esa-sceva/satcom-chunk-collection huggingface dataset:
# Viscoelastic Strains of Palaeozoic Shales under the Burger's Model Description
###### Abstract  
This article presents the results of creep studies of Palaeozoic shales from the Baltic Basin in which the exploitation of shale gas in Poland was planned. Knowledge of instantaneous and long-term properties investigated in triaxial stress conditions is important from the point of view of exploitation techniques related to hydraulic fracturing. Rheological phenomena also play an important role in the analysis of the initial stress in shales, the knowledge of which is indispensable in the hydraulic fracturing process. The tests were carried out on samples representing four siltstone-claystone lithostratigraphic units occurring in the Baltic Basin. The studies and analyses were aimed at determining the character of creep in shales, selection of the appropriate rheological model for the analyzed rocks, and determination of the threshold of the linear creep under triaxial compression conditions. An original approach together with analysis results are presented here, which enable the separation and monitoring of shear and volume creep effects, and on this basis, the determination of the significance of the contribution of volume creep in the entire creep process. A relatively simple methodology for determination of the parameters of the Burgers model using this division is presented. The original value of the article is also due to the test results themselves and the parameter values of the analyzed model for triaxial creep of shales, which are not numerous in the literature. The investigations were performed at various loading levels in relation to the triaxial strength of the shales. Depending on the load, at its low values up to 0.7 (\(\sigma_{1}-\sigma_{3}\))\({}_{\text{max}}\), creep had a determined character and did not show features of progressive creep. The linear creep threshold was also analyzed in this range. The loading level of 0.7 (\(\sigma_{1}-\sigma_{3}\))\({}_{\text{max}}\) was the limit of linear creep. Exceeding this load resulted in the loss of the linear character of creep, which in consequence lead to the subsequent third creep phase ending with rock damage. Parameters of the [PERSON]'s model for gas shales from the Baltic Basin (northern Poland) were identified. There are significant differences in the behavior of shales depending on the lithostratigraphic unit from which the samples were collected. The mineral composition of the shales also influenced their behavior.  
shale gas; creep; Burgers model; triaxial stress; Baltic Basin 2023
## 6 Computational Experiments
### Comparison with Actual Layout  
The actual layout of the hospital is depicted in Figure 6, and the total movement distance and total closeness rating score are easy to calculate, 20,397,091.34 and 34.8, respectively. The comparison is showed in Table 13. In terms of the two objectives, our method can improve upon the actual layout by up to 37.71% and 83.91%, with an average of 20.96% and 60.16%. This result means that our method can significantly reduce the unnecessary movement of patients, thus improving their medical experience and relieving their burden. The solution with minimal total movement distance is decoded into a layout in Figure 7. As we can see, the biggest difference is that the distance between the obstetrics and gynecology department and the B-ultrasound room, and the distance between the laboratory and the radiology department are reduced, between which the flow of patients is relatively high. As for closeness, the layouts obtained by our method are also better than the actual layout in terms of cleanness, pollution, noise, and other qualitative requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Total Movement Distance** & **Total Closeness Rating Score** \\ \hline Actual layout & & 20,397,091.34 & 34.8 \\ \hline Results of our method & Best & 12,705,847.76 & 64 \\  & AVG & 16,121,966.65 & 55.7368 \\ \hline Improvement & Best & 83.91\% & 60.16\% \\  & AVG & 37.71\% & 20.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison with actual layout.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Standard NSGA-II** & \begin{tabular}{c} **NSGA-II with a Local Search** \\ **Performed in Every Iteration** \\ \end{tabular} & \
\begin{tabular}{c} **NSGA-II with** \\ **Adaptive Local Search** \\ **Search** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Search** \\ \end{tabular} \\ \hline Standard NSGA-II & 0 & 0.095238095 & 0.047619048 & 0.142857143 \\ NSGA-II with a local search performed in every iteration & 0.157894737 & 0 & 0.19047619 & 0.348370927 \\ NSGA-II with adaptive local search & 0.157894737 & 0.095238095 & 0 & 0.253132832 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Convergence metric.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total Movement Distance** & **Total Closeness Rating Score** \\ \hline Actual layout & & 20,397,091.34 & 34.8 \\ \hline Results of our method & Best & 12,705,847.76 & 64 \\  & AVG & 16,121,966.65 & 55.7368 \\ \hline Improvement & Best & 83.91\% & 60.16\% \\  & AVG & 37.71\% & 20.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison with actual layout.
## 4 Experimental Results  
In this section, the DCGAN-based augmented WBMs and CAE-based augmented WBMs were found through the CNN described in Section 3.5, and then the classification accuracy and PGI of each model were used to compare and analyze the performance of the generative model.  
Our experimental environment was Windows OS ([PERSON], Microsoft, WA, USA) with an Intel i9-10920 \(\times\) 3.60 GHz CPU, GeForce RTX 3060 GPU, and 64 GB of memory. The models were trained on Keras 2.5.1 (with Tensorflow 2.5) using Python 3.8.5.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction  
### Acceleration Sensors  
An accelerometer is a device that measures an object's proper acceleration relative to an observer in free fall. For example, an accelerometer at rest on the surface of the Earth will measure an acceleration of approximately 9.81 m/s\({}^{2}\).  
Accelerometers are used in industry, consumer products, and science and technology. They are used for navigation and stabilizing systems aboard aircrafts, rockets, and unmanned aircraft vehicles. In consumer electronics, they are used inside smartphones and video cameras to help stabilize and detect the orientation of these devices. Industry applications of accelerometers include the vibration detection of industrial machinery such as motors or conveyors. An essential application of accelerometer and vibration measurements is detecting ground movement, especially earthquakes.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 5 Performance Evaluation
### Simulation Results
#### 5.2.2 Success Rates of Preamble Transmission and Random Access  
Figure 8 shows the RA performance during the 2000 RA cycle. In Figure 8a, it can be seen that the baseline and ACB and BO schemes showed a nearly zero preamble transmission rate in high access intensity. Due to the preamble codeword expansion, the CeRA schemes showed outstanding performance in respect of the preamble transmission. The legacy CeRA method showed a larger number of successful preamble transmissions than the proposed CeRA-eSP method owing to the low preamble collision rate of the legacy CeRA method. This is because legacy CeRA utilizes 2916 preamble codeword sets without considering preamble codeword utilization. Despite the number of successful preamble transmissions being larger than the CeRA-eSP method, the BS could not allocate PUSCH resources to all successful preamble codewords owing to PUSCH resource limitations. Therefore, Figure 8b shows that the proposed CeRA-eSP method achieved a larger number of successful RAs than the legacy CeRA method. Due to the proposed CeRA-eSP method selecting the optimal preamble codeword set by considering PUSCH resource limitations, a higher number of successful RAs can be achieved. In the case of the baseline method and the ACB and BO method, the simulation results showed that the number of successful RAs was lower than the CeRA schemes in high-access-intensity situations as the preamble transmission rate was low.  
Figure 7: (**a**) Average delay for different consistency. numbers of devices and (**b**) RA failure rate for different numbers of devices.  
In both the legacy CeRA and proposed CeRA-eSP methods, fluctuations occurred at certain points as the access intensity increased. The fluctuation point occurred when the penalty owing to a lack of PUSCH resources was reversed, and the gain owing to a decrease in the preamble collision rate of the CeRA was reversed. Because both schemes have the same PUSCH resource limit, the performance curve after the fluctuation at \(RAO=300\) showed a similar shape. This fluctuation occurred when the preamble codeword transmitted from one RAO exceeded the allocable PUSCH resources. Because both schemes had the same PUSCH resource limit, the performance curve after the fluctuation showed a similar tendency.
# Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning
## 2 Field II and Simulation Setup
Once the simulation was completed, for each point of the space  
Figure 1: Simulation setup: the calculation path of the cross-correlation results shown in Figures 3–5; the vertical section A of the typical 4 m x 4 m x 3 m room along which the results displayed in Figure 6 are computed; the horizontal section B where the results displayed in Figure 7 are computed. Figure 2: Example of a defined emitting transducer: circular and planar piston transducer with a diameter of D = 25 mm divided into square mathematical elements. considered in the simulation, the behavior over time of the acoustic pressure generated by the complete excitation signal was obtained. This allowed any subsequent evaluation and postprocessing of the signal to be obtained. For example, the peak pressure and the total signal strength at each point can be calculated. Subsequently, an ideal receiver was assumed that linearly transduced the pressure signal into an electrical signal, downstream of a suitable sampling and numerical quantization, so that the cross-correlation vector C could be calculated.
# An Evaluation of the Efficiency of Compartmented
## 3 Results
### SCB Test
#### 3.2.2 Fracture Toughness (\(K_{lc}\)) and Fracture Energy (\(G_{lc}\))  
Tables 2 and 3 summarise the fracture energy and fracture toughness results. The results confirm that the asphalt mix containing fibre has a lower fracture toughness and energy, and thus a reduced crack velocity (Figure 10) in comparison to the control asphalt mix. However, a detailed statistical analysis of these results shows significant variation between tests. The coefficient of variation ranges from 25% at the initial test to 97% at the second test after 20 h of healing for the control mix and from 27% to 74% for the fibre mix under the same conditions. Therefore, it is questionable whether this test can be used to adequately analyse the healing efficiency of the asphalt mixtures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Spec No.**} & \multicolumn{3}{c}{**Fracture Toughness \(K_{lc}\) (Pa-m\({}^{1/2}\))**} & \multicolumn{3}{c}{**Fracture Energy \(G_{lc}\) (J/m)**} \\ \cline{2-7}  & **1 st Test** & **2 nd Test** & **3 rd Test** & **1 st Test** & **2 nd Test** & **3 rd Test** \\ \hline
1 & 6.37 & 2.80 & 1.93 & 431.19 & 167.80 & 105.32 \\
2 & 10.28 & 8.87 & 4.29 & 1217.65 & 574.29 & 160.75 \\
3 & 7.75 & - & - & 646.19 & - & - \\
4 & 8.81 & - & 1.96 & 1114.53 & - & 126.35 \\
5 & 4.98 & 0.91 & - & 492.16 & 113.21 & - \\
6 & 10.55 & 4.09 & 2.00 & 1804.93 & 440.80 & 105.39 \\
7 & 11.05 & 7.71 & - & 1591.86 & 992.21 & - \\
8 & 11.86 & 2.39 & 2.33 & 1127.41 & 97.53 & 115.49 \\
9 & - & - & - & - & - & - \\
10 & 11.58 & - & - & 1369.73 & - & - \\
11 & 10.56 & - & - & 1968.69 & - & - \\
12 & 7.61 & 1.83 & 2.64 & 707.56 & 80.75 & 205.55 \\ Mean & 9.15 & 4.09 & 2.16 & 1133.81 & 352.37 & 116.98 \\ Std. Dev & 2.30 & 3.05 & 1.26 & 523.95 & 340.70 & 62.99 \\ Coef. Var. & 0.25 & 0.75 & 0.58 & 0.46 & 0.97 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control Mix-Fracture Toughness and Fracture Energy results.  
Figure 10: Crack speed.
## 3 Current State of Object Detection Algorithms
### Yolo
#### 3.2.3 YOLOv5  
YOLOv5 [41] offers significant improvements over its predecessor, YOLOv4. At the core of YOLOv5 is the combination of the CSP-Darknet53 network and the Focus layer, which form an efficient and highly accurate backbone. This backbone solves the problem of repeating gradient information in large networks, resulting in faster inference times, improved accuracy, and reduced model size. One of the key changes in YOLOv5 is the replacement of the BottleneckCSP block with the C3 block, which features three convolutional layers and enhances the information flow. PANet acts as a bottleneck, improving the precision of object localization, especially for tiny and large objects. The SPP layer helps overcome the limitation of fixed network size, and the use of the SiLU activation function instead of the Mish activation function adds to the efficiency of the system. Another innovative feature of YOLOv5 is its use of mosaic augmentation, where the network is trained on images created from four training photos. This allows for smaller training batch sizes and enables the use of GPUs with lower memory. YOLOv5 also offers a range of five different architecture options, including YOLOv5 Nano, Small, Medium, Large, and XLarge. Each architecture is designed to meet different size, speed, and precision requirements.
## 1 Introduction  
Liquid nitrogen is widely used as an insulation cooling medium in high-temperature superconducting power equipment, such as a superconducting power transformer. A large number of suspended nitrogen bubbles will be produced when superconductors are quenched. Bubbles tend to deform and coalesce under the action of the electric field, which seriously affects the insulation performance of liquid nitrogen [1, 2, 3, 4].  
The conventional finite element method (CFEM) was often used to calculate the three-dimensional (3D) transient electric field distribution in the presence of bubbles in liquid nitrogen. CFEM has been widely used in the numerical calculation of electric fields with its concise principle and reliable results. However, limited by the continuous shape function, the boundary of elements in CFEM needs to fall on the interface of different materials. This may increase the number of elements in the case of multiple bubbles and dense material interfaces. Due to the rapid change of field near the bubbles' interfaces, more elements are needed to ensure the accuracy of the results.  
An extended finite element method (XFEM), which is more suitable for weak discontinuities in electromagnetic fields, is gradually developed based on CFEM [5, 6]. In previous studies, an improved XFEM to better deal with the dense interface problem was proposed. It has been applied to the study of the one-dimensional (1D) eddy current field and the quasi three-dimensional magnetic field [7, 8, 9, 10].  
Based on this research in 1D and quasi-three dimensions, an improved 3D XFEM is proposed to study the effect of bubble deformation on the electric field in liquid nitrogen. The interfaces are described by a four-dimensional (4D) level set function. A special enrichment term is constructed to reflect the discontinuity characteristics. This change effectively avoids the mesh repartitioning problem caused by the material interface movement.  
Three numerical models of single bubble, multi-bubbles, and deformed bubbles in liquid nitrogen are established to illustrate the reliability of 3D improved XFEM.
## 5 Experiments
### Visualization  
#### 5.3.1 Feature Maps Visualization  
To clearly demonstrate the effectiveness of the method introduced in this paper, we randomly selected pedestrian images from the Market-1501 dataset for feature extraction. Subsequently, we visualized the extracted feature maps. As shown in Figure 7, Figure 7a shows the original pedestrian images, Figure 7b shows the feature maps extracted by the baseline, and Figure 7c shows the feature maps extracted by our proposed method. In these visualization images, we use chromatic gradients to indicate the different levels of attention paid by the neural network to different parts of the image. Specifically, the dark red color indicates that the network pays the most attention to the region, while the dark blue color indicates that the network pays the least attention to the region, thus creating a clear contrast.  
As shown in Figure 7, the baseline model tends to focus on large regions of the image where pedestrians might be present, but it fails to effectively focus on the pedestrians themselves or fully consider potential auxiliary information. This limitation restricts the overall feature extraction capability. In contrast, our method integrates pedestrian boundary information, allowing us not only to locate pedestrian positions from a global perspective and effectively focus on the pedestrians themselves, but also to capture subtle differences at the local level, revealing more detailed pedestrian features. Notably, the introduction of the IREM enhances the semantic coherence and depth of feature extraction by deepening the understanding of spatial relationships between different parts of the pedestrian. In summary, compared to the baseline, our approach generates richer and more discriminative feature representations, significantly improving the performance in person re-identification tasks.
# Aortic Pressure Control Based on Deep Reinforcement Learning for _Ex Vivo_ Heart Perfusion
## 3 Results
### Mixed Conditions  
Figure 8 illustrates the comparison of the tracking results of AoP between the PD-DDPG method proposed in this paper and the control effects of the PID method and the fuzzy PID methods in mixed-condition experiments. The stability of the method proposed in this paper in mixed factors is validated through the selection of diverse combinations of conditions, including stepwise increases and decreases in the target MAP value and increases and decreases in the coronary resistance. The proposed method in this paper is observed to achieve a rapid and stable pressure tracking effect, with an insignificant overshoot value of MAP within a range of 3.5 mmHg, in comparison to the traditional PID method and the fuzzy PID method. Furthermore, when the target MAP value and the trend in the change in coronary artery resistance are consistent, the tracking results of the PID and fuzzy PID methods demonstrate a significant overshoot of approximately 10 mmHg, which may potentially lead to damage to the myocardium and impact the perfusion results. In contrast, the pressure tracking results of the PD-DDPG method are relatively optimal.  
In order to facilitate a comparative analysis of the control performance of the three control methods, namely PD-DDPG, fuzzy PID, and PID, under mixed conditions, four performance indicators are selected for evaluation. The following parameters of rise time, settling time, overshoot, and mean absolute percentage error (MAPE) are considered over the 20 s following the step change. The evaluation results are presented in Figure 9. With the exception of the rise time, the values of the remaining three indicators exhibit considerable variation. The settling time for the PD-DDPG method is 2.56 s, while the PID and fuzzy PID methods require 8.16 s and 7.62 s, respectively. The overshoot of the PD-DDPG control of aortic pressure is 6.57%, while the other two methods exhibit values of 22.09% and 14.31%, respectively. The proposed method also exhibits the lowest MAPE value of 2.46%, indicating superior accuracy in control outcomes compared to the other two methods. Therefore, the PD-DDPG method not only demonstrates precise control of the aortic pressure but also facilitates rapid and smooth pressure changes.  
Figure 7: Tracking result for step changes in \(R_{CA}\). (**a**,**c**,**e**): The MAP tracking results for \(R_{CA}\) value from 1.2 to 1.0, 0.8, and 0.6 mmHg:s/mL; (**b**,**d**,**f**): The mean AoF tracking results for \(R_{CA}\) value from 1.2 to 1.0, 0.8, and 0.6 mmHg:s/mL.  
Figure 9: The comparative results of PID, fuzzy PID, and PD-DDPG methods.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 2 Development of AI-FEM
### Defining the Crack Growth Characteristics  
Once the structure and crack shape and crack tip mesh are determined, the crack growth characteristics must be defined, which can be determined through the submenu AI-FEM. AI-FEM consists of crack growth rule, crack front smoothing option, and run option.  
In the \"Crack growth rule\" category, the relationship between SIF and crack growth rate is defined, and SCC growth and mechanical FCG can be simulated through this. The \"Crack front smoothing option\" category provides a function to compensate if the node arrangement of the grown crack tip is not suitable for the mesh for the next crack growth. The smoothing option is applied as default and can be turned off if not required. A description of the functions is as follows:  
1. Twist crack front: If crack propagation is large at a specific region in one cycle, normal directions at adjacent nodes may cross each other, which may cause twisting of the crack tip. This option helps to smooth the mesh of the crack tip by creating one node at the average point of the twisted node coordinates.
2. Concave/convex: When the curvature between the nodes forming the crack tip is small, the two nodes are integrated to make the crack tip smooth.
3. Low curvature: When nodes are concentrated in one area, the average point of the nodes within a certain distance is calculated and then combined into one node.
4. Winding: If the curvature of the crack tip is repeated in an S-shape, the angle between the two points is relaxed.  
Lastly, in the \"Run option\" category, the Abaqus user-subroutine file can be inserted, and additionally, stress fields such as residual stress in a structure can be mapped. The stress field is repeatedly inserted in every crack growth cycle.