esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 2 Related Work
### Individual Deep Learning Algorithms Implementation  
Other researchers have focused on comparing how different deep learning algorithms performed in DDoS attack detection in SDNs. A case in point was [11]. They reached the simple neural network, CNN, and RNN in DDoS attack detection in the CSE-CIC-IDS2018 dataset, which simulated diverse attack scenarios, including brute-force, DoS, infiltration, DDoS, Heartbleed, and botnet attacks. Findings from the study revealed that simple neural networks outperformed CNN and RNN, attributed to the detection of malware by generating an accuracy of 82% and a precision of 42%. The authors argued that the more popular RNN and CNN could have performed better due to the challenges of overfitting, which led to high false positives and negative rates [11]. In another study, Ref. [12] also compared the performance of several algorithms to facilitate the detection of DDoS attacks in SDN environments. In particular, it was also considering the Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN) classification models. In addition, the researchers adopted feature selection techniques to simplify the models used in the experiments to ensure model simplification, enhance their interpretation, and facilitate a shorter training time [12]. Findings reported showed that wrapper feature selection combined with the KNN classifier generated the highest score of 98.3% accuracy in DDoS-attack detection. Analytically, the results underscored the performance improvement associated with combining deep learning with feature selection techniques when detecting DDoS attacks to facilitate reduced load processing times [12]. Furthermore, there has been critical research [13]; the authors proposed an approach against DDoS attacks by adopting the Gated Recurrent Units (GRU) method and compared it with other deep learning algorithms by explaining how their approach outperforms.  
Findings from the study showed that the GRU-RNN did not adversely affect network performance and led to high accuracy performances of 99% and 89% for the CICIDS2017 and NSL-KDD datasets ([PERSON] et al., 2019). In previous research, Ref. [14] reported an accuracy of 99.63% for the CNN-RF model and 99.58% for the RF-MLP using the same dataset--CICIDS2017. Analytically, the comparison of [1,14] revealed, using the same dataset, different hybrid deep learning algorithms that generated similar performances--99% for both the CNN-RF and GRU-RNN in both cases. Such insights are essential in the current study, as they reveal that differences in implementation of the hybrid deep learning algorithms did not vary significantly despite using different deep learning algorithms.
# Decolorization of Orange-G Aqueous Solutions over C\({}_{60}\)/Mcm-41 Photocatalysts
## 2 Experimental
### Photocatalytic Tests  
The photocatalytic tests were performed at 400 mL aqueous suspensions containing 25 uM of orange G (OG, Merck, Darmstadt, Germany), 0.5 mM of ascorbic acid (AA, Sigma, China), and 300 mg of the photocatalyst. A glass semi-batch photoreceptor was used (Heraeus, Hanau, Germany) equipped with magnetic stirrer (Figure S1), sampling system and a proper socket for glass electrode to measure the suspension's pH. The lamp, TQ 150 (power: 150W), situated in a glass cooling jacket filled with re-circulated deionized water, is located inside the reactor. Thus, the emitted UV irradiation in wavelengths lower than 300 nm was cut off (Figure S2). Before each experiment the photoreceptor was covered by aluminum paper to avoid the effect of external light. The reactor was fed with 15 mL/min air regulated by a mass flow controller ([PERSON], Holland). The temperature of the suspension during the whole experimental procedure was maintained at 25\(\pm\)0.5 \({}^{\circ}\)C and the pH=4. Liquid samples were taken at various time intervals. These were filtered (Millipore cellulose filters 0.22 nm, Ireland) for removing catalytic particles and then their spectra were recorded in the range 200-600 nm using a UV-Vis spectrophotometer ([PERSON], model Cary 3, Australia).  
Each kinetic experiment was initially carried out for more than 180 min in dark in order to determine the extent of decolorization which is due to the adsorption of the Orange G on the photocatalyst's surface. During this time, liquid samples were taken at various time intervals and analyzed as mentioned above. Then the lamp was switched on and kinetic procedure was performed under irradiation in order to monitor decolorization via photocatalytic oxidation.
## 2 Material and Methods
### Methods  
The framework shown in Figure 2 describes the overall process of studying the spatio-temporal variability of WUE and its controlling mechanisms among various vegetation types across the FPENC. The process is divided into three main steps. The first step is to analyze the spatio-temporal variation of WUE in the FPENC based on MODIS GPP and ET product. Long-term trends were assessed using the non-parametric Theil-Sen slope and Mann-Kendall test. To eliminate the influence of autocorrelation, the seasonal and trend decomposition using the LOESS (STL) method was adopted [44] The second step was to detect the influence of biophysical drivers on WUE. Using random forest and multiple regression methods, five environment factors (PPT, Temp, VSWC, Rn, and VPD) and two biotic factors (LAI and EVI) were selected for the driving force analysis. The third step was to explore the characteristics of ecosystem resilience in different vegetation types over FPENC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Dataset/Product**} & \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**Resolution**} & \multirow{2}{*}{**Data Source**} \\  & & & & & **Spatial** & & **Temporal** \\ \hline \multirow{4}{*}{1.} & Gross primary & & & & & & \
\begin{tabular}{} \end{tabular} \\  & productivity & MODIS GPP(MOD17A2H) & 2003–2022 & 500 m & 8 days & & &
\begin{tabular}{} \end{tabular} \\  & & & & & & & \\  & (GPP) & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\
2. & Evapor- & transpiration & MODIS ET (MOD16A2) & 2003–2022 & 500 m & 8 days & &
\begin{tabular}{} \end{tabular} \\  & & & & & & & & \\  & (E1) & & & & & & \\  & & & & & & & \\
3. & Leaf area index & & & & & & \\  & (LAI) & MODIS LAI (MOD15A2H) & 2003–2022 & 500 m & 8 days & &
\begin{tabular}{} \end{tabular} \\  & & & & & & & & \\  & (LAI) & & & & & & \\  & & & & & & & \\  & Enhanced & & & & & & \\
4. & vegetation index (EV) & MODIS EVI (MOD13A1) & 2003–2022 & 500 m & 16 days & &
\begin{tabular}{} \end{tabular} \\  & & & & & & & & \\  & & & & & & & \\
5. & Rainfall & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\
6. & Temperature & ERA-5 Land monthly averaged & 2003–2022 & 0.1 & Monthly & & \\  & & & & & & \\  & & ERA-5 Land monthly averaged & 2003–2022 & 0.1 & Monthly & & \\  & & & & & & & \\  & & & & & & \\
7. & Soil moisture & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\
8. & Net radiation & & & & & \\  & & & & & & \\  & & & & thermal radiation data & & \\  & & & & & & \\  & & & & & & \\  & TerraClimate: Monthly Climate & & & & & & \\  & & & & & & \\
9. & Vapor pressure & & & & & & & \\ deficit (VPD) & Water balance for global & & & & & \\ terrestrial surfaces VPD & & & & & & \\ \hline \multirow{4}{*}{10} & \multirow{4}{*}{Land use classes} & MODIS Land Cover Type & 2022 & 500 m & Annual & & &
\begin{tabular}{} \end{tabular} \\  & & & & & & & \\ \cline{1-1}  & Standardized & & & & & & & \\ \cline{1-1}  & precipitation & & & & & & \\ \cline{1-1}  & evapotranspiration & & &
# Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
## 3 Edge Line Segment Extraction Based on Quadratic Curve Fitting  
From the derivation of the above mathematical equations, it is clear that each edge line segment to be extracted in the actual image should satisfy the quadratic curve model. The parameters of a quadratic curve model are calculated according to Equation (8) using at least three non-co-linear pixel points. Therefore, we can take the detected edge contour in the image, select three pixel points randomly from it, and estimate the optimal parameters of the quadratic curve model by continuously iterating. At the same time, this optimal model is used to find the continuous and longest image sequence in the edge contour, which is used as an edge segment of this contour. In addition, in order to avoid the problem that the edge contour contains multiple edge segments (as shown in Figure 1), which affects the reliability of subsequent detection, each edge segment extracted is removed from the edge contour, and the remaining pixel sequences constitute the new edge contour. Then, repeat the above steps of edge segment extraction until no more edge segments can be extracted from the edge contours. The above process is implemented as follows:  
Step 1: The Canny operator [22] is used to detect the edge features in the input image, and the Moore-Neighbor algorithm [23] is used to find the edge contours. The edge contours whose length is smaller than \(\dfrac{1}{20}\) of the image size are removed. Suppose M edge contours are extracted by the above steps (noted as the set \(\left\{C_{i}|i=1,2,3,...,M\right\}\)), where \(C_{i}\) is any one edge contour. Step 2: For the edge contour \(C_{i}\), assume that it consists of n pixel points, and that it is denoted as \(C_{i}=\left\{(x_{j},y_{j})|j=1,2,3,...,n\right\}\). From \(C_{i}\), select three randomly non-co-linearpixel points \((x_{1},y_{1})\), \((x_{2},y_{2})\), and \((x_{3},y_{3})\) and bring them into Equation (9) to calculate the quadratic curve equation coefficients \(\varepsilon_{k}\), \(f_{k}\), and \(g_{k}\). Based on the current solution we obtain \(e_{k}\), \(f_{k}\), and \(g_{k}\). Together with Equation (4), we define the error function as follows:  
\[E=x_{j}^{2}+y_{j}^{2}+e_{k}x_{j}+f_{k}y_{j}+g_{k} \tag{10}\]  
When the pixel points in \(C_{i}\) satisfy Equation (10), then E's value is smaller; otherwise, E's value is larger. Therefore, by setting a threshold \(\varepsilon\) for the quadratic curve fitting error, it is possible to search for L from \(C_{i}\) based on the magnitude of E, where L indicates the longest and continuous pixel sequence that satisfies Equation (10). In addition, this sequence can be recognized as an edge segment.
## 1 Body Area Network Propagation Channel
In contrast to those research concepts, in order to gain more quantitative insight into the influence of the reflecting multipath environment and its particular elements, an all-in-one numerical FDTD model was developed. The goal of this research was to find a simplified model of the complex building structure that is capable of giving simulation results useful for indoor BAN designers. Thus the number of obstacles (walls and ceiling) that should be included in the model was analyzed. The model consists of both a human body phantom with a pair of transmitting and receiving antennas and several examples of the surrounding multipath environment, including walls, ceiling, and floor. In this investigation the ISM 2.4 GHz band is considered. This is a remarkably important band for medical applications. Its proper use requires a reliable channel model for the radio link budget analysis, important not only for transmission reliability but also for radio node energy consumption optimization. Very intense use of the ISM 2.4 GHz frequency range by other systems also calls for interference analysis (electromagnetic compatibility) in BAN, which also requires a reliable propagation channel model. In the paper, the properties of BAN radio channel are considered as a function of link attenuation _vs._ frequency for the radio link between the transmitting and the receiving antennas located on the body in two different configurations following from three antenna locations shown in Figure 1.  
Figure 1: Graphical representation of the NMR Hershey body phantom in Remcom XFdtd® (10 mm voxels) and antenna location: 1–chest, 2–back, 3–arm.
## 5 Conclusions  
In this paper, we propose a deep reinforcement learning-based image restoration algorithm. To deal with multi-image problems in complex situations, we divide the restoration tasks into three categories: (1) the image deblurring restoration task, (2) the image defect restoration task, and (3) the image denoising and enhancement restoration task. First, a finger vein deblurring generation adversarial network based on DeblurGAN-v2 network with Inception-Resnet-v2 backbone is proposed, which is used for the image deblurring restoration task with the deblurring Gaussian restoration task. Second, a finger vein feature-guided restoration network is proposed to achieve defect image restoration, which is designed in two stages: feature image restoration and original image restoration. After the feature image restoration, the feature texture information is followed to guide the restoration of the original finger vein image. Finally, we propose a novel deep reinforcement learning-based finger vein image restored method called DRL-FVRestore. The method treated the proposed restoration task as a behavior and the input low-quality image as a state. The method trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image.  
In this study, we performed extensive experiments with the proposed method on four public datasets, (Section 3.1). The experimental results show that the proposed deblurring network, damaged restoration network in this paper is an effective and reliable method for finger vein image restoration to effectively deal with the existence of a single image problem. With regards to a complex situation where there are multiple image problems, the DRL-FVRestore method proposed in this paper demonstrates better restoration performance. It can adaptively select the above effective restoration task to perform the restoration process based on the state of the input image. In future work, we plan to further research how to reduce the time complexity and space complexity of the model. A lighter, more real-time approach to finger vein image repair is a challenge we need to address.  
Conceptualization, R.G. and H.L.; methodology, R.G.; software, R.G., Y.L., and C.Z.; validation, H.L. and R.G.; formal analysis, R.G.; investigation, H.L. and C.Z.; resources, Y.L.; data curation, R.G.; writing--original draft preparation, R.G.; writing--review and editing, R.G., H.L. and A.A.-A.; visualization, Y.L. and A.A.-A.; supervision, H.L. and A.A.-A.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Key R&D Program of Science and Technology Development Plan of Jilin Province of China (No. 20200401103 GX).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Performance Evaluation
### Outage Probability
\begin{array}{c}\sum\limits_{t=0}^{K-1}\sum\limits_{m=1}^{M} \left(-1\right)^{m-1}\frac{C_{M}^{m}}{t!}\left(\frac{\lambda_{\text{RD}}\lambda _{\text{BR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}\frac{2m\lambda_{\text{ SR}}}{m\left(\lambda_{\text{SR}}+\lambda_{\text{RD}}\right)-\lambda_{\text{ RD}}}K_{1-t}\left(2\sqrt{\frac{\lambda_{\text{RD}}\lambda_{\text{BR}}\rho}{\kappa \Delta}}\right)\\ \hskip 14.226378 pt+\sum\limits_{t=0}^{K-1}\sum\limits_{n=1}^{N}\sum\limits_{m =1}^{M}\left(-1\right)^{m+n-1}\frac{C_{N}^{n}C_{M}^{m}}{t!}\left(\frac{\lambda _{\text{BR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}\left(\frac{\rho}{n \lambda_{\text{RD}}\delta\Delta+\lambda_{\text{RD}}\rho}\right)^{\frac{1-t}{2 }}\\ \hskip 14.226378 pt\times\frac{2m\lambda_{\text{SR}}\lambda_{\text{RD}}}{m \left(\lambda_{\text{SR}}+\lambda_{\text{RD}}\right)-\lambda_{\text{RD}}}K_{1- t}\left(2\sqrt{\frac{\lambda_{\text{BR}}}{\kappa\Delta}\left(n\lambda_{ \text{RP}}\delta\Delta+\lambda_{\text{RD}}\rho\right)}\right)\\ \hskip 14.226378 pt+\sum\limits_{t=0}^{K-1}\sum\limits_{n=1}^{N}\sum\limits_{m =1}^{M}\left(-1\right)^{m+n-1}\frac{C_{N}^{n}C_{M}^{m}}{t!}\left(\frac{\lambda _{\text{BR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}\left(\frac{\rho}{n \lambda_{\text{RP}}\delta\Delta+m\left(\lambda_{\text{SR}}+\lambda_{\text{ RD}}\right)}\right)^{\frac{1-t}{2}}\\ \hskip 14.226378 pt\times\frac{2m\left(m-1\right)\lambda_{\text{SR}}\left( \lambda_{\text{SR}}+\lambda_{\text{RD}}\right)-\lambda_{\text{RD}}}{K_{1-t}} \left(2\sqrt{\frac{\lambda_{\text{BR}}}{\kappa\Delta}\left(n\lambda_{\text{ RP}}\delta\Delta+m\left(\lambda_{\text{SR}}+\lambda_{\text{RD}}\right)\rho \right)}\right)\end{array} \right\}.\]  
**Proof. ** Firstly, relaxing the dependence between \(\gamma_{\text{SR}_{c}}\) and \(\gamma_{\text{R}_{c}\text{D}}\), we have the following approximation:  
\[ \begin{array}{c}\text{OP}_{\text{C}-\text{ORS}}&\approx 1-\text{Pr}\left( \gamma_{\text{SR}_{c}}\geq\frac{\rho}{\Delta Z_{1}}\right)\text{Pr}\left( \gamma_{\text{R}_{c}\text{D}}\geq\frac{\rho}{\Delta Z_{2}}\right)\\ &\approx 1-\text{Pr}\left(Z_{1}\geq\frac{\rho}{\Delta\gamma_{\text{SR}_{c}}} \right)\text{Pr}\left(Z_{2}\geq\frac{\rho}{\Delta\gamma_{\text{R}_{c}\text{ D}}}\right).\end{array}  \tag{72}\]  
Our next objective is to calculate \(\text{Pr}\left(Z_{1}\geq\frac{\rho}{\Delta\gamma_{\text{SR}_{c}}}\right)\) and \(\text{Pr}\left(Z_{2}\geq\frac{\rho}{\Delta\gamma_{\text{R}_{c}\text{D}}}\right)\), i.e.,  
\[ \begin{array}{c}\text{Pr}\left(Z_{1}\geq\frac{\rho}{\Delta\gamma_{\text{SR}_ {c}}}\right)&=\int_{0}^{+\infty}\left(1-F_{Z_{1}}\left(\frac{\rho}{\Delta y} \right)\right)f_{\gamma_{\text{SR}_{c}}}\left(y\right)dy,\\ \text{Pr}\left(Z_{2}\geq\frac{\rho}{\Delta\gamma_{\text{R}_{c}\text{D}}} \right)&=\int_{0}^{+\infty}\left(1-F_{Z_{2}}\left(\frac{\rho}{\Delta y} \right)\right)f_{\gamma_{\text{R}_{c}\text{D}}}\left(y\right)dy.\end{array}  \tag{73}\]  
Using ([34], Equation (2)), we obtain PDF of \(\gamma_{\text{SR}_{c}}\) and \(\gamma_{\text{R}_{c}\text{D}}\), respectively, as  
\[
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
## 4 Conclusions  
After the analysis of the results obtained both in the comparison of cupola slag with other admixtures, and in the comparison with Portland cement, the following conclusions have been obtained:  
* The recovery of cupola slag and its use in construction materials means a reduction in CO\({}_{2}\) emissions generated by the manufacturing of cement, and preventing this waste from going to landfills.
* The diffractogram, the chemical composition, and the infrared spectrum confirm the presence of an amorphous structure in the slag cupola that makes it susceptible to reacting.
* The cupola slag studied does not pose any environmental risk since concentrations of dangerous elements in the leachate test are very low.
* Compared to limestone filler and fly ash admixtures, the evolution of strength is similar up to ages close to 7 days, but more than 20% higher in the long-term, for ages close to 60 days. This shows a greater hydraulicity of the slag cupola with respect to the other admixtures used.
* A greater replacement of cupola slag by Portland cement produces a greater consistency in the fresh mortar, but a loss of compressive strength of up to 27% after 7 days. The slowness of the pozzolanic reactions makes the resistances tend to converge at 90 days for any replacement.
* Future work will attempt to quantitatively clarify the environmental and economic costs associated with the recovery process, which is very important to establish the sustainability of the material.
* The results obtained show that it is feasible to use the slag cupola in the manufacturing of mortars/concrete when high initial strengths are not required or when a binder that generates less heat of hydration than Portland cement is desired.  
Conceptualization, J.A.P. and C.T.; methodology, C.T. and I.S.; validation, J.A.P. and J.S.; formal analysis, I.S. and P.T.; investigation, I.S.; data curation, I.S.; J.A.S.-A. and A.C.; writing--original draft preparation, I.S. and P.T.; writing--review and editing, C.T. and P.T.; visualization, I.S.; supervision, J.A.P. and C.T.; project administration, C.T. and J.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The authors of this research would like to thank GLOBAL STEEL WIRE for the EAF slag supply and Saint Gobain Pam Espana for the Cupola Furnace Slag as well as ROCACERO for providing the cement, natural aggregates, and superplasticizer additive.  
The authors declare no conflict of interest.
## 1 Introduction
[25] evaluated the reliability of the transmission tower structure considering the mechanical model uncertainty caused by the connection details. [PERSON] et al. [26] improved the calculation expression of K-joint displacement of transmission tower, and verified the theoretical calculation through numerical simulation and full-scale test. The mechanical analysis and comparison of several commonly used auxiliary materials are manufactured. The research model is plane tower leg, which does not take into account the impact of the actual spatial structure on the mechanical performance of tower leg. [PERSON] et al. [27] studied the mechanical properties of a transmission tower under load by using finite element method considering the experimental sliding behavior of bolted joints of the transmission tower. Research on the connection joints of transmission towers at home and abroad mostly focuses on the test and numerical simulation of circular steel pipe joints, angle steel bolt joints, I-steel joints and box joints. In this paper, a new type of double-limb double-plate connection joint [28] connecting circular steel pipe and angle steel is studied. The mechanical characteristics of the new type of double-limb double-plate joint and its influence on the stable bearing capacity of quadrilateral tower are studied through test, numerical simulation and theoretical analysis. The research results can provide reference for the engineering application and design of double-limb double-plate connection joint.