esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Registration of MR Scans
### MR-PET Images Denoising Procedure
* \(\delta_{3}=0.001\)--assigning low weight to the adversarial loss, primarily used to refine texture and perceptual realism while avoiding instability in training. These values provide a starting point and may be further fine-tuned empirically depending on the specific noise characteristics of the MR-PET data and the desired balance between fidelity and perceptual quality. The mean squared error loss  
\[L_{MSE}=\|I_{clean}-\hat{I}\|_{2}^{2} \tag{12}\]  
ensures pixel-wise fidelity to the ground truth, while the perceptual loss \(L_{perc}\) computes the distance between high-level feature representations extracted from a pre-trained VGG-19 network [13]:  
\[L_{perc}=\sum_{\hat{I}}\|\phi_{\hat{I}}(I_{clean})-\phi_{\hat{I}}(\hat{I})\|_{2 }^{2}, \tag{13}\]  
with \(\phi_{\hat{I}}(\cdot)\) denoting activations at the \(\hat{I}\)-th layer. The adversarial loss \(L_{adv}\) follows the WGAN paradigm with gradient penalty [19]:  
\[L_{adv}=\mathbb{E}_{l_{clean}}[D_{\phi}(l_{clean})]-\mathbb{E}_{l_{obs}}[D_{\phi }(G_{\theta}(l_{obs}))]+\lambda_{gp}\mathbb{E}_{\hat{I}}[(\|\
abla_{\hat{I}}D_{ \phi}(\hat{I})\|_{2}-1)^{2}], \tag{14}\]  
where \(\hat{I}\) are samples interpolated between clean and generated images, and \(\lambda_{gp}\) is the gradient penalty coefficient. In this expression, \(L_{adv}\) represents the adversarial loss used to encourage the generator \(G_{\theta}\) to produce denoised outputs \(\hat{I}\) that are indistinguishable from clean images \(l_{clean}\), as judged by the discriminator \(D_{\phi}\). The first two terms compute the Wasserstein distance between the real data distribution and the generated distribution. The final term introduces a gradient penalty with coefficient \(\lambda_{gp}\), which enforces the Lipschitz constraint on \(D_{\phi}\), stabilizing GAN training [19]. The interpolated samples \(\hat{I}\) are randomly chosen points along straight lines between real and generated images and are used to compute the gradient penalty. The denoised images \(\hat{I}\) serve as input to the subsequent joint sparsity [27; 31] and super-resolution modules. The removal of Rician noise is critical for preserving the low-rank Hankel structures exploited by the joint MR-PET reconstruction [27], as residual noise would violate the sparsity assumptions and degrade reconstruction quality. In summary, the proposed GAN-based denoising framework effectively handles the complex statistical characteristics of MR-PET noise [52; 53], providing high-quality inputs for the downstream joint super-resolution pipeline [27; 31], thereby enhancing the final anatomical and functional image fidelity.
# Experimental Research on the Properties of Rock-Filled Concrete
## 3 Experimental Tests for the Properties of Rock-Filled Concrete
### Water Pressure Test  
According to the \"Design specification for concrete gravity dams (SL319-2005)\" issued by the Ministry of Water Resources, the permeability of RFC should be tested by permeability tests, but representative specimens are very difficult to choose, due to the large defects contained in RFC, consequently a water pressure test is applied to measure the permeability of RFC. Meanwhile, the uniformity and density of RFC can be also explored indirectly. The water pressure test was conducted in the borehole, in accordance with \"Code of water pressure test in borehole for water resources and hydropower engineering (SL31-2003)\" (Ministry of Water Resources of the P.R.C. 2003) [26]. The permeability rate of boreholes changed with the depth, which is shown in Figure 7 by a histogram.  
From the pressure water tests in six boreholes shown in Figure 7, the permeability rate of RFC is 2.41-42.39 Lu, with a wide fluctuation, especially the permeability rate change in boreholes Nos. 2, 3, 4 is particularly significant, but in boreholes Nos.1, 5, 6 the change is not obvious. Through statistical analysis, the average permeable rate of RFC is 11.32 Lu, with a standard deviation of 12.20 Lu. The permeability rate of the dam is mainly distributed between 2.41-10.41 Lu, but at the depth of 6.3 m-10.9 m in boreholes Nos. 2, 3, 4, the permeability rate of the dam is more than 25.52 Lu, which indicates that there exists some penetrating fractures in some places within the dam body,  
Figure 6: Relationship between porosity of RFC and depth.  
such as good interconnected interfaces or no dense construction pouring joint, causing dam to display relatively high permeability. The permeability data also shows a high dispersion, indicating obvious non-homogeneity of the RFC.
## 5 Result and Discussion
### Performance Analysis Based on TP  
Figure 4 showcases the results of using the BCHO-TCN LightGBM model for predicting out-of-stock occurrences. Figure 4a shows the accuracy for epochs 100, 200, 300, 400, and 500 as 89.78%, 89.8%, 93.92%, 94.09%, and 94.52%, respectively, with a TP of 90. Likewise, Figure 4b displays the outcomes of the BCHO-TCN LightGBM model in terms of NPV with a TP of 90, with values of 86.04%, 86.76%, 92.68%, 93.91%, and 95.16%. The results shown in Figure 4c for epoch values of 100, 200, 300, 400, and 500 are 87.90%, 91.43%, 92.60%, 94.60%, and 94.81% for PDV at a TP of 90. Similarly, Figure 4d illustrates the findings of the BCHO-TCN LightGBM model in terms of threat score with a TP of 90, with values of 86.63%, 91.68%, 93.00%, 93.79%, and 95.76%.  
Figure 4: Performance analysis based on TP: (**a**) accuracy, (**b**) NPV, (**c**) PDV, and (**d**) threat score.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 2 Analysis of Models and Limitations in VLC Systems
### Free-Space Channel Model and Underwater Channel Model  
In the VLC communication system, the most common channel is free space, and the loss of this channel has the aforementioned effects on waveform transmission. For visible light transmission, due to the rectilinear propagation characteristic, this free-space channel can be treated as an LOS channel in which the main restation is signal loss. The gain in the LOS channel can be expressed as below [25]:  
\[\mathrm{PL_{LOS}}=10\log_{10}\left(\frac{\mathrm{A_{PD}(m+1)}}{2\pi\mathrm{D}^ {2}}\cos^{(m)}(\alpha)\cos(\beta)\mathrm{G_{ct}}\mathrm{G_{cr}}\right), \tag{1}\]  
where \(\mathrm{A_{PD}}\) is the PD area; \(\mathrm{D}\) is the distance between the light source and PD; \(\alpha\) and \(\beta\) are the emission and receiving angles, respectively, with \(\cos(\varphi)\)\(=\cos(\psi)\)\(=\) h/D in which h is equal to D when the PD aligns to the LED; and \(\mathrm{m}\) is the Lambert emission order. \(\mathrm{G_{ct}}\) is the gain in the lens at the transmitter that is utilized to increase the intensity by narrowing the half-power angle, while \(\mathrm{G_{cr}}\) is the gain in the non-imaging concentrator that is applied before PD [25].  
Another typical channel for a VLC system is water due to the low loss of visible light in water. For an underwater VLC, path loss consists of attenuation loss and geometrical loss, which should be considered for diffused light sources, such as LEDs [25]. The geometrical loss for LED can be represented by the LOS channel loss since it is caused by spreading during transmission. The attenuation loss is determined by [26]:  
\[\mathrm{PL_{AT}}=10\log_{10}\left(\mathrm{e}^{-c(\lambda)\mathrm{D}}\right), \tag{2}\]  
which is the Beer-Lambert law. \(\mathrm{c(\lambda)}\)\(=\) a(\(\lambda\))\(+\)b(\(\lambda\))\(+\)a\({}_{\mathrm{T}}(\lambda)\)[27], which is the extinction coefficient representing the summation of water absorption coefficient \(\mathrm{a(\lambda)}\), water scattering coefficient \(\mathrm{b(\lambda)}\), and tank attenuation coefficient \(\mathrm{a_{T}}(\lambda)\). The final overall channel loss of the underwater channel is:  
\[\mathrm{PL_{Water}}\mathrm{=PL_{AT}+PL_{LOS}}. \tag{3}\]  
With the path loss equations of both the free-space channel and the underwater channel, the path loss variance with distance of these two types of channels could be simulated. The parameters of the VLC link model that applied to the simulation are summarized in Table 1, and the corresponding curves are presented in Figure 4. For a channel length equal to 3 m, the path loss of the free-space channel was 57.4 dB, while that of the underwater channel was 62.5 dB.  
Figure 3: Compact equivalent circuit model of PD.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 5 Architecture and Implementation
### Model Training Component
#### 5.2.1 Data Structure  
The amount of input data from a single stroke is dependent on the duration and gesture length. For example, if a user just clicks a button with his finger, one position is reported, but if a scroll through a website occurs, multiple positions are reported for the specific event. A lot of gestures are made up of 1 to 100 data points, and a high percentage of them only contain a single position. On the other hand, gestures with significantly longer duration consisting of more than 200 data points are sparse. The inclusion of gestures with very little data points may significantly alter the prediction performance. For example, if gestures with five or fewer data points are left out because they contain too little information, there will be fewer usable gestures available for the training and verification process.  
If we look for the _x_position_, the _y_position_ or the _accelerometer_x_ values, we see that their distribution could be approximated by one or multiple Gaussian curves. This effect can be observed when looking at the distribution of multiple gestures. The more gestures are included, the more generalized the plot is going to look. Since we are using HMM as a classifier, the data distribution plays an important role, in selecting the correct parameters for the model. The _hmmlearn_ Python package ([[https://github.com/hmmlearn/hmmlearn](https://github.com/hmmlearn/hmmlearn)]([https://github.com/hmmlearn/hmmlearn](https://github.com/hmmlearn/hmmlearn)), accessed on 4 December 2021) offers models for different kinds of emissions to learn from. Emissions are the observable output values, that occur depending on the internal state of the model ([PERSON] et al., 2021).  
Since the different features captured by the recording application do not have the same range, the data are normalized after being cleaned. This has to be done because, for example, when the display size is measured in pixels and the accelerometer data in m/s\({}^{2}\)  
Figure 3: Multiple gestures visualized at display scale, showing that a user most of the time interacts with a specific portion of the screen.  
the different value ranges may result in the display size being a far more significant feature (due to larger values and therefore substantial value changes) than the accelerometer data. As an example of our implementation, we use the min-max method, which normalizes data values to a range between zero and one.
## 2 Materials and Methods
### Specim Preparation  
Sample size was calculated using Pass version 11 (NSS Statistical software, Kaysville, UT, USA), employing one-way ANOVA with 99% confidence interval, 99% power of the test, and with means and standard deviation of _C. albicans_ viable cells in groups: DW = \(7.47\times 10^{5}\), DC = 4.82 \(\times\) 10\({}^{5}\), DW with MW 1 min = 4.49 \(\times\) 10\({}^{5}\), DC with MW 1 min = 2.64 \(\times\) 10\({}^{5}\), DC with MW 2 min = 0.00, DW with MW 3 min = 0.00, DC with M 3 min =0.00 [16]. Sample size was calculated with at least 10 specimens per sub-group (total 12 subgroups). However, a total of 168 specimens were fabricated (_n_ = 14).  
Test specimens (_n_ = 168) of PMMA acrylic resin denture base were fabricated with the help of modelling wax (Yeti Dental GmbH, Engen, Germany) melted in a wax pot (Manfredi, San secondo di Pinerolo TO, Italy) and poured into a three-part preformed metal mold (30 \(\times\) 30 \(\times\) 15 mm). Wax patterns were impressed in a metallic denture flask filled with type III dental stone (Garrico Lab Stone, Heber Springs, AR, USA) to produce PMMA samples. De-waxing was performed using boiling water for 6 min. Heat polymerized PMMA acrylic resin was mixed and packed at a doughy stage according to manufacturer recommendations at a powder: liquid ratio, 2.3 g of polymer powder to 1 mL of liquid monomer (heat-cured acrylic provided by MR Dental, Plymouth, UK). A Hydraulic press was used for packing the denture base resin with a sheet of separating plastic between the two halves. Heat cure PMMA was polymerized in a thermostatically controlled water bath (Manfredi-Acrydig 12) and processed for 74 \({}^{\circ}\)C for two hours followed by 100 \({}^{\circ}\)C for one hour (Figure 1).  
All specimens were allowed to cool at room temperature before de-flasking and were immersed in distilled water at room temperature for 48 h for residual monomer elimination. The excess resin was trimmed with a metal bur (Denfac Acrylic trimming burs). All PMMA specimens were wet-polished with abrasive paper (# 1200, water resistant) and buffed (Dialap ML150P; Maruto, Tokyo, Japan). Final finishing was performed with an alumina-based abrasive (particle size: 0.3 \(\upmu\)m). Post-finishing, all specimens were autoclaved at 121 \({}^{\circ}\)C for 15 min. For the purpose of reliability, a single operator prepared all the specimens.
## 2 Material and Methods
### Methods
#### 2.3.3 Correlation Analysis  
This study employed a correlation analysis to examine the relationships between the NDVI, EVI, and SIF with regard to climate factors, the nine land use types, and socio-economic factors across China from 2000 to 2019. The interannual correlations were calculated accordingly. They were calculated as follows:  
\[\mathrm{R}_{\mathrm{xy}}=\frac{\sum_{i=1}^{\mathrm{n}}\left(\mathrm{x}_{ \mathrm{i}}-\mathrm{\overline{x}}\right)\left(\mathrm{y}_{\mathrm{i}}- \mathrm{\overline{y}}\right)}{\sqrt{\sum_{i=1}^{\mathrm{n}}\left(\mathrm{x}_{ \mathrm{i}}-\mathrm{\overline{x}}\right)^{2}\sum_{i=1}^{\mathrm{n}}\left( \mathrm{y}_{\mathrm{i}}-\mathrm{\overline{y}}\right)^{2}}} \tag{2}\]  
In the formula, \(\mathrm{R}_{\mathrm{xy}}\) represents the correlation coefficient between x and y, with coefficient values ranging between \(-1\) and \(1\), and n denotes the research period. Variables x and y correspond to temperature, precipitation, relative humidity, land use types (9 categories), and human activity factors (population density and GDP). The variable i represents the annual values of NDVI, EVI, and SIF. The symbols \(\mathrm{\overline{x}}\) and \(\mathrm{\overline{y}}\) respectively represent the mean values of x and y. A positive correlation is indicated by R > 0, while a negative correlation is represented by R \(\leq\) 0.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 1 Introduction
### Existing Works  
As a core cybersecurity practice, penetration testing employs offensive strategies to evaluate system vulnerabilities. The objective is to detect vulnerabilities in a given network environment, evaluate security risks, and implement measures to reduce them [6]. In recent years, penetration testing has emerged as a critically important domain. A multitude of studies have been carried out and implemented with the aim of enhancing the security of networks [7; 8; 9; 10; 11; 12; 13].  
[PERSON] [7] and [PERSON] et al. [8] systematized security testing techniques, categorizing penetration testing into black-box, white-box, and gray-box approaches. Recent studies, such as [PERSON] et al. [10], advanced dynamic vulnerability analysis for mobile applications, while [PERSON] [12] developed cloud-specific penetration testing frameworks for AWS and Azure. IoT security research by [PERSON] et al. [11] proposed modular testing methods to address device heterogeneity. However, these works predominantly focus on conventional IT systems, neglecting the unique challenges of satellite networks. For example, existing frameworks omit analysis of RF physical-layer vulnerabilities, orbital-specific attack vectors, and interdependencies between space-ground-user segments. A comparative summary of limitations is provided in Table 1.  
Following penetration testing, vulnerability assessment is a crucial step. Vulnerability assessment is critical for evaluating system security, prioritizing remediation, and strategizing defenses. The Common Vulnerability Scoring System (CVSS)--a widely adopted benchmark--quantifies vulnerabilities (0-10 scale) using three metric groups: base (intrinsic attributes), temporal (evolving exploitability), and environmental (organization-specific context) [14; 15; 16; 17]. While CVSS versions (v2.0 to v4.0 [18]) excel in traditional IT systems, they face limitations in satellite networks. The inherent complexity of satellite architectures--interconnected space-ground-user segments, nonlinear risk propagation, and mission-critical interdependencies--renders linear CVSS scoring inadequate. This underscores the necessity for domain-specific assessment methods tailored to satellite ecosystems.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 4 Results and Discussion
### Process of Porosity Change  
The porosity of the entire buffer layer ranged from 0.35 to 0.4, as shown in Figure 4. Under the combined effect of expansion and suction, the porosity decreased near the heater and the buffer layer shrank. Near the hydraulic boundary, groundwater caused the buffer layer to expand, leading to an increase in porosity. The porosity gradually increased along the radial direction, and the impact of porosity changes on the seepage field led to faster saturation of the buffer layer near the hydraulic boundary. [PERSON] discovered that mineral dissolution induced slight changes in the bentonite porosity in [51]. [PERSON] discovered the high stability of bentonite from the 10 th Khutor deposit under model conditions, which was confirmed by modeling. The processes of concrete leaching were revealed, which changed the composition and acidity of the synthetic water and led to an increase in the porosity of the material in contact with the solution [52]. [PERSON] found that the change in bentonite porosity was caused by decay heat, which is consistent with the research findings of this article [15].  
The variation curve of porosity at each point on section A over time is shown in Figure 5a. With the continuous input of water, the porosity first showed an increasing trend, and then gradually stabilized. The results show that the porosity near the water injection boundary increased rapidly. The closer to the water injection boundary, the faster the porosity increased, and the steeper the curve. At the initial stage of the experiment, the porosity near the heater remained almost unchanged. After about 100 days, the porosity slowly increased, and the rate of increase was relatively small near the hydraulic boundary. The final porosity of the buffer layer near the heater was about 0.37, and the final porosity of the buffer layer near the hydraulic boundary was about 0.40. The entire buffer layer had no plastic strain, and THM coupling led to uneven porosity of the buffer layer.  
During the first 100 days of irrigation (Figure 5b), the porosity near the hydraulic boundary rapidly increased, while the porosity of the buffer zone near the heater remained almost unchanged. After 360 days, the porosity of the buffer zone near the heater significantly increased, reaching 0.36, and the porosity of the buffer zone near the hydraulic boundary was 0.38. After 1000 days, the porosity of the buffer zone near the hydraulic boundary was 0.40.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 2 Preliminaries
### Attacker Model  
To examine the security of our protocol, we describe the Dolev-Yao (DY) model [17], which is described as follows:  
* An adversary is able to eavesdrop, intercept, modify, delete, or insert messages exchanged through an open channel.
* An adversary is able to obtain the lost or stolen mobile device of legitimate mobile users [18; 19] and can extract the important data stored in the mobile device by utilizing a power-analysis attack [20; 21].
* An adversary is able to perform various types of attacks, including replay, masquerade, man-in-the-middle (MITM) and mobile device theft attacks.
## Materials and Methods
### GC-MS Analysis  
The desorption of SPME fibers was carried out with a 7820 A gas chromatograph coupled with a 5977E mass spectrometer (both from Agilent Technology, Santa Clara, CA, USA) operating in EI ionization mode at 70 eV energy. A DB-Wax (60 m \(\times\) 250 \(\upmu\)m \(\times\) 0.5 \(\upmu\)m, Agilent J&W) column was used for analytes separation. The other instrumental parameters were set as follows: injector temperature 260 \({}^{\circ}\)C, splitless mode and carrier flow (He) of 1.2 mL min\({}^{-1}\). The oven temperature program was initially set at 40 \({}^{\circ}\)C for one minute, then increased by 5 \({}^{\circ}\)C/min until 210 \({}^{\circ}\)C, and then of 10 \({}^{\circ}\)C/min until 260 \({}^{\circ}\)C, at which temperature, it was held for 10 min, with a total run time of 48 min. The mass spectra were acquired in the 29-205 \(m/z\) range at three scans sec\({}^{-1}\). Data were analyzed using the Agilent Mass Hunter software (Qualitative Analysis-Version B.06.00; Quantitative Analysis-Version B.07.01/Build 7.1.524.0), and the analytes were identified by matching their mass spectra and retention indices with those reported in NIST 11 spectral database library. Information from fragmentation patterns, retention times and data available from scientific literature was used for final identification [79; 80]. The amounts of monoterpenes and monoterpenoids (MTs), expressed as peak areas, were reported both as single compounds and total MTs, and they have been related to Total Ion Current (TIC).
## 5 Experiments
### Traces Description  
As illustrated in Figure 1, ten sensor nodes and multiple gateways are used in our experiments. Traces are collected from each of these nodes in the following way: as the nodes have limited on-board storage and solely for the testing purposes of packet trace collection, a wired USB connection between each node and a PC is provided. Whenever a node transmits or receives a message, the message content is recorded in the PC, alongside with the timestamp of the message (for both transmission and reception) and the RSSI value of the message (for reception only).  
Packet traces have been collected from each of the nodes in the network, over a period of two days, and with differing test scenarios. During certain periods in time, one of the gateways may be deliberately switched off, to simulate the effect of an actual gateway that has depleted its energy in the outdoor deployment. The routing protocol is expected to recover in such scenarios by re-routing data packets to the only other existing gateway in the network.  
Figure 1: Experiment environment.