esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Variant Seismic Fragility of Offshore Continuous Beam Bridges Based on Collapse Analysis
## 3 Fragility Analysis  
### Fragility Analysis Method  
The fragility curve expresses the probability that the structure will exceed a specific limit damage state under a specific level of earthquake action, as shown in Equation (25).  
\[P_{f}=P(R\geq LS|IM=x) \tag{25}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Steel Types** & \(\mathbf{X_{c}}\) & \(\mathbf{w/c}\) & \(\mathbf{\Delta A_{s0}}\) & \(\mathbf{\Delta A_{s}}\) & \(\mathbf{x}\) & \(\mathbf{t}\) \\ \hline Stirrup & 90 & 0.5 & 1.904\(\pi\) & \(32\pi\times-4\pi\times 2\) & 0.240 & 1.495 \\ Longitudinal reinforcement & 106 & 0.5 & 2.452\(\pi\) & \(64\pi\times-4\pi\times 2\) & 0.126 & 0.761 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter analysis when the concrete surface crack reaches 1 mm.  
Figure 2: Longitudinal reinforcement and stirrup material properties: (**a**) Corrosion rate; (**b**) Diameter; (**c**) Elastic modulus; (**d**) Yield strength.  
where \(P_{f}\) is the exceeding probability; \(R\) (Response) represents the seismic response of the structure or component; \(LS\) (Limit State) represents a specific damage limit state; \(IM\) (Intensity Measure) is the ground motion intensity parameter, namely the peak ground acceleration (PGA); \(LS|IM\) notation in this equation means the specific failure state \(LS\) of the structure at ground motion intensity \(IM\); \(x\) is the specific ground motion intensity level. Assume that the bearing capacity of the structure and the demand of seismic follow a log-normal distribution [28], as shown in Equation (26).  
\[P_{f}[D\geq C|IM]=P_{f}\left(\frac{S_{c}}{S_{d}}\leq 1\right)=\Phi\left[ \left(\frac{\ln\mu_{D}/\mu_{C}}{\beta}\right)\right]=\Phi\left[\left(\frac{ \ln\mu_{D}/\mu_{C}}{\sqrt{\beta_{D}{}^{2}+\beta_{C}{}^{2}}}\right)\right] \tag{26}\]  
where \(D\) refers to the demand of seismic; \(C\) refers to the bearing capacity of the structure; \(IM\) refers to the parameter of ground motion intensity, namely, PGA [12]; \(\mu_{C}\), \(\beta_{C}\) and \(\mu_{D}\), \(\beta_{D}\) refers to the mean and logarithmic standard deviation of the seismic capability and seismic demand of the structure, respectively, according to HAZUS99 [29], when choosing PGA as the ground motion intensity parameter, \(\sqrt{\beta_{D}{}^{2}+\beta_{C}{}^{2}}\) is 0.5; \(\Phi\) (\(\cdot\)) refers to the standard normal cumulative distribution function (CDF).  
[PERSON] et al. [30] and [PERSON] et al. [31] assume that \(D\) and \(IM\) are linearly correlated on a logarithmic scale, so \(D\) can be expressed as a function of \(IM\), as shown in Equation (27).  
\[\ln(\mu_{D})=\ln a\ +b\cdot\ln(IM) \tag{27}\]  
where \(a\) and \(b\) are coefficients determined by regression analysis of the nonlinear time-history analysis results. substitute Equation (27) into Equation (26) to obtain.  
\[P_{f}[D\geq C|IM]=\Phi\left[2\ln\left(aIM^{b}/\mu\right)\right] \tag{28}\]
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 4 Reference Channel Model
### Description of the Reference Propagation Environment  
The proposed channel simulator in this work is based on the reference model for vehicular communications presented in [31]. Such a model considers velocity variations and non-linear trajectories of the MSs. This model is particularly well suited for analyzing the channel's non-stationarities arising at a small-scale level due to the time-varying nature of the propagation delays [42] and the frequency dependence of the Doppler shift [50]. In this model, the transmitted signal reaches the receiving antenna by single interactions with \(\mathcal{L}\) fixed (static) IOs randomly located in the propagation environment. As a result of such interactions, \(\mathcal{L}\) replicas of the transmitted signal are observed at the receiving antenna.  
Figure 3 shows the reference propagation environment when the MSs begin the transmission at \(t=0\). The black dots represent the IOs, whereas the black triangle and the black square denote the transmitter (\(T_{X}\)) and receiver (\(R_{X}\)) stations, respectively. The initial positions of \(T_{X}\) and \(R_{X}\) are represented  
Figure 2: LTE-V2X frame structure with a 10 MHz bandwidth [14].  
by \(\mathbf{\mathcal{O}}\) y \(\mathbf{\mathcal{O}}^{\prime}\), respectively, and they are separated by a distance \(D\). Vectors \(\mathbf{v}_{q}\) and \(\mathbf{a}_{q}\) for \(q\in\{T,R\}\) represent the velocity and constant acceleration components, respectively, of \(T_{X}\) and \(R_{X}\). The velocity vectors define the initial speeds and directions of motion of the MSs. The initial speeds of \(T_{X}\) and \(R_{X}\) are, respectively, \(v_{T}\) and \(v_{R}\). The angles \(\gamma_{T}\) and \(\gamma_{R}\) correspond to the directions of the velocity components of \(T_{X}\) and \(R_{X}\), respectively. On the other hand, the acceleration components define the magnitudes and directions for the MS changes of motion over time. The acceleration magnitudes of \(T_{X}\) and \(R_{X}\) are \(a_{T}\) and \(a_{R}\), respectively. The directions of the acceleration vectors are \(\rho_{T}\) and \(\rho_{R}\) for \(T_{X}\) and \(R_{X}\), respectively. Furthermore, \(\mathbf{p}_{\ell}^{\mathbf{T}}\) denotes the position of the \(\ell\)-th IO with respect to the reference point \(\mathbf{\mathcal{O}}\), and \(\mathbf{p}_{\ell}^{\mathbf{R}}\) describes the \(R_{X}\) position seen from the \(\ell\)-th IO. The \(\ell\)-th IO is separated by a distance \(d_{\ell}^{T}\) from \(T_{X}\) and a distance \(d_{\ell}^{R}\) from \(R_{X}\). Finally, \(\phi_{\ell}^{T}\) represents the angle of departure (AOD) and \(\phi_{\ell}^{R}\) the angle of arrival (AOA), both of them from the interaction with the \(\ell\)-th IO. The vectors introduced in this section, as well as the constants that complete the description of the propagation environment are summarized in Table 2, where the notation \(\mathbf{x}=M_{x}\angle\Theta_{x}\) indicates a vector \(\mathbf{x}\) with magnitude \(M_{x}\) and direction \(\Theta_{x}\).
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 3 Results and Discussion
### Acoustic Emission Characteristics  
#### 3.3.1 Water-Immersed Sample  
During long-term creep testing, acoustic emission (AE) monitoring was synchronously employed to measure fracture patterns during coal-rock creep. Due to a prolonged test duration, only AE data from the initial 67 days were compiled, as shown in Figure 7. A total of 8508 AE events were recorded in this period. The analysis reveals pronounced AE activity during the initial testing phase (transient creep stage), with instantaneous AE ring-down counts reaching 1734 times per sec and AE energy peaking simultaneously. During the strain reduction phase (1-30 d), both AE ring-down counts and energy gradually increased over time. Conversely, the strain increase phase (30-40 d) exhibited minimal AE activity, indicating that compaction hardening between bedding planes dominated deformation without new fracturing. Steady-state creep displayed insignificant AE signals.  
Investigating AE ring-down count and energy evolution, it is revealed that in the transient creep stage (1 d), the closure of pre-existing fractures and stress concentration induced high-frequency AE signals [39]. Throughout the strain reduction phase (1-30 d), water-induced non-uniform weakening triggered localized fracturing, progressively increasing AE activity. During the strain hardening phase (30-40 d), internal compensation mechanisms suppressed AE generation as specimens underwent structural reorganization into steady deformation. Subsequent AE events displayed low-energy characteristics, signaling stabilized deformation progression.
# Making Optimal Location-Sizing Decisions for Deploying Hybrid Renewable Energy at B&Bs
## 5 Results and Discussion
### Comparison of the Base Case and the Unconventional Technology Case  
From the data in Tables 3 and 7, which present the optimal solution for the base and the unconventional technology case, respectively, the following conclusions can be drawn:  
* Both scenarios generate more energy than the B&B requires (50,217 kWh/year). Since the Wind Tree has higher efficiency than a wind turbine, wind energy can be produced even though the wind speed is below 3.0 m/s at certain time periods. This capability increases the annual volume and share of wind energy in the RE system, from 10,287 kWh/year (8.8%) in the base case to 10,736 kWh/year (9.4%) in the unconventional technology case.
* As the share of wind energy increases in the unconventional technology case, the complementarity between solar and wind energy in the RE system also increases, resulting in a decrease in the volume of unused energy in the system. The volume of unused energy in the unconventional technology case is 3.7% less than in the base case.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Locations** & **RFT** & **BLC** & **FRP** & **OBQ** & **DCK** & **OFP** & **WP** \\ \hline Area (m2) & 136 & 65 & 275 & 225 & 185 & 225 & 300 \\ Solar panel & x & ✓ & x & ✓ & ✓ & ✓ & x \\ Wind turbine & x & x & x & ✓ & ✓ & ✓ & x \\ Tesla Solar Roof & ✓ & x & x & x & x & x & x \\ Ciel \& Terre Floating Solar & x & x & x & x & x & ✓ \\ Wind Tree & x & x & ✓ & x & x & x & x \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **System Configuration** & & & **Battery** & & **Annual Energy** & & & \\  & **(Unit)** & & & **Capacity** & & **Generation (kWh/Year)** & & & \\ \hline
**SP** & **WT** & **TSL** & **C\&T** & **WTR** & **(kWh)** & **Solar** & **Wind** & **Total** & **Solar** & **Wind** & **Battery** & **Total** \\ \hline
328 & 7 & 81 & 250 & 1 & 123 & 103,738 & 10,736 & 114,474 & 14,158 & 4028 & 6484 & 24,670 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The optimal solution for the unconventional technology case.  
* The increasing complementarity also affects the battery capacity in the system, from 126 kWh to 123 kWh in the base case and the unconventional technology case, respectively.
* The annual costs of the unconventional technology case are 14.7% higher than in the base case. However, fewer solar REGDs are required in the unconventional technology case than in the base case (799 units vs. 659 units), affecting the area required to install solar REGDs (927 m\({}^{2}\) vs. 796 m\({}^{2}\)).
* The required area to install REGDs in the unconventional technology case is 873 m\({}^{2}\), which is 14.2% less than in the base case.  
Overall, the comparison between the base and the unconventional technology case is listed in Table 8
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 4 Discussion
### Comparative Analysis  
Table 8 provides a qualitative comparison of different QKD protocols in terms of security model, scalability, complexity, and key rate efficiency. Trusted-Node QKD relies on classical relays, making it highly scalable but vulnerable to node compromises. MDI-QKD and TF-QKD improve security by removing detector vulnerabilities, but they require complex setups, with TF-QKD additionally demanding phase stabilization for long-distance communication. In contrast, Loop-Back QKD offers a multiuser scheme that maintains quantum integrity without trusted nodes, achieving high scalability in local and metropolitan networks while maintaining moderate complexity. This trade-off makes Loop-Back QKD a practical alternative for scalable, secure key distribution in short-to-medium-range quantum networks.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**QKD Protocol** & **Security Model** & **Scalability** & **Complexity** & **Efficiency** \\ \hline Trusted-Node [24] & Trusted Relays (Nodes Process Keys) & High (Classical Relays) & Low (Relays Only) & High (Depends on Relays) \\ \hline MDI [17] & Measurement-Device Independent (Requires Central Node) & Moderate (Requires Central Node) & High (Bell-State Detection) & Moderate (\(O(\eta^{2})\), Single-Photon Detection) \\ \hline TF [19] & Twin-Field Interference (Stability Issues) & Moderate (Stability Issues) & Very High (Long-Distance Control) & High (\(O(\sqrt{\eta})\), Scalable with Distance) \\ \hline Loop-Back & Basis-Dependent Polarization (Multiuser Support) & High (Multiuser Support) & Low (No Intermediate Nodes) & Low (\(O(1/2^{n+1})\), Multi-Pulse Filtering) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different QKD protocols based on their security model, scalability, implementation complexity, and asymptotic efficiency order.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
###### Abstract  
On the basis of the complexity of the pore structure characteristics of a coal reservoir, coal samples with different ranks were selected to study the difference in pore structures and permeability using nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), and permeability measurement. Porosity and pore size distribution (PSD) above 20 nm can be analyzed by the improved NMR equation, and the results were basically consistent with that of SEM and MIP. The NMR spectra of the coal samples from the same location were close, but the difference between the coal samples from different locations was quite large, which indicated that the heterogeneity of a coal reservoir was strong. An empirical equation of movable fluid porosity was proposed, which can be used to evaluate the fluid migration characteristics of the coal reservoir, and the porosity of movable fluid mainly came from the contribution of fissures and micro-fissures. The average movable fluid porosity of the coal samples from the Chengzhuang (CZ) coal mine, Wuyang (WY) coal mine, and Yujialiang (YJL) coal mine was 1.37%, 0.67%, and 4.26%, respectively. Although the permeability is related to the NMR porosity and movable fluid porosity, it was difficult to establish a widely used mathematical equation correlating permeability and porosity based on the experimental data.  
low-field NMR; pore type; porosity; permeability +
Footnote †: journal: Applied Sciences
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 2 The ROck Slope Kinematic Analysis (ROKA) Algorithm
### Ad Hoc Kinematic Analysis Process  
After defining the input data and the preprocessing calculation, the algorithm performs the ad hoc Kinematic Analysis (KA) for each point of the point cloud representing the rock slope (Figure 1).  
In particular, for each point of the cloud, the algorithm performs the following:  
1. Selects all the points of the model contained in the moving scan-sphere, which has a scan-radius defined by the user (see Figure 3a-c);
2. Calculates the mean orientation of every portion of the slope using the normal vectors of the points ('normal2 attitude.m' Matlab function) and creates a moving scan-plane (see Figure 3c,d);
3. Verifies if the discontinuity planes and intersections cross the scan-plane ('intSPdisc.m' and 'intSPinters.m' Matlab functions), considering the defined cutoff value of the discontinuities;
4. Performs the KA of the possible Mode Of Failure (planar failure, flexural toppling, wedge failure, or direct toppling) considering the scan plane's attitude and of the intersected discontinuity plane and intersection ('KinematicAnalysis.m' Matlab function). If the scan plane has an overhanging attitude, the algorithm considers its attitude as vertical (dip equal to \(90^{\circ}\)) and facing toward the outside the rock slope.
## 5 Reactor Systems and Process Parameters
### Operational Parameters
Increasing the dissolved oxygen content from 2.8 to 3.9 mg/L, resulted in an increase in the degradation rate constant from 0.035 to 0.062 min\({}^{-1}\) in graphene oxide-based photocatalysis of methylene blue [101]. Ensuring adequate oxygen levels in the reaction medium is, therefore, vital for effective photocatalysis. The starting concentration of pollutants affects also the photocatalytic performance. High contaminant levels can saturate the catalyst's surface, leading to reduced active sites and diminished degradation rates. Additionally, elevated pollutant concentrations may cause light attenuation, further hindering the process. Studies have shown that higher initial concentrations of contaminants result in lower degradation efficiencies due to these factors [156]. A study of [PERSON] et al. [93] investigated the photocatalytic degradation of diclofenac (DCF) using Mn-WO\({}_{3}\) under LED light irradiation. The results demonstrated that as the initial concentration of DCF increased from 1 to 10 mg/L, the degradation rate decreased. This decline was attributed to the saturation of active sites on the photocatalyst and reduced generation of hydroxyl radicals at higher contaminant concentrations. Research on the photocatalytic oxidation of butyldehyde (BUTY) performed by [PERSON] et al. [157] revealed that increasing the initial pollutant concentration led to a decrease in the removal efficiency. At a constant flow rate, higher BUTY concentrations resulted in lower degradation rates due to the limited availability of active sites and reduced contact time with the reactive species. An experimental investigation into the degradation of clofibric acid (CA) showed that at initial concentrations of 5 and 10 mg/L, approximately 100% elimination was achieved after 15 min [158]. However, when the initial concentration was increased to 300 mg/L, only 87% removal was observed, indicating that higher pollutant loads can hinder photocatalytic performance. Optimizing the initial pollutant load is necessary to maintain effective photocatalytic activity.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 4 Diagnostic Approaches of Foodborne Pathogens
### Culture Techniques
According to the studies conducted by the Department of Disease Control in Paris, France, Rambach agar (CHROMagar, Paris, France) and the SM-ID medium were the first chromogenic media used to detect non-typhi salmonella strains in 1993 [116]. In 1994, a specially designed system called CHROMagar Candida was developed to distinguish and separate different Candida species. It is now widely accepted that there are a variety of commercialized chromogenic and fluorogenic media available on the market that can be used to detect pathogens in various samples based on the sample matrices used in various laboratories. As a matter of fact, the culture-based approach continues to be the standard method for determining the viability of pathogens; however, microorganisms' ability to detect damage--or a state in which they are viable but not cultureable--can be affected substantially. Bacteria enter the non-culturable state when environmental conditions are negative, and these bacteria are unable to grow on regular nutrition medium due to their inability to survive [117]. It can also take a long time for bacteria to grow enough to form a visible colony and also for a chromogenic substrate to be added to a culturing solution, depending on (i) how fast a colony grows to form a visible colony, and (ii) how long it takes for an enzyme to produce color or fluorescence when the chromogenic substrate is added [118]. This method of culturing requires a lot of equipment to ensure that the cultures grow well and the results can be interpreted correctly. Other factors that can make it difficult to isolate pathogens in food samples are the low number of pathogens and the uneven distribution of them, the presence of native bacteria and the fact that food matrices can vary greatly. Microbiologists and clinical diagnosticians have focused more on culture-independent methods in the past few decades due to the sluggish and limited accuracy of microbe detection that has been experienced in food microbiology [119; 120]. Despite their low cost, sensitivity and selectivity with chromogenic media-, culture- and colony-based methods are labor-intensive, time-consuming and may result in microbial contamination, which can inhibit growth of bacteria of interest and presence of viable but non-culturable bacteria.
## 2 Materials and Methods
### Hermite Transform  
The Hermite transform is a specific type of polynomial transform and can be regarded as a model for image representation. This transformation serves as a method for signal decomposition and involves two main steps. Initially, the input signal \(L(x,y)\) is combined with a window function through multiplication,  
\[v(x-p,y-q), \tag{1}\]  
at the positions \(p\) and \(q\).  
The goal is to achieve a comprehensive representation of the signal. This process is repeated at multiple positions spaced evenly across the image, forming a sampling grid  
Figure 1: Representative examples of skin lesion images from the ISIC 2019 dataset used in this study. These images illustrate the visual variability across lesion types, including differences in size, shape, color, and texture, which the proposed hybrid feature extraction method is designed to handle.  
\(S\). At each \((x,y)\), the pixel coordinates and the input signal are multiplied by the window function, while the original signal is  
\[L(x,y)=\frac{1}{W(x,y)}\sum_{p,q\in S}L(x,y)v(x-p,y-q), \tag{2}\]  
where  
\[W(x,y)=\sum_{p,q\in S}v(x-p,y-q) \tag{3}\]  
is a weighting function.  
The only requirement is that Equation (3) must be nonzero for all \((x,y)\). Next, the signal within the window is expressed as a weighted sum of \(G_{m,n-m}(x,y)\), with degrees \(m\) and \(n-m\) relative to \(x\), \(y\), respectively. These polynomials are determined by the window function.  
\[\int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty}v^{2}(x,y)G_{m,n -m}(x,y)\times G_{l,k-l}(x,y)dxdy=\delta_{nl}\delta_{mk}. \tag{4}\]  
Here, \(n,l=0,\ 1,\ 2,\ldots,\infty\) and \(m\), \(k=0,\ 1,\ 2,\ldots,\ \infty\), while \(\delta_{nl}\), \(\delta_{mk}\) represent the Kronecker delta functions, and \(\times\) denotes element-wise multiplication. The process of converting the input signal into a weighted sum of polynomials, referred to as polynomial coefficients, is called the direct polynomial transform. These polynomial coefficients, \(L_{m,\ n-m}(p,q)\), are obtained by convolving the original image with the analysis filters.  
\[D_{m,n-m}(x,y)=G_{m,n-m}(x,y)v^{2}(-x,-y) \tag{5}\]  
That is, for everything, \((p,q)\in S\),  
\[L_{m,n-m}(p,q)=\int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty}L(x,y)\times D_{m,n-m}(x-p,y-q)dxdy \tag{6}\]  
where  
\[G_{m,n-m}(p,q)=\frac{1}{\sqrt{2^{n}(n-m)!m!}}\times H_{m}\Big{(}\frac{x}{ \sigma}\Big{)}H_{n-m}\Big{(}\frac{y}{\sigma}\Big{)}, \tag{7}\]  
\[v(x,y)=\frac{1}{\sqrt{\sqrt{\pi\sigma}}}e^{-\frac{x^{2}+y^{2}}{2x^{2}}}, \tag{8}\]  
where \(v(x,y)\) is the Gaussian window function, \(\sigma\) is the standard deviation of the Gaussian window function and,  
\[H_{n}(x)=(-1)^{n}e^{x^{2}}\frac{d^{n}}{dx^{n}}e^{-x^{2}},\quad n=0,1,2,\ldots \tag{9}\]  
where \(H_{n}(x)\) is the n-th Hermite polynomial.  
Using the convolution form in Equation (6) it is defined as,  
\[L_{m,n-m}(p,q)=L(x,y)*D_{m,n-m}(x,y). \tag{10}\]