esa-sceva/satcom-chunk-collection huggingface dataset:
# Decreasing the Rail Potential of High-Speed Railways Using Ground Wires
## 1 Introduction
The grounding types include lightning rod and arrester grounding, and working grounding. Devices are installed in Japanese stations to prevent excessive rail potential, which can be achieved through the equipotential bonding of up and down rails and decentralized grounding of rails to ensure the normal operation of the return current system. References [15; 16] utilized Matlab/Simulink to run a rail potential model of a railway system and compared the effects of different amounts of leakage conductance on the distribution of the rail potential. Reference [17] analyzed the influence on the rail potential distribution by changing the potential partition coefficient of high-speed railway rails. The rail potential problem has a very long research history [18]. For high-speed railways, due to the heavy loads, large short-circuit currents and high rail-ground leakage resistance, the rail potential can be considerably higher than that of conventional speed electric railways. High-speed railways normally adopt a 2 \(\times\) 25 kV auto-transformer (AT) power feeding system, whose traction network is of a multi-mesh type. For Chinese high-speed railways, the traction current return configuration and grounding system are similar to those used in France. However, the OCS structure in China mainly adopts a stitched simple catenary with larger tensions of the contact and messenger wires. To obtain the rail potential distribution, one must look to computer simulation programs. In this paper, firstly, a brief theoretical review of the rail potential problem is provided, and then a simulation program, TRANAS, is introduced. Some calculation results are presented, which are used to evaluate the effects of various suppression countermeasures including cross-bonding of tracks, inserting CPWs, grounding using catenary masts, laying bare buried ground wires and connecting to dedicated grounding grids.
## 2 The System
### Sensing System  
#### 2.2.1 Eye Movement Tracking  
Currently, four standard eye-tracking methods are used. Electro-oculography is the earliest tracking method used. This method is simple to use but has the disadvantage of poor accuracy. A scleral search coil method can improve accuracy, but its disadvantage is that it is invasive. The use of eye tracker technology improves the shortcomings of the above two methods. However, its disadvantage is that the user must wear the device on the head correctly and without deviation. Video-based pupil/corneal reflection combination is currently the most advanced and widely used method. The Tobii EyeX, which is the eye-tracking device used in this study, is one such device (Figure 3). It uses near-infrared light (800-1500 nanometers) to track the movement and gaze of the user's eyes. When the user's line of sight moves, the cursor will move to the corresponding position on the computer screen. A plug-and-play USB device records the subject's visual field and eye movements. The device only captures eye gaze data (not the user's face), so there are no privacy concerns. Figure 4 shows part of the user's captured gaze data (two-dimensional X- and Y-axes).  
Figure 4: Part of the user’s captured gaze data (two-dimensional X- and Y-axes).  
Figure 3: Tobii EyeX controller.  
Figure 2: The simulation system includes the steering wheel, accelerator, and brake pedal.
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 3 Results
### System Detection Accuracy
In Figure 7, the overall vision accuracy percentage across all trials is calculated and presented for each laptop. GATE1 and GATE2 emerged as the best-performing laptops, achieving an overall vision accuracy percentage of 70.2% and 71.3%, respectively. On the other hand, the lowest overall detection rate, 38.5%, was observed in the data for DELL2. A closer look at the differences between the higher- and lower-performing laptops revealed that GATE1 featured a silver case and silver screws, GATE2 had a black case and silver screws, while DELL2 had black cases with black screws. Additionally, it was noted that GATE1 had a mix of surface screws and deep screws, whereas COMP1 had no surface screws. DELL2 also had deeper screws located near other features that were being recognized as false detections. Both of these factors were identified as potential contributors to the differences in detection rates, and they required further investigation. It should be noted that these overall vision accuracy percentages are an average of particular laptop trials, and an individual trial could have yielded higher percentages. The vision accuracy percentage for all the individual trials can be found in S2 - Vision Accuracy.lskx. To further investigate the effect that light has on detection accuracy, a few high-performing trials were chosen at two different velocities (V10 and V15), and the lighting parameter was varied (nl and wl). Figure 8 displays the vision accuracy percentage broken down by laptop. For example, two trials for the Gate1 laptop (V10_S15_XL_nl and V15_S15_XL_nl) achieved vision accuracy of 95%. The results highlight that DELL2 and HP1 tended to be the laptops with the lowest accuracy amongst this subset of trials. Both DELL2 and HP1 demonstrated improved detection accuracy when additional lighting was \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Vision Accuracy by Velocity**} \\
**Trial Name** & **V5** & **V10** & **V15** & **V20** & **Average of All Trial Velocities** \\ \hline S15\_S\_nl & 48.7 & 58.1 & 41.2 & 29.7 & 44.4 \\ S15\_S\_wl & 37.8 & 62.7 & 54.3 & 38.6 & 48.3 \\ S15\_XL\_nl & 64.7 & 69.9 & 61.9 & 42.1 & 59.7 \\ S15\_XL\_wl & 65.1 & 72.7 & 68.7 & 58.4 & 66.2 \\ \hline S25\_S\_nl & 45.8 & 58.1 & 40.0 & 28.1 & 43.0 \\ S25\_S\_wl & 24.9 & 52.0 & 50.9 & 43.9 & 42.9 \\ S25\_XL\_nl & 60.8 & 68.8 & 61.7 & 47.6 & 59.7 \\ S25\_XL\_wl & 55.4 & 66.1 & 73.4 & 54.6 & 62.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average vision accuracy for all laptops broken down by velocity. introduced. Interestingly, two laptops experienced decreased accuracy when exposed to light, while all the other laptops either performed better or maintained their vision accuracy. Specifically, GATE1 and DELL1 were the only two laptops that showed a decline in their vision accuracy percentage. It should be noted that both of these laptops are a lighter color, and when subjected to additional light, they exhibited more false positives than increases in true positives.
###### Abstract  
The development of promising new high-performance materials, such as composite materials made of fibers in a polymeric resin (fiber-reinforced polymer, or FRP), has transformed the structural rehabilitation and upgrade industry for buildings and infrastructure. Conventional materials and construction techniques (e.g., strengthening with externally bonded steel plates, known as beton plaques, or steel/concrete jackets) have been supplanted by innovative ones. The most important emerging techniques involve the use of externally bonded composites as a quick and effective option for repairing and upgrading existing structures. Several issues regarding FRP-substrate interaction, mainly related to brittle failure mechanisms due to fracture phenomena, remain the focus of a great deal of research. This paper presents an overview of debonding behavior and its predictive modelling in externally bonded FRP systems.  
retrofitting; FRP; debonding phenomena +
Footnote †: journal: Applied Sci.  
10 May 2021  
Accepted: 7 July 2021  
Submitted: 8 July 2021  
**Problems:**  
Several experimental investigations have been performed on strengthened concrete elements [1; 2; 16; 17; 18; 19] and, more recently, on reinforced masonry members [3; 4; 20; 21]. These investigations have shown that the rupture of retrofitted existing elements is, indeed, not characterized by global failures (such as a failure in shear or in flexure) but by local ones, due to debonding phenomena.  
The mechanisms, or modes, of debonding have been investigated in various studies, from a theoretical point of view, in order to formulate reliable mechanical models, based either on identification of a direct Cohesive Zone Model (CZM) by means of shear interface stresses and slips [16; 17; 18; 19; 20; 21; 22; 23], or on the primary analytical relationship between the J-integral (first introduced by [PERSON]) and the cohesive traction [24; 25; 26; 27; 28].  
Recent studies on the direct evaluation of the interface law via a shear interface stress-slip diagram [20; 21; 22; 23] have been based on the predictive approximated nonlinear strain law (proposed by [PERSON] et al. [19]) in the stress transfer zone. The use of this method has led to the relevant dispersion of experimental longitudinal strains with respect to the predictive non-linear strain distribution [19; 20; 21; 22; 23].  
On the contrary, approaches based on the J-integral [24; 25; 26; 27; 28] are less approximate, due to a limited dispersion of the experimental J-integral values with respect to the predictive J-integral distribution.  
On the other hand, more accurate methods to predict debonding failure are still needed. The design procedures proposed in [12; 13; 14; 15] suggest conservative approaches which place significant limitations on the strain achieved (and, thus, the stress achieved) in the FRP material, to take into account debonding failure modes. The above-mentioned recent developments provide more thorough methods for predicting debonding via a cohesive law.
## 3 Results and Discussion
### Broadband Dielectric Spectroscopy
For the 298 K data series, the fitting parameters were found to be \(\sigma_{\text{max}}\) = 1.423(33), \(c_{\text{max}}\) = 1.536(40), and \(D\) = 0.248(13). As mentioned, the discrepancies between Equations (1) and (2) are caused by the assumption made in Equation (2) that there is no interaction between IL and EC and negligence of complex morphologies caused by high ion concentrations due to interactions between ions, EC molecules and interactions between IL and EC [34]. The temperature also plays a key role in ionic conductivity. Typically, ILs follow the Vogel-Fulcher-Tamman (VFT) equation [16] as follows:  
\[\sigma=\sigma_{\infty}\text{exp}\left(-\frac{\text{E}_{\text{a}}}{\text{k}_{ \text{B}}\cdot(\text{T}-\text{T}_{0})}\right) \tag{5}\]  
where \(\sigma_{\infty}\) is the ionic conductivity at infinity temperature, T\({}_{0}\) is the ideal glass-transition temperature or Vogel temperature, and E\({}_{\text{a}}\) is the activation energy per molecule. The data series corresponding to x\({}_{\text{IL}}\) = 1.0, 0.7, 0.5, and 0.15 are represented in Figure 5c and the VFT parameters for all samples are compiled in Table 6.  
Although the VTF equation fits the temperature dependence of the electrical conductivity of all the mixtures very well, the trend between VFT parameters and IL molar fraction is irregular, meaning that the VFT equation cannot describe the effect of IL concentration on ionic conductivity as also shown in other works [19,41]. Regarding ternary mixtures, their behaviour as a function of temperature is represented in Figure 5d. As expected, adding lithium salt to binary mixtures decreases ionic conductivity by increasing the viscosity of the system [37,38]. However, the addition of EC leads to an increase in conductivity of IL+lithium salt mixtures; remarkably, at 298 K, thus, the conductivity of ternary mixtures is more than twice that of the binary mixture without EC, as reported in previous studies [19] and comparable to other ternary mixtures with water [10,12,14], but avoiding the risks of water content in lithium batteries. The findings presented herein, in conjunction with the identified need for further investigation into their physicochemical properties, electrochemical performance, and electrodes compatibility suggest that these ternary mixtures possess considerable potential for application as next-generation electrolytes in advanced energy storage systems.
# Mechanisms and Models of Attenuation of Shock Waves through Rock Formations
## 4 Discussion
### Model Establishment
where \(\sigma_{s}\) is the maximum bearing stress of the support system of the roadway roof and \(\sigma_{0}\) is the impact strength of the rock layer of the roadway roof itself. Bringing Equation (1) and Equation (3) into Equation (4), the following can be obtained:  
\[\sigma_{A}\cdot\prod_{i=1}^{n}h_{i}^{-\eta_{i}}+\gamma h\left(1-\frac{r^{2}}{R^ {2}}\right)\leq\sigma_{s}+\sigma_{c} \tag{6}\]  
Since the calculation here is the surrounding rock force of the roadway support body system, the strength balance position calculated here is between the roadway roof support system and the non-supported body, so the size of R in the equation should be the sum of the anchor length \(l\) and the radius r of the roadway, and the left side of Inequality (4) can be converted as follows:  
\[\sigma_{A}\cdot\prod_{i=1}^{n-1}h_{i}^{-\eta_{i}}+\gamma h\left(1-\frac{r^{2}} {\left(l+r\right)^{2}}\right)=\sigma_{h_{n}}+\sigma_{r} \tag{7}\]  
where \(l\) is the length of the longest anchor or cable. When the inequality relationship of Inequality (8) is satisfied, i.e., the strength of the shock wave propagated to the roadway envelope is greater than the impact strength of the roadway envelope and support system, the roadway is damaged. Otherwise, the roadway will be stable. Inequality (8) becomes a criterion for testing whether the roadway will be damaged under the influence of a shock wave. \[\sigma_{A}\cdot\prod_{i=1}^{n-1}h_{i}^{-\eta_{i}}+\gamma h\left(1-\frac{r^{2}} {\left(l+r\right)^{2}}\right)\left\{ \begin{array}{l}\leq\sigma_{s}+\sigma_{ c}\\ >\sigma_{s}+\sigma_{c}\end{array} \right. \tag{8}\]  
According to the corresponding mechanical model, the attenuation of the stress of the shock wave after passing through the rock formation was analyzed. The attenuation characteristics of the shock wave after passing through the rock layer were analyzed by substituting different rock formations. The mechanical attenuation parameters associated with this article were obtained through numerical simulations; the attenuation rates of shock waves in actual rock formations were not measured due to equipment factors. In the future, our model can be confirmed by setting up several vibration monitoring probes in the rock formation by certain means.
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## 4 Numerical Simulations
### Reconstruction Results  
To recover the initial pressure \(\mathbf{f_{\star}}\), we apply the steepest descent method (Algorithm 1) with \(a_{\star}=0.8\) to the point-wise approximation \(\mathbf{h}\simeq\mathcal{W}\mathbf{f_{\star}}\) that is recovered in step one. Reconstruction results after 5 iterations are shown in Figure 5. The top row shows reconstruction results from simulated data and the middle row shows reconstruction results from noisy data, both using the correct sound speed \(\mathbf{c_{\star}}\) for the reconstruction process. One observes, that the reconstruction results from simulated as well as from noisy data are quite accurate. This demonstrates that the proposed algorithm is efficient, accurate and stable with respect to noise. In both cases, the whole reconstruction procedure takes about \(90\,\mathrm{min}\) on a standard desktop PC.  
The bottom row in Figure 5 shows reconstruction results using the wrong constant sound speed \(\mathbf{c}_{0}\) in Algorithm 1 (while data generation still uses the inhomogeneous sound speed \(\mathbf{c}_{*}\)). In this case, the reconstruction error is much larger, showing the relevance of integrating sound speed variations in image reconstruction. Finally, in Figure 6 we show reconstruction results with the previous Fourier method that has been derived in [31] under a constant sound speed assumption. The results with the Fourier method show a large reconstruction error and are very similar to the reconstruction result using the steepest descent method assuming constant sound speed. This demonstrates that the artifacts in both cases are due to the wrong wave propagation model, which further supports the importance of taking sound speed variations into account in FFD-PAT image reconstruction.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Digital Core Analysis
#### 3.3.3 X-Ray Computed Tomography (CT) Scan  
CT scanning is a non-destructive technique for detecting the internal structure of rocks [55]. The first CT scanner was introduced by Hounsfield in 1969 and became commercially available in 1972 [56; 57]. It was first used in the oil industry in 1980 [58; 59] to determine rock characteristics [60]. [PERSON] and [PERSON] [61] used a low-power CT scanner to investigate the severity and location of formation damage. [PERSON] [62] evaluated the damage caused by injecting brines of different mineralization using a CT scanner. He observed a greater degree of damage within cores that are injected with highly mineralized brine. [PERSON] et al. [63] evaluated the change in core porosity before and after the particle transport damage using a modified medical CT scanner. [PERSON] et al. [64] used CT scans to study the damage occurring in inhomogeneous and homogeneous porous media during injection and found inhomogeneous damage patterns in homogeneous core samples. [PERSON] and [PERSON] [65] used a modified core repulsion device and CT scan analysis to study the extent of formation damage caused by various aggravators. [PERSON] et al. [66] used a nanoVoxel-4000 series high-resolution X-ray 3D microscope to perform high-resolution imaging of the samples to study the interaction between guanine-dine gum-breaking fracturing fluid and formation. The results show that the guanidine gum-breaking fracturing fluid makes the core less stable, and the clay minerals absorb the water and swell. Eventually, this blocks the pore throat, which reduces the pore space of the core, making it less connected. Figure 4 shows the nanoVoxel-4000 high-resolution X-ray 3D microscope CT scan results of the rock samples in Well Ke 206 conglomerate reservoir before and after the exposure to damage caused by guar gel-breaking fracturing fluid. Figure 4a-c represents the core before and after flooding by water and guar gel-breaking fracturing fluid. Figure 4d-f represents the pore size measurement of the core before and after the flooding by water and guar gel-breaking fracturing fluid. The size of the pore radius became smaller after flooding by guar gel0 breaking fracturing fluid. Figure 4g-i representing the 3D reconstruction of CT images from various sections following the core axis that was performed to analyze the changes done to 3D pore distribution.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 6 Conclusions  
Wireless sensor networks are deployed to support a variety of precise monitoring applications in smart factories, and require energy-efficient and no-entropy-loss data aggregation. In this paper, we propose a data aggregation scheme based on the overlapping rate of sensing area, named AggOR. In the transmission hierarchy diagram, some candidate parent nodes as well as appropriate neighbor nodes, whose overlapping rates of sensing area are not smaller than a preset threshold \(OR_{T}\), may be selected as the candidate gathering nodes. It guarantees that the sensor data in a gathering area are extremely correlative, and there exist a large amount of redundant data to be cleaned. Member nodes transfer their data to gathering nodes through one hop, and only duplicated data are removed by aggregation, ensuring a short end-to-end delay and a high data accuracy. A large quantity of experiments on OPNET modeler show that AggOR has a better data accuracy and a shorter delay than compared schemes, while keeping similar network lifetime.  
Figure 12: The numbers of independent nodes in three scenarios. (**a**) Scenario SP; (**b**) Scenario SC; (**c**) Scenario SS.  
Figure 13: Sketch maps of three scenarios. (**a**) Scenario SP; (**b**) Scenario SC; (**c**) Scenario SS.  
However, the specific relation between \(OR_{T}\) and network density still requires further study. In addition, considering the occurrence of multiple events at the same time [27], how to optimize multi-event data collection by analyzing overlapping sensing area is another research topic for the future.  
We gratefully acknowledge the support from the National Natural Science Foundation of China (61502320, 61373161 & 61572060), Science & Technology Project of Beijing Municipal Commission of Education in China (KM201410028015), Youth Backbone Project of Beijing Outstanding Talent Training Project (2014000020124G133), 973 Program (2013 CB035503), CERNET Innovation Project (NGII20151004, NGII20160316), Beijing Advanced Innovation Center for Imaging Technology, Cultivation Object of [PERSON] Scholar of Capital Normal University and Science Foundation of Shenzhen City in China (JCYJ20160419152942010).  
[PERSON] conceived and designed the study; [PERSON] performed the experiments and wrote the paper; [PERSON] and [PERSON] supervised the research; [PERSON] revised the paper.  
The authors declare no conflict of interest.
## 3 Experimental Results
To avoid this phenomenon, the frequency range should be expanded to ensure that the frequency of interest is within the measured frequency, when modeling the transfer function and calculating the filter coefficients. The theoretical results are the off-line simulation results based on measured data. Figure 5a,b shows that the theoretical results are in good agreement with the experimental results. However, the experimental noise reduction in the proposed SFR-ACC algorithm is smaller than the theoretical noise reduction, for two reasons. On one hand, the robustness of this algorithm is determined by both the ACC algorithm and the LS algorithm, which leads to sensitivity to environmental disturbances. On the other hand, the consistency of the secondary loudspeaker array is poor.