esa-sceva/satcom-chunk-collection huggingface dataset:
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Connection Probability Using Dynamic Communication Ranges
#### 3.5.2 Case 2: Random Distributed Communication Range
\begin{cases}\sqrt{\bar{a}(2\pi)^{1- \bar{a}}}\mathrm{G}_{1,\dot{\alpha}}^{\frac{\delta}{2},1}\begin{bmatrix}1\\ \frac{1}{2},...,1+\frac{1}{\beta}\\ \end{bmatrix}\begin{bmatrix}\left(\frac{\bar{\epsilon}}{\bar{\pi}}\right)^{ \ast}\\ \Delta\Gamma\left(1+\frac{2}{C}\right)\\ \end{bmatrix},\;\beta=0\;(\mathrm{Exp.})\\ \\ \frac{\dot{\alpha}\sqrt{\bar{\epsilon}(2\pi)^{2-\bar{a}}}}{\sqrt{\pi}}\mathrm{G}_ {1,\frac{\alpha}{2}}^{\frac{\delta}{2},1}\begin{bmatrix}1\\ \frac{1}{2},...,1+\frac{1}{\beta}\\ \end{bmatrix}\begin{bmatrix}\left(\frac{\sqrt{\bar{\epsilon}\bar{\pi}}}{\Delta \Gamma\left(1+\frac{2}{C}\right)}\right)^{\frac{\zeta}{2}}\\ \end{bmatrix},\;\beta=1\;(\mathrm{GUE}).\end{cases}  \tag{36}\]  
**Case 2.c: Rayleigh distributed range. ** For Rayleigh distribution, the probability of connection using the general mapped expressions and substituting either \(m=1\) into (34) or \(C=2\) into (36) gives (37), where \(\Phi(. )\) is the error function. \[\mathrm{P}_{\mathrm{c}}^{\mathrm{Ray}}= \begin{cases}\sqrt{\bar{a}(2\pi)^{1- \bar{a}}}\mathrm{G}_{1,\dot{\alpha}}^{\alpha,1}\begin{bmatrix}1\\ \frac{1}{2},...,1\\ \end{bmatrix}\begin{bmatrix}1\\ \bar{\Lambda}\left(\frac{\rho}{\alpha}\right)^{\alpha}\\ \end{bmatrix}=\underbrace{\frac{\rho\sqrt{\pi}e^{-\frac{\rho^{2}}{2\Lambda}}}{2 \sqrt{\Lambda}}\Phi\Big{(}\frac{\rho}{2\sqrt{\Lambda}}\Big{)}}_{\alpha=2},\; \beta=0\;(\mathrm{Exp.}),\\ \\ \frac{\dot{\alpha}\sqrt{\bar{\epsilon}}}{\sqrt[4]{(2\pi)^{\bar{a}}}}\mathrm{G}_{1, \dot{\alpha}}^{\frac{\delta}{2},1}\begin{bmatrix}1\\ \frac{1}{2},...,1+\frac{1}{\beta}\\ \end{bmatrix}\begin{bmatrix}1\\ \bar{\Lambda}\left(\frac{\rho\sqrt{8}}{\sqrt{\alpha\pi}}\right)^{\alpha}\\ \end{bmatrix}=\underbrace{\frac{(2\rho)^{3}}{\sqrt{\pi(4\rho^{3}+\Lambda\pi)^{3 }}}}_{\alpha=2},\;\beta=1\;(\mathrm{GUE}).\end{cases}  \tag{37}\]  
**Case 2.d: Lognormal distributed range. ** Using exponential inter-vehicle distance (27) and lognormal distributed range PDF in (17), connectivity probability is;  
\[\mathrm{P}_{\mathrm{c}}^{\mathrm{Log}}=\int_{0}^{\infty}\mathbf{Q}\left(\frac{ \ln(\Lambda x^{\bar{a}})}{\sigma}\right)\rho e^{-\rho x}dx \tag{38}\]  
which can numerically be solved simply. For synchronised GUE distribution,\[\mathrm{P}_{\mathrm{c}}^{\mathrm{Log}}=\frac{32\rho^{3}}{\pi^{2}}\int_{0}^{\infty} \mathbf{Q}\bigg{(}\frac{\ln(\Lambda x^{\alpha})}{\sigma}\bigg{)}x^{2}e^{\left(- \frac{4\rho^{2}\pi^{2}}{\pi}\right)}dx, \tag{39}\]  
which has no closed-form solution. Swapping \(\dot{\Lambda}\) for \(\Lambda\) gives bus-shadowed connectivity probability. We give the summary table for connectivity probability in Table 3.
## 5 Conclusions  
In this paper, an NTSM controller was designed for the trajectory tracking problem of underactuated USVs in the absence of disturbance, then a nonlinear disturbance observer was devised to observe wind, wave, and current disturbances. The designed control strategy was strictly proved to be asymptotically stable using the Lyapunov stability theory. Finally, the NTSM and the nonlinear disturbance observer were combined, and the output of the disturbance observer was used to replace the actual environmental disturbance. The following conclusions are drawn from the theoretical analysis and numerical simulations of circular and linear trajectories:  
1. The trajectory tracking results were obtained by Simulink numerical simulation using the parameters of an existing USV model. In the case of no disturbance, the errors in the linear trajectory and circular trajectory speeds converged to zero quickly while the error in the position results converged to zero within 10 s to follow the the predefined trajectory, which verifies the feasibility of the controller.
2. Numerical simulations of both constant and time-varying external environmental wind, wave, and current disturbances were performed, and estimates were obtained with the designed disturbance observer. In both cases, the disturbance errors converged rapidly to a value close to zero, indicating the capability of the disturbance observer with regard to external disturbances.
3. With constant and time-varying disturbances, the USV completed the trajectory tracking task within 10 s, and the errors in velocity and position were very close to zero. The robustness of the system was improved by incorporating a disturbance observer to estimate environmental disturbances and compensating for external disturbances in the controller.  
Conceptualization, D.X. and X.Z.; data curation, Z.L. and L.Y.; formal analysis, Z.L.; funding acquisition, D.X. and X.Z.; methodology, D.X., L.Y. and L.H.; resources, D.X. and X.Z.; writing--original draft, D.X. and Z.L.; writing--review and editing, D.X. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science Fund Project of Heilongjiang Province (LH2021E087) and the National Natural Science Foundation of China (51779055).  
Not applicable.  
Figure 8: Surge force and yaw moment in three cases.
# Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks
###### Abstract  
Underwater acoustic networks (UAN) allow for efficiently exploiting and monitoring the sub-aquatic environment. These networks are characterized by long propagation delays, error-prone channels and half-duplex communication. In this paper, we address the problem of energy-efficient communication through the use of optimized channel coding parameters. We consider a two-layer encoding scheme employing forward error correction (FEC) codes and fountain codes (FC) for UAN scenarios without feedback channels. We model and evaluate the energy consumption of different channel coding schemes for a \(K\)-distributed multipath channel. The parameters of the FEC encoding layer are optimized by selecting the optimal error correction capability and the code block size. The results show the best parameter choice as a function of the link distance and received signal-to-noise ratio.  
Keywords:underwater acoustic networks; forward error correction codes; fountain codes; energy efficiency +
Footnote †: journal: Sensors
# The Emerging Roles of RNA Modifications in Glioblastoma
## 4 Potential Clinical Implications of RNA Modifications in GBM
### Potential Diagnostic Implications of RNA Modifications in GBM  
Epigenetic alterations are considered as promising markers for GBM diagnosis [159; 160; 161; 162], although this study is just the first step for their possible applications in GBM diagnosis. Besides, some epigenetic status really account for the outcome of GBM. For example, the epigenetic silencing of the O\({}^{6}\)-methylguanine-DNA methyltransferase (MGMT) significantly affects the TMZ treatment of GBM [163]. Therefore, MGMT promoter methylation status is an important biomarker to predict survival and TMZ response in patients with GBM [164].  
As a kind of chemical modifications in RNA, the alterations of their regulations may also predict the prognosis or be used for diagnosis of GBM. Actually, m\({}^{6}\)A in in peripheral blood RNA has shown to be a predictive biomarker for gastric cancer [165]. Methylation levels in miRNAs are also potential diagnostic biomarkers for early-stage gastrointestinal cancers [166]. The m\({}^{6}\)A erasers ALKBH5 and FTO are also shown to be prognostic biomarkers in patients with clear cell renal carcinoma [167]. From Table 1, some RNA modification enzymes are also tightly related to the grades and may predict the prognosis of GBM. However, the specificity of their expression should be further confirmed. Besides, some RNA modifications in peripheral blood RNA may also be promising methods for GBM diagnosis. In the future, it can be suspected that RNA modification marks as well as their RMPs' expression may be important biomarkers for the diagnosis of GBM.
## 5 Conclusions  
We presented an investigation of the ionization and dissociation dynamics in CH\({}_{3}\)I molecule with an X-ray pump and X-ray probe scheme. To illustrate the charge dynamics and nuclear kinetics, we showed the charge state and the KE distribution for selected charge states as a function of pump-probe delay time. We observed the creation of ions with low KE at a relatively long delay time, probing the long-range potential curves of molecular high charge states. We also observed the suppression of high charge states at short delay time and an increase of the high charge states at long delay time, indicating the time scale of the C-I charge transfer. X-ray pump-probe experimental approach combined with coincidence measurements for resolving fragmentation channels could hold the promise for unambiguous time-resolved investigation of molecular dynamics, including decoupled dissociation and charge transfer dynamics.  
We acknowledge the XRSD commissioning team: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. This work was funded by the Department of Energy, Office of Science, Basic Energy Sciences (BES), Division of Chemical Sciences, Geosciences, and Biosciences under a SISGR program1 No. DE-SC0002004, under grant No. DE-SC0012376 and for the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, under Contract No. DE-AC02-76 SF00515. E.K. acknowledges the support by the Academy of Finland. L.F. acknowledges the support by Defense Advanced Research Project Agency Contract 12-63-PULSE-FP014, and by National Nuclear Security Administration Cooperative Agreement DE-NA0002008.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Species** & **KE (eV) 1** & **Species** & **KE (eV) 1** & **Species** & **KE (eV) 1** & **Species** & **KE (eV) 2** \\ \hline C\({}^{+}\) (-I\({}^{n+}\)) & 5.0 \(\pm\) 0.9 & I\({}^{n+}\) (-C\({}^{+}\)) & 0.5 \(\pm\) 0.3 & I\({}^{25+}\) & 49 \(\pm\) 9 & C\({}^{+}\) (-I\({}^{+}\)) & \(\sim\)6 \\ C\({}^{2+}\) (-I\({}^{n+}\)) & 19.1 \(\pm\) 2.3 & I\({}^{n+}\) (-C\({}^{2+}\)) & 1.8 \(\pm\) 0.2 & I\({}^{26+}\) & 61 \(\pm\) 10 & C\({}^{2+}\) (-I\({}^{5+}\)) & \(\sim\)47 \\ C\({}^{3+}\) (-I\({}^{n+}\)) & 42.5 \(\pm\) 7.9 & I\({}^{n+}\) (-C\({}^{3+}\)) & 4.0 \(\pm\) 0.4 & I\({}^{27+}\) & 55 \(\pm\) 10 & C\({}^{3+}\) (-I\({}^{7+}\)) & \(\sim\)100 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Kinetic energy (KE) of carbon and iodine ions. The values for the current work were obtained from the spectrum with single pulse. The charge states in the parenthesis are the species associated those for which the KEs are presented.  
**Author Contributions**: [PERSON] conceived the experiment; [PERSON], [PERSON], E.K. and N.B. performed the experiments; [PERSON] and [PERSON] analyzed the data; E.K. did the simulation; [PERSON] wrote the paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model
## 2 Continuum Damage Theory
### Gradient-Enhanced Damage Models  
It is well known that local damage models lead to ill-posed boundary value problems and associated numerical difficulties such as mesh-dependent results in computational modeling. Non-local damage models as proposed [26; 27; 28; 29; 30] restore the well-posedness of the boundary value problem by introducing an intrinsic length scale which smear the crack over a certain width. In such models, the loading function is therefore expressed in terms of non-local equivalent strain \(\bar{\varepsilon}_{eq}(\mathbf{x})\) which in turn depend on the local effective strain and a weighting function \(g\left(\bar{\mathbf{\xi}}\right)\) governing the domain of non-locality:  
\[\bar{\varepsilon}_{eq}(\mathbf{x})=\frac{1}{V}\int\limits_{V}g\left(\bar{\mathbf{\xi} }\right)\varepsilon_{eq}\left(\mathbf{x}+\bar{\mathbf{\xi}}\right)dV \tag{8}\]An alternative to such strongly nonlocal models are weakly nonlocal approaches [26; 28; 31] which are based on Taylor series expansions to approximate the effective strain. Substituting these into the Equation (8) yields a different expression for the non-local equivalent strain. However, it is well known that such formulations require \(C^{1}\)--continuity for second-order gradient models (\(C^{2}\)--continuity for fourth-order gradient enhancements), which complicates their implementation in Lagrange polynomial based finite element analysis. Implicit formulations overcome these difficulties. Differentiating twice and neglecting higher-order gradient terms of the local equivalent strain, the implicit second-order gradient model reads:  
\[\varepsilon_{eq}=\overline{\varepsilon}_{eq}-\frac{1}{2}l_{c}^{2}\
abla^{2} \overline{\varepsilon}_{eq}(\mathbf{x}) \tag{9}\]  
where the parameter \(l_{c}\) indicates the intrinsic length scale, which can be regarded either as purely numerical regularization parameter or material parameter which needs to be determined through experiments or other theoretical considerations. For concrete materials, [PERSON] et al. related \(l_{c}\) to the maximum aggregate size [32]. Furthermore, the following von Neumann boundary conditions need to be satisfied [28; 33]:  
\[\
abla\overline{\varepsilon}_{eq}\cdot\mathbf{n}=0 \tag{10}\]  
It can be shown that the gradient enhanced damage model, Equation (9), can be expressed in terms of the principal directions and an anisotropic weight function:  
\[\varepsilon_{eq}(x_{1},x_{2})=\overline{\varepsilon}_{eq}(x_{1},x_{2})-c_{1} \frac{\partial^{2}\overline{\varepsilon}_{eq}(x_{1},x_{2})}{\partial{x_{1}} ^{2}}-c_{2}\frac{\partial^{2}\overline{\varepsilon}_{eq}(x_{1},x_{2})}{ \partial{x_{2}}^{2}} \tag{11}\]  
where \(c_{1}\), \(c_{2}\) are the weighting factors. More details about the derivation of above described gradient-enhanced damage model and its implementation can be found for instance in [26].
## 5 Conclusions  
This study examines the effects of different exercise interventions on participants' physical and mental health during fragmented exercise sessions. A 16-week follow-up investigation was conducted, and the conclusions drawn have practical significance for the development of public health. The findings can be summarized as follows:  
1. Exercise interventions have been proven to be an effective method for improving both physical and mental health. They can reduce BMI and body fat levels, enhance metabolic health, cardiovascular health, and cardiorespiratory fitness, and have a positive impact on quality of life and mental well-being.
2. Among the three exercise interventions--HIIT-LISS, MLCT-LISS, and HIIT-MICT--the HIIT-LISS is the most effective in improving participants' BMI, showing a reduction of 5.37% compared to pre-intervention levels. Similarly, alternating between two types of exercise interventions yields the best results for lipid profiles and VO2 max, with an increase of 9% in HDL cholesterol, a reduction of approximately 7% in LDL cholesterol, and a 14% improvement in VO2 max. Additionally, the HIIT is most effective in reducing body fat percentage and blood pressure, with a maximum reduction of 5.65% in body fat and nearly 9 mmHg in blood pressure. This exercise mode shows a slightly greater effect on diastolic blood pressure compared to systolic blood pressure. In contrast, the MLCT-LISS combination has a greater impact on heart rate, reducing it by 8 BPM. Although the different exercise interventions do not show significant effects on psychosocial health, there is a notable improvement of over 10% in psychological well-being compared to the control group.
3. The comprehensive evaluation results indicate that the HIIT-MICT is the most effective, followed by HIIT. The HIIT-MICT-LISS has a greater effect than LISS alone, while MLCT performs better than MLCT-LISS. Our study demonstrates that LISS, when used alone, is  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 2 Materials and Test Setup  
Three type of metallic samples were selected for the experimental investigation: aluminum (AL 6061-T6, 97.2% Al, 1% Mg, 0.6% Si, 0.3% Cu, 0.3% Fe, 0.2% Cr, 0.2% Zn, 0.1% Mn, 0.1% Ti), cold rolled steel (CRS, 1018, 99% Fe, 0.8% Mn, 0.2% C) and brass (C36000, 61.5% Cu, 35.4% Zn, 2.7% Pb).  
The dynamic tensile fatigue tests were performed with a sinusoidal deformation at a frequency of 5 Hz with \(R=0.1\) and a distance between grips of 24 mm on an Acumen 3 (MTS, Eden Prairie, MN, USA) mechanical tester in the strain controlled mode. The time signals for the force and deformation under dynamic testing were recorded via a custom written LabView program with a sampling rate of 200 points/cycle using oversampling [30]. Using a custom written MATLAB script, the nonlinear parameters (\(I_{2/1}\) and \(I_{3/1}\)) were calculated via Fourier transform. The tests were performed at room temperature (RT \(\approx\) 23 \({}^{\circ}\)C) and followed by a thermal camera (FLIR ThermoVision A320). More information on the experimental set-up and data analysis can be found in our previous works [19; 21; 26; 31; 32].  
The fracture surface was investigated via microscopy (Olympus SZ-PT).
## 3 Modeling of SMEHS  
In Figure 1, the SMEHS consists of a hydraulic cylinder, a servo valve 1 at the metering-in side of the hydraulic cylinder, a servo valve 2 at the metering-out side, a hydraulic pump, and a relief valve. The two valves are used to control the cylinder. The two signals control the two independent valve spools, so the SMEHS has two degrees of freedom and it provides more flexibility and controllability. The relief valve is used to maintain a constant pressure of the system. The dynamics of the servo valve can be described by a second-order system, which yields the following equations, since the response frequency of the servo valve is much higher than that of the entire hydraulic system.  
\[K_{s1}\omega_{v1}^{2}u_{1}=\ddot{x}_{v1}+2\xi_{v1}\dot{x}_{v1}+a_{v1}^{2}x_{v1} \tag{17}\]  
\[K_{s2}\omega_{v2}^{2}u_{2}=\ddot{x}_{v2}+2\xi_{v2}\dot{x}_{v2}+a_{v2}^{2}x_{v2} \tag{18}\]  
Figure 5: The system block diagram with the active damping strategy.  
where \(x_{v1}\) and \(x_{v2}\) are the displacement of the servo valve spool, \(K_{s1}\) and \(K_{s2}\) are the servo valve gain, \(\omega_{v1}\) and \(\omega_{v2}\) are the natural frequency, \(\xi_{v1}\) and \(\xi_{v2}\) are damping ratio, and \(u_{1}\) and \(u_{2}\) are the input signal to servo valve.  
The pressure dynamics can be obtained, as follows:  
\[\dot{p}_{1}=(q_{1}-A_{1}\dot{x}_{L})\frac{\beta_{e}}{V_{L01}+A_{1}x_{L}}+ \Delta_{1} \tag{19}\]  
\[\dot{p}_{2}=(q_{2}+A_{2}\dot{x}_{L})\frac{\beta_{e}}{V_{L02}-A_{2}x_{L}}+ \Delta_{2} \tag{20}\]  
where \(\beta_{e}\) is the effective bulk modulus of oil, \(p_{1}\) is the pressure in piston chamber, \(p_{2}\) is the pressure in rod chamber, \(V_{L01}\) and \(V_{L02}\) are the initial volume of cylinder chambers, separately, including the chamber volume of connected pipeline, \(A_{1}\) is the area of cylinder piston, \(A_{2}\) is the effective actuating area of rod end in cylinder, \(x_{L}\) is the displacement of cylinder rod, \(\Delta_{1}\) and \(\Delta_{2}\) are uncertain terms due to parameter perturbation, modeling errors, and other disturbance.  
While ignoring the mass of oil, we obtain the force balance equation following [PERSON]'s second law:  
\[\ddot{x}_{L}=\frac{1}{m}(p_{1}A_{1}-p_{2}A_{2}-b\dot{x}_{L}-f_{L})+\Delta_{3} \tag{21}\]  
where \(m\) is the total mass of the piston, \(f_{L}\) is the load force, \(b\) is viscous damping coefficient, \(\Delta_{3}\) is the uncertain item, consisting of the external disturbance and unmodeled uncertainties.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 4 The Proposed Method
### Training Loss
#### 4.3.1 Scale-Invariant Loss  
The scale-invariant loss [40] measures the relationships between points regardless of the absolute global scale. Hence, in the monocular depth estimation tasks [30; 34; 39] where inherent scale ambiguity exists, this type of loss function is widely employed. In our work, we employ it to ensure reliable results when the input depth is extremely sparse or absent. The scale-invariant loss is defined by  
\[L_{SIL}=\frac{1}{n}\sum_{i}g_{i}^{2}-\lambda\frac{1}{n^{2}}\left(\sum_{i}g_{i }\right)^{2}, \tag{7}\]  
where \(g_{i}=\log z_{i}-\log z_{i}^{*}\), \(z_{i}\) is an estimated depth value at pixel \(i\), \(z_{i}^{*}\) is the ground truth, and \(n\) is the pixel number.
## 2 Materials and Methods
### Description of Test Sites
#### 2.1.1 Site A: Seogwipo, Jeju Province  
For DEM generation, Seogwipo, the second-largest city located in the southern part of Jeju Island, was chosen. This volcanic island houses a volcanic mountain, called Mt. Halla, at the center of the island (Figure 1), which has a height of 1950 m above sea level with an inclination of approximately 3-5\({}^{\circ}\) from the peak [40].  
Figure 1: Map of South Korea highlighting the selected test sites for interferometry using Sentinel-1 synthetic aperture radar (SAR) data and open-source SeNtinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) tools.
# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
## 3 Research Process  
### Datasets and Evaluation Metrics  
The data distribution of the training set, development set, and evaluation set for the PA task of the ASVspoof 2021 challenge is shown in Table 1. The training set provided in the ASVspoof 2019 PA dataset consisted of 5400 genuine and 48,600 spoofed utterances, while the development set consisted of 5400 genuine and 24,300 spoofed utterances. The final evaluation set was based on the ASVspoof 2021 PA dataset. In this paper, the training set was used to train the replay speech spoofing detection model, and the evaluation set was utilized to submit the final results.  
The ASVspoof challenges use two evaluation metrics [8]. The primary evaluation metric for the PA task is the Minimum Tandem Detection Cost Function (min t-DCF) [34], which is defined as in Equation (3):  
\[t-DCF(\theta)=C_{1}P_{miss}^{cm}(\theta)+C_{2}P_{fa}^{cm}(\theta) \tag{3}\]  
In this equation, \(P_{miss}^{cm}(\theta)\) and \(P_{fa}^{cm}(\theta)\) represent the false rejection rate and false acceptance rate of the speech replay detection system, respectively; \(\theta\) is the threshold of the model. Additionally, the min t-DCF metric does not require a preset threshold.  
The secondary metric for the PA task is the EER, which is defined as follows:  
\[P_{fa}(\theta)=\frac{\#\{\mathit{fake\ trials\ with\ score>\theta}\}}{\#\{ \mathit{total\ fake\ trials}\}} \tag{4}\]  
\[P_{miss}(\theta)=\frac{\#\{\mathit{genuine\ trials\ with\ score<\theta}\}}{\#\{ \mathit{total\ genuine\ trials}\}} \tag{5}\]  
where \(P_{fa}(\theta)\) and \(P_{miss}(\theta)\) represent the false acceptance rate and false rejection rate at threshold \(\theta\), respectively. Additionally, the EER corresponds to the threshold \(\theta_{EER}\) at which the false acceptance rate and false rejection rate are equal. In addition, the ASVspoof competition also sets up a parameter-fixed ASV system to help researchers to focus on the design of the replay speech detection model.