esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of the Broadcast Ephemeris Correction with the PPP-B2b Service
### Correction Algorithm  
After the PPP-B2b corrections are matched, we obtain the corrected satellite position with the orbit correction message:  
\[X_{orbit}=X_{broadcast}-\delta X \tag{1}\]  
where \(X_{broadcast}\) denotes the 3D coordinates of satellite positions calculated through the broadcast ephemeris. \(\delta X=\left[e_{radial}\quad e_{cross}\quad e_{along}\right]\cdot\delta O\) is the orbit correction message, where \(e_{radial}=\frac{r}{|r|}\), \(e_{cross}=\frac{r\times\dot{r}}{|r\times r|}\), and \(e_{along}=e_{cross}\times e_{radial}\), with \(r\) being the 3D position coordinate vector and \(\dot{r}\) being the 3D velocity coordinate vector in the ECEF coordinate system. These vectors are obtained from the broadcast ephemeris. \(e_{i}\) denotes a unit vector with the subscript \(i=\{radial,along,cross\}\) indicating the direction in the centroid orbit coordinate system. \(\delta O\) is the 3D orbit correction vector broadcast by the PPP-B2b service. The equation for obtaining the clock offset of PPP-B2b is  
\[t_{satellite}=t_{broadcast}-\frac{c_{0}}{c} \tag{2}\]  
where \(t_{satellite}\) is the clock offset of PPP-B2b, \(t_{broadcast}\) is the uncorrected clock offset calculated from the broadcast ephemeris, \(c\) is a constant representing the speed of light, and \(c_{0}\) is the clock offset correction. The equation of the DCB correction algorithm is  
\[\widetilde{l}=l_{broadcast}-DCB_{B2b} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Content of Correction** & **Message Type** & **Nominal Validity** & **Update Interval** \\ \hline Satellite mask & 1 & 86,400 s & 48 s \\ Orbit correction & 2, 6, 7 & 96 s & 48 s \\ Clock correction & 3 & 86,400 s & 12 s \\ Differential code bias & 4, 6, 7 & 12 s & 6 s \\ User ranging accuracy index & 2, 5, 6, 7 & 96 s & 48 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validity periods of various corrections.  
where \(l_{broadcast}\) denotes the code measurements corresponding to the ranging signals, \(\widetilde{l}\) denotes the corrected code measurements corresponding to the ranging signals, and \(DCB_{B2b}\) denotes the DCB correction message of the corresponding ranging signal. The dual-frequency ionosphere-free combination code measurements obtained after the DCB correction are  
\[\widetilde{l}=\frac{\gamma\widetilde{l}_{1}-\widetilde{l}_{2}}{\gamma-1}=\frac{ \gamma l_{1}-l_{2}}{\gamma-1}-\frac{\gamma DCB_{1}-DCB_{2}}{\gamma-1} \tag{4}\]  
\[\gamma=\frac{f_{1}^{2}}{f_{2}^{2}} \tag{5}\]  
where \(f_{1}\) is the center frequency of carrier phase \(l_{1}\) and \(f_{2}\) is the center frequency of the carrier phase and \(DCB_{1}\) and \(DCB_{2}\) are the corresponding DCB correction messages. The DCB correction is used to correct pseudo-range observations. It is also used to correct the clock offset when evaluating the PPP-B2b clock offset in this study.
## 3 Results and Discussion
### Mechanical Characterization Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **Source** & **Sum of Squares** & **Df** & **Mean Square** & **F-Ratio** & \(p\)**-Value** \\ \hline Flexural Strength (MPa) & A: Dosage & 7.954410 & 1 & 7.954410 & 539.28 & 0.0000 \\  & B: Fiber type & 0.414408 & 1 & 0.414408 & 28.10 & 0.0007 \\  & AB: Interactions & 0.000075 & 1 & 0.000075 & 0.01 & 0.9449 \\  & Residual & 0.118000 & 8 & 0.014750 & & \\  & Total (Corrected) & 8.486890 & 11 & & & \\ \hline Compression Strength (MPa) & A: Dosage & 193.218000 & 1 & 193.218000 & 1212.41 & 0.0000 \\  & B: Fiber type & 0.911905 & 1 & 0.911905 & 5.72 & 0.0507 \\  & AB: Interactions & 0.337345 & 1 & 0.337345 & 2.12 & 0.1838 \\  & Residual & 1.274940 & 8 & 0.159367 & & \\  & Total (Corrected) & 195.742000 & 11 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of variance (ANOVA) for flexural and compressive strength. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & **Dosage** & **Count** & **LS Mean** & **LS Sigma** & **Homogeneous Group** \\ \hline Flexural Strength & B & 6 & 3.45000 & 0.0495816 & X \\  & A & 6 & 5.07833 & 0.0495816 & X \\ \hline Compression Strength & B & 6 & 10.17130 & 0.1629760 & X \\  & A & 6 & 18.19670 & 0.1629760 & X \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multi-range test for flexural and compressive mechanical strengths (MPa) considering the cement/aggregate ratio. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & **Fiber** & **Count** & **LS Mean** & **LS Sigma** & **Homogeneous Group** \\ \hline Flexural Strength & NF & 6 & 4.08733 & 0.0495816 & X \\  & AF & 6 & 4.45000 & 0.0495816 & X \\ \hline Compression Strength & NF & 6 & 13.9050 & 0.1629760 & X \\  & AF & 6 & 14.1250 & 0.1629760 & X \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multi-range test for flexural and compressive mechanical strengths (MPa) considering the incorporation of aramid fibers. As can be seen in Figure 5a, the mortars made with recycled concrete aggregate have a correct setting process that is reflected by the formation of Ettringite crystals in the mortar matrix [80]. In addition, it can be seen in Figure 5b how the cementitious material wraps and adheres firmly to the surface of the recycled aggregates, resulting in a good cohesion between both. Figure 5c,d shows the interaction between the aramid fiber and the mortar matrix, where it can be appreciated the good mortar-fiber adhesion that has favored the increase of the mechanical flexural strengths [81].
## Materials and Methods
### Bending Tests for the PEDOT:PSS-C6 Films  
We evaluated the flexibility of the films using a bending test apparatus (STS-RT-5 AXIS, Scinectown, South Korea). The PEDOT:PSS and PEDOT:PSS-C6 films coated on a PET film were cut into a rectangular piece, 1.5 cm \(\times\) 15 cm, and the silver paste was applied at 1 cm from both ends of the films in the vertical direction (i.e., the gap between the silver pastes was 13 cm). The film was attached to the bending machine, which was adjusted to 3R (curvature radius of 3 mm) with the PEDOT:PSS coated side facing down. Therefore, PEDOT:PSS was folded by a 'outfolding' manner, as shown in Figure 1. The silver paste was connected to a multimeter (289 Fluke View, Fluke, Everett, WA, USA) to measure changes in the electric resistance. Changes of the electric resistance were recorded and stored by a computer connected to the multimeter, and analyzed. The bending test was conducted up to 50,000 cycles under 120 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Reaction Conditions**} \\ \cline{3-6}
**Code** & \begin{tabular}{c} **Carbon** \\ **Numbers of** \\ **Epoxyalkanes** \\ \end{tabular} & \begin{tabular}{c} **Weight of** \\ **PEDOT:PSS** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **Weight of** \\ **Epoxyalkanes (g)** \\ \end{tabular} & \begin{tabular}{c} **Washing** \\ **Solvent** \\ \end{tabular} & \
\begin{tabular}{c} **Weight of** \\ **Products** \\ **Obtained (g)** \\ \end{tabular} &
\begin{tabular}{c} **Conductivity b** \\ **(S-m\({}^{-1}\))** \\ \end{tabular} \\ \hline PEDOT:PSS & - & 50 g & - & IPA & 0.51 & 132 \(\pm\) 31 \\ PEDOT:PSS-C4 & 4 & 50 g & 1.19 & IPA & 0.46 & 350 \(\pm\) 57 \\ PEDOT:PSS-C6 & 6 & 50 g & 1.65 & IPA & 0.55 & 268 \(\pm\) 16 \\ PEDOT:PSS-C8 & 8 & 50 g & 2.11 & IPA & 0.62 & 114 \(\pm\) 15 \\ PEDOT:PSS-C12 & 12 & 50 g & 3.03 & IPA & 0.58 & 94 \(\pm\) 39 \\ PEDOT:PSS-C16 & 16 & 50 g & 3.96 & Acetone & 0.48 & 55 \(\pm\) 11 \\ \hline \hline \end{tabular}  
* Solid content is 1.2 wt.% b The average and standard deviation values were calculated from 3 different samples independently prepared.  
\end{table}
Table 1: Reaction conditions and conductivities of PEDOT:PSS derivative.
## 2 Materials and Methods
### Spectral Preprocessing Methods  
In hyperspectral data analysis, data preprocessing can effectively solve the problems of noise interference, the scattering effect and inter-sample heterogeneity in spectral data, and guarantee the model prediction accuracy and robustness. The Savitzky-Golay (S-G) algorithm smooths the spectral data through local polynomial fitting, which effectively suppresses random noise while retaining the detailed features of the spectral signal to the greatest extent. The multiplicative scatter correction (MSC) algorithm can eliminate the multiplicative scattering interference caused by the difference of sample particle size and surface roughness [25]. The standard normal variable transformation (SNV) algorithm eliminates the additive baseline offset between samples by centralizing and standardizing each spectrum separately [26].  
In view of the fact that it is difficult for a single preprocessing method to completely remove noise interference, the combined use of multiple methods can often achieve better denoising results [27]. Based on this, this study not only used independent preprocessing methods, but also combined the S-G algorithm with other methods. By evaluating the impact of different preprocessing strategies on model performance, the optimal data processing scheme was selected.
## 3 Results
Regarding CMR-FT, there was a significant decrease in GLS after Cone repair (\(-15.01\%\) vs. \(-14.53\%\), \(p=0.0155\)), while significant improvements were noted in GRS and GCS (15.00% vs. 17.83%, \(p=0.0202\) and \(-8.82\%\) vs. \(-13.02\%\), \(p=0.0026\)), as illustrated in Figure 2. \begin{table}
\begin{tabular}{l c c c} \hline
** CMR Parameters** & **Preoperative (n = 18 *)** & **Postoperative (n = 18 *)** & **\(p\)-Value** \\ \hline Aorta flow netto indexed (mL/m\({}^{2}\)) & 38 (23–48) & 40 (20–54) & 0.1066 \\ Aorta flow antegrade indexed (mL/m\({}^{2}\)) & 38 (23–48) & 41 (20–54) & 0.1058 \\ Cardiac index aorta (L/min/m\({}^{2}\)) & 2.8 (1.8–4.7) & 2.8 (1.6–5.0) & 0.5619 \\ Heart rate & 82 (56–98) & 73 (59–98) & 0.1099 \\ \hline \end{tabular} Data are presented as median (min, max). CMR, cardiovascular magnetic resonance; RV, right ventricle; EE, ejection fraction; EDVi, indexed end-diastolic volume; ESVi, indexed end-systolic volume; SWi, indexed stroke volume; LV, left ventricle; TI, tricuspid insufficiency; PA, pulmonary artery. * for RVGS and RVGS n = 17 and RVGS n = 15. SD, standard deviation; CMR, cardiovascular magnetic resonance.  
\end{table} 
Table 2: _Cont._  
Figure 2: Global radial (**a**), global circumferential (**b**), and global longitudinal strain (**c**) before and after Cone repair. The line in the middle of the boxplot represents the median; the X represents the mean.
## Nomenclature  
### Greek symbols  
\begin{tabular}{l l} \(\alpha\) & Angstrom exponent (dimensionless) \\ \(\beta\) & Angstrom turbidity coefficient (dimensionless) \\ \(\gamma\) & Solar elevation or solar height or solar altitude (degrees) \\ \(\lambda\) & Geographical longitude (degrees, positive in east of Greenwich) \\ \(\varphi\) & Geographical latitude (degrees, positive in Northern Hemisphere) \\ \(\mathit{Latin symbols}\) & \\ \(B\) & Schiepp turbidity factor (dimensionless) \\ \(B_{\mathrm{e}}\) & Direct horizontal irradiance (Wm\({}^{-2}\)) \\ \(B^{\mathrm{*}}\mathrm{e}\) & Direct (normal) irradiance in a dust-free atmosphere (Wm\({}^{-2}\)) \\ \(D\) & Day of the year (dimensionless); \(D=1\) for 1 January, 365 for 31 December in \\  & a non-leap year and 366 in a leap year \\ \(D_{\mathrm{e}}\) & Diffuse horizontal irradiance (Wm\({}^{-2}\)) \\ \(e_{\mathrm{m}}\) & Partial water-vapor pressure (hpa) \\ \(e_{\mathrm{s}}\) & Saturation water-vapor pressure (hpa) \\ \(G_{\mathrm{e}}\) & Global horizontal irradiance (Wm\({}^{-2}\)) \\ \(G_{\mathrm{e,extra}}\) & Extra-terrestrial solar irradiance (Wm\({}^{-2}\)) = \(S\)\(G_{\mathrm{e,o}}\) \\ \(G_{\mathrm{e,o}}\) & Solar constant = 1361.1 Wm\({}^{-2}\) \\ \(m\) & Optical air mass (dimensionless) \\ \(m^{\prime}\) & Pressure-corrected optical air mass (dimensionless) \\ \(\overline{k_{r}}\) & Mean attenuation of the direct solar radiation due to Rayleigh scattering \\ \(k_{\mathrm{d}}\) & Diffuse fraction (dimensionless) \\ \(k^{\prime}_{\mathrm{d}}\) & Modified diffuse fraction (dimensionless) \\ \(k_{\mathrm{t}}\) & Clearness index (dimensionless) \\ \(K^{\prime}_{\mathrm{t}}\) & Modified clearness index (dimensionless) \\ \(P_{\mathrm{o}}\) & Sea-level atmospheric pressure = 1013.25 hpa \\ \(P_{\mathrm{z}}\) & Atmospheric pressure at height z (hpa) \\ \(RH\) & Relative humidity (\%) \\ \(S\) & Correction of the Earth-Sun distance (dimensionless) \\ \(T_{\mathrm{i}}\) & Atmospheric transmittance due to ith atmospheric constituent \\ \(T_{\mathrm{L}}\) & Linke turbidity factor (dimensionless) \\ \(t\) & Ambient temperature (K) = 273.15 + t\({}^{\prime}\) \\ \(t^{\prime}\) & Ambient temperature (\({}^{\mathrm{\circ}}\)C) \\ \(T_{\mathrm{UM}}\) & Unsworth-Monteith turbidity coefficient (dimensionless) \\  & Atmospheric column content due to ith atmospheric constituent (atm-cm, or \\ \(u_{\mathrm{i}}\) & cm, depending on the constituent) \\ \(z\) & Altitude (m) \\ \end{tabular}
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 5 Results and Discussion
### Route Selection Based on Profiles  
In this work, we propose using the Profile Operator (see Section 4.5) to apply our model in individual route recommendations. This operator combines the attention that us given by a user to the experience or price-related metrics. Figure 10 shows the influence of the profiles for the three already described traffic scenarios. In a real-world system, the profile operator should be calibrated with usage data by applying different learning techniques, such as the one that was proposed in the FAVOUR algorithm [5]. Great variations to the duration metric can be observed in Figure 10, this happens since trips with different lengths, which take varied time to be performed, are evaluated. Conversely, the price metric does not vary much, because, for most of the transport modes, the duration does not have a direct impact on the price (e.g., bus, walk, and the initial hiring fee for Ubers).  
The profile operator fuses the utility functions after the normalization using a z-score. Next, the \(\zeta\) profile operator is evaluated, i.e., the route option with the smallest value for each trip is selected for the evaluation--omitting the Uber option, which is used as the baseline. We computed the difference for every metric and then normalized it (divided by the value of the Uber option). We use Equation (19) to calculate the impact in trips, where \(I\) is the impact, \(r^{\prime}\) is the Uber option for the trip, and \(f(r)\) is the cost function studied (i.e., \(c_{r}\), \(t_{r}\), or \(T(r)\)).  
\[I=\frac{\min_{\forall r\in R-\{r^{\prime}\}}f(r)}{t_{r^{\prime}}}. \tag{19}\]  
For all of the traffic scenarios, we present the expected results. When \(p\) increases, the duration and user experience drop, and the cost values rise. This reflects the idea that users  
Figure 10: Relative impact of different users’ profiles in the route selection.  
expecting a better QoE might pay more for the trip and vice versa. Yet, there are specific situations where the price and duration of trips are reduced together, which results in a cheaper trip with better QoE. For instance, when there is congestion in the streets, leaving a vehicle and choosing a different mode, like a bicycle, may result in a faster trip; since the usage of a hired vehicle is reduced, and the price of the trip decreases.  
The median values were plotted over the graphs to facilitate the visualization of this behavior. Most of the observations for all scenarios lie between \(-1\) and \(1\) (81.7% of the evaluated flows), which means that there are no high gains or losses. All of the median is under the zero mark for the low peak scenario, meaning that gains were obtained in more than 50% of the trips for all metrics. In the High and Weekend peak scenarios, only the metrics duration and price had the same performance, while the median for user experience was a little above the zero mark, even though these median values were not too high.
# Multimodal Tucker Decomposition for Gated RBM Inference
## 5 Materials and Methods
### Decomposing the Three-Way Tensor in a Gated RBM  
The central idea of this research is to represent the required three-way interaction tensor in the gated RBM model using far fewer parameters through its Tucker Decomposition. The energy function in a gated RBM (Equation (5)) captures all possible correlations among the components of the \(\mathbf{x}\) (input), \(\mathbf{y}\) (output), and \(\mathbf{h}\) (hidden) layers. In this function, parameter \(\mathbf{\mathscr{W}}\) defines a three-way interaction tensor that learns the importance of correlations between layers \(\mathbf{x}\) and \(\mathbf{y}\). However, despite its appealing modeling power, a fully parametrized gated RBM suffers from an explosion in the number of parameters, quickly becoming intractable because the size of the full tensor \(\mathbf{\mathscr{W}}\) is prohibitive using common dimensions for textual, visual, or output spaces.  
As we will see, it is possible to use much fewer parameters by factorizing the multi-way interaction tensor via Tucker decomposition. We can plug the Tucker decomposition Equation (16) into the energy function of the gated RBM Equation (5). Then, the energy of a joint configuration of the visible (input/output) and hidden units is defined as:  
\[-E(\mathbf{y},\mathbf{h};\mathbf{x})=\underbrace{\left[\mathbf{\mathscr{G}}_{pqr} \times_{P}\mathbf{A}_{ip}\times_{Q}\mathbf{B}_{jq}\times_{R}\mathbf{C}_{kr} \right]}_{\mathbf{\mathscr{V}}_{ijk}}\mathbf{x}_{i}\mathbf{y}_{j}\mathbf{h}_{k}\]  
Using the distributive law, this can be rewritten as:  
\[-E(\mathbf{y},\mathbf{h};\mathbf{x})=\mathbf{\mathscr{G}}_{pqr}\times_{P}\mathbf{ x}_{i}^{T}\mathbf{A}_{ip}\times_{Q}\mathbf{y}_{j}^{T}\mathbf{B}_{jq}\times_{R} \mathbf{h}_{k}^{T}\mathbf{C}_{kr}\]  
We can drop subindices for clarity and get:  
\[-E(\mathbf{y},\mathbf{h};\mathbf{x})=\mathbf{\mathscr{G}}\times_{P}(\mathbf{x}^{T }\mathbf{A})\times_{Q}(\mathbf{y}^{T}\mathbf{B})\times_{R}(\mathbf{h}^{T} \mathbf{C}) \tag{21}\]  
It is possible to simplify the notation in (21) if we define:  
\[\hat{\mathbf{x}}=\mathbf{x}^{T}\mathbf{A},\]  
\[\hat{\mathbf{y}}=\mathbf{y}^{T}\mathbf{B},\text{ and }\]\[\mathbf{\hat{h}}=\mathbf{h}^{T}\mathbf{C}.\]  
Then the energy function in Equation (21) is given by:  
\[-E(\mathbf{y},\mathbf{h};\mathbf{x})=\boldsymbol{\mathscr{G}}\times_{P}\mathbf{ \hat{x}}\times_{Q}\mathbf{\hat{y}}\times_{R}\mathbf{\hat{h}} \tag{22}\]
## 3 IPv6 Communications Requirements for M2M
### Basic IP Parameters  
Several mechanisms exist for the auto-configuration of basic IP parameters (address, mask, default route) for a device. A rough classification groups them depending on their capacity to maintain a state related to the parameters assigned to a device. For example, DHCPv6 protocol falls within a _stateful_ group, since it maintains an address assigned to a device, at a specific DHCPv6 server. The alternative _stateless_ group does not maintain such a state: a router provides a prefix to the device to form an address without further assistance from other entities.  
Figure 2: Overall view of the functional elements involved in the process of network parameters configuration. The DHCPv6 server and relay are in the operator domain, while the machine-to-machine (M2M) gateway (mobile router) along with the attached devices are in the user domain.  
The vehicular eHealth scenario considers IP devices deployed in a vehicle equipped with a gateway offering long-range connectivity. In such a scenario, auto-configuration mechanisms are needed: the gateway needs not only one address for itself, but a set of addresses for the IP eHealth devices. The mechanisms to achieve such auto-configuration are named _prefix delegation_. This is an extension to the DHCPv6 protocol [27]. In addition to the typical functionality of DHCPv6 to assign an IP address, this extension allows the assignment of a set of prefixes to a client. The DHCPv6 protocol is specified to work with relay and server entities as described in this recent reference [28].  
Prefix delegation for network mobility [29] is a specification of the behavior for the existing DHCPv6 prefix delegation in the context of network mobility. Network mobility (NEMO) is an extension to the mobile IP protocol to support groups of devices moving together; such a group can be understood as a capillary network and/or as a vehicular network. This particular prefix delegation mechanism specifies the roles of the requesting router (mobile router) and of delegating router (home agent), as well as the placement of the DHCPv6 relay (mobile router).
# Percolation Threshold of Red-Bed Soft Rock during Damage and Destruction
## 3 Results and Discussion
### Analysis of the Influencing Factors on Percolation Threshold
#### 3.4.2 Water  
To determine the influence of water on the percolation threshold of red-bed soft rock, the percolation threshold under water-stress coupled triaxial compression was compared with that under conventional triaxial compression. It can be seen from Table 4 that the percolation threshold increases with increases in confining pressure, but the percolation threshold is lower than that under the conventional triaxial compression test. When the confining pressure was 0 (without the direct effect of confining water), the percolation threshold under uniaxial compression after 48 h of water saturation was approximately 20% lower than that of the sample that was unsaturated. When the confining pressure was 1 MPa (the confining water is in direct contact), the percolation threshold of soft rock under water-stress coupling triaxial compression after 48 h of water saturation was about 34% lower than that under traditional anhydrous triaxial compression. Similarly, when the confining pressure was 3 MPa, the percolation threshold decreased by about 31%, as shown in Figure 12.  
It can be seen that the percolation threshold of red-bed soft rock decreased by about 20% after 48 h of water saturation alone. Under the joint action of 48 h of water saturation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confining Pressure (MPa)**} & \multirow{2}{*}{**Conventional Triaxial Compression**} & \multirow{2}{*}{
\begin{tabular}{c} **Water-Stress Coupled** \\ **Triaxial Compression** \\ \end{tabular} } \\  & & & **Percolation Threshold** \\ \hline
0 & 0.0081 & 0.320 & 0.250 \\
1 & 0.0146 & 0.464 & 0.304 \\
3 & 0.0153 & 0.504 & 0.350 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percolation threshold and failure strain under two kinds of triaxial compression.  
Figure 12: Percolation threshold of triaxial compression test under different confining pressures.  
and direct contact with confining water pressure, the percolation threshold of red-bed soft rock can be decreased by 34%, which shows that water has a great influence on the percolation threshold of red-bed soft rock. This decrease in percolation threshold is mainly due to the following reasons: when red-bed soft rock has been saturated with water for 48 h, water enters the soft rock and reduces the strength of its cement. When water acts as a confining pressure, it directly acts to further erode and soften the soft rock, reducing its compressive strength as well as the percolation threshold at which damage occurs.