esa-sceva/satcom-chunk-collection huggingface dataset:
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 5 Experimental Evaluation
### Testing Framework and Methodology  
The testing framework for YouGovern combined both automated and manual approaches to ensure a comprehensive evaluation of the system. Unit testing was carried out using the Mocha framework, allowing each smart contract component to be independently verified for correctness and reliability. These tests helped confirm that the contracts performed as intended across various scenarios and maintained proper interaction with the BSC. After unit testing, integration tests were conducted to evaluate how system components interacted, helping to detect any functional mismatches or performance issues.  
At the same time, the React-based user interface underwent thorough usability testing to verify the proper functioning of graphical elements. This included checks for interface responsiveness, input validation, and the overall navigation flow to ensure a smooth user experience. To strengthen security, independent blockchain security firms conducted audits targeting potential vulnerabilities, such as reentrancy attacks, arithmetic overflows, and inadequate access control. Additionally, automated tools like MythX and OpenZeppelin were used for static analysis and security scanning, further reinforcing the system's resilience against threats.  
The experimental evaluation of YouGovern confirmed the reliability, usability, and security of the system across multiple dimensions. All smart contract components passed unit and integration tests without failure, and transactions were consistently processed with low latency on the Binance Smart Chain. The React-based interface was well-received in user testing, demonstrating clear navigation, efficient identity management, and responsive design. Security audits conducted by third-party firms revealed only low-severity issues, all of which were promptly addressed. No critical vulnerabilities were found, affirming the robustness of the design and implementation. User feedback highlighted the benefits of self-sovereignty, particularly in terms of privacy and control, while also praising the ease of use and intuitive interface.  
Figure 11: Upload IPFS.  
These findings support YouGovern's readiness for real-world deployment. The scalability and cost-efficiency of BSC provide a solid foundation for expanding the system to broader user bases without compromising performance. The secure architecture, combined with user-centric design, positions YouGovern as a compelling solution for decentralized identity management. Figures 12 and 13 illustrate how governmental verification and access delegation processes are seamlessly handled within the platform. In summary, the detailed testing and evaluation of YouGovern underscore its efficacy as a self-sovereign identity solution, validating both its technical and functional capabilities and reinforcing its potential to revolutionize identity management practices globally.
###### Abstract  
In this paper, reversible data-hiding (RDH) systems with modified fluctuation functions and rate-matched Reed-Solomon (RS) codes are proposed to enhance the data recovery from encrypted images. The modified fluctuation functions are used for estimating embedded codeword bits from the correlation of pixels. Instead of direct data-bit embedding, codeword bits of RS codes are embedded by a data-hider. With the help of the error-correcting capability of RS codes, the encrypted message can be recovered from the weak correlation of adjacent pixels in the image. In the experimental results, bit error rate (BER) and peak signal to noise ratio (PSNR) performances of the proposed system are better than those of referenced data-hiding systems for three images. The proposed schemes based on the modified fluctuation function or rate-matched codes can be applied to various RDH systems with better data transmission and image recovery performance.  
bit error rate; data-hiding; encryption; fluctuation functions; PSNR; reversible data-hiding
## 5 Influence of Pressure Filtration Parameters on Mud Cake Deformation
### Optimization of the Pressure Filtration Parameters  
The parameters involved in the pressure filtration treatment of waste mud slurry include the dewatering time, initial mud cake thickness, and filtration pressure, all of which have mutual constraints on the improvement of treatment efficiency. Treatment efficiency can be regarded as the amount of mud slurry treated per unit of time, and for this paper, the mud cake only changes in thickness, and the ratio \(v\) between the initial mud cake thickness and dewatering time can be used as the basis for judging the construction efficiency according to Equation (8).  
\[v=\frac{h_{0}}{t} \tag{8}\]  
Figure 11: Mud cake deformation over time under different filtration pressures.  
Figure 12: Variation in dewatering time with filtration pressure under different initial thicknesses.  
According to the data in Figure 12, the treatment efficiency \(v\) corresponding to different initial thicknesses and filtration pressures when the mud cake is pressed to 70% water content is calculated, as shown in Table 4.  
Figures 10 and 12 and Table 4 together reflect the effects of the dewatering time, filtration pressure, and initial cake thickness on the pressure filtration effect:  
* An increased initial cake thickness will lead to a non-linear increase in the dewatering time, resulting in a reduction in the processing efficiency; the initial cake thickness should be appropriately reduced.
* Under the same initial mud cake thickness, a continuous increase in filtration pressure after the pressure is increased to the critical value has less effect on the dewatering time.  
Combined with the above analyses, when the initial cake thickness exceeded 240 mm, the processing efficiency decreased significantly at all filtration pressures, and the reduced thickness increased the treatment efficiency. Meanwhile, the efficiency increase with filtration pressure was no longer significant beyond 1.0 MPa for each initial thickness. Therefore, in order to improve the efficiency of pressure filtration while maintaining low energy consumption, an initial cake thickness of 180 mm\(\sim\)240 mm and a filtration pressure of 1.0 MPa are recommended. Under these pressure filtration parameters, the time to achieve deformation stability for the initial cake thicknesses of 180 mm and 240 mm is calculated to be 21.3 min and 28.2 min, respectively, and the corresponding efficiencies \(v\) are 8.45 and 8.51, respectively. For the mud slurry used in this study, the optimal values for the initial mud slurry bag thickness, filtration pressure, and dewatering time are 240 mm, 1.0 MPa, and 30 min, respectively, yielding a final mud cake water content of 58.7% after filtration.  
The above pressure filtration parameter values take into account the impact of processing energy consumption and processing efficiency, which can provide a reference for the parameter values in the pressure filtration part of the PF-FSCM for waste mud slurry treatment.
## 4 Results
### Model Complexity Analysis  
Figure 6 presents a parallel coordinate plot used to examine the relationship between the cardinality of the model sets--P, S, T, and K--and the corresponding computational time, measured in minutes. Each vertical axis represents one set, with individual lines corresponding to different problem instances. The color of each line encodes the computational time.  
According to Figure 6, the solution time increases substantially with the number of characteristics (K) and stations (S), which are the most significant contributors to model dimensionality. These two parameters directly influence the number of decision variables and constraints, leading to an exponential growth in problem size. This is particularly evident for configurations where both K and S are large (e.g., K = 200 and S = 50), which resulted in models with over 900,000 constraints and similar numbers of variables. In such cases, the solution times exceeded 100 min. In contrast, increasing the number of products (P) and time periods (T) had a more modest effect on the computational performance. While these dimensions increase the total number of indexed variables and constraints, their relative contribution to model complexity appears to be lower than that of characteristics and stations. The parallel coordinate visualization also illustrates that there is a strong correlation between the total model size (in terms of constraints and variables) and the solving time, with darker (shorter) time durations associated with smaller-scale instances. However, some variability remains across configurations with similar structural sizes, suggesting that internal sparsity patterns or solver dynamics may also influence performance.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring
#### 5.3.3 Airports  
This section reports an overview of research about the use of PSI techniques in airport monitoring, with a focus on runways and their surrounding areas. [PERSON] et al. [103] present a case study on the deformation monitoring and the analysis of the geological environment at the Shanghai Pudong International Airport, China. A qualitative spatial-temporal analysis of the ground deformations found a correlation between the InSAR measurements and the variations observed in the geological environment. This was achieved by implementing a multi-temporal InSAR approach to identify several down-lifting displacement areas. [PERSON] et al. [104] present an integrated analysis of SAR interferometric and geological data for the investigation of long-term reclamation settlements at the Chek Lap Kok Airport, Hong Kong. Relying on a dataset of ascending and descending images acquired by the ENVISAT ASAR mission and a sparse levelling campaign, the authors could effectively link the deformation trend observed in the areas of the airport to the geotechnical properties of the soils, with a special focus on the area reclaimed by the sea. The same settlement was examined by [PERSON] et al. [105] through an extensive InSAR analysis covering two decades of observations. The authors merged information from various multi-temporal datasets of different sources (i.e., ERS-2, ENVISAT ASAR, COSMO-SkyMed and Sentinel-1A). The outcomes were compared to the data collected using GNSS stations located in the airport area as well as to available geological data. The scope was to monitor the ground settlements following a sea reclamation land from construction of the Xiamen New Airport, China. The study reports significant information for airport land reclamation design purposes and the next stages of the construction process. [PERSON] et al. [106] present research on the monitoring of the Beijing Capital International Airport, China, which is affected by severe down-lifting deformations. The authors demonstrate that the observed settlements were related to the variation of the groundwater levels. In general, existing research has widely proven the effectiveness of the InSAR analyses for the interpretation of geological and geotechnical features in areas of interest for the airports [107; 108]. These studies also clearly demonstrate the reliability of the InSAR technique in reconstructing the actual vertical displacements, with very limited differences across the adopted frequency band and the acquisition geometry, given the quasi-vertical principal direction of the deformations.
# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 3 Preliminary
### Facial Expression Recognition Module  
Human facial expressions contain emotion, mentality, etc., and many service scenarios obtain useful information by recognizing facial expressions. Thus, we took facial expression recognition as an example to complete the work of service quality maintenance [53; 54; 55].  
We define the expression set \(L\) to contain seven elements: \"angry\", \"disgust\", \"fear\", \"happy\", \"sad\", \"surprise\", and \"neutral\". The essence of facial expression recognition is to realize classification tasks [56; 57]. Compared to other kinds of neural networks, Convolutional Neural Networks (CNNs) [56; 58] show better performance in this work [59; 60]. CNNs are made up of several convolutional layers with pooling layers and fully connected layers. Each convolutional layer extracts features from the input data, and the final target representative features are reflected in the highest convolutional layer. Then, the output enters the fully connected layers to complete the mapping from the input image to the expression tag. The weights of the network were iteratively adjusted through backpropagation and trained and tested on the Kaggle facial expression recognition challenge dataset [61].  
The workflow of the expression recognizer is illustrated in Figure 1. For the initial image, the pre-trained Haar cascade classification model [62] is used to detect the facial region in the gray image and return the boundary rectangle of the detected face. This step is very necessary so that we can reduce the dimension of the original image to a form similar to the image of the facial expression recognition network training set, effectively improving the reliability of facial expression recognition. Taking the cropped image \(x\) as the input, the output \(\textit{res}(x)\) of the model will obtain the values of seven neurons for facial expression recognition for this image. As shown in Equation (4), we selected the index of the maximum value and took the corresponding expression as the result of facial expression recognition \(E(x)\).  
\[E(x)=L_{k},\quad\text{where }k=\arg\max_{i}(\textit{res}(x)_{i}) \tag{4}\]
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 2 Materials and Methods
### Validation
#### 2.3.1 Accuracy  
In order to demonstrate the accuracy of our federated GLM, we generated three models, each from a different family (with its corresponding linking function) and with an appropriate dataset. These are summarized as follows.  
Figure 1: Required server–client architecture needed for executing the presented federated GLM algorithm. Notice that the data never leave their corresponding party.  
```
1:Initialization Server
2:initialize \(\beta^{(0)}\)
3:Initialization Node \(m\)
4:initialize \(\eta_{(m)}=X_{(m)}\beta^{(0)}\)
5:initialize \(\mu_{(m)}=g^{\prime}(\eta_{(m)})\)
6:initialize \(dev_{(m)}^{(0)}=f(y_{(m)}h_{(m)},w_{(m)})\)
7:loop
8:Node \(m\)
9: compute \(z_{(m)}=\eta_{(m)}+\frac{y_{(m)}-H_{(m)}}{\Delta g_{(m)}^{(m)}}\)
10: compute \(W_{(m)}=w_{(m)}\frac{\Delta g_{(m)}^{(m)}}{\Delta Var(\mu_{(m)})}\)
11: compute and return to Server \(\left(X_{(m)}^{T}W_{(m)}X_{(m)}\right)\) and \(\left(X_{(m)}^{T}W_{(m)}z_{(m)}\right)\) Server
12: update \(\beta^{(t+1)}=\left[\sum_{m=1}^{M}\left(X_{(m)}^{T}W_{(m)}X_{(m)}\right)\right] ^{-1}\left[\sum_{m=1}^{M}\left(X_{(m)}^{T}W_{(m)}z_{(m)}\right)\right]\)
13: return to Nodes \(\beta^{(t+1)}\)
14:Node \(m\)
15: compute \(\eta_{(m)}=X_{(m)}\beta^{(t+1)}\)
16: compute \(\mu_{(m)}=g^{\prime}(\eta_{(m)})\)
17: calculate \(dev_{(m)}^{(t+1)}=f(y_{(m)}\mu_{(m)},w_{(m)})\)
18: return to Server \(dev_{(m)}^{(t+1)}\)
19:Server
20: compute \(dev^{(t+1)}=\sum_{m=1}^{M}dev_{(m)}^{(t+1)}\)
21:if\(|dev^{(t+1)}-dev^{(t)}|<\epsilon\)thenreturn\(\beta^{(t+1)}\) breakloop
22:else
23:\(t=t+1\)
24:endif
25:endloop
```  
**Algorithm 2** Federated GLM
## 2 Study Area and Methods
### Data Sources  
Land surface temperature (LST) data and the NDVI data, provided by NASA as part of the Modis data products, were taken from the 16d synthesized version of the MOD13A3 product and the 8d synthesized version of the MOD11A2 product, respectively. Pre-processing operations, including format conversion, splicing, projection, cropping, etc., were performed on the HDF-type data through the MODIS Reprojection Tool (MRT). The maximum value synthesis technique was employed to synthesize NDVI and LST data into monthly values. Finally, the LST is corrected, and the correction formula is shown in reference [25]. Evapotranspiration data (ET) were derived from the MOD16A2 product on the NASA website.  
Monthly data provided by the China Meteorological Data Center ([[http://www.resdc.cn/User](http://www.resdc.cn/User)]([http://www.resdc.cn/User](http://www.resdc.cn/User)) (accessed on 29 August 2023)) for the period 2001-2020 is the basis for the temperature and precipitation data, and meteorological data for 30 stations in the research region were acquired (Figure 1c). Using professional meteorological interpolation software ANUSPLIN (ANUSPLIN VERSION 4.4, The Australian National University Fenner School of Environment and Society, Canberra, Australia) for spatial interpolation, heat and precipitation data were interpolated from point scale to 1 km resolution raster data using a 90 m STRM DEM (digital elevation model) as a covariate. The seasons were classified as follows: winter (January, February, and December of this year), spring (March to May), summer (June to August), and autumn (September to November).  
The soil texture data are derived from the National Soil Survey and Soil Series of China data. It employs adaptive depth function fitting, ensemble machine learning, and other methods to generate 250 m resolution raster data for soil sand, clay, and silt content in the 0-30 cm layer. Textural categorization consists of five categories based on USDA classification guidelines: sandy soil, loamy sandy, sandy loam, silty loam, and loam (Figure 1d). Land use data with a 1 km resolution were obtained from the Resource and Environmental Science and Data Centre ([[http://www.redc.cn/](http://www.redc.cn/)]([http://www.redc.cn/](http://www.redc.cn/)) (accessed on 15 July 2023)). The Intersect  
Figure 2: (**a**) Land use types that have not changed from 2000 to 2020. (**b**) The percentage of different land utilization categories in various years.  
tool was used to identify regions where land use types remained relatively unchanged between 2000 and 2020 (Figure 2a), excluding water bodies. The data were reclassified into seven categories: cropland, forestland, grassland, water bodies, built-up land, desert land, and unused land, accounting for 14.61%, 2.37%, 58.71%, 0.56%, 19.25%, and 3.31%, respectively (Figure 2b).
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 6 HWLNets in IoT Applications  
Supporting high data requirements in 4K/8K video streaming, holographic display, and other high-speed applications in IoT is becoming more and more important. LiFi has proven to be successful in the transmission of holographic data [76]. HWLNets can be used in a vast number of applications:  
* hospitals: where RF is prohibited or cannot be used due to RF interference;
* LNG ships and platforms: due to the risk of RF;
* planes and airports: again, due to the risks involving RF in planes and congestion of traffic in airports;
* stadiums: excessive number of visitors in a given moment with different data requirements;
* conference halls: stationary users with high data and throughput requirements.  
Where it can at least relieve part of the resource shortage and pressure on WiFi or provide additional service in areas where it was deemed impossible.  
In IoT, user applications rely on two basic functionalities: indoor positioning and physical security.
# Systematic Analysis and Design of Control Systems Based on [PERSON]'s Direct Method
## 2 Methods
### Sum-of-Squares Decomposition  
One way to numerically implement the definiteness tests associated with Lyapunov methods is the _Sum-of-Squares Decomposition_ (SOS). It is checked whether a given polynomial can be decomposed into a sum of squares. From such a representation, definiteness properties can be read off, which are needed for the stability conditions.  
Let \(\mathcal{P}\) be the set of all polynomials in the considered variables. Then,  
\[\mathcal{S}=\{p\in\mathcal{P}|p=\sum_{i=1}^{k}q_{i}^{2},\quad q_{i}\in \mathcal{P}\} \tag{20}\]  
denotes the set of _SOS polynomials_. From the definition (20) it follows immediately that every polynomial \(p\in\mathcal{S}\) is positive semidefinite. The converse case does not hold, because the so-called _Motzkin polynomial_[42]  
\[p_{\mathbb{M}_{\chi}}(x)=(x_{1}^{2}+\dots+x_{\chi-1}^{2}-\chi x_{\chi}^{2})x_{ 1}^{2}\dots x_{\chi-1}^{2}+x_{\chi}^{2\chi} \tag{21}\]  
with \(\chi\geq 3\) is positive definite, but cannot be represented as a sum of squares [43]. A more detailed consideration of the differences between positive definite and SOS polynomials can be found in [44].  
Each SOS polynomial can be expressed in the form  
\[p(x)=m(x)^{T}Q\,m(x), \tag{22}\]  
with a positive semidefinite matrix \(Q\) and a vector \(m(x)\) consisting of all monomials up to degree \(\frac{1}{2}\deg(p(x))\)[45]. The search for a positive semidefinite matrix \(Q\) can be transformed into a semidefinite optimization problem [45]. Efficient tools exist for solving such semidefinite programs: _SeDuMi_[46], _CSDP_[47], _SDPNAL_[48], _SDPNAL_+ [49], _SDPA_[50], _CDCS_[51], or _SDPT3_[52]. The transformation of a SOS problem into a semidefinite program is possible with the toolbox _SOSTOOLS_ under _MATLAB_ (see Figure 1). For this purpose, the respective conditions formulated with SOS polynomials are defined under _MATLAB_. These can subsequently be transformed into a semidefinite program using _SOSTOOLS_. This program can then be solved with one of the previously mentioned tools. The solutions obtained in this way are thereupon transformed into the solution of the original SOS constraints by _SOSTOOLS_. Through this interaction of the individual tools, an efficient solving of SOS programs is possible.  
The resulting semidefinite programs can in principle be solved in polynomial time. However, the size of these programs grows rapidly with the dimension of the state space, because the matrix \(Q\) of a polynomial in \(n\) variables with polynomial degree \(2d\) has dimension \((\genfrac{}{}{0.0 pt}{}{n+d}{d})\times(\genfrac{}{}{0.0 pt}{}{n+d}{d})\). Therefore, when applying the sum-of-squares decomposition, thoughtful problem formulations are not only useful but also necessary.  
Figure 1: Relationship between the SOS program, the semidefinite program (SDP) and the MATLAB Toolbox SOSTOOLS [53; 54].
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 5 Experimental Results for Aerial Surveillance Application  
In this section, we compare the detection performance provided by a SF operation and the proposed MF integration strategies against experimental data collected by a DVB-T-based PR in an aerial surveillance scenario.  
Figure 4: Actual false alarm probability obtained by means of Monte Carlo simulations for different processing approaches for \(N=4\), nominal \(P_{\beta i}=10^{-5}\) and Q = 100 secondary data in the case of: (**a**) exponentially correlated interference affecting the secondary data with varying one-lag correlation coefficient; (**b**) a non-homogenous region contaminating 20 secondary data in two out of the four DVB-T channels and characterized by additive noise with circular symmetric complex Gaussian zero mean PDF and a power level that deviates from the homogeneous background by \(\Delta\) dB.