esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
###### Abstract
The second reason for the proposed approach is that the classical Shannon entropy measure is only based on the histogram of GLCM elements while it would also be valuable to evaluate the sequences of GLCM elements since they may provide further information on the presence of the attack. For this reason, the 2D Dispersion Entropy (2 DDE) was introduced in the study. As described in Section 3.4 later in this paper, 2 DDE allows to analyze irregularity of images on the basis of the frequency of patterns in the image, which can provide more information than the classical Shannon entropy. This study uses the CIC-IDS2017 data set [5], which has been recently published (2017) and it has been increasingly used by the IDS research community. The results shown in this paper demonstrate that this approach manages to remain competitive in terms of detection performance in comparison to more sophisticated and computing demanding approaches based on Deep Learning (DL) applied to the same data set [6; 7]. To summarize, the contributions of this paper are following:  
* GLCM is applied to an IDS problem where the network traffic features are transformed to grayscale images on which GLCM is applied. An extensive evaluation of the GLCM hyperparameters on detection accuracy is implemented. To the knowledge of the authors this is the first time that the GLCM in combination with 2 DDE is used for the IDS problem. This is also the first time that the authors submitted this study for review and the authors did not publish this work before. * 2D Dispersion Entropy (2 DDE) is used as additional GLCM feature. We demonstrate that the use of this entropy measure contributes significantly to the capability of the proposed approach to detect a cybersecurity attack. * The study uses the recent IDS CIC-IDS2017 data set instead of older data sets, which may not be representative any longer of modern networks. We highlight that the approach is based only on the network flow features and it does not attempt to perform a deep-packet inspection on the network traffic. In addition, it is limited in scope to two specific attacks of the CIC-IDS2017 data set: DDoS and Port Scan attack since they are the ones with the most significant number of samples in the data set and they are the ones where the research community has given much attention [7; 8; 9; 10], which is relevant for the comparison of the results of this paper with literature (see Section 4). The structure of this paper is the following: Section 2 provides the literature review. Section 3 describes the overall workflow of the approach, the concept of GLCM, the definition of 2D Dispersion Entropy and the materials (i.e., CICIDS2017 data set) used to evaluate the approach. In addition, Section 3 describes the machine learning algorithms adopted for the detection and the evaluation metrics. Section 4 presents the results, including the findings from the hyperparameters optimization phase and the comparison to the other approaches used in literature. Finally Section 5 concludes this paper.
# Possibilities of Evaluating the Effectiveness of Noise Barriers in Slovakia
## 5 Conclusions  
The main outputs of the research are:  
* Noise barriers' effectiveness in Slovakia can be evaluated by three methods: the direct and indirect method in accordance with the standard ISO 10847, by simulation modelling according to the CNOSSOS method and in accordance with the standard EN 1793-6 (Adrienne method).
* By comparing the measurement methods of noise barriers' acoustic characteristics, it can be stated that the method of ISO 10847 can be used in different measurement locations and only the reference point above the barrier is defined. The Adrienne method precisely defines the distances of the noise source and nine microphones from the noise barrier and its direct output is a value of the airborne sound insulation index obtained in situ for the whole structure. The simulation according to the CNOSSOS method is used for modeling the sound field. It enables comparison of the simulated values of the insertion loss of a barrier with the values measured according to the ISO 10847 standard and to assess whether the barrier satisfies the expectations.
* The measurement methods were used on real noise barriers on selected highway sections, thus real values of the insertion loss and airborne sound insulation index were obtained and gave a picture of their sound performance. The researched noise barriers were proven to be good quality.
* Construction materials that have given acoustic properties from the manufacturer (declared airborne sound insulation category D0 to D4) may lose these values over time due to material degradation. In addition, during the construction of noise barriers, defects can occur that contribute to reduced effectiveness. Measurement according to the EN 1793-6 Adrienne method is used to determine the quality of a noise barrier and its actual values of the airborne sound insulation index in situ. So, the Adrienne method can evaluate (degradation) the acoustic properties of a barrier in situ during a longer time period.  
Conceptualization, A.P. and J.S.; methodology, A.P. and J.G.; software, J.S.; validation, A.P., J.G. and J.S.; formal analysis, A.P.; investigation, A.P.; resources, J.S.; data curation, J.S.; writing--original draft preparation, A.P.; writing--review and editing, A.P and J.G.; visualization, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON]; project administration, A.P.; funding acquisition, A.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovak Grant National Agency VEGA, project No. 1/0006/20, Constructions of transport structures in terms of dynamic analysis of real operational states.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 2 Diffusion Model of an SDN Switch
As a consequence, \(p\) is the probability that the service time is a constant \(KT\), representing the case when all the flow entries in the table are examined without success, while with probability \((1-p)\) the packet's flow match is found in a time that is uniformly distributed in \([T,KT]\)--i.e., on average in time \((K+1)T/2\) and variance \((K^{2}-1)T^{2}/12\). Sophisticated hardware means may also be designed to improve this performance but are not considered in this paper. Figure 1: The architecture of an SDNswitch [1]. To analyze this system, we use a continuous state space and continuous time diffusion process \(\{X(t),t\geq 0\}\) to replace the discrete state-space buffer queue, where the increments \(dX(t)=X(t+dt)-X(t)\) are normally distributed, with mean \(\beta dt\) and variance \(\alpha dt\), which appear in the diffusion Equation (1). Assuming an arrival rate \(\lambda\) and average service time \(\mu^{-1}\), the changes in a small time interval \(\Delta T\) tend to a normal distribution with mean \((\lambda-\mu)\Delta T\) and variance \((\lambda^{3}\sigma_{A}^{2}+\mu^{3}\sigma_{B}^{2})\Delta T=(\lambda C_{A}^{2}+ \mu C_{B}^{2})\Delta T\), where \(\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) are the variances of the interarrival and service times, and \(C_{A}^{2}\) and \(C_{B}^{2}\) are the corresponding squared coefficients of variation. Therefore, for the diffusion process we have \(\beta=\lambda-\mu\) and \(\alpha=\lambda C_{A}^{2}+\mu C_{B}^{2}\)[64]. The buffer's size is limited to \(N\) packets, therefore the diffusion process resides in the interval \([0,N]\), and we use a diffusion process with returns from the barriers at \(x=0\) and \(x=N\) to represent the jumps that occur when the buffer queue is empty and a packet arrives, and when the queue is full and a service occurs as in [65], leading to the equations:  
\[ \begin{array}{lcl}\frac{\partial f(x;x;x_{0})}{\partial t}&=&\frac{\partial^ {2}f(x;x;x_{0})}{\partial x^{2}}-\beta\frac{\partial^{2}f(x;x;x_{0})}{\partial x }+\lambda p_{0}(t)\delta(x-1)+\mu p_{N}(t)\delta(x-N+1),\\ \frac{dp_{0}(t)}{dt}&=&\lim_{x\to 0}[\frac{\alpha}{2}\frac{\partial f(x;x_{0})}{ \partial x}-\beta f(x,t;x_{0})]-\lambda p_{0}(t)\\ \frac{dp_{N}(t)}{dt}&=&\lim_{x\to N}[\frac{\alpha}{2}\frac{\partial f(x;x_{0} )}{\partial x}-\beta f(x,t;x_{0})]-\mu p_{N}(t),\end{array}  \tag{1}\]  
where \(f(x,t;x_{0})\) is the probability density function (pdf) of the diffusion process; \(p_{0}(t)\) and \(p_{N}(t)\) are, respectively, the probabilities that the process is at the barrier at \(x=0\) or \(x=N\) at time \(t\), corresponding to probabilities that the system is empty or saturated; and \(\delta(x)\) is the Dirac delta function. The first of the above equations defines the pdf of the diffusion process with jumps from \(x=0\) to \(x=1\) (arrival of the first customer after the idle period) with intensity \(\lambda\) and from \(x=N\) to \(x=N-1\) (departure of a customer ending the saturation period) with intensity \(\mu\). The next two equations represent the probability balance of the barriers.
## 4 Data Processing
### Pre-Processing
#### 4.2.2 Assembling the FAPAR Data Cube  
The next step consists in merging data from the two relevant MISR Paths. Raster images of five samples by five lines, centered on the geographic location of the flux tower, were extracted from the FAPAR product for each of the 842 available Orbits and reprojected onto a common geographical grid, since both the MISR data and the MISR-HR products are provided in the SOM projection specific to each Path. Figure 6 explains this process graphically: Three grids are superimposed on a background map of the area around Skukuza, symbolized by the black triangle at the center of the display: the yellow and blue grids show idealized representations of the locations of the areas observed by MISR in Path 168 and 169 acquisitions, respectively. The red grid is the common geographical projection used as the target, and all grid squares are 275 m on the side, the ground sampling distance of MISR-HR products. This process roughly doubles the number of observations at each location, and helps constrain the phenology.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 3 Evaluation of Model Simulations
### Surface Currents  
#### 3.2.1 HF-Radar versus Drifter Observations  
Figure 2 shows the drifter velocity magnitude versus the HF radar velocity as scatter plots generated for the whole period of drifter deployment. For the study period, 334 collocations of radar and drifter data were available for comparison. The velocity data of the drifters were filtered using a Savitzky-Golay filter [54] with a quadratic fit and a window length of \(\sim\)2.3 days in order to remove outliers. The velocity of all nine drifters (regarding their deployment period or quality) was used in the scatter diagram to demonstrate the statistical robustness of the method. A detailed analysis of the separate trajectories of the Albatros drifters for the deployment periods was reported [27].  
The HF radar currents should be interpreted as Eulerian currents [55] (i.e., without including the Stokes drift). However, it has also been argued that the HF radar velocity partially contains the Stokes drift, in which case the HF radar measurements contain a filtered component of the Stokes drift [56]. On the other side, comparisons of HF radar velocity measurements with drifter observations demonstrated that the presence of the Stokes drift in HF measurements is not settled. In Figure 2 and Figure S2 we compare HF radar velocities interpreted as Eulerian currents against the velocity estimated by the nine Albatros drifters. With a standard deviation of 15 cm/s and a bias of 2.7 cm/s, the drifter and the HF radar are in reasonably good agreement. A noticeable feature is a slight underestimation of HF velocity at higher values. There are several reasons for the disagreement between HF and drifters when the speed is over 0.6 m/s: (i) the HF radar retrieves the currents at a depth of around 1 m, but the drifter's sails are located at 0.5 m. An underestimation of the HF radar regarding the drifter measurements may be expected. (ii) Due to strong wind stress during storm events, taking into account that the current profile over depth is logarithmic, a stronger current gradient between 1 m (HF) and 0.5 m (drifters) depth is expected. (iii) During breaking events in storm conditions, the drifter and the sail can surf over the sea surface, while the HF radar measures the current speed under the surface.
## 1 Introduction  
Gastric cancer or stomach cancer is one of the leading cancers in males which accounts for considerable mortality globally. Genetic factors are an important regulator of the carcinogenic responses in the stomach; among these, the most prominent is the mutations of a tumor suppressor gene, CDH1, that encodes the E-cadherin-class proteins [1]. Apart from these factors, infections of _Helicobacter pylori_ and certain human papillomavirus are also reported to have a significant association with gastric carcinoma [2,3]. Dietary habits and other infections are also known to be associated with the incidence of the onset and advancement of gastric neoplasms [4]. Increased consumption of fatty foods [5], and diets rich in sugars [6] and salts [7] are reported to increase the incidence of gastric cancer. In addition, several oncogenic signaling pathways are also found to be upregulated in gastric cancers; these include the TGF-beta signaling family [8], receptor tyrosine kinase family proteins [9], especially epidermal growth factor receptor [10], and various growth factors [11]. According to recent reports from the USA, gastric cancer is the fifth most common cancer with an annual occurrence of over 1 lakh new cases per year and accounts for about 7 lakh deaths annually [12,13]. The overall five-year survival rates of gastric cancer are also estimated to be lower with an average of 31% for metastatic cases and less than 67% for non-metastatic cases [14]. Hence, the treatment of gastric cancer is of greater importance and therefore various therapeutic strategies are being employed for this tumor [15]. Among the forerunners of cost-effective anticancer drugs, the natural products isolated from plants are the most important compounds.  
Various plants are being utilized for cancer prevention and therapy by traditional practitioners in Ayurvedic and Chinese medicines; however, the main limitation of their application is the reduced scientific support for their efficacy. The Western Ghats are an important source of such medicinal plants and the present study evaluated the possible application of a widely used anti-inflammatory medicinal plant _Azima tetracantha_ for its possible application in gastric cancer control. The plant is widely used in Ayurvedic medicines and folk medicinal systems as a dietary supplement [16]; however, limited studies have analyzed their application as a dietary ingredient. The plant is a rich source of a bioactive compound called friedelin; the antioxidant and anti-inflammatory properties of the isolated friedelin from _A. tetracantha_ have also been reported [16,17]. The gastro-protective effect of this plant, whereby it inhibits gastric ulceration and inflammatory damages in animal models, has also been reported [18]. However, there are no further reports available on the plant for further anticancer properties or other biological efficacies. It is noteworthy that the plant, being a dietary supplement and gastro-protective agent, may be useful, and it may emerge as an anticancer agent against gastric cancer.
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 3 The Estimation of Iridium Satellite Signal Doppler-Shift in Weak Signal Environment
### The Principle of Estimating Iridium Satellite Signal Doppler-Shift in Weak Signal Environment
Expression (11) can also be written as:  
\[r_{p}(n)=a\sin c(f_{e}\cdot T_{coh})\exp^{j[2\pi f_{e}(t_{1}+\frac{T_{coh}}{ 2})+\theta_{e}]} \tag{12}\]  
The expressions of the output of the coherent integration in \(I\) and \(Q\) arms are:  
\[I_{p}(n)=a\sin c(f_{e}\cdot T_{coh})\cos(\phi_{e}) \tag{13}\]  
\[Q_{p}(n)=a\sin c(f_{e}\cdot T_{coh})\sin(\phi_{e}) \tag{14}\]  
where \(\phi_{e}=2\pi f_{e}\Big{(}t_{1}+\frac{T_{coh}}{2}\Big{)}+\theta_{e}\) is the phase difference. The correlation amplitude between input signal and local duplicated signal can be written as:  
\[E(n)=\sqrt{I_{p}{}^{2}(n)+Q_{p}{}^{2}(n)}=a\Big{|}\sin c(f_{e}\cdot T_{coh}) \Big{|} \tag{15}\]  
In (15), we can see that the degree of correlation between real signal and the local signal depend on \(\sin c(f_{e}\cdot T_{coh})\). When \(T_{coh}\) is constant, the closer the local signal frequency is to the actual signal frequency, the larger the value of \(\Big{|}\sin c(f_{e}\cdot T_{coh})\Big{|}\). During the acquisition process, the local signal frequency is continuously adjusted and the maximum correlation amplitude is searched to complete the Doppler-shift estimation.
## 2 Background  
### Surface Roughness  
Most industrial ultrasonic inspections involve reflection and transmission on coupled interfaces. The states of most surfaces of workpieces are by definition rough while considering the irregular microstructures of macroscopic scale workpiece surfaces. For example, the micro irregular texture characteristics caused by different processing methods normally appear on the surfaces of workpieces during manufacturing, such as convex, groove, and scratches. These characteristics directly affect the performance and life of the machine and instrument. Metal cooling and solidification of cast parts generate rough surfaces. Surface roughness is a comprehensive evaluation parameter for micro-sizes of machined surfaces. It is therefore necessary to characterize the state of a rough surface by a set of common surface statistic parameters. Then, any surface condition can be assigned to a statistical class.  
A considerable number of reports deal with different methods of numerically generating rough surface shapes, and a statistical characteristic is adopted here. As can be seen in Figure 1, the variation in height of the two-dimensional surface profile is defined in the \(z\)-axis and the shape of rough surface fluctuation is given along the \(x\)-axis. \(\mathbf{R_{a  
where \(m\) is the number of sampling points, \(i\) represents incremental changes along the \(x\)-axis, and \(Z_{i}\) represents the height of a single discrete point on the surface, as shown in Figure 1. The second parameter, \(R_{sm}\), is used to describe the characteristics along the surface in the horizontal direction (\(x\)-axis). A series of normally distributed discretized intervals is used to define \(R_{sm}\)[34]:  
\[R_{sm}=\frac{1}{n}\sum_{j=1}^{n}X_{sj} \tag{1}\]  
where \(n\) represents the number of profile elements and \(j\) represents incremental changes along the \(x\)-axis. \(X_{sj}\) is the spacing length of a profile element. It can be seen that \(R_{sm}\) represents the spacing length between two points on the mean plane, and it is set as an isotropic correlation length. The parameters \(R_{a}\) and \(R_{sm}\) are widely used in the machining process for general quality control and other fields for surface quality assessment. The statistical characteristics described in this manner can be related to the wave scattering behavior.
## 2 Overview of IoV
### VANETs to IoV Transition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interaction** & **Description** & **Function** & **Communication Protocol** \\ \hline \multirow{3}{*}{V2V} & \multirow{3}{*}{Vehicle-to-Vehicle} & Supports the wireless communication across vehicles for sharing location data, and it consists of the speeds, coordinates, road condition, and traffic information & \multirow{3}{*}{WAVE} \\  & & & traffic information \\ \hline \multirow{3}{*}{V2R} & \multirow{3}{*}{Vehicle-to-roadside Unit} & Supports the wireless interaction between vehicle and RSUs & \multirow{3}{*}{WAVE} \\  & & & vehicles and the supporting infrastructure (base stations, grid, networks, devices, etc.) & \\ \hline \multirow{3}{*}{V2H} & \multirow{3}{*}{Vehicle-to-Human} & Supports mobility and awareness for the nearest users, such as pedestrians, cyclists, and drivers & \multirow{3}{*}{NFC} \\  & & & \\ \hline \multirow{3}{*}{V2S} & \multirow{3}{*}{Vehicle-to-Sensor} & Support the bi-directional communication between different kinds of sensors and onboard terminals for real-time insights & \multirow{3}{*}{Ethernet and Wi-Fi,} \\  & & & terminals for real-time insights & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the different communication dynamics of an IoV system.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Image Encryption Result
#### 5.2.6 Comparative Evaluation  
Using several standard security metrics, the proposed method's performance was compared with cryptographic and steganographic techniques published in 2024-2025.  
As shown in Table 8, the proposed method outperforms recent methods in terms of NPCR and PSNR and maintains comparable entropy and UACI values, confirming its superior security and robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Proposed** & **Ref. [44]** & **Ref. [51]** & **Ref. [42]** \\ \hline House & 6.8632 & - & 8.935 & - \\ \hline Baboon & 7.5058 & 8.64496 & - & 8.7880 \\ \hline Peppers & 8.1567 & 7.44729 & 8.1156 & 8.6210 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Differentiating PSNR Values from Those Found in the Research Studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Channel** & **Correlation Value** \\ \hline Peppers & \begin{tabular}{c} Red \\ Green \\ Blue \\ \end{tabular} & \begin{tabular}{c} \(-\)0.0015 \\ 0.0047 \\ 0.0042 \\ 0.0007 \\ 0.0007 \\ 0.0019 \\ \end{tabular} \\ \hline House & \
\begin{tabular}{c} Red \\ Green \\ Blue \\ \end{tabular} &
\begin{tabular}{c} \(-\)0.0018 \\ 0.0007 \\ 0.0019 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test of Correlation Between Encrypted and Primary Images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Proposed** & **Ref. [33]** & **Ref. [48]** & **Ref. [49]** & **Ref. [50]** \\ \hline House & 13,389.4508 & 3287.6925 & 8454.3259 & - & 8771.9 \\ \hline Baboon & 11,547.7449 & 3236.6520 & - & - & - \\ \hline Peppers & 9940.4937 & 3245.6325 & 8193.0659 & 7274.44 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: MSE Values Compared with Those in the Literature.
## 4 Implementation and Results
### Implementation and Discussion
#### 4.2.2 Wall detection results and discussion
Thus, non-uniform illumination on surfaces of structural elements should be avoided when taking photos. Figure 5: The wall detection result of the No. 1 image in Table 2 (the sub-region 4 is a wall): (a) the preprocessed image; (b) variance of \(\mathrm{GLHs}\); (c) skewness of \(\mathrm{GLHs}\); (d) kurtosis of \(\mathrm{GLHs}\). Figure 6: The wall detection results: (a) the No. 3 image in Table 2, (b) the No. 7 image in Table 2.