable tool in analyzing and interpreting deflection time-history curves in structural engineering, which is used to identify the temporal patterns and magnitudes of deflections caused by different loading conditions. For instance, the deflection time-history curve under temperature load revealed distinct diurnal patterns; however, the deflection time-history curve under vehicle load demonstrated the transient nature of vehicular impacts.  
Figure 6: The deflection time–history curve for temperature load.
# LoRa 2.4 GHz Communication Link and Range
## 4 Path Loss Modeling
### Urban Environment  
An urban path loss model is used in the third scenario to evaluate the range of LoRa at \(2.4\,\mathrm{GHz}\) in an outdoor city-scale environment. The Okumura-Hata Urban Path Loss model is an empirical model that is often used in sub-GHz wireless communication systems. While the COST-231 urban model extended its use up to \(2\,\mathrm{GHz}\), the Electronic Communication Committee (ECC) modified the original Okumura-Hata model to work with frequencies up to (and beyond) \(3\,\mathrm{GHz}\) in the ECC-33 model [30; 31]. Therefore, the ECC-33 model is suitable to evaluate the maximum communication range of LoRa at \(2.4\,\mathrm{GHz}\) in an urban environment. The path loss equation for this model is given by  
\[L_{p,urban}(d)=A_{fs}+A_{bm}+G_{b}+G_{m}, \tag{15}\]  
where \(A_{fs}\) is the free space attenuation, \(A_{bm}\) is the basic median path loss, \(G_{b}\) is the base station height gain factor and \(G_{r}\) is the receiver height gain factor, which can be calculated as  
\[A_{fs} = 92.4+20\log_{10}(d)+20\log_{10}(f), \tag{16}\] \[A_{bm} = 20.41+9.83\log_{10}(d)+7.894 log_{10}(f)\] (17) \[+9.56[\log_{10}(f)]^{2},\] \[G_{b} = \log_{10}(\frac{h_{b}}{200})\{13.958+5.8[\log_{10}(d)]^{2}\}, \text{and}\] (18) \[G_{m} = [42.57+13.7\log_{10}f][\log_{10}(h_{m})-0.585] \tag{19}\]  
for medium-sized urban environments. Given the complexity of this set of equations, we extract the maximum range by iterating over values of \(d\) from \(1\,\mathrm{m}\) to \(10\,\mathrm{km}\) and solving the optimization problem given a certain path loss \(L_{p,urban}(d)\).