esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
combined MobileNetV3-large with the CBAM attention mechanism to achieve highly accurate localization and classification of different crack images [28]. All of the above research has fully demonstrated the potential of LCNNs in crack detection. Crack image classification is a fundamental task in image recognition and is the basis for the subsequent implementation of crack target detection and semantic segmentation. However, the evaluation of LCNNs for image classification of tunnel lining cracks is still lacking. As a data-driven method, LCNNs based on deep learning depend on the quantity and quality of training data, and limited samples can lead to overfitting or low accuracy of LCNNs. Therefore, researchers have explored image augmentation methods. Recently, unsupervised learning-based methods for generating virtual crack images have been proven by several studies [29; 30; 31] to be a worthwhile method. For example, [PERSON] et al. proposed a method combining variational auto-encode (VAE) and deep convolutional generative adversarial networks (DCGANs) to generate virtual pavement crack images, which outperformed traditional data augmentation methods [32]. [PERSON] et al. used DCGAN to generate crack-labeled images and pixel-to-pixel models to generate corresponding virtual crack images, providing a new way to reduce the cost of crack labeling [33]. [PERSON] et al. proposed WGAN-RE model for generating images to address the problem of data scarcity in tunnel lining crack detection. However, the studies above ignored the interference background of the tunnel [34], and the extension of crack image generation under interference backgrounds remains underexplored. Based on the literature review above, this paper aims to overcome the challenge in tunnel lining crack classification: the scarcity of datasets with tunnel lining crack images under various interference backgrounds. Meanwhile, an LCNN model named ShuffleNetV2-1.0-SE was proposed to classify tunnel lining cracks accurately. The main contributions of this paper can be summarized as follows:1. A novel dataset for classification was constructed by manually taking field photographs of tunnel lining cracks under various interference backgrounds. 2. A publicly available concrete dataset with a small sample size was used to pretrain DCGAN, resulting in higher quality synthetic images of tunnel lining cracks. 3. The ShuffleNetV2-1.0 network was improved by incorporating a squeeze-excitation (SE) attention module, which improved its crack feature extraction and balanced its accuracy and speed. The rest of this paper is organized as follows. Section 2 provides a method overview, including the overall steps and the specific theory for each step. Section 3 shows the experimental configuration and detail. Section 4 shows the results of our model and compares them with other LCNNs. Section 5 outlines the conclusions and outlook.
## 3 Results
### Performance Variation with Slit Height and Jet Injection Volume
The decrease in torque appeared larger when the slit height was higher for all \(C_{j}\), and it was confirmed by previous results that this due to the injection pattern of the jet. The increase in thrust and the decrease in torque are not linear based on \(C_{j}\) for all slit heights, and the higher \(C_{j}\) is, the smaller the increase in thrust is, and the larger the decrease in torque is. At the same \(C_{j}\), the power of the pump is the highest at \(h_{S}=0.2\) mm. As can be seen in Table 5, the slit area is small, and the jet speed is the fastest, which lead to increase in the required power of the pump. Not only that, due to the injection pattern, but the reduction in torque is also not very large, so the efficiency consi \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(h_{S}\)**(mm)** & \(C_{j}\) & \(K_{T}\) & \(\Delta K_{T}\)/\(C_{j}\) & \(10K_{Q}\) & \(\Delta 10K_{Q}\)/\(C_{j}\) & \(P_{Pump}\)**(W)** & \(\eta_{O}\) \\ \hline \multirow{4}{*}{1.0} & 0.0 & 0.055 & - & 0.077 & - & - & 0.454 \\  & 0.05 & 0.081 & 0.518 & 0.037 & \(-\)0.797 & 15.60 & 0.591 \\  & 0.125 & 0.113 & 0.467 & \(-\)0.067 & \(-\)1.153 & 61.64 & 0.556 \\  & 0.2 & 0.138 & 0.414 & \(-\)0.170 & \(-\)1.237 & 124.76 & 0.385 \\ \hline \multirow{4}{*}{0.6} & 0.0 & 0.060 & - & 0.079 & - & - & 0.487 \\  & 0.05 & 0.086 & 0.524 & 0.069 & \(-\)0.192 & 20.13 & 0.412 \\  & 0.125 & 0.117 & 0.453 & 0.020 & \(-\)0.466 & 79.58 & 0.271 \\  & 0.2 & 0.137 & 0.387 & \(-\)0.039 & \(-\)0.590 & 161.56 & 0.184 \\ \hline \multirow{4}{*}{0.2} & 0.0 & 0.063 & - & 0.079 & - & - & 0.507 \\  & 0.05 & 0.091 & 0.566 & 0.076 & \(-\)0.066 & 34.87 & 0.311 \\ \cline{1-1}  & 0.125 & 0.114 & 0.412 & 0.038 & \(-\)0.328 & 137.84 & 0.152 \\ \cline{1-1}  & 0.2 & 0.132 & 0.346 & \(-\)0.011 & \(-\)0.451 & 278.96 & 0.096 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of propeller performance change according to \(C_{j}\) at each slit height. pump is reduced for all \(C_{j}\). However, when \(h_{S}\) = 1.0 mm, the efficiency of the propeller increased when injecting a jet. This result is found not only because the torque is greatly reduced through jet injection, but also because the jet speed is relatively slow, and the required power of the pump is small.
## 3 Results
### Physiological Characteristics for the Entire Test Group  
In the course of the 12-MCTRE, the subjects rowed an average distance of 2413 m, produced an average power of 112 W, and burned an average of 136 kcal. The average (HRavg) and maximum (HRmax) heart rate values were 167 and 187 bpm, respectively. Plotting the duration of exercise across intensity zones, it was found that the subjects spent most of their exercise time (370 s on average) in the 162+ bpm zone (Table 1).  
Table 2 shows the characteristics (parameters) of the subjects (PA, anthropometric and physiological characteristics) by group. Group 1 (G1)--no COVID-19, Group 2 (G2)--non-hospitalized COVID-19 sufferers, and Group 3 (G3)--hospitalized COVID-19 sufferers. Significant differences were found between the groups for most of the characteristics tested.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristic** & **Mean** & **SD** & **Min.** & **Max.** & **As * \\ \hline \multicolumn{6}{c}{**PA and Anthropometric Characteristics**} \\ \hline Age [years] & 19.93 & 1.66 & 17.4 & 24.0 & 0.694 \\ PA [METs/min/week] & 1975.33 & 429.48 & 750.0 & 3100.0 & \(-\)0.379 \\ Body height [cm] & 179.75 & 5.69 & 168.0 & 191.0 & \(-\)0.178 \\ Body mass [kg] & 77.10 & 10.96 & 55.9 & 111.0 & 0.551 \\ TBW [kg] & 46.46 & 5.63 & 32.9 & 57.8 & \(-\)0.054 \\ Proteins [kg] & 12.61 & 1.53 & 8.9 & 15.7 & \(-\)0.046 \\ Minerals [kg] & 4.38 & 0.59 & 3.2 & 5.7 & 0.094 \\ BFM [kg] & 13.64 & 6.16 & 4.0 & 31.8 & 0.908 \\ FFM [kg] & 63.46 & 7.75 & 45.0 & 79.2 & \(-\)0.046 \\ SMM [kg] & 36.04 & 4.65 & 24.7 & 45.5 & \(-\)0.073 \\ BMI [kg/m\({}^{2}\)] & 23.83 & 2.96 & 19.2 & 32.4 & 0.851 \\ PBF [\%] & 17.29 & 6.02 & 6.0 & 30.6 & 0.391 \\ Target weight [kg] & 74.92 & 7.41 & 62.1 & 93.2 & 0.379 \\ Weight control [kg] & \(-\)2.18 & 5.76 & \(-\)17.8 & 7.9 & \(-\)0.767 \\ BFM control [kg] & \(-\)3.14 & 5.18 & \(-\)17.8 & 4.9 & \(-\)1.082 \\ FFM control [kg] & 0.96 & 2.02 & 0.0 & 9.1 & 2.407 \\ BMR [Kcal] & 1740.70 & 167.32 & 1342.0 & 2082.0 & \(-\)0.044 \\ WHR & 0.85 & 0.07 & 0.7 & 1.0 & 0.529 \\ Visceral Fat Level & 5.08 & 2.91 & 1.0 & 14.0 & 0.846 \\ \hline \hline \multicolumn{6}{c}{Physiological characteristics} \\ \hline Distance [m] & 2412.61 & 377.16 & 1769.0 & 3115.0 & 0.007 \\ Power [W] & 112.05 & 50.78 & 42.0 & 227.0 & 0.407 \\ Calories [kcal] & 135.73 & 34.94 & 87.0 & 215.0 & 0.398 \\ S/M [strokes/min] & 27.20 & 3.23 & 20.0 & 34.0 & \(-\)0.203 \\ Pace/500 m [s] & 150.74 & 25.51 & 104.0 & 203.0 & 0.229 \\ HRavg [bpm] & 166.50 & 17.24 & 109.0 & 197.0 & \(-\)0.81 \\ HRmax [bpm] & 186.70 & 13.47 & 144.0 & 219.0 & \(-\)0.570 \\ \hline \multicolumn{6}{c}{Intensity of effort [s]} \\ \hline Zone 1: 90–108 [ppm] & 17.82 & 65.03 & 0.0 & 429.0 & 5.365 \\ Zone 2: 108–125 [bpm] & 52.97 & 105.85 & 0.0 & 474.0 & 2.932 \\ Zone 3: 126–144 [bpm] & 128.96 & 174.44 & 0.0 & 607.0 & 1.606 \\ Zone 4: 145–162 [bpm] & 149.83 & 164.25 & 0.0 & 603.0 & 1.154 \\ Zone 5: \textgreater{}162 [bpm] & 370.42 & 286.31 & 0.0 & 715.0 & \(-\)0.189 \\ \hline \hline \end{tabular} Note. * AS—skewness coefficient.  
\end{table}
Table 1: Descriptive statistics for the entire test group.
### Multi-Sensor Fusion  
Introducing assistant sensor data can improve the robustness of the SLAM system. Currently, for LiDAR-SLAM and Visual-SLAM, the most commonly used assistant sensors are encoder and Inertial Measurement Unit (IMU), which can provide additional motion data of the robot. SLAM systems [2; 5; 17; 18; 20; 19] with such assistant sensors usually perform better.  
In recent years, based on the works of LiDAR-SLAM and Visual-SLAM, some researchers have started to carry out the research of integrating such two main sensors [21; 22; 23; 24; 25]. In [21], the authors applied a visual odometer to provide initial values for two-dimensional laser Iterative Closets Point (ICP) on a small UAV, and achieved good results in real-time and accuracy. In [22], a graph structure-based SLAM with monocular camera and laser is introduced, with the assumption that the wall is normal to the ground and vertically flat. [23] integrates different state-of-the art SLAM methods based on vision, laser and inertial measurements using an EKF for UAV in indoor. [24] presents a localization method based in cooperation between aerial and ground robots in an indoor environment, a 2.5D elevation map is built by RGB-D sensor and 2D LiDAR attached on UAV. [25] provides a scale estimation and drift correction method by combining mono laser range finder and camera for mono-SLAM. In [26], a visual SLAM system that combines images acquired from a camera and sparse depth information obtained from 3D LiDAR is proposed, by using the direct method. In [27], EKF fusion is performed on the poses calculated by LiDAR module and vision module, and an improved tracking strategy is introduced to cope with the tracking failure problem of Vision SLAM. As camera and LiDAR becoming standard configurations for robots, laser-vision fusion will be a hot research topic for SLAM, because it can provide a more robust result for real applications.
## 2 System Overview
### UAC Modem  
The UAC modem adopts the ARM+DSP+FPGA architecture, which can realize dual-channel transmission and eight-channel reception. In this paper, in order to reduce energy consumption, only one channel of the communicator is used. However, the UAC modem must switch between sending mode and receiving mode through a switcher because it can only work in half-duplex mode. The underwater acoustic communication system parses the received signal and sends it to the Raspberry Pi through the serial port, which is further processed and forwarded to each module by the Raspberry Pi.
## 3 Performance Analysis
### Error Performance for Negligible Phase Noise
#### 3.1.2 Approximate ASEP Expression  
In order to avoid the cumbersome integration procedure in Equation (18), we proceed to an approximation. To this end, we exploit that for \(M\geq 4\), the conditional SEP in Equation (15) can be approximated by [5; 23]:  
\[p_{I}(I)\cong\lambda_{M}\text{erfc}\left(\frac{I\sqrt{\mu_{NZB}}\sin\frac{\pi}{ M}}{A_{q}}\right), \tag{19}\]  
where \(\lambda_{M}=1\); for BPSK schemes, the relationship still holds true provided that \(\lambda_{M}\)= \(1/2\). Hence, after expressing the complementary error function in Equation (19) by its Meijer-Gfunction [20] and averaging with respect to \(I\) through (Equation (07.34.21.0013.01) of Wolfram Function site in [13]), the approximated ASEP for each hop is obtained as:  
\[p_{I}\cong\frac{\lambda_{M}\vec{\tau}^{2}A_{\text{I,tot}\in\mathbb{R}}}{2\pi^{ 3/2}}\sum\limits_{(\mathbb{N}\text{ or }\mathbb{R})}2^{\pi+3}d_{\mathbb{R}(\mathbb{N}\text{ or }\mathbb{R})}B_{(\mathbb{N}\text{ or }\mathbb{R})}^{-\frac{\pi+3}{2}}G_{63}^{2.5}\left(\frac{16\mu_{NZB}(1+q^{-2 })^{2}\sin^{2}\frac{\pi}{M}}{(\varepsilon+\Omega)^{2}B_{(\mathbb{N}\text{ or }\mathbb{R})}^{3}}\right]\begin{array}{c}2-\vec{\tau}^{2},\\ 2\end{array}\frac{1-q}{2},\frac{2-q}{2},\frac{1-k}{2},\frac{2-k}{2},1\\ 0,\frac{1}{2},\frac{q-k}{2}\end{array}\right). \tag{20}\]  
Consequently, for \(N\) individual hops connected by \((N-1)\) DF serial relays, the ASEP can be expressed as [24]:  
\[\begin{array}{rl}p_{I,tot}&\cong\sum\limits_{i=1}^{N}\left[p_{I,i}\prod \limits_{j=i+1}^{N}\left(1-2p_{I,j}\right)\right]\\ &=\frac{\lambda_{M}}{2\pi^{3/2}}\sum\limits_{i=1}^{N}\left\{\Sigma_{i}\,\xi_{i }(\mu_{i,NZB})\prod\limits_{j=i+1}^{N}\left[1-\frac{\lambda_{M}}{\pi^{3/2}} \Sigma_{j}\,\xi_{j}(\mu_{j,NZB})\right]\,\right\},\end{array} \tag{21}\]  
where:  
\[\Xi_{i}=q_{i}^{2}A_{i(\mathbb{N}\text{ or }\mathbb{N})}\sum\limits_{( \mathbb{N}\text{ or }\mathbb{N})}2^{q_{i}+k_{i}-3}a_{kJ(\mathbb{N}\text{ or }\mathbb{N})}B_{i(\mathbb{N}\text{ or }\mathbb{N})}^{-\frac{q_{i}+k_{j}}{2}} \tag{22}\]  
and:  
\[\xi_{i}(\mu_{i,NZB})=G_{6,3}^{2.5}\left(\frac{16\,\mu_{i,NZB}(1+q_{i}^{-2})^{ 2}\sin^{2}\frac{\pi}{M}}{(c_{i}+\Omega_{i})^{2}B_{i(\mathbb{N}\text{ or }\mathbb{N})}^{2}}\right]\begin{array}{c}1-\frac{q_{i}^{2}}{2},\frac{1-q_{i} }{2},\frac{2-q_{i}}{2},\frac{1-k_{j}}{2},\frac{2-k_{j}}{2},1\\ 0,\frac{1}{2},\frac{q_{i}^{-2}}{2}\end{array}\right). \tag{23}\]  
For the special case, where \(p_{I,i}=p_{I,i}\), \(i\
eq j\), which means that \(\xi_{1}(\mu_{1,NZB})=\xi_{2}(\mu_{2,NZB})=\ldots=\xi_{i}(\mu_{i,NZB})=\xi(\mu_ {NZB})\) and \(\Xi_{1}=\Xi_{2}=\ldots=\Xi_{i}=\Xi_{s}\), we simply obtain:  
\[p_{I,tot}\cong\frac{\lambda_{M}\Xi}{2\pi^{3/2}}\,\xi(\mu_{NZB})\sum\limits_{i= 1}^{N}\prod\limits_{j=i+1}^{N}\left[1-\frac{\lambda_{M}\Xi}{\pi^{3/2}}\,\xi( \mu_{NZB})\right]=\frac{1}{2}\left[1-\left(1-\frac{\lambda_{M}\Xi}{\pi^{3/2}} \,\xi(\mu_{NZB})\right)^{N}\right]. \tag{24}\]
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 6 _P. ginseng_ in ROS-Mediated Diseases
### Antioxidant Activities of P. ginseng in Cancer
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cancer Cell** & **Ginseng/Ginsenosides** & **Mechanisms** & **Effect** & **References** \\ \hline
**Leukemia Jurkat** & & & ROS\(\uparrow\), MTP\(\downarrow\), & Stimulated mitochondrial & \\
**cells** & & & caspase-3/9\(\uparrow\), Bax/Bcl-2\(\uparrow\), & ROS that inhibited cell proliferation & [149] \\
**Acute** & & & Cyt C\(\uparrow\) & and induced apoptosis. & \\
**Lymphoblastic** & 20(S)-Ginsenoside & & ROS\(\uparrow\), MTP\(\downarrow\), Cyt C\(\uparrow\), & Generated mitochondrial ROS & \\
**Lymphoblastic** & & & caspase-3/9\(\uparrow\), LC3-I\(\uparrow\), & that inhibited autophagy & [150] \\
**leukemia cells** & & & LC3-II\(\uparrow\), Atg\(\downarrow\), Bclin-1\(\uparrow\) & and induced apoptosis. & \\  & & & Increased \(\gamma\)-ray-induced & \\  & & & apoptosis by enhancing & \\
**Lung cancer** & & & & \\
**NCI-H460 cell** & & & ROS\(\uparrow\), MMP\(\downarrow\), caspase-3\(\uparrow\) & intracellular ROS production & [151] \\  & & & & that reduced MMP and increased & \\  & & & & caspase-3. & \\  & & & & Reduced the formation of & \\
**Colorectal** & & & ROS\(\downarrow\), Bax/Bcl-2\(\uparrow\), & ROS which activated & \\
**cancer HCT-116** & & & capase-3\(\uparrow\), G1\(\downarrow\), S \(\downarrow\) & autophagy and antiproliferative & \\  & & & effects. & \\
**Colorectal** & & & & & \\
**cancer** & & & & ROS production activated the NF-\(\kappa\)B & \\  & & & & pathway that & \\  & & & & induced paraptosis. & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & & \\
**Colon cancer** & & & & & \\
**HCT-116 cells** & & Compound K & & Stimulated TRAIL-induced & \\  & & & & apoptosis by upregulating DR5 in & \\  & & & & & both autophagy in a dependent and & \\  & & & & ERK\(\downarrow\), P38\(\uparrow\), P33\(\uparrow\), DR5\(\uparrow\), & dependent manner. & \\  & & & & CHOP\(\uparrow\) & \\
**Hepatoma** & & & Caspase-3/9\(\uparrow\), cytosol Cat & Mitochondrial apoptotic & \\  & & & & & and ROS-accumulation- & \\
**HepG2 cells** & & & MTP\(\downarrow\), Bid\(\uparrow\), & \(\text{Bid}\)\(\uparrow\), Cyt C\(\downarrow\) & pathway-induced apoptosis. & \\
**Hepatoma** & & & & & \\
**HepG2 cells** & & & & & \\  & & & & & \\
**Hepatoma** & & & & & \\
**Hep93B cells** & & & & \\
**Breast cancer** & & & & & \\
**MCF-7 cells** & & Compound K & & COX-2\(\downarrow\), PGE2\(\downarrow\) & \\  & & & & \\  & & & & \\
**Breast cancer** & & & & & \\
**MDA-MB-231 cells** & & & & \\  & & & & \\  & & & & \\
**Breast cancer** & & & & \\
**MCF-7 cells** & & & & \\  & & & & \\
**Cervical** & & & & \\
**carcinoma HeLa** & & & & \\
**cells** & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\
**Bladder cancer** & & & & \\
**T24 cells** & Compound K & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 2: ROS-associated effects of ginseng and ginsenosides on cancer cells.
## Appendix A The Hermite Transform  
Hermite transform [38; 39] is an image model bio-inspired in the human vision system (HVS). It extracts local information of an image sequence \(I(X,t)\) at time \(t\), by using the Gaussian window:  
\[v^{2}(X)=\left(\frac{1}{\sigma\sqrt{\pi}}\exp\left(-\frac{X^{2}}{2\sigma^{2}} \right)\right)^{2}, \tag{1}\]  
where \(X=(x,y)^{\top}\) are the spatial coordinates.  
Some studies that suggest that adjacent Gaussian windows separated by two times the standard deviation \(\sigma\) represent a good approximation of the overlapping receptive fields in the HVS [40].  
Then, a family of polynomials, orthogonal to the window, is used to expand the localized image information yield the Hermite coefficients:  
\[I_{m,n-m}(X,t)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}I(X,t)D_{m,n-m}(X )dX, \tag{2}\]  
where \(m\) and \((n-m)\) denote the analysis order in \(x\) and \(y\) respectively, \(n=0\),..., \(\infty\), \(m=0\),..., \(n\).  
\(D_{m,n-m}(X)=D_{m}(x)D_{n-m}(y)\) are the 2D Hermite filters, which are separable due to the radial symmetry of the Gaussian window. Thus, the 1D Hermite \(D_{k}\) of \(kth\) order for the coordinate \(x\) is given by:  
\[D_{k}(x)=\frac{(-1)^{k}}{\sqrt{2k!}}\frac{1}{\sigma\sqrt{\pi}}H_{k}\left(\frac {x}{\sigma}\right)\exp\left(-\frac{x^{2}}{\sigma^{2}}\right), \tag{3}\]where \(H_{n}\left(\frac{x}{\sigma}\right)=(-1)^{n}\exp\left(-\frac{x^{2}}{\sigma^{2}} \right)\,\frac{d^{n}}{dx^{n}}\exp\left(-\frac{x^{2}}{\sigma^{2}}\right)\) represents the generalized Hermite polynomials with respect to the Gaussian function.  
To recover the original image \(I(X,t)\) an inverse Hermite transform is applied to the Hermite coefficients by using the reconstruction relation [38]:  
\[I(X,t)=\sum_{n}^{\infty}\sum_{m=0}^{n}\sum_{(X_{0})\in S}I_{m,n-m}(X_{0},t)\cdot P _{m,n-m}(X-X_{0}), \tag{10}\]  
where \(S\) is the lattice of sampling and \(P_{m,n-m}\) are the synthesis Hermite filters which are defined by:  
\[P_{m,n-m}(X)=\frac{D_{m,n-m}(X)}{V(X)} \tag{11}\]  
and \(V(X)\) is a weight function:  
\[V(X)=\sum_{(X_{0})\in S}v^{2}(X-X_{0})\
eq 0. \tag{12}\]
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 4 Smart Glove  
To demonstrate the potential of the one-single layer pressure sensors, the proposed sensor is integrated into a smart tactile glove (Figure 9a) in order to distinguish objects with different weights during a grasping task. We used a hot melt double-sided adhesive tape to attach the sensor onto the glove by heating it at 90 \({}^{\circ}\)C for 15 s (with an iron). Two electrode threads of the sensor were connected to two electrical wires of the Arduino Nano (5 V-16 Mhz) using a silver paste (from KLK Ltd., Jeonnam, Korea). As shown in Figure 9b, the hardware platform of experimental is an integrated circuit, composed of some main components such as Arduino, Bluetooth module, Micro SD Card, and a lipo-battery (3.7 V). Using a voltage divider circuit, the electrical resistance variation of the sensors has been converted into a voltage variation. Data are sampled and digitized and thus converted into digital values. For resolution reasons, mathematical mapping of voltage values between 0 to 5 V into digital values between 0 to 1023 (5/1023-0.005 V or 5 mV per unit) has been made by pre-calculating the actual data. It is calculated to take about 0.01 s (10 ms) to read an analog signal input, and a maximum reading rate is about 100 times per second. Subjects are instructed to grasp a paper water cup (Figure 9c) for an amount of time, and the electrical signal is continuously recorded. The weights of paper water cup are 110 g, 160 g, 210 g, and 310 g, respectively. Figure 9d shows the amplitude signal during the scenarios. The change in electrical resistance is found to be higher when the subject grasps the heavier object. From the results, it is clear that this one-layer pressure sensor could be used in soft, wearable sensors or actuator applications for control purposes.  
Figure 8: (a) Resistance change at different temperatures and (b) Comparison of the thickness with other studies.
## 4 Results and Discussion
### Performance of Polymer Curing Agent
#### 4.1.3 Waterproof Performance  
Test results of water-absorbing rate are shown in Figure 9.  
Figure 8: Test results of bonding strength of different polymer–cement ratios.  
Figure 9: Relationship between polymer–cement ratio and water-absorbing rate.  
In Figure 9, the water-absorbing rate of specimen is relatively large when polymer-cement ratio is relatively small. However, it gradually decreases and tends to be stable as the polymer-cement ratio increases. The reason is that when the polymer-cement ratio is relatively small, less polymer emulsion and more cement results in more hydration products with network structure. A small amount of polymer emulsion cannot completely fill the network structure, so the relatively small layer is generated on the polymer network structure. Thus, the water-absorbing rate of the specimen is relatively large. As the polymer-cement ratio continues to increase, the polymer emulsion is dominant in the polymer curing agent, causing the polymer material to react with the hydroxyl groups on the surface of the weak rock with a waterproof layer formed. Therefore, the water-absorbing rate gradually decreases until it tends to be stable.