esa-sceva/satcom-chunk-collection huggingface dataset:
# Drag Reduction in Compressible Channel Turbulence with Periodic Interval Blowing and Suction
## 2 Computational Details
### DNS Method and Physical Model
The flowchart for the DNS analysis is presented in Figure 1.  
The physical model models the turbulence in an infinitely long channel and it is assumed to be fully developed. A periodic unit of the channel is selected as the computational domain, and is displayed in 2D form in Figure 2. Periodic boundary conditions are used in the streamwise and spanwise directions, and isothermal and no-slip boundary conditions are used on the top and bottom wall. Given that the flow state in the channel is periodic, this simplification enables the turbulence to attain a fully developed state with reduced computational resources. The periodic motion of the channel turbulence is driven by the external body force \(s_{x}\). This force is an artificial virtual force introduced to prevent the pressure gradient from disrupting the compressible turbulence. With a given target flow rate \(q_{0}\), \(s_{x}\) will increase/decrease if the actual flow rate is inferior/superior to \(q_{0}\). During the computation, \(s_{x}\) is corrected at every time step until the turbulence reaches a fully developed state. The lengths of the cuboid computational domain in three directions are \(\left(L_{x},L_{y},L_{z}\right)=2\pi\times 2\times 2\pi\), and the grid number of these three directions are \(\left(N_{x},N_{y},N_{z}\right)=128\times 150\times 128\). Uniform grids are used in the streamwise and spanwise directions, while the distribution of grids in the normal direction is controlled by the following hyperbolic tangent function:  
\[y_{j}=\tanh\left(\frac{4(j-1)}{N_{y}-1}-2\right)/\tanh(2),j=1,2,\ldots,N_{y} \tag{3}\]  
where \(y_{j}\) is the normal coordinate of the grids, and \(j\) is the normal number of the grid. Hence, the set of grids is sparser in the centerline and densest near the wall (\(y^{+}=0.2\)). The scale of the first layer of grids at the wall satisfies \(\Delta y_{1}^{+}\approx 0.4\). The superscript \"+\"  
Figure 1: Flowchart of DNS analysis. Figure 2: Schematic diagram of the X-Y cross section of the computational domain. suggests that the wall viscous scale \(\
u_{\rm w}/u_{\rm T}\) is used as the dimensionless parameter, where \(\
u_{\rm w}\) represents the kinematic viscosity at the wall and the wall friction velocity \(u_{\rm T}\) is defined as \(u_{\rm T}=\sqrt{\tau_{\rm w}/\rho}\).
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### BrickerBot  
BrickerBot, discovered in 2017, raised a new wave of threats against the ever-expanding Internet of Things (IoT) ecosystem. Gaining notoriety for its ability to \"brick\" or render IoT devices non-operational, BrickerBot shed light on the pressing security challenges of IoT. Variations include BrickerBot.1, BrickerBot.2, BrickerBot.3, and BrickerBot.4.  
BrickerBot's access method is to brute force the telnet password, then run commands using BusyBox to corrupt MMC and MTD storage, delete all files, and disconnect the device from the Internet. BrickerBot was specifically designed to compromise Linux-based IoT devices.
_Applied_  
_sciences_  
_Article_  
**Polyurethane-Supported Graphene Oxide Foam**  
**Functionalized with Carbon Dots and TiO\({}_{2}\) Particles**  
**for Photocatalytic Degradation of Dyes**  
**[PERSON] 1,2, [PERSON] 1,2, [PERSON] 3, [PERSON] 1,2,*, [PERSON] 1,2,* and [PERSON] 4,*+**  
1 State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China; [EMAIL_ADDRESS] (T.L.); [EMAIL_ADDRESS] (S.S.)  
2 Beijing Key Laboratory of Advanced Functional Polymer Composites, Beijing University of Chemical Technology, Beijing 100029, China  
3 School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China. [EMAIL_ADDRESS]  
4 Faculty of Production Engineering, University of Bremen, D-28359 Bremen, Germany  
*Correspondence: [EMAIL_ADDRESS] (P.L.); [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (G.W.)  
Received: 11 December 2018; Accepted: 11 January 2019; Published: 15 January 2019  
**Featured Application: The CQDs/TiO\({}_{2}\)/GO foam reported in this paper has the advantages of convenient recovery, remarkable effect, and degradation under visible light. It has great potential to be used in the treatment of wastewater containing organic dyes in actual industrial production.**  
**Abstract:** The design and optimal synthesis of functional nanomaterials can meet the requirements of energy and environmental science. As a typical photocatalyst, TiO\({}_{2}\) can be used to degrade dyes into non-toxic substances. In this work, we demonstrated the in-situ hydrothermal synthesis of carbon quantum dots (CQDs)-modified TiO\({}_{2}\) (CQDs/TiO\({}_{2}\)) particles, and the subsequent fabrication of three-dimensional (3D) graphene oxide (GO) foam doped with CQDs/TiO\({}_{2}\) via a facile strategy. By making full use of the up-conversion characteristics of CQDs, the synthesized CQDs/TiO\({}_{2}\) exhibited high catalytic activity under visible light. In order to recover the photocatalyst conveniently, CQDs/TiO\({}_{2}\) and GO were mixed by ultrasound and loaded on 3D polyurethane foam (PUF) by the multiple impregnation method. It was found that GO, CQDs/TiO\({}_{2}\), and PUF reveal synergistic effects on the dye adsorption and photocatalytic degradation processes. The fabricated 3D CQDs/TiO\({}_{2}\)/GO foam system with a stable structure can maintain a high photocatalytic degradation efficiency after using at least five times. It is expected that the fabricated 3D materials will have potential applications in the fields of oil water separation, the removal of oils, and the photothermal desalination of seawater.  
**Keywords:** graphene oxide; carbon quantum dots; TiO\({}_{2}\); photocatalysis; water purification
## 5 Results and Analysis
### Q-Learning Global Path Planning Results and Analysis  
Under identical configurations, Q-learning generated two optimized paths through 60,000 policy iterations (Figure 11). Action space mapping: 0--left, 1--down, 2--right, 3--up. The action sequences are  
Results demonstrate Q-learning's convergence to Manhattan-optimal paths under exploration-exploitation tradeoffs, with multi-modal trajectories highlighting RL's adaptability in dynamic environments. Compared with \(A^{**}\)'s deterministic paths, Q-learning achieves equivalent optimization through online learning mechanisms.
## 3 Cascaded Phase Change Heat Storage Dehumidifier and Its Experimental System
### Working Principle of Cascaded Phase Change Heat Storage Dehumidifier  
The working principle of the cascaded phase change heat storage dehumidifier is shown in Figure 4. Paraffin is a commonly used low-temperature phase change material with a relatively high latent heat of the phase change, and the phase change temperature of paraffin is around 323.15 K. During the experiments, the temperature of the hot and wet air at the inlet of the dehumidifier is 333.15-343.15 K. Within this temperature range, the paraffin can just be used for storing the latent heat of the water vapor. Therefore, paraffins with phase transition temperatures of 325.15 K, 323.15 K and 321.15 K were selected as the cascaded phase change materials, and they are represented by PCM1, PCM2 and PCM3, respectively. The hot and wet air flows from left to right at the bottom of the dehumidifier to exchange heat with the finned heat pipe for cooling. Part of the water vapor in the hot and wet air condenses into fresh water and falls to the bottom of the dehumidifier. The  
Figure 3: Structural diagram of a cascaded phase change heat storage dehumidifier.  
Figure 2: Exergy efficiency of the thermal fluid, \(\eta_{n}\)/%.  
condensation latent heat of the water vapor is successively transferred to PCM1, PCM2 and PCM3 at the upper part of the heat pipe, which melts PCM, and the heat is stored by PCMs.  
In Figure 4, for the dehumidifier, \(\dot{M}_{\text{in}}\) and \(\dot{M}_{\text{out}}\) are the air mass flow at the inlet and outlet, kg/s; \(H_{\text{in}}\) and \(H_{\text{out}}\) are the total enthalpy of air at the inlet and outlet, kW; \(p_{\text{in}}\) and \(p_{\text{out}}\) are the air pressure at the inlet and outlet, Pa and \(T_{\text{in}}\) and \(T_{\text{out}}\) are the air temperatures at the inlet and outlet, K.  
Figure 5 is a schematic diagram of the three-phase change materials PCM1, PCM2 and PCM3 in different arrangements. Among them, CASE1-CASE3 are the single-stage layouts, and CASE4 is the three-stage layout.
## 3 Results
### OPLS-DA IV Scaling, VIP, and S-Plot  
In the OPLS-DA Score plot using UV scaling (Figure 3), the patient samples (exacerbations and non-exacerbations) were completely separated along the X-axis. This indicates that the OPLS-DA model was able to successfully differentiate between the two categories based on the defined exacerbation status. After obtaining the OPLS-DA results, the VIP (Variable Importance in the Projection) diagram was generated (Figure 4). The VIP chart provides a summary of the most important features that contribute to the separation observed in the OPLS-DA model. In this chart, features with VIP values greater than 1.0 are  
Figure 2: (**A**) The PCA score plot in Pareto Scaling; (**B**) the PLS-DA score plot in Pareto Scaling; (**C**) The Permutation test for Pareto Scaling, performed by SIMCA.  
considered statistically significant, indicating their importance in distinguishing between the two categories. On the other hand, features with VIP values less than 0.5 are considered non-statistically significant and may exert a diminished impact on the separation. From the VIP diagram, a table with the VIP values and the MZ_RT (Table 2) was automatically generated. In addition to the VIP diagram, the S-plot was utilized to display the covariance and correlation of the characteristics. The S-plot helps identify the features that exhibit high differentiation and contribute the most to the separation observed in the OPLS-DA model. Features located at the edges of the S-plot, further away from the center, are the ones that show the greatest covariance and correlation with the defined categories. From the S-Plot, a table with the MZ_RT (Table 3) was automatically generated. The combination of two tables, namely Tables 2 and 3, resulted in the creation of a third table, denoted as Table 4, that provides a summary of the characteristics derived from the sample analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RT (min)** & **Characteristics (M/Z)** \\ \hline
6.5 & 55 \\
8.3 & 57, 58, 67, 112 \\
8.9 & 77, 80, 92, 93, 94, 122 \\
9.4 & 55, 67, 68, 70, 81, 82, 95, 98 \\
10 & 85, 112, 119 \\
10.8 & 41, 44, 56, 118, 123 \\
12 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary table of the characteristics obtained from the analysis of the samples.  
Figure 4: (**A**) VIP Plot chart; (**B**) S-Plot chart (both were generated by Simca program).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MZ\_RT** & **MZ\_RT** \\ \hline
98\_9.4 & 67\_9.4 \\
80\_8\_9 & 112\_8.3 \\
55\_6.5 & 44\_10.8 \\
122\_8.9 & 95\_9.4 \\
68\_9.4 & 118\_10.8 \\
85\_10 & 119\_10 \\
81\_9.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: S-Plot-Statistically Significant Characteristic.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Research Questions and Objectives
#### 1.2.1 Research Questions  
To achieve the overarching goal of improved ransomware detection, this research investigates the following key questions:  
1. To what extent does GAN-based data augmentation mitigate pre-encryption data scarcity and enhance ransomware early detection?
2. How can mutual information feature selection techniques be integrated into a DBN-based ransomware detection model to enable dynamic feature relevance reassessment with the incorporation of new data?
3. How can an uncertainty-based estimation approach be utilized to determine the optimal number of training epochs for a DBN-based ransomware detection model?
## 3 Proposed Method
### The Authentication Algorithm  
The pseudocode for the authentication algorithm is as follows:  
1. The image is divided into parts: inner part, outer part with the same principles as in embedding
2. Make pairs \((B\_Ik,B\_Ok)\), \(B\_Ik\) from inner part, \(B\_Ok\) from outer part using the given key \(S\_K\)
3. Load the given \(PSNR\)
4. \(PSNR\_length\)\(\leftarrow\)\((PSNR\_length)\)
5. For each block \(B\_Ik\) from inner part (a) Find out DWT \((B\_Ik)\)  
Figure 2: Inner block size is less than outer block size.  
* Obtain binary image \(Bin\_LL1\_B\_Ik\) from \(LL1\) band of DWT (\(B\_Ik\))
* \(DCT\_B\_Ok\_M\gets DCT(B\_Ok)\), where \(B\_Ok\) is corresponding block from outer part
* \(V\_DCT\_B\_Ok\leftarrow\)take value from(\(Z\_DCT\_B\_Ok\)), excluding first \(V\_exclude\) and last \(V\_exclude\) values
* While \((Processed\_Start<=(Length(V\_DCT\_B\_Ok)-PSNR\_length))\)
* \(Val\_PSNR\leftarrow\)take values of length \(PSNR\_length\)from\(V\_DCT\_B\_Ok\)
* if \(Correlation\_value1>=Correlation\_value2\)
* \(Extracted\_Bitmap\leftarrow\)Put the bit value in proper place in the order it is embedded
* If \((Bit\_Diff\_Sum/BitMapImageNumberOfBits)>0.15/^{*}Threshold=0.15^{*}/\)
* Mark the inner and outer block as changed
* Embed robust watermark into center blocks for the purpose of proving ownership as in [20]  
Both the embedding and extraction algorithms should be robust and secure in the watermarking process. The authentication process is important to find out embedded secret information in the cover images for copyright protection. In the proposed methodology, the cover image is divided into inner and outer blocks. Each block is transformed into the discrete wavelet transformation (DWT). The DWT gives low frequencies (LL band) and high frequencies (HL, LH, and HH bands). The LL bands were transformed into the DCT using zigzag order. If the correlation of the inner block is greater than the correlation of the outer block, then the pixel value is assigned to 1; otherwise 0. In the extraction process, the similarity ratio (SR) is used for quality measurement. For binary watermarking, an SR value of 1 demonstrates the best quality of the extracted watermark.  
Figure 3: Inner and outer blocks in embedding process.
# High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm
## 4 The Proposed Algorithm
### Surrogate Models
#### 4.2.1 Kriging  
The Kriging model is one of the most well known spatial interpolation models for substituting the numerical relationship between input and output variables. It loses a lot of efficiency when dealing with significant design variances. On the other hand, Kriging surrogate models can significantly reduce the computational cost of regression because they need significantly fewer sample data to be generated. Additionally, more trustworthy prediction results can be produced because the Kriging fitting technique concentrates on more representative samples.  
The Kriging model regards the function of interest as a realized random function (stochastic process). As a result, a linear combination of a global model and deviations, Equation (6), is presented as the Kriging mathematical model:  
\[y(x)=f(x)+Z(x) \tag{6}\]  
where \(y(x)\) is the unknown deterministic response, \(f(x)\) is a known (usually polynomial) function of \(x\), and \(Z(x)\) is a realization of a stochastic process with mean zero, variance \(\sigma^{2}\), and non-zero covariance.
## 7 Results of the InSAR Processing
### Vertical and Horizontal Displacements
As regards the sites where slope failures occurred, it was found from the displacement map and the respective sections and graphs that in the Aylakia area (A3-A3\({}^{\prime}\) and B3-B3\({}^{\prime}\)) (Figure 31) the slope failures occurred in an area that suffered a subsidence of \(-9\) cm (SARscape) and \(-5\) cm (SNAP). Similarly, for the slope failures that occurred at the Tsambou Vourliotes beach (A4-A4\({}^{\prime}\) and B4-B4\({}^{\prime}\)) (Figure 31), the area suffered the maximum detected subsidence on the affected island; more specifically, -12 cm from the SARscape software and -6 cm from the SNAP software.
# Modulated Noncollinear Optical Parametric Amplifier Output Induced by Stimulated Raman Scattering
## 4 Results and Discussion
### Mechanism of Stimulated Raman Processes in the Parametric Amplification  
We have clarified the mechanism of the formation of the modulated structure in NOPA spectrum. This phenomenon is due to the interaction between the Raman field and parametric medium, BBO. BBO can be regarded as a molecular crystal composed of BaB\({}_{2}\)O\({}_{4}\) molecular ensemble and the representative vibrational modes are as mentioned before in Table 1[36]. The abundant molecular vibration modes are coupled to the virtual electronic transition via vibronic coupling introducing the modulated features in the OPA spectrum as mentioned before with Figure 2. There are several important reports related to the subject discussed here in this paper. The shortened titles of them are \"Mode-specific excited state dynamics based on Raman loss spectroscopy\" [39] and \"Snapshot of structure during phonon creation with ultrafast Raman spectroscopy\" [40], and \"Ultrashort laser-driven stable-buckling of blisters in chirped mirror\" [41] are some examples of them. There are other important papers to be mentioned such as [42; 43; 44; 45; 46].  
Figure 8: (**a**) Amplified signal spectrum. (**b**) Two-dimensional results of a FROG measurement.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### 2d-Ssa
#### 2.2.3 Grouping  
A subsequent operation is eigenvalue grouping, during which the total set of \(M_{x}M_{y}\) individual components in (10) are divided into \(m\) subsets, designated as \(S=\left[S_{1},S_{2},\ldots,S_{m}\right]\). By selecting one or more elementary matrices \(\mathfrak{Z}^{b}_{i}\) from each subset, it is possible to derive the main information contained in an image without being disturbed by high noise levels. As a result, the trajectory matrix \(\mathfrak{Z}^{b}\) can be represented as follows:  
\[\mathfrak{Z}^{b}=\mathfrak{Z}^{b}_{S_{1}}+\mathfrak{Z}^{b}_{S_{2}}+\cdots+ \mathfrak{Z}^{b}_{S_{m}} \tag{11}\]  
The reconstruction of a single band scene of HSI using various numbers of components (\(\mathfrak{Z}^{b}_{i}\)) is compared in Figure 3. In general, the component with the highest eigenvalue is the most informative one, containing key features with the lowest noise contribution. With the inclusion of additional components, the reconstructed scene begins to resemble the actual scene. The reconstructed image obtained by grouping the 1 st-5 th components and 1 st-10 th components are very similar with marginal differences (Figure 3c,d). Hence, a small number of key components are sufficient to reconstruct the scene satisfactorily.
## 6 Simulations Results and Discussions
### Remaining Network Energy over the Rounds  
Figure 9 presents the remaining network energy over the rounds. Along the y-axis we have the total initial energy of the WSNs (which is different for homogeneous, two-tier, and three-tier heterogeneous WSNs). As we move along the x-axis (rounds spent), the network energy is reduced. Due to the heterogeneous nature of WSNs in the case of EEECA-THWSN and SEP protocols, they have more initial network energy than the others, but their performance is still significantly lower than our proposed EEUCH protocol. This is because in EEUCH the UAV can move dynamically towards the ground SNs for data packets' collection, while in the case of the benchmark protocols the data packets are transmitted by the CH to the BS (located at longer distance from the CH).  
Figure 8: A comparative analysis of dead SNs over the number of rounds.  
Figure 7: A comparative analysis of the number of alive SNs over the number of rounds.
## Appendix A RL Agents
### Ppo  
PPO [26] is a policy gradient method that improves the data efficiency and stability of traditional policy gradient algorithms. It belongs to the family of actor-critic methods, where the actor updates the policy while the critic evaluates it. PPO is designed to address some of the challenges faced by Trust Region Policy Optimization (TRPO), such as complexity and inefficiency in handling large models. PPO introduces a simpler objective with a clipped probability ratio, keeping updates within a safe range, avoiding excessively large updates that can destabilize learning. This method alternates between data collection via environmental interaction and multiple epochs of policy optimization using the collected data.  
The core update in PPO uses a _clipped surrogate objective_, which is designed to limit policy updates without requiring the complexity of TRPO's constraints. The surrogate objective is defined as  
\[L^{CLIP}(\theta)=\mathbb{E}_{t}\big{[}\min\big{(}r_{t}(\theta)\hat{A}_{t},\text {clip}(r_{t}(\theta),1-\epsilon,1+\epsilon)\hat{A}_{t}\big{)}\big{]} \tag{10}\]  
where \(r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{\text{old}}}(a_{t }|s_{t})}\) is the ratio of the new to old policy probabilities. \(\hat{A}_{t}\) is the estimated advantage at time \(t\), and \(\epsilon\) is a small hyperparameter, typically set to 0.1 or 0.2, which controls the range of allowed updates.  
PPO limits the change in policy by clipping \(r_{t}(\theta)\), preventing large deviations from the previous policy during optimization. This balances exploration and exploitation without the need for complex constraints like in TRPO.  
One of the key advantages of PPO is that it is easier to implement compared to TRPO, requiring fewer computational resources while still providing similar stability. The clipped objective prevents overly aggressive policy updates, ensuring that learning remains smooth and stable even during multiple epochs of optimization. This makes PPO particularly robust across different environments and tasks, requiring less hyperparameter tuning compared to other policy gradient methods. Moreover, the ability to perform multiple updates using the same data improves sample efficiency, a critical factor in environments where interactions are expensive, such as in portfolio optimization.  
Despite its robustness, PPO is not immune to certain drawbacks. The algorithm's performance can be sensitive to the choice of clipping parameter \(\epsilon\), which requires careful tuning. If the clipping threshold is too tight, the algorithm may fail to make meaningful policy updates, slowing down learning. On the other hand, if the clipping is too loose, it can lead to unstable updates similar to standard policy gradient methods. Additionally, PPO, while more efficient than TRPO, may still be outperformed by algorithms specifically designed for high-dimensional continuous control tasks, such as SAC.