esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The State-Dependent Constitutive Model for Saturated Sand
### State-Dependent Plastic Modulus  
The plastic modulus \(K_{P}\) in this paper is defined as follows:  
\[K_{p}=\frac{dq}{de_{d}^{p}}=\frac{hGe^{m\psi}}{\eta}\big{(}Me^{-m\psi}-\eta \big{)} \tag{6}\]where \(dq\) and \(de_{d}^{p}\) are the deviatoric stress increment and plastic shear strain increment. Where \(n\) is a material constant, \(h\) is changed by the density of the soil. This variable is simplified as follow:  
\[h=h_{1}-h_{2}e \tag{7}\]  
The value of \(h\) varies linearly with the void ration \(e\), where \(h_{1}\) and \(h_{2}\) are two material constants about shear hardening. Equation (4) considered \(\psi\)-dependency on the basis of the previous plastic modulus by [PERSON] et al. [23], which can unify the models of both strain-hardening and strain-softening behavior of remaining sands in different densities, respectively. The equation before suggests that the plastic modulus is subject to the difference of current \(\eta\) for a 'virtual' peak stress ratio \(Me^{-m\psi}\), which keeps changing during the shearing of the soil [13]. As a consequence, the key following features associated with the plastic hardening behavior of sand in shear can be reproduced:  
1. For the initial state of sand in each horizon, \(K_{P}=\infty\) at \(\eta=0\) which is successive with the behavior of sand showing \(de_{q}^{p}=0\) induced by a tiny but nonzero \(d\eta=0\)[24];
2. For the initial state of sand in each horizon, \(K_{p}=0\) at the critical state (\(\eta=M\) and \(\psi=0\)), because of the term \(d\eta/de_{q}^{p}\);
3. For loose sand, by \(\eta\) always smaller than \(Me^{-m\psi}\), during the entire process of shearing. \(K_{p}>0\) and the sand is in a strain-hardening state.
4. For relatively dense sand, the formulation allows a smooth transition from \(K_{p}>0\) to \(K_{p}<0\), in which the sand changes from strain-hardening state to strain-softening state, when \(\eta<Me^{-m\psi}\) and \(\eta>Me^{-m\psi}\), respectively.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 4 Circuit Design  
### The Basic Circuit Unit Design of Gaussian Convolution  
According to Equations (1) and (2), Gaussian convolution basically consists of Gaussian multiplication and addition operations. In this presented circuit, the multiplication is implemented by Gilbert Gaussian multipliers. Due to the current mode output, the addition operation is simply realized by connecting the multipliers' outputs together, according to [PERSON]'s current law. The Gaussian convolution unit is illustrated in Figure 5[15], where the numbers (from 1 to 3) means the different spread factors. According to [21], it is easy to prove that the Gilbert Gaussian circuit works as a multiplier between the tail current and the Gaussian function, using the exponential transfer characteristic of the subthreshold region of transistors. Thus, the tail current could be replaced by the pixel output to perform the Gaussian multiplication on pix data. Because of the current-mode input, the active pix sensor (APS) architecture could be used as the front end. The differential voltage input of the multiplier is used to tune the scale or the spread factor \(\sigma\) of the Gaussian kernel function.  
Figure 4: The model of analog-to-Information processing (AIP) architecture in fully flexible system.  
Once the transistors' parameters are decided, a default \(\sigma\) is determined. In this work, the multiplier's input-output relation is simulated to be:  
\[I_{\text{out}}=I_{\text{in}}\times\exp\Bigl{(}-6.6\ \times\ \Delta V^{2} \Bigr{)}. \tag{5}\]  
If we need an arbitrary \(\sigma\) to perform multi-scale filtering, we only need to calculate the input \(\Delta V\) by:  
\[\Delta V\ =\ \frac{1}{\sqrt{13.2\sigma}}.x \tag{6}\]  
By substituting Equation (6) to (5), it is easy to see the relation transform to:  
\[I_{\text{out}}\ =\ I_{\text{in}}\ \times\ \exp\Bigl{(}-\frac{x}{2\sigma^{2}} \Bigr{)} \tag{7}\]  
The \(x\) is an integer in \([-n,\ n]\), representing the corresponding position in one-dimension convolution mask. Its value range is in proportion to the mask size. This transformation is easy to be generalized to the two-dimensional (2-D) situation. When the factor \(\sigma\) and the position of the multiplier are decided, the input voltage \(\Delta\)V can be calculated via Equation (6). Therefore, it is easy to implement on circuit via on-chip memory and DAC, which have been demonstrated on flexible technology. The output is read out in voltage to match the input of MLP.
# Exploring the Effect of Virtual Environments on Passive
## 2 Related Works
### Consistency between Real and Virtual Environments  
With the recent and ongoing advances in sensing technologies, developments that enhance users' perceptions in the interactive VR environment have become more feasible [10]. Therefore, many researchers have considered system configurations that evaluate the participant's sense of presence when interacting in virtual environments [11]. In one notable piece of representative research, an interactive locomotion technique called redirected walking was proposed for virtual environments to allow users to walk in a virtual space larger than the real walking space [12]. In this study, they attempted to manipulate participants in the virtual environment into walking and interacting along an arc while giving the perception of walking straight. Another useful study showed a method for evaluating users' perception of virtual objects using heterogeneous augmented reality (AR) devices to develop interactive AR content [3]. They divided several parameters related to the user's perception such as color and scale. In the result, participants in the scale perception experiment thought the large virtual object was equal in size to the small real object. People tended generally to perceive the virtual object as small and to underestimate its dimensions. [PERSON] et al. proposed the effects of an \"out of body\" tactile illusion to create feedback using vibration devices in the case of dynamic virtual objects detached from the body [13]. Their approach can be applied to generate phantom tactile sensations that seem to arise from an external virtual object. More recently, many researchers have attempted to align different physical spaces and to adapt the teleported avatar to remote sites for collaborative environments [14]. [PERSON] et al. introduced an adjustment of the position of the teleported avatar into the local space with the corresponding information in teleconference environments [1]. Comprehensive research in terms of developing passive haptic feedback in a more economical way has yet been conducted. Our work was designed to explore the effects of passive haptic parameters on the user's perception, comparing actual and virtual objects in relation to transformation. Specifically, in our paper, we investigate the correla  
Figure 1: An example of passive haptic perception with an immersive virtual reality environment. This figure presents a scene in which a user interacts with a virtual object using a head-mounted display. Then, the user determines which among different-sized virtual objects feels the same comas the real object. Furthermore, VR content should create the feeling of being in a virtual environment; thus, the virtual object must be appropriately transformed (i.e., the scale of the virtual object must be adjusted to increase the level of presence).  
tion in visual perception between real and virtual objects, aiming to determine whether the participants felt the same way within a certain range to give sufficient sensory stimulation.
# Continuous-Wave THz Imaging for Biomedical Samples
## 2 CW THz Single-Point Scanning Imaging
### CW-THz Near-Field Microscopy Imaging
The resolution depended on the size of the focused THz beam spot, and it broke the Abbe diffraction limit. The sub-wavelength structural information in the tissues was clearly viewed from the THz images, such as veins. The THz intensity of the leaf veins was greater than the surrounding area, which may be related to the difference in water content, density, and tissue structure.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 2 Preliminaries  
In this section, we will introduce the threat model used in this paper and review some basic definitions concerning the Chebyshev polynomial, chaotic maps, and PUF.
# The Sonic Resonance Method and the Impulse Excitation Technique: A Comparison Study
## 3 Results
The test scores \(t\) for different samples were calculated according to the equation  
\[t=|f_{IET}-f_{SRM}|\sqrt{\frac{n}{s_{IET}^{2}+s_{SRM}^{2}}}, \tag{3}\]  
where \(s_{IET}\) and \(s_{SRM}\) are the standard deviations. Because all t-test scores \(<t_{\text{crit}}=2.07\), the mean values obtained by SRM and IET can be considered as equivalent. The differences between the measurement results can be considered in the scope of the measurement errors. To compare the deviations of measured data, [PERSON]'s test was employed [25; 26]. The F-test score was calculated for degrees of freedom \(k_{SRM}=n_{SRM}-1=11\) and \(k_{IET}=n_{IET}-1=11\) for the significance level \(\alpha/2=0.025\), for which \(F_{\text{crit}}=3.47\). The F-test score was calculated according to the formula:  
\[F=\frac{s_{1}^{2}}{s_{2}^{2}}, \tag{4}\]  
where \(s_{1}\) and \(s_{2}\) are standard deviations (\(s_{IET}\) and \(s_{SRM}\)) and \(s_{1}>s_{2}\), therefore \(F>1\). The F-test scores (see Table 2) were \(<\)\(F_{\text{crit}}=3.47\); therefore, it can be considered that standard deviations of SRM and IET are very close to each other. Consequently, both methods are equivalent for measuring the resonant frequency. The comparative tests confirmed that SRM and IET are equivalent methods that give identical values of the resonant frequency. This is also shown graphically in Figure 3. The relationship between resonant frequencies measured by SRM and IET for different samples must be presented by the line with a slope = 1. As can be seen, the experimental points are lying on the line with a slope 0.9993 \(\approx\) 1, which confirms the equivalence of the SRM and IET methods. The data for samples 6 and 9 are very close to each other and merge to one point in the graph. Figure 3: Dependence of resonant frequencies for samples measured by IET and SRM. [PERSON]'s moduli calculated from measured resonant frequencies (by SRM and IET) for the tested samples (Table 2) are given in Table 3, where they are compared to [PERSON]'s moduli for similar materials from the literature. It can be seen that [PERSON]'s moduli obtained by SRM and IET are close to each other, and also are in good agreement with already published results. The differences are mainly caused by different porosity and chemical composition.
## 5 Fragility and Leakage Risk Assessment of the NCS
### Fragility Assessment Results  
#### 5.2.1 Probability Distribution of the Pressure Capacity of the NCS  
To perform the fragility assessment, it is necessary to clarify the probability distribution of the pressure capacity of the NCS. Figure 13 plots the frequency histogram and the fitted distribution curve of the pressure capacity. It can be found that the pressure capacity of the NCS under different degrees of liner corrosion is in good agreement with the lognormal distribution. Moreover, the Lilliefors test is used to further examine whether the lognormal distribution is persuasive [36]. The results show that the pressure capacity of the NCS under all liner corrosion conditions does not reject the hypothesis of obeying a lognormal distribution at the 5% significance level. In summary, it is reasonable to use a lognormal distribution to characterize the pressure capacity of the NCS under liner corrosion conditions.  
Figure 12: Flowchart of the automated script for the fragility assessment.  
Figure 13: Probability distribution of the pressure capacity.
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 5 Data Analysis
### Prediction
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Arm Cir. Mov. & 0.88 & 0.92 & 0.90 & 0.90 \\ \hline Walking & 0.94 & 0.97 & 0.95 & 0.96 \\ \hline Grasping & 0.92 & 0.95 & 0.94 & 0.94 \\ \hline Leg Flexion & 0.90 & 0.93 & 0.92 & 0.92 \\ \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics. First, Random Forest is known for its robustness, and tends to be less prone to overfitting than models like neural networks. This feature may explain its superior performance in analysis. In addition, Random Forest is more interpretable than neural networks, allowing a better understanding of the features that significantly influence predictions. Another aspect to consider is training time--neural networks often require longer training times and more computational resources than Random Forest. These factors lead to the conclusion that Random Forest is an excellent choice for asset classification. Considering its characteristics compared to a logistic regression model, which represents another type of machine learning algorithm, the validity of the choice can be confirmed in relation to the type of movements examined. In particular, several advantages emerge related to the ability to manage nonlinearities, provide estimates of the importance of variables and tolerate missing data, as well as versatility. The management of nonlinearities is particularly effective with Random Forest, which manages data with nonlinear relationships and complex interactions between variables. This is crucial for motion detection, where patterns can be highly non-linear. In contrast, logistic regression, being a linear model, can struggle to capture complexities in motion data. Another important aspect is robustness and accuracy. Random Forest, being a set of decision trees, tends to be more robust and less prone to overfitting than individual models. This leads to greater accuracy in forecasting. On the other hand, logistic regression can be more susceptible to overfitting, especially with complex, non-linear data. Random Forest provides an estimate of the importance of variables, allowing one to identify which characteristics of movements (such as grip, arm movement, leg flection, walking) are most relevant to predictions. Logistic regression, while it can provide coefficients for variables, does not offer the same clarity and interpretability in relation to relative importance as Random Forest. When it comes to handling missing data, Random Forest is more tolerant, and can handle it more effectively during training. Logistic regression, on the other hand, requires the more careful management of missing data, such as imputation, which can introduce bias. Finally, versatility is another point in favor of Random Forest, which can be used for both classification and regression problems, offering more flexibility for any future extensions of the project. Logistic regression, on the other hand, is limited to binary or multi-class classification problems.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### Static PPP Results
#### 4.4.1 Positioning Accuracy  
Taking IGS daily coordinates as the reference value, the coordinate difference between the estimated coordinates with PPP and the IGS daily coordinates was calculated. The coordinate error series of GPS, GLONASS, and GPS+GLONASS modes on N, E, and U components of SOD3, WTZR, and SPTU are shown in Figure 12.  
As shown in Figure 12, the GLONASS coordinate error series of SOD3 is as steady as that of GPS, while the coordinate error of WTZR and SPTU estimated with GLONASS observations is much more dispersed than other estimate modes. In addition, the magnitude of the error series on U components of three positioning modes at the SPTU station is larger than at the other stations.  
The RMSE of N, E, and U components, together with the 3D RMSE for all the stations, are shown in Figure 13. The mean RMSE of N, E, and U components, together with the 3D RMSE for each network, are calculated and listed in Table 9.  
Figure 13 and Table 9 illustrate the RMSE of coordinates estimated with PPP. The GLONASS estimated coordinates have comparable accuracy with GPS in the high latitude network. The accuracies on N and E components are 13.62% and 0.87% better than that of GPS, but the accuracies on U and 3D components are 2.83% and 1.37% worse than that of GPS. GPS+GLONASS presents the best coordinate estimates in the high latitude network. The percentages of improvement over GPS on N, E, and U components are 18.78%, 10.37% and 7.87%, respectively, and the 3D positioning accuracy is 9.35% better than that of GPS. GPS stand-alone mode presents the best coordinate estimates except for the E component in the middle latitude network. The coordinate accuracy on the E component of combined GPS+GLONASS constellations has an advantage of 2.99% over GPS. The accuracies of GLONASS on N, E, and U components are worse than that of GPS, at 19.83%, 21.77%, and 20.40%, respectively. The 3D positioning accuracy of GPS+GLONASS is 1.52% lower than that of GPS. GLONASS shows the worst coordinate estimates among the three positioning modes in the low latitude network. The 3D positioning accuracy is 7.58% lower than that of GPS; however, the 3D positioning accuracy improvement of GPS+GLONASS is 11.40% over GPS. The positioning performance of PPP with different modes in different latitudes is basically consistent with that of the DD network solutions.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 7 Simulation Results and Discussions
The proposed RLPC schemes are still better than the corresponding ARQ schemes. That makes sense since a bigger \(W\) means the transmitter could send more packets during a transmission; it reduces the number of ACKs. There is a turning point for the J-ARQ scheme. This is because \(N_{g}\) will decrease with the increase in group size \(W\). When \(W=10\), \(N_{g}=4\) and it turns to \(N_{g}=3\) for \(W=12\). The throughput efficiency of the J-ARQ scheme decreases as \(N_{g}\) becomes smaller since it leads to more idle time. Figure 5 shows the impact of transmission distance on throughput efficiency. Naturally, the throughput efficiency decreases with the increase of \(d\), since it leads to an increase in propagation delay \(T_{d}\). The performance of the RLPC-ARQ scheme is always better than the SR-ARQ scheme. For the J-ARQ scheme, the throughput efficiency becomes larger at \(d=5000\) m because \(N_{g}\) becomes larger. The transmitter sends two group packets during a round-trip time with \(d=4500\) m, while it sends three groups with \(d=5000\) m. The same situation also occurs at \(d=7000\) m. For the RLPC-J-ARQ scheme, the performance continuously decreases since we only consider that two blocks are transmitted simultaneously. However, its throughput efficiency is still better than that of the J-ARQ scheme when \(d\) is less than 7000 m.  
Figure 3: Throughput efficiency as a function of the number of source data \(W_{s}\). Finally, we analyze the impact of steady-state probability \(\pi_{0}\), which reflects the effect of UWA channels. Figure 6 illustrates the throughput efficiency as a function of steady-state probability \(\pi_{0}\). From Figure 6, we find the performance of all schemes will deteriorate with the increase of \(\pi_{0}\). A larger \(\pi_{0}\) means the channel is more likely to be bad. The performance gain between with RLPC and without RLPC also increase as \(\pi_{0}\) increase. This is because the RLPC schemes under the bad channel could improve the successful decode probability by increasing the transmitted packets to reduce the retransmission. However, the conventional ARQ schemes need to retransmit many times. Thus, the superiority of RLPC schemes is more reflected in the case of poor channels. After all, the conventional ARQ scheme also does not need to be retransmitted when the channel is good. Figure 4: Throughput efficiency as a function of the number of group packets \(W\). Figure 5: Throughput efficiency as a function of transmission distance \(d\). This paper proposed two ARQ schemes with RLPC for UWA communications. The limitations of the proposed method are as follows. First, the sender knows the transition matrix of the channel state in advance, which needs to do a lot of experiments. Second, the proposed methods were based on the DP algorithm. The computational complexity is very high with a large system state. Thus, it is better to solve the problem with deep learning method.
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
## 2 Experimental Details  
### Materials  
Transparent PET was purchased from Suzhou Aokai Polymer Material Co., Ltd. (Suzhou, China) (Thickness: 0.025 mm). Graphene suspension was purchased from xfnano (diameter: 1-5 \(\upmu\)m; thickness: \(<\)10 nm, Suzhou, China). Anhydrous ethanol (C\({}_{2}\)H\({}_{5}\)OH, analytical purity, Chengdu Kolon Chemical Co., Ltd., Chengdu, China), sodium dodecyl sulfate (SDS, 92.5-100.5%, Aladdin Industries, Shanghai, China), and deionized water were used as received.
## 4 Advanced In-Vehicle Displays
### In-Vehicle Display of Roadway Conditions  
With the development of highly advanced automobile technologies, such as connected vehicles, IVISs have also been proposed as a potential alternative, or supplement, to road signage [23; 79; 80; 81]. Compared to conventional signs, IVIS displays have the advantages of being less susceptible to poor weather conditions and presenting messages that are tailored to traffic conditions (current and anticipatory) as well as driver information needs. For example, lodging information can be displayed more frequently to a driver during evening vs. daytime hours. However, only a small number of studies have been conducted to guide the presentation of in-vehicle displays of roadway conditions. In one study, [PERSON] and his colleagues found that in-vehicle messages, such as warnings about \"icy roadway\" and \"accident in lane\", were much more effective when presented as redundant information, in addition to road signs, than when presented alone [56]. [PERSON] and colleagues examined the effectiveness of in-vehicle displays of traffic light notifications and found that notifications presented 8 to 12 s before arriving at an intersection reduced the frequency of drivers running yellow lights [79]. In a study by [PERSON] & [PERSON], drivers were provided with in-vehicle speed limit information [80]. Although the in-vehicle presentation did not lead to significant improvements in driver speed control, drivers rated the in-vehicle information as favorable and helpful when following an unfamiliar route. In-vehicle presentation of information about surrounding traffic has also been found to improve driver anticipatory behavior in both manual driving [82] and driving with automation [83]. As compared to receiving takeover requests and automation capability information, drivers would be more likely to take over an automated vehicle when also provided with surrounding traffic information [83]. This finding highlights the importance in considering communicating roadway information to cognitively engage drivers in automated driving. The questions are when and how to present such information. For example, how frequently should roadway information be presented, and what roadway information (safety-critical vs. non-safety-critical) should be presented.