esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
###### Abstract  
With the development of coal resource extraction and wellbore construction proceeding towards deeper depths, the stability of drilling wellbore structures has become increasingly severe, even posing a barrier to the use of drilling method technology in deep wellbore construction. To address this issue, this study raised an optimized constraints method involving pre-throwing cement slurry to the bottom before wellbore decent, altering bottom constraints. Firstly, the critical depth and instability criterion of this optimized method was derived by catastrophe theory. Subsequently, the role of single-factor and multi-factor sensitivity analyses on critical depth was discussed. The engineering effects of optimized constraint methods were contrasted and examined in several drilling projects. Finally, the characteristic values of real engineering were computed using numerical techniques and ABAQUS2020 software, and the efficacy of optimization approaches was examined and validated. The results revealed that the critical depth increased by 41.39 \(\pm\) 5%. The influence factors described in order of the degree were the counterweight water height, the elastic modulus, the thickness of the wellbore, and the self-weight of the wellbore, sequentially. The conclusion on structural stability between the numerical calculation solution and theoretical calculation solution was completely the same. The optimized constraints method can effectively improve the stability of the wellbore structure.  
optimized constraints method; deep drilled wellbore; cusp catastrophe theory; vertical stability +
Footnote †: journal: Applied Sciences
## 2 Governing Equations
### k-Omega SST Turbulence Model  
This model was developed to combine the accuracy of the k-\(\omega\) model near the wall and the robustness of the k-\(\varepsilon\) in the free-stream. It contains some different terms with respect to the standard k-\(\omega\) formulation. A blending function activates the transformed models k-\(\omega\) and k-\(\varepsilon\) depending on the local value of y+, i.e., near or far from the wall [33]. A different formulation for the eddy viscosity and modified constants are introduced. The transport equations are:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\Gamma_{k}\frac{\partial k}{ \partial x_{j}}\Bigg{]}+G_{k}-Y_{k}+S_{k} \tag{11}\]  
\[\frac{\partial}{\partial t}(\rho\omega)+\frac{\partial}{\partial x_{j}}\big{(} \rho\omega u_{j}\big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[}\Gamma_{ \omega}\frac{\partial\omega}{\partial x_{j}}\Bigg{]}+G_{\omega}-Y_{\omega}+D_ {\omega}+S_{\omega} \tag{12}\]  
The diffusivity is obtained by the following equations:  
\[\Gamma_{k}=\mu+\frac{\mu_{t}}{\sigma_{k}} \tag{13}\]\[\Gamma_{\omega}=\mu+\frac{\mu_{t}}{\sigma_{\omega}} \tag{14}\]  
The eddy viscosity is determined with:  
\[\mu_{t}=\frac{\rho k}{\omega}\frac{1}{max\left[\frac{1}{a^{\pi}},\frac{S_{F}}{ \sigma_{1}\omega}\right]} \tag{15}\]  
The model constants are: \(\sigma_{k,1}=1.176\), \(\sigma_{\omega,1}=2.0\), \(\sigma_{k,2}=1.0\), \(\sigma_{\omega,2}=1.168\), \(\alpha_{1}=0.31\), \(\beta_{i,1}=0.075\) and \(\beta_{i,2}=0.0828\).
## 2 Experimental Section
### Problem Statement
#### 2.1.3 Charging Efficiency and Charging Demand  
The data sink node is an important piece of equipment in wireless sensor networks. Sensors within the sensing area can collect data and send data back to the data sink node by multi-hops. If a sensor is close to the data sink node, then it will facilitate other sensors to forward data to the sink frequently, thus incurring more power consumption. The charging efficiency defines the charging ability of a charger, and different manufacturers have different charging efficiencies. The charging efficiency can be defined as the following Equation (4):  
\[P(W(j))=\begin{cases}\sigma\cdot W(j),&W(j)\leq W_{\max}\\ \sigma\cdot W_{\max},&W(j)>W_{\max}\end{cases} \tag{4}\]  
The \(\sigma\) is a rechargeable factor and \(W_{max}\) is the charging efficiency limit of the sensor. In fact, for different brands of sensors, the values of \(\sigma\) and \(W_{max}\) are different. Here, however, we assume that all sensors have the same \(\sigma\) and \(W_{max}\) in the sensing region. From Equation (4), we can know that the charger does not have unlimited charging efficiency, so there is no instant charging in this setting. The sensors must have enough charge to maintain their normal operations, and if there is excess charge power, the excess power will be wasted. In a real environment, the charging demand of a sensor is related to the distance between the sensor and the data sink node, so we define charging demand as Equation (5). If the distance to the sink node is \(f\), the charging demand is \(\gamma\) and \(\mu\) is added/subtracted when the distance is greater/smaller than \(f\).  
\[H(dist(a_{i},Y))=\begin{cases}\gamma+\mu,&dist(a_{i},Y)<f\\ \gamma,&dist(a_{i},Y)=f\\ \gamma-\mu,&dist(a_{i},Y)>f\end{cases} \tag{5}\]  
Due to the charging power being a continuous value, deployed positions will have infinite points. Without discretization, a large amount of computation time is unavoidable. In order to design an efficient algorithm, we will further elaborate the discretization in the fourth chapter. Table 2 briefly explains all the symbols used in this study.  
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Corresponding Meaning** \\ \hline \(Z\) & Selected 2D region \\ \(x\) & Number of sensors \\ \(s_{j}\) & The sensor j \\ \(m\) & The number of chargers \\ \(a_{i}\) & The charger i \\ \(\gamma\) & The data sink node \\ \(\theta\) & Charging angle \\ \(r\) & Charger radius \\ \(\overset{\rightharpoonup}{\gamma}\) & Direction vector of a charger \\ \(\overset{\rightharpoonup}{u}\) & The vector of the charger and sensor \\ \(\sigma\) & The factor affecting charging effect \\ \(W_{\max}\) & Upper bound value of charging power \\ \(\gamma\) & The average charging demand \\ \(\mu\) & The charging demand adjustment coefficient \\ \(f\) & The specific distance which influences the charging demand \\ \hline \end{tabular}
\end{table}
Table 2: Symbols used in this research.
# Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model
## 3 Proposed Pose Estimation Algorithm
### Pose Estimation Process
Based on the camera-based coordinate frame \(x_{c}y_{c}z_{c}\) defined in Figure 4, CoM D is described as follows:  
\[CoMD(\mathbf{v})=\frac{\left\|\left(x_{c}^{CoM},\,y_{c}^{CoM}\right)-\frac{ \left(x_{c}^{i,fl},\,y_{c}^{i,fl}\right)+\left(x_{c}^{i,fl},\,y_{c}^{i,fl} \right)}{2}\right\|_{2}}{l_{leg}} \tag{8}\]  
where \(\left(x_{c}^{CoM},\,y_{c}^{CoM}\right)\) is the coordinate of the humanoid's CoM projected onto the floor; \(\left(x_{c}^{i,fl},\,y_{c}^{i,fl}\right)\), \(i=l,r\) denotes the center of the floor coordinates of the left and right feet; and \(l_{leg}\) denotes the length of the leg attained by summing the three links in the leg, i.e., \(l_{leg}=l_{fe}+l_{tb}+l_{ca}\), which is necessary for normalization. As the third metric of loss function, penalty functions concerning the suitable bounds of some joint angles are newly proposed to make it possible to find the best fit among several identical 3D poses for an estimated MPP pose. Figure 7 shows three types of penalty functions for joint angles. The single-sided negative (positive) penalty function \(P_{sn}(P_{sp})\) and the double-sided penalty function \(P_{d}\) are defined as follows:  
\[P_{sn}(\theta,\sigma_{n})= \begin{cases}|\theta-\sigma_{n}|,&\theta<\sigma_{n} \\ 0,&\theta\geq\sigma_{n}\end{cases}  \tag{9}\]  
\[P_{sp}(\theta,\sigma_{p})= \begin{cases}|\theta-\sigma_{p}|,&\theta>\sigma_{p} \\ 0,&\theta\leq\sigma_{p}\end{cases}  \tag{10}\]  
\[P_{d}\big{(}\theta,\sigma_{n},\sigma_{p}\big{)}= \begin{cases}|\theta-\sigma_{ n}|,&\theta<\sigma_{n}\\ |\theta-\sigma_{p}|,&\theta\geq\sigma_{p}\\ 0,\sigma_{n}\leq\theta<\sigma_{p}\end{cases}  \tag{11}\]  
where \(\sigma_{n}\) and \(\sigma_{p}\) represent negative and positive threshold values for joint angle \(\theta\), respectively. \(P_{sn}\big{(}P_{sp}\big{)}\) plays the role of informing uDEAS by increasing the loss function that an unrealistic pose is generated when a specific joint angle falls (increases) below (above) the assigned threshold angle. For example, when a human is standing, a sagittal torso angle \(\theta_{tr}\) smaller than \(-20^{\circ}\), i.e., leaning back, is rare, and thus adding \(P_{s}(\theta_{tr},-20^{\circ})\) to the loss function may prevent the generation of an unnatural pose by uDEAS. On the other hand, \(P_{ds}\) is a useful function for creating the most realistic pose when a certain joint angle is within the two angles \(\sigma_{n}\) and \(\sigma_{p}\). For instance, a sitting pose generally has the range of coronal torso angle \(\phi_{tr}\) between \(-10^{\circ}\) and \(10^{\circ}\), and thus adding \(P_{s}(\phi_{tr},-10^{\circ},10^{\circ})\) to the loss function will help uDEAS select the feasible \(\phi_{tr}\). The total loss function is formulated in Section 4. Figure 6: Pose comparison in the 2D frontal plane between the MPP pixel model (solid line) and the fitted model (dashed line) contracted from the initial humanoid model (dash-dotted line).
# The Fractal Characteristics of Ground Subsidence Caused by Subway Excavation
## 4 Fractal Interpolation Simulation
### The Fractal Characteristics  
To investigate the fractal characteristics of ground subsidence caused by subway excavation, we obtained the deformation curve with different monitoring frequencies: once a day, once every two days, once every four days, once every six days, once every eight days, once every ten days, once every twelve days, once every fourteen days, once every sixteen days, once every eighteen days, once every twenty days, and once a month (that is, f = 2n, n = 1-10), as shown in Figure 13. As can be seen from the graph, the curve becomes flatter as the monitoring interval increases. Accordingly, less and less detail and complexity can be captured in the curve.  
We used the algorithm mentioned in Section 5.2 to calculate the fractal dimension of these deformation curves, the result is listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Monitoring Frequency** & **Fractal Dimension** \\ \hline
1 d/time & 1.2418 \\
2 d/time & 1.2231 \\
4 d/time & 1.2261 \\
6 d/time & 1.2098 \\
8 d/time & 1.2109 \\
10 d/time & 1.2055 \\
12 d/time & 1.1998 \\
14 d/time & 1.2087 \\
16 d/time & 1.2139 \\
18 d/time & 1.2026 \\
20 d/time & 1.2283 \\
30 d/time & 1.1921 \\ \hline \end{tabular}
\end{table}
Table 3: Fractal dimension for the deformation curve with different monitoring frequencies.  
Figure 12: Fitted fractal dimension of Von Koch curve.  
We found that the deformation and the fractal dimension have a similar fluctuation pattern. That is, when the monitoring frequency is once every 2 days, 12 days, and 18 days, the deformation trend turns, and the corresponding fractal dimension curve also appears as an \"inflection point\" (Figures 14 and 15). It can be found from Figure 15 that when the monitoring frequency is once every 2 days, the fractal dimension rises slightly; when the monitoring frequency is once every 12 days and 18 days, the fractal dimension rises significantly. Therefore, to monitor the key information of surface deformation, you should ensure that the monitoring period is less than 12 d in the field monitoring. If you want to capture subtle changes in deformation, the monitoring frequency should be once every 2 days.  
Figure 13: The deformation curve with different monitoring frequencies.  
In general, the larger the monitoring frequency is, the less the fractal dimension, Hence, we derive the expression of the fitting function: \(y=1.2438\times\mathrm{x}^{-0.0017}\), as shown in Figure 15.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Robustness Analysis Under UWA Environment Mismatches  
This subsection examines the performance of the proposed model under channel environment mismatches, a crucial factor in real-world UWA communication systems. In Figure 21, training and testing on the same channel (NOF1) results in a substantial decrease in BER, reaching around \(10^{-2}\) at \(\text{SNR}=10\,\text{dB}\), showcasing the model's optimal performance when channel conditions align. However, when trained on NOF1 and tested on BCH1, the BER remains much higher, with a clear performance drop as the SNR increases, reflecting the model's struggle with mismatched environments.  
Similarly, Figure 22 illustrates the drastic difference in performance between matching and mismatched environments. In Figure 22 (BCH1), the AUC drops from 0.95 (same channel) to 0.81 (mismatched), confirming that channel mismatch significantly impairs the model's classification ability. A similar trend is seen in Figure 22  
Figure 21: BER vs. SNR under channel environment mismatches. (**a,b**) compare training and testing on same vs. different channel scenarios between NOF1 and BCH1.  
Figure 20: BER vs. SNR for an OFDM receiver with single-channel training and testing on the NOF1 channel.  
falls from 0.99 to 0.83 under mismatched conditions, further emphasizing the challenge of generalizing across diverse channels. These results underline the importance of training on a variety of channels or adopting a transfer learning technique. With transfer learning, the model can leverage knowledge from one environment and adapt it to a new, unseen channel, significantly enhancing its robustness and real-world applicability.
## 3 Proposed Procedure
### CityGML Transformation and Schema Validation  
LoD1-level 3D objects generated in this study are converted into CityGML 3.0 standard format, with schema validation performed to ensure data consistency and interoperability. CityGML, an international standard for storing structured 3D city model data, is designed to represent various spatial elements including buildings, roads, and terrain. This study automated the CityGML conversion process and implemented validation procedures to ensure compliance with CityGML 3.0 schema Table 12.
## 3 Results
### Multi-Task Classification of APS Types (NS, EAS, MS) and PA Types (SS, SB, TR)
#### 3.3.3 Independent LSTM RNN for EE Estimation  
In the independent EE regression task, PLS is used to narrow down the most informative features. The regression model achieved an RMSE of 0.666 \(\frac{\text{cal}}{\text{hr}\cdot\text{kg}}\). Figure 4d shows the architecture of the independent LSTM RNN model for regression. Figure 10a compares the EE estimation using the independent LSTM RNN model and the measured EE by the indirect calorimeter (Cosmed K5) for an independent testing data for an individual subject running on the treadmill and experiencing EAS. Figure 10b compares the EE estimation using the multi-task LSTM RNN model and the measured EE by the indirect calorimeter for the same subject.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 3 Performance Evaluation
### Satellite Channel Model
As illustrated in [26], the channel parameters \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]1, \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]2 and \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]3 of the satellite links can be determined with respect to the elevation angles \(\theta_{i}\), which can be calculated by the following identities over the range \(20^{\circ}\leq\theta_{i}\leq 80^{\circ}\) as  
\[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]4 \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]5 \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]6 \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]7 \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]8 \[G_{i}=LG_{\text{U}}G_{\text{S, max}}\left(\frac{I_{1}\left(u_{i}\right)}{2u_{i}}+36 \frac{I_{3}\left(u_{i}\right)}{u_{i}^{3}}\right)^{2}, \tag{14}\]9  
where the frequent heavy shadowing (FHS), average shadowing (AS) and infrequent light shadowing (IFL) scenarios can be precisely simulated corresponding to the low, medium and high elevation angles [26].
# 3D Positioning of Drones through Images
## 3 Methods
### Overall Structure  
Given a set of UAV images and a satellite image containing the UAV image, we aim to locate the position of the UAV image within the satellite image. Assuming the satellite image is already stored in a database with calibrated coordinates, we can calculate the UAV image's position to achieve localization. As shown in Figure 5, the backbone network for feature extraction is designed for UAV positioning and altitude determination at multiple  
Figure 4: Satellite images of different sizes randomly shifted corresponding to the drone images in the test set.  
heights, which we call MH-PCPVT. Compared to the backbone OS-PCPVT from OS-FPI, we have added an additional dimension of input.  
The input consists of one satellite image and two UAV images. The relationship between Drone and drone1 is as follows: Drone is a \(256\times 256\) UAV image, while drone1 is a smaller \(128\times 128\) UAV image cropped from the center of the Drone.  
The overall architecture of this paper is shown in Figure 7. The input to the backbone network consists of two UAV images of size \(96\times 96\times 3\) and one satellite image of size \(384\times 384\times 3\). These three images are divided into \(4\times 4\times 3\) patches. Through linear transformation, they are reshaped into vectors of size N\({}^{\mathrm{z}}\)\(\times\) C1 and N\({}^{\mathrm{z}}\)\(\times\) C1. The patches are then merged and input into the Transformer encoder for feature extraction, establishing communication channels between UAV and satellite features for information exchange.  
After the first Transformer encoder in each stage, a Position Encoding Generator (PEG) is employed. With each stage, the size of the feature maps decreases while the number of channels increases.  
After the feature maps of three scales are extracted through the backbone network, these feature maps are used in the feature fusion network, and the information interaction between the UAV and the satellite features is established. The feature map of the satellite map adopts the feature pyramid structure. Feature maps of different scales have different levels of semantic information. When feature map scales are compressed, deep feature maps may be damaged, but shallow feature maps can be preserved. Therefore, the feature pyramid structure is used to recover the compressed feature map and finally fuse the information to improve the accuracy of the result. The feature maps of the three branches are fused to obtain a heat map. By observing the heat distribution results in the heat map, the feature map is finally restored to the size of the satellite map by interpolation, and the relative position of the UAV on the satellite view is obtained.
## 2 Materials
### Monitoring
\begin{table}
\begin{tabular}{l} \hline \hline
**Outdoor Environment** \\ \hline \textless{} **+Weather station [65]**: Log duration: 2 years; Log frequency: 10 min.; Location: Outdoor, in an unobstructed area. \\ \textless{} **.Air dry-bulp temperature** (\(T_{\text{out}}\)): Resol.: 0.1 \({}^{\circ}\)C; Range: \(-40\) to \(+65\)\({}^{\circ}\)C; Accur: 0.5 \({}^{\circ}\)C. \\ \textless{} **.Air relative humidity** (RH): Resol.: 1%; Range: 0 to 100%; Accur: 3\%. \\ \textless{} **.Global radiation** (on a horizontal plane, \(E_{\text{sol}}\)): Resol.: 1 \(\text{W/m}^{2}\); Range: 0 to 1800 W/m\({}^{2}\); Accur: 5\%. \\ \textless{} **.Precipitation** (R): Resol.: 0.2 mm; Range: 0 to 6553 mm; Accur: 4\%. \\ \textless{} **.Wind speed** (10 m, WS): Resol.: 0.1 m/s; Range: 1 to 80 m/s; Accur: 5\%. \\ \textless{} **+Atmospheric CO\({}_{2}\) concentration [66]** (\(c_{\text{atm}}\)): Log details: 15 days periods in autumn, winter and spring; Resol.: 1 ppm; Range: 0 to 10,000 ppm; Accur.: \(\pm 30\) ppm. \\ \hline
**Indoor environment [67]** \\ \hline \textless{} **+Air temperature and CO\({}_{2}\) concentration**: Log duration: 2 years; Log frequency: Sequence event recording implemented by the building management system; Location: Classrooms’ interior walls, approx. 2.5 m above floor. \\ \textless{} **.Dry-bulp temperature** (\(T_{\text{in}}\)): Resol.: 0.1 \({}^{\circ}\)C; Range: \(-20\) to \(+60\)\({}^{\circ}\)C; Accur.: 0.4 \({}^{\circ}\)C. \\ \textless{} **.CO\({}_{2}\) concentration** (\(c\)): Resol.: 1 ppm; Range: 0 to 2000 ppm; Accur.: \(\pm 50\) ppm. \\ \hline
**Classroom occupancy** \\ \hline \hline \end{tabular}  
\(\textless{} **+Metabolic CO\({}_{2}\) production** (\(S_{c}\)): Students’ number and class grade were obtained from logs of lesson attendance for each class that took place in the monitored classrooms. Academic records were used to determine mean students’ age and mean ratio of girls–boys per class. The rate of production of metabolic CO\({}_{2}\) was determined considering the average height and mass of male and female students at different ages [68; 69; 70] in the context of sedentary school work.  
\end{table} 
Table 2: Characteristics of the sensors and details of occupancy data gathering.
# A Fully Automated Pipeline for a Robust Conjunctival Hyperenia Estimation
## 2 Materials and Methods
### Image Processing Pipeline
#### 2.4.3 Step 3--Redness Features  
The redness measurements are standardly performed on the whole area occupied by the conjunctiva, despite the most informative section being given by the only vessels network component. Therefore, for all the features, only the pixels belonging to the vessels network were included.  
[PERSON] et al. propose in [9] a redness measure given by a combination of the RGB channels. The authors extract this score on the manually segmented conjunctival area without any preprocessing step, obtaining promising results on the prediction of the conjunctival redness. The core assumption behind the relation between this feature and the conjunctival redness is the perception of the red color into the conjunctiva area: the mathematical formula tends to emphasize the R intensity using a weighted combination of the three channels. This starting point is generally true in the major part of slit lamp images, but it can suffer from the image light exposition: the RGB channels do not respond in the same way to the image brightness, and this behavior can affect the robustness of the measure.  
In our work, we evaluated the same score using the mask provided by the vessels network segmentation: in this way, we can focus our evaluation of the redness only in the most informative area of the conjunctiva, minimizing possible light exposition artifacts. We also improved this measure applying the preprocessing light standardization. Thus, the first feature extracted is the redness score given by  
\[score_{1}=\frac{1}{n}\sum_{i=1}^{n}\frac{2R_{i}-G_{i}-B_{i}}{2\times(R_{i}+G_ {i}+B_{i})} \tag{1}\]  
where we denote with R, G, B the red, green, and blue channels of the standardized image, respectively. The \(n\) value represents the amount of pixels in the mask produced by the conjunctival segmentation step.