esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Second Phase of the Study
#### 4.2.2 Data Analysis Process  
Our research first validated the reliability and validity of the data through multiple steps including reliability analysis, descriptive statistical analysis, common method bias test, reliability analysis, validity analysis, and correlation tests. The results indicated high levels of reliability and validity of the data, with significant correlations observed among various dimensions. Finally, a regression analysis was employed to verify the impact relationships between different dimensions and the adoption intention, ensuring the reliability of the conclusions drawn. These analyses provided robust scientific support for the study.
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
###### Abstract  
Aiming at the complex structure of the space information networks (SIN) and the dynamic change of network topology, in order to design an efficient routing strategy, this paper establishes a SIN management architecture based on Software-defined Networking (SDN). A routing algorithm flow of the spatial information network based on a snapshot sequence is designed. For different spatial tasks with different Quality of Service (QoS) requirements, the concept of integrated link weight is proposed. The Warshall-Floyd algorithm is used to design the optimal routing strategy. A Task-oriented Bandwidth Resource Allocation (TBA) algorithm is proposed for multiple spatial tasks in the same link. Simulation results show that the algorithm can effectively guarantee the priority transmission of important tasks and avoid the unnecessary waste of bandwidth resources.  
Keywords:space information networks; SDN; snapshot sequence; link weight; Warshall-Floyd algorithm +
Footnote †: journal:
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 3 Performance Analysis of Cover Surveillance Video Transmission
### Analysis of Covertness Performance
(a) Case 1: If we have \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[j]\
eq 0\) and \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[k]\
eq 0\), the KL divergence can be expressed as  
\[\mathcal{D}(Q_{1,w}\|Q_{0,w}) =\iint\mathcal{Q}_{1,w}(x,y)\log\frac{Q_{1,w}(x,y)}{Q_{0,w}(x,y)} \mathrm{d}x\mathrm{d}y \tag{29}\]9  
and the transmission rate is given by  
\[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]0 \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]1 \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]2  
It is imperative to emphasize that the mutual information is represented as \(\mathcal{O}(1)\), indicating a fixed value that remains invariant with changes in encoding length. This produces an exceptionally low covert signal transmission rate, typically in the order of \(\mathcal{O}(n)\) in traditional signal transmission scenarios. It is also a significantly low rate within the domain of covert communication, often characterized by \(\mathcal{O}(\sqrt{n})\). In this case, the covertness of video transmission relies on the adoption of extremely low video signal transmission rates. This implies that within the parameters outlined in Case 1, the scenario falls short of attaining the standard covert communication rate, i.e., \(\mathcal{O}(\sqrt{n})\).
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods
#### 1.2.2 Pattern Matching and Signature-Based Limitations  
_Evasion through obfuscation:_ traditional signature-based detection methods struggle against sophisticated obfuscation techniques that disguise malicious payloads while preserving their functionality [10].  
_Limited coverage of attack vectors:_ recent research by [PERSON] et al. demonstrates that pattern-based approaches cannot keep pace with the rapidly evolving XSS attack patterns, particularly DOM-based vectors [11].  
_Polymorphic XSS dhallenges:_ as shown by [PERSON] et al., attackers can use polymorphic techniques to generate functionally equivalent but syntactically different attack payloads that evade signature detection [12].
## 3 Material and Methods
### Surface Roughness  
An optical profilometry test (NANOVEA PS50 Profilometer) took place to obtain data on the topography of the metallic surface of the sphere present in the bearing. This test consists of the data acquisition without physical contact with the part through the emission of a laser on the surface (see Figure 3). As seen in [PERSON]'s literature [14], to model a contact process, it is necessary to have adequate knowledge of the topography of the surfaces. Thus, the main roughness parameters must be adequately treated to represent the profile of the asperity heights as faithfully as possible. The requirements were met through the test performed via an optical profilometer.  
The distribution of the heights (peaks and valleys) acquired through the test was the data set used in the modeling of the sphere's surface on a microscopic scale (see Figure 4). This component belongs to the bearing model KC45-58, basic size 203, shaft diameter 5/8 inches (17 mm) with standard bearing model KMB45-58-PA from the manufacturer Rexnord MB line Link-Belt Pillow Block Ball Bearings.  
From the optical profilometry test, an output file of extension _.txt_ is obtained through the 3D Profiler Software NANNOVEA version 3.2.7. In this output file, there is the point cloud containing the x-, y- and z-coordinates of the 0.4 \(\times\) 0.4 mm\({}^{2}\) scanned element of the ball surface. To model a surface with the flattest possible, i.e., with minimal interference from the undulation, we sought to employ the points of the scanned area of 0.4 \(\times\) 0.4 mm\({}^{2}\). The data were treated so that the z-axis values represented the roughness parameters because the z-coordinate values in the point cloud correspond to the color scale related to the chromatic confocal measurement technique used by the profilometer.  
Based on this, scripts in Python were elaborated and imported into Abaqus software that contained the x- and z-coordinates of the point cloud for each y-coordinate, creating the rough surface. In the optical profilometry test, for each value of y, the laser would travel a length in x, measuring the height of the roughness, then return to the starting point of that line and move on to the next value on the y-axis. Each imported script creates a new part of the line type. Then, these parts will be joined together to become a shell or more realistic surface.  
The surface parameters calculated from the ball scan are the average roughness \(R_{a}=0.949\) um and average quadratic roughness \(R_{q}=1.159\) um.  
Figure 3: Data acquisition. (**a**) Optical profiling test of the sphere surface by 3D NANNOVEA Profilometer (model PS50). (**b**) Example of roughness topography using Software NANNOVEA version 3.2.7 (scan area of 0.5 \(\times\) 0.5 mm\({}^{2}\)).  
When meshing the computational model for the sample surface of \(0.4\times 0.4\) mm\({}^{2}\) to achieve analysis convergence, the number of elements was more than \(10^{5}\), making the simulation unfeasible. So, it was decided to cut the model size and consider a final inspected area of \(0.075\times 0.075\) mm\({}^{2}\) (see Figure 5).
# An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference
## 5 Conclusions  
This paper proposed an adaptive reversible image hiding method using AMBTC compression by considering the quantization level difference of each block. According to our adaptive embedding strategy, more bits are embedded into the image sub-block located in regions where the value of QLD is larger. However, in order to ensure the quality of the image, when the value of the QLD of a sub-block is greater than the threshold, this  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Lena** & **F-16** & **Saillboat** & **Girl** & **Toys** & **Barbara** & **Average** \\ \hline Our scheme & 32.59 & 32.22 & 30.16 & 33.30 & 32.12 & 28.82 & 31.54 \\ \hline [PERSON] et al.’s [4] & 31.05 & 30.23 & 28.43 & 31.11 & 28.73 & 25.53 & 29.18 \\ \hline [PERSON] et al.’s [20] & 32.54 & 31.33 & 30.41 & 32.76 & 31.15 & 29.82 & 31.34 \\ \hline [PERSON] et al.’s [25] & 31.61 & 29.84 & 27.25 & 31.43 & 34.84 & 28.42 & 30.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance of PSNR (dB) for six test images between our method and other schemes.  
Figure 9: Comparative performance of pure hiding capacity for six test images between our method and other methods. (**a**) Comparisons with ours and other schemes based on BTC and (**b**) comparisons with ours and other schemes which are not based on BTC.  
block will not embed information. Experimental results showed that the performance of the proposed scheme is better than the previous schemes in terms of payload and modified image quality.  
Conceptualization, Y.-H.C. and C.-C.C.; methodology, C.-C.L.; software, Z.-M.W.; validation, C.-C.L.; writing--original draft preparation, Y.-H.C. and Z.-M.W.; writing--review and editing, C.-C.L.; project administration, C.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available in a publicly accessible repository.  
The authors declare no conflict of interest.
## 2 Experimental Materials and Methods
### Anisotropy of Compressive Strength  
The variation curves obtained for shale compressive strength at different \(\theta\) under triaxial monotonic loading (ML) and triaxial incremental cyclic loading and unloading (CL) are shown in Figure 5. As \(\theta\) increases, the compressive strength under two loading conditions tends to decrease and then increase again, with the maximum and minimum compressive strengths under the two loading conditions observed at \(0^{\circ}\) and \(60^{\circ}\), respectively. Compared to triaxial monotonic loading, the average compressive strength of shale with \(\theta\) of \(0^{\circ}\) and \(90^{\circ}\) decreased by 8.81% and 3.11%, while that with \(\theta\) of \(45^{\circ}\) and \(60^{\circ}\) increased by 7.36% and 11.84%, respectively, under triaxial incremental cyclic loading and unloading. A slight increase of 0.5% was observed in the average compressive strength for shale with a \(\theta\) of \(30^{\circ}\). The degree of anisotropy in the compressive strength \(R_{c}\)[24] is defined using:  
\[R_{C}\ =\ \frac{\sigma_{c(0)}}{\sigma_{c(\min)}}, \tag{1}\]  
where \(\sigma_{c(0)}\) and \(\sigma_{c(\min)}\) represent the compressive strength of shale with a \(0^{\circ}\) bedding inclination angle and the minimum compressive strength of shale with different bedding inclination angles under the same test conditions, respectively.  
The anisotropy \(R_{c}\) in the shale compressive strength under the three types of confining pressures is shown in Figure 6. The results show, under triaxial monotonic loading, the \(R_{c}\) decreases from 1.62 to 1.33 as the confining pressure increases from 10 to 30 MPa, which is a decrease of 17.9%. However, the \(R_{c}\) initially increases followed by a decrease as the confining pressure increases from 10 to 30 MPa under triaxial incremental cyclic loading and unloading, with a difference of 8.5% between the maximum and minimum values. Compared with monotonic loading, the \(R_{c}\) decreased by 29.69%, 11.13%, and 11.59% at confining pressures of 10, 20, and 30 MPa, respectively, under triaxial incremental cyclic loading and unloading, suggesting weakened anisotropy in the shale compressive strength under triaxial incremental cyclic loading and unloading conditions.  
Figure 5: Variation curve of compressive strength at different bedding inclination angles.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## Appendix A Derivation of (13)  
In accordance with [28], the differential of \(\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})\) is given by  
\[\mathrm{d}[\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})]=\mathrm{tr}\Big{[}\
abla \mathrm{T1}(\mathbf{F}_{\mathrm{RF}})\mathrm{d}\Big{(}\mathbf{F}_{\mathrm{RF} }^{H}\Big{)}\Big{]}. \tag{11}\]  
By defining \(\mathbf{B}_{12}=\mathbf{I}_{N_{\mathrm{S}}}+\left(\mathbf{W}^{H}\mathbf{H}_{2} \overline{\mathbf{G}}\mathbf{H}_{1}\mathbf{F}_{\mathrm{RF}}\right)\mathbf{B}_ {11}^{-1}\left(\mathbf{W}^{H}\mathbf{H}_{2}\overline{\mathbf{G}}\mathbf{H}_{ 1}\mathbf{F}_{\mathrm{RF}}\right)^{H}\), (11) can be rewritten as  
\[\mathrm{d}[\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})]=\mathrm{d}\Big{[}\mathrm{tr} \Big{(}\mathbf{B}_{12}^{-1}\Big{)}\Big{]}\!=\!-\mathrm{tr}\Big{(}\mathbf{B}_ {12}^{-2}\mathrm{d}\mathbf{B}_{12}\Big{)}. \tag{12}\]  
The differential of \(\mathbf{B}_{12}\) can be expressed as  
\[\mathrm{d}\mathbf{B}_{12}=\mathbf{W}^{H}\mathbf{H}_{2}\overline{\mathbf{G}} \mathbf{H}_{1}\mathbf{F}_{\mathrm{RF}}\bigg{[}\mathrm{d}\mathbf{B}_{11}^{-1} \Big{(}\mathbf{W}^{H}\mathbf{H}_{2}\overline{\mathbf{G}}\mathbf{H}_{1} \mathbf{F}_{\mathrm{RF}}\Big{)}^{H}\!\!+\!\mathbf{B}_{11}^{-1}\mathrm{d} \mathbf{F}_{\mathrm{RF}}^{H}\Big{(}\mathbf{W}^{H}\mathbf{H}_{2}\overline{ \mathbf{G}}\mathbf{H}_{1}\Big{)}^{H}\bigg{]}. \tag{13}\]  
Due to the fact that \(\mathrm{d}\mathbf{X}^{-1}=-\mathbf{X}^{-1}(\mathrm{d}\mathbf{X})\mathbf{X}^{-1}\), we have the differential of \(\mathbf{B}_{11}^{-1}\) as \(\mathrm{d}\mathbf{B}_{11}^{-1}=-\mathbf{B}_{11}^{-1}(\mathrm{d}\mathbf{B}_{11} )\mathbf{B}_{11}^{-1}\). In addition, \(\mathrm{d}\mathbf{B}_{11}\) can be expressed as  
\[\mathrm{d}\mathbf{B}_{11}=b_{11}\mathrm{d}\mathbf{F}_{\mathrm{RF}}^{H} \mathbf{F}_{\mathrm{RF}}. \tag{14}\]  
Substituting (13) and (14) into (12) yields  
\[\mathrm{d}[\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})]=\mathrm{tr}\Big{[}\Big{(}b_ {11}\mathbf{F}_{\mathrm{RF}}\mathbf{B}_{11}^{-1}\mathbf{F}_{\mathrm{RF}}^{H} -\mathbf{I}_{N_{\mathrm{T}}}\Big{)}\mathbf{H}_{1}^{H}\overline{\mathbf{G}}^{ H}\mathbf{H}_{2}^{H}\mathbf{W}\mathbf{B}_{12}^{-2}\mathbf{W}^{H}\mathbf{H}_{2} \overline{\mathbf{G}}\mathbf{H}_{1}\mathbf{F}_{\mathrm{RF}}\mathbf{B}_{11}^{- 1}\mathrm{d}\mathbf{F}_{\mathrm{RF}}^{H}\Big{]}. \tag{15}\]  
Finally, comparing (15) with (11), we can obtain the Euclidean gradient of \(\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})\) with respect to \(\mathbf{F}_{\mathrm{RF}}\) as  
\[\
abla\mathrm{T1}(\mathbf{F}_{\mathrm{RF}})=\Big{(}b_{11}\mathbf{F}_{\mathrm{ RF}}\mathbf{B}_{11}^{-1}\mathbf{F}_{\mathrm{RF}}^{H}-\mathbf{I}_{N_{\mathrm{T}}} \Big{)}\mathbf{H}_{1}^{H}\overline{\mathbf{G}}^{H}\mathbf{H}_{2}^{H}\mathbf{W} \mathbf{B}_{12}^{-2}\mathbf{W}^{H}\mathbf{H}_{2}\overline{\mathbf{G}}\mathbf{ H}_{1}\mathbf{F}_{\mathrm{RF}}\mathbf{B}_{11}^{-1}. \tag{16}\]
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 5 Conclusion  
In this paper, we presented the design of a field monitoring network system for forest ecology based on the communication functionality of the Beidou satellite and GPRS. This design achieves real-time monitoring, cloud storage, dynamic query, statement generation, and big data analysis of parameters such as atmosphere, soil, light, and plants. The system solves the step-problem in quantitative scoring of the interval of fire weather indexes using the logistic regression function and achieves a fine forecasting of fire weather grades by introducing the factor of litter layer soil moisture. During the experiment conducted at the Beijing Jiufeng National Forest Park and Inner Mongolia, the system could stably and reliably obtain 11 types of microclimate data of forest with a correct rate of transmission of over 98.57% by the Beidou satellite and over 99.89% by GPRS. The system can be used for the big data monitoring of field ecology, an assessment of ecological restoration, meteorological disaster monitoring, accurate irrigation, etc.  
Conceptualization, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); Methodology, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); Software, S.L.; Validation, S.L.; Investigation, W.L.; Resources, W.L.; Writing-Original Draft Preparation, Y.Z. ([PERSON]); Writing-Review & Editing, Y.Z. ([PERSON]) and R.Y.; Visualization, Y.Z. ([PERSON]); Supervision, Y.Z. ([PERSON]); Project Administration, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); Funding Acquisition, Y.Z. ([PERSON]) and Y.Z. ([PERSON]).  
This paper was supported by the Fundamental Research Funds for the Central Universities (No. 2015 ZCQ-GX-04), the National Key R&D Program of China (2017 YFD0600901), and the National Natural Science Foundation of China (Grant No. 31670719).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Contact Angle Measurements  
Contact angle (\(\theta\)) measurements were carried out with a portable See SYSTEM E instrument. The included software enables the calculation of the surface energy based on the most often used models: Owens-Wendt-Rable-Kaeble, Lifshitz-van der Waals/acid-base, Li-Neumann, Kwok-Neumann, Wu Equation of State, and Zisman. Regression variants of Owens-Wendt and acid-base models are also supported. Additionally, the instrument is provided with a color UVC camera: color 2 Mpix (1600 \(\times\) 1200) UVC camera with a high-resolution glass objective lens, which captures high-resolution images of the analyzed drops on the surface of the grapes. Ultrapure Water Type I was used as a surface contact liquid. A drop of 3 \(\mu\)L was added to the grape surface, and the contact angle was measured. Six individual coated grapes were used to measure the contact angle, in six random zones at different storage times (1, 3, 5, 9, and 11 days), since after each measurement, the grapes were discharged. For comparative purposes, the study also included non-coated grapes.
# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 3 Related Work
### Object Detection and Counting Using Deep Learning Techniques
The test results reveal that the authors' algorithm outperforms others (such as Background Subtraction and Haar Cascade) in terms of vehicle detection accuracy and counting on the test set, showing that the suggested technique has better detection performance. However, the authors have used limited traffic scenarios to evaluate their model, and they have to test their model's efficiency for larger diversified datasets. Similarly, [PERSON] [43] has proposed a vehicle tracking system based on intelligent video surveillance. To monitor automobiles, the suggested system employs a neural network, image-based tracking, and YOLOv3 (You Only Look Once). With diverse datasets, they train the suggested system. Moreover, the authors tested the proposed system's performance using real-world video sequences of traffic. The results of the study revealed that the suggested system can identify, track, and count cars in a variety of situations with acceptable results. Most of the models discussed in the literature exhibit the following pitfalls. Sometimes when the environment information is not clear, the data stored in the image frame gets scattered distorting object information. The tracker tries to accumulate the scattered information into a single piece of information. This process might decrease the accuracy of classification or the models dismiss the information as false alarms. In some cases, the models misclassify the objects leading to inaccurate environment information. Object counting models, which use ML approaches take more time to train the models. Moreover, more cost is involved to establish the appropriate infrastructure to implement CNN-based models. Some models use small datasets in simulated environments to evaluate their performance. This practice hinders estimating the actual performance and efficiency of the object counting and detecting models. In the proposed system, we incorporated customized YOLOv5, which has more accuracy compared to previous versions. Most of the authors in the literature used earlier versions of YOLO. We introduce the feature of the crossing line, where we track counted objects for a short period. This approach reduces the computational power required to operate the proposed system; hence, it can be deployed in mobile phones or UAVs. Tracking only counted objects addresses two main causes of tracking failures namely, the increased tracking time which might result in object loss, and an increase in the error rate of the object to template assignment due to instant tracking of multiple objects.
## 3 Results and Discussion
### Adsorption Equilibrium  
Figure 5 showed the different adsorption performance of BPA onto Mag-PVP and Fe\({}_{3}\)O\({}_{4}\) nanospheres. The adsorption capacity of BPA increased with an increase in the concentration of BPA 2.5 to 50 mg/L (Figure 5a). The \(Q_{e}\) of Mag-PVP reached 87 mg/g in BPA solution with a concentration of 50 mg/L, which was more than that of the Fe\({}_{3}\)O\({}_{4}\) nanospheres (39 mg/g). Adsorption equilibrium data were modeled by the [PERSON] [34] and [PERSON] [35] isotherm models, as depicted by Equations (3) and (4), respectively:  
\[Q_{e}=Q_{m}K_{L}C_{e}/(1+K_{L}C_{e}) \tag{3}\]  
\[Q_{e}=K_{F}C_{e}^{l/n} \tag{4}\]  
where \(Q_{e}\) (mg/g) was the equilibrium adsorption capacity and \(Q_{m}\) (mg/g) was the maximum adsorption capacity of the polymers. \(C_{e}\) (mg/g) was the equilibrium concentration of BPA. \(K_{L}\) (L/mg) was the Langmuir binding constant, which was related to the energy of adsorption. \(K_{F}\), the Freundlich constant, was related to the adsorption capacity. The constant \(n\) in the Freundlich equation was an indication of the favorableness of the adsorbent/adsorbate interaction. The Langmuir and Freundlich fits were shown in Figure 5a, and the regression data of the two models for all adsorbents were listed in Table 1. The adsorption of BPA on Mag-PVP and Fe\({}_{3}\)O\({}_{4}\) nanospheres were more adequately represented by Langmuir than Freundlich. The higher regression coefficient of the Langmuir model (\(R^{2}\) = 0.94) indicated that the adsorption process for Mag-PVP was a theoretical monolayer adsorption. The maximum adsorption capacity (\(Q_{m}\)) calculated by the Langmuir model for Mag-PVP and Fe\({}_{3}\)O\({}_{4}\) nanospheres were 115.87 and 42.05 mg/g, respectively. The results suggested that Mag-PVP had a better adsorption capacity than Fe\({}_{3}\)O\({}_{4}\) nanospheres. The enhancement of adsorption was attributed to the coating of poly-divinylbenzene-2-vinylpyridine of the Fe\({}_{3}\)O\({}_{4}\) nanospheres, which could provide more interaction sites.  
Figure 4: Thermogravimetric analysis (TGA) of Fe\({}_{3}\)O\({}_{4}\), \(\gamma\)-MPS-Fe\({}_{3}\)O\({}_{4}\), and Mag-PVP.