esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation on the Leakage-Induced Collapse of Segmental Tunnels
## 4 Tunnel Collapse Process Analysis
### Strata Response
#### 4.1.2 Soil Arching Effect  
To analyze the change in the soil arching effect, the change in contact force chain between soil particles during tunnel leakage was analyzed, as shown in Figure 10, where the width of the contact force chain indicates the magnitude of the contact force. It can be seen from the figure that, along with the development of seepage erosion, the distribution of soil contact force chain shows obvious stage changes:  
1. Before the start of leakage, the distribution of the contact force showed a trend of large contact force at the bottom and smaller contact force as it went upward, as shown in Figure 10a. The reason is that the soil stress at the bottom is larger due to gravity.
2. After the leakage, a visible soil arch first appeared near the leakage point, and the soil arch feet fell at the top as well as the bottom of the tunnel, which generated an initial soil cave in the external strata, as shown in Figure 10b.  
Figure 9: Porosity changes during the process: (**a**) Leakage at the tunnel top; (**b**) Second soil cave; (**c**) Secondary soil cave; (**d**) Surface subsidence.  
3. The initial soil arch was destabilized with the development of seepage erosion, and then a secondary soil arch was formed. The width and height of the secondary soil arch increased relative to the initial soil arch, and the position of the soil arch's feet shifted toward the shoulder and bottom of the tunnel, as shown in Figure 10. At the same time, there was almost no contact force chain inside the soil arch, indicating that the particles inside the soil arch were in a state of dispersion.
4. After the destabilization of the secondary soil arch, the third soil arch appeared at a higher position, with no visible change in the width of the soil arch, the foot of the arch stopped moving, but the height of the soil arch increased, as shown in Figure 10. At this time, a contact force chain was observed inside the soil arch, indicating that the soil destabilized and fell in the form of a whole piece, rather than scattered in the form of loose particles.
5. When the third soil arch reached the critical height, the soil arch destabilized completely. At this time, there was no complete contact force chain from the strata surface to the tunnel leakage point, as shown in Figure 10.  
In summary, the microscopic mechanism that induces the development of strata erosion after tunnel leakage is the continuous destabilization and reappearance of the soil arching effect formed by soil particles. In the process of soil arch development, the height and width of the soil arch increase continuously, which leads to a decrease in its stability and eventually triggers surface subsidence.
## 2 Preliminaries and Problem Statement
### Recovery Scheduling  
The scheduling problem is first formulated. Suppose that \(\mathcal{N}\triangleq\{1,\ \ldots,\ N\}\) is the set of UAVs in the swarm. The mapping from the recovery serial number to the UAV index can be defined as:  
\[i=g(s),i,s\in\{1,\ 2,\ \ldots,\ N\} \tag{1}\]  
where \(i\) is a UAV index; \(s\) is a recovery serial number; \(S=(g(1),\ g(2),\ \ldots,\ g(N))\) represents a feasible recovery sequence of the aerial recovery problem.  
In real-world applications, not all of the UAVs can be recovered by one mother aircraft because they have inappropriate positions or orientations and the mother aircraft has a limited recovery capability. Thus, the goal of the scheduling layer is to search for a recovery sequence to recover most UAVs as efficiently as possible. Two criteria are considered in this problem, namely, the number of recovered UAVs and the time cost of the aerial recovery process. The two objective functions can be given by  
\[f_{1}(S)=\max\sum_{s=1}^{N}c_{sg(s)} \tag{2}\]  
\[f_{2}(S)=\min t_{N} \tag{3}\]  
subject to  
\[c_{sg(s)}\in\{0,1\},\quad\forall(s,g(s))\ \in\mathcal{N}\times\mathcal{N}, \tag{4}\]  
\[\sum_{s=1}^{N}c_{sg(s)}\leq N, \tag{5}\]  
\[t_{N}>\sum_{s=1}^{N}c_{sg(s)}\tau_{g(s)} \tag{6}\]  
where \(c_{sg(s)}\) indicates whether the UAV \(g(s)\) can be recovered based on the serial number \(s\); \(\tau_{g(s)}\) is the recovery time cost taken by the mother aircraft to recover the UAV \(g(s)\) and prepare for the next UAV; \(t_{N}\) indicates the whole time of the recovery process and it is given by  
\[t_{s}=\begin{cases}\min\Bigl{(}[t_{s-1},+\infty)\cap W_{g(s)}\Bigr{)}+\tau_{g (s)},&[t_{s-1},+\infty)\cap W_{g(s)}\
eq\varnothing\\ t_{s-1},&\text{otherwise}\end{cases} \tag{7}\]  
subject to  
\[t_{0}=0, \tag{8}\]  
\[s\in\mathcal{N} \tag{9}\]  
where \(W_{g(s)}\) indicates the feasible recovery time window of UAV \(g(s)\). To simplify the multi-objective optimization problem, the two objective functions can be normalized and combined by using the linear weighted method. The new objective function of the scheduling problem can be rewritten as  
\[S^{*}=\arg\!\max_{S\in\mathbb{S}}\!\!\left(\alpha\times(f_{1}(S)/N)+\beta \times\Bigl{(}\tau_{g(s)}\times f_{1}(S)/f_{2}(S)\Bigr{)}\right) \tag{10}\]  
subject to  
\[\alpha+\beta=1, \tag{11}\]  
\[0\leq\alpha,\beta\leq 1 \tag{12}\]  
where \(\mathbb{S}\) is the decision space that contains all of the feasible recovery sequences; \(\alpha\) and \(\beta\) are the weight coefficients of the two objective values.
## 4 Synthesis and Summary
### Future Directions
#### 4.4.7 Brief Summary
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Direction** & **New Trends** & **Effect** \\ \hline \multirow{4}{*}{Enhanced Intelligence and Autonomy} & Make autonomous decisions and execute tasks like autonomous cruising and autonomous maintenance & Alleviate the burden on operators, enhance efficiency and reduce costs autonomous maintenance & Alleviate the burden on operators, enhance efficiency and reduce costs \\ \hline \multirow{4}{*}{Highly Integrated Sensor Systems and Multi-Modal Task Execution} & Marine conservation, military operations, underwater exploration, subsea operations, cargo transportation, search and rescue & \multirow{2}{*}{Necessitate advanced adaptive control systems and task-planning algorithms} \\ \cline{2-3}  & More efficient battery technology and the integration of renewable energy sources & Allow them to carry out longer-duration missions \\ \hline \multirow{4}{*}{Satellite Communication and Interconnectivity} & Real-time communication and collaborative operations with other USVs, vessels and terrestrial stations; & A recipe for the ground-breaking progress for USV \\ \hline \multirow{4}{*}{Eco-Friendly and Sustainable Practices} & Emissions reduction the adoption of renewable energy sources, mitigating marine pollution; new energy converter Intelligent feeding & More green to the world Cut down the cost \\ \hline \multirow{4}{*}{Safety and Defense} & Enhanced Safety Anti-Jamming Communication Automatic Missile Defense Systems Surveillance and Reconnaissance Autonomous Swarming Cost-Effective Alternatives & \multirow{4}{*}{Protect the lives of operators, countries and the sea} \\ \cline{1-1} \cline{3-3}  & Emissions reduction The adoption of renewable energy sources, mitigating marine pollution new energy converter Intelligent feeding & \multirow{4}{*}{More green to the world Cut down the cost} \\ \cline{1-1} \cline{3-3}  & Intelligent feeding & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The summary of future directions.
## 4 Results and Discussion
### Comparison between Simulated and Measured Results of the Pipes with Pre-Existing Cracks
#### 4.3.3 Effect of Crack Location
The simulation curves in Figure 25a,b show that when the crack is located at the crown (0\({}^{\circ}\)), the largest strain is on the inner and outer walls of the crown; however, when the crack is located at the invert (180\({}^{\circ}\)), the largest strain occurs on the inner and outer walls of the pipe of the invert; when the crack is located at the springline (90\({}^{\circ}\)), the largest strain is again on the inner and outer walls of the crown. Moreover, the strain distribution of the results of the measured curve is approximately consistent with that of the simulated curve, which proves that different crack positions have a great influence on the strain distribution of the pipe. In addition, it can be found out that when the crack is located at the springline (90\({}^{\circ}\)), the strain inside and outside the pipe is clearly smaller than when  
Figure 25: Circumferential strain curves of the pipes containing cracks at different positions: (**a**) outer wall and (**b**) inner wall. the crack is located either at the crown (0\({}^{\circ}\)) or at the invert (180\({}^{\circ}\)). Also, according to the test, when the crack is located at the springline, the largest ultimate bearing capacity of the pipe is achieved. Therefore, when the crack is located either at the crown (0\({}^{\circ}\)) and or at invert (180\({}^{\circ}\)), more attention should be paid to the stress condition of the pipe. Through the comparative analysis of the tests and simulations, the failure loads of the concrete pipes under different working conditions are summarized in Table 6. The vulnerable parts of the concrete pipes are the outer wall at the springline, the inner wall either at the crown or at the invert. Under the action of an external load, the concrete pipe is first destroyed at the crack position. The ultimate bearing capacity of the concrete pipes with cracks is greatly reduced, and the ultimate bearing capacity of the pipes varies with the length, depth, and location of cracks. According to the overall comparison between the experimental and simulated data, the simulated and measured data closely match; however, the mechanical properties obtained by the concrete constitutive model and the experimental concrete pipe are not completely consistent. Moreover, due to the influence of the manufacturing process, field environment, data collection, and other factors, the experimental and simulated data still contain errors.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Unmanned Ground Vehicle in UAV-Aided NTN Communication
#### 4.1.2 NTN Communication in Unmanned Ground Vehicle Control
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Use Case**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{2-6}  & **Exchange** & **Exchange** & **Data Rate** & **Max. Delay** & **Reliability** \\ \hline Command and control & \multirow{2}{*}{high} & \multirow{2}{*}{stream} & \multirow{2}{*}{28 kbps} & \multirow{2}{*}{20 ms} & \multirow{2}{*}{99.9\%} \\ \cline{1-1} Video streaming of FPV & & & & & \\ \hline Offloading processing tasks & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak service requirements for UGV use cases in NTN communication. of vehicular networks. Vehicular Ad hoc Network (VANET) shows promise for future intelligent transportation systems. Vehicle-to-vehicle communication is utilized to collect and share information. For UGV applications supported by NTN, a variety of security issues remain challenging [127; 128; 129].
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gender** & **Number** & **Age (Y)** & **CTDIvol (mGy) Supine** & **CTDIvol (mGy) Prone** & **Effective Dose (mSv)** \\ \hline M & 25 & 58.64 \(\pm\) 15.2, 58 (29–83) & 6.7 \(\pm\) 4.42, 5.63 (2.27–15.56) & 6.5 \(\pm\) 4.1, 5.6 (2.1–15.58) & 10.3 \(\pm\) 7.95, 6.92 (2.17–31.9) \\ \hline F & 30 & 57.4 \(\pm\) 12.3, 55 (24–82) & 7.84 \(\pm\) 3.64, 8.02 (2.79–18.3) & 8.06 \(\pm\) 4.3, 7.61 (2.8–19.76) & 12.6 \(\pm\) 7.55, 10.99 (3.88–31.39) \\ \hline Total & 55 & 57.96 \(\pm\) 13.6, 56 (24–83) & 7.33 \(\pm\) 4, 7.02 (2.2–18.3) & 7.37 \(\pm\) 4.3, 7.11 (2.2–19.7) & 11.57 \(\pm\) 7.75, 9.25 (2.17–31.93) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean \(\pm\) sd, median and range of patient radiation dose values according to the patient gender. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Standard Imaging Protocol**} & \multicolumn{4}{c}{**Modified \({}^{\star}\)**} \\ \hline
**Authors** & **Country** & \begin{tabular}{c} **CTDIvol** \\ **(mGy)** \\ \end{tabular} & **DLP (mGy.cm)** & \begin{tabular}{c} **Effective** \\ **Dose (mSv)** \\ \end{tabular} & \begin{tabular}{c} **CTDIvol** \\ **(mGy.cm)** \\ \end{tabular} & \
\begin{tabular}{c} **DLP** \\ **(mGy.cm)** \\ \end{tabular} &
\begin{tabular}{c} **Effective Dose** \\ **(mSv)** \\ \end{tabular} \\ \hline [PERSON] et al. [22] & USA & 5.3 & 239 & 3.6 & 4.1 & 197 & 3.0 \\ \hline [PERSON] et al. [19] & Japan & 2.37 & 118.8 & 1.78 & 1.22 & 47.4 & 0.9 \\ \hline [PERSON] et al. [19] & Japan & 2.53 & 128.2 & 1.92 & 0.92 & 42.2 & 0.7 \\ \hline [PERSON] et al. [19] & Japan & 2.63 & 130.4 & 1.96 & 0.77 & 36.4 & 0.6 \\ \hline [PERSON] et al. [19] & Japan & 2.51 & 123.9 & 1.86 & 0.62 & 29 & 0.5 \\ \hline [PERSON] et al. [18] & Italy & 3.87 & 179.3 & 2.69 & 1.32 & 65.3 & 1.0 \\ \hline [PERSON] et al. [20] & The Netherlands & N/A & 303.3 & 4.55 & N/A & 193.3 & 3.0 \\ \hline [PERSON] et al. [23] & USA & 6.7 & 327.8 & 4.91 & 2.7 & 129.1 & 1.9 \\ \hline Current study & Saudi Arabia & 7.3 & 385.5 & 5.7 & 7.3 & 385.5 & 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the average radiation dose values with six worldwide studies per series.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### GNSS Code and Phase Observations
#### 3.1.2 Geometric-Free (GF) Code and Phase  
The GF combination is constructed by taking the difference between the observations of two frequencies, where \(\alpha_{GF}=1\) and \(\beta_{GF}=-1\). This approach effectively removes the geometric component and frequency-independent terms, leaving behind frequency dependent terms, such as ionospheric delays and signal biases. GF combinations are useful in cycle slip detection and ionospheric total electronic content estimation.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications
### Modulation Methods of Hybrid Optical Wireless Communications
#### 2.2.1 Hybrid Wireless Optical System in OFDM Modulation
The first integrated scheme using OOK for data modulation is discussed in [35]. On this basis, OFDM technology was used in hybrid IWOC/PLC system to decrease inter-symbol interference [33]. In [34], an integrated PLC and IWOC communication system based on OFDM modulation was proposed to provide electronic medical services inside the hospital. For cascaded BPLC and IWOC communication systems, the performance of binary phase shift keying (BPSK) OFDM for subcarrier modulation is studied to reduce the impact of impulse noise and ISI. In [36], the opportunity of PLC and IWOC channel integration was investigated. Literatures [37; 38] analyzed the capacity of hybrid IWOC/PLC systems based on AF and DF. First, the source node passes the information to the relay node through the PLC link, and then the relay node amplifies [39] or decodes [20] the transmitted information, and finally forwards the information to the destination node through the IWOC link. A hybrid system of IWOC and RF is considered in the indoor environment [40] to improve the average throughput and interrupt throughput of each user. In [1], a cascaded IWOC/PLC system is considered, which is compatible to provide a variety of services with different service quality requirements.
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 4 Virtual and Augmented Reality Technologies in Underwater Archeology
### Advantages of Virtual and Augmented Reality Technology
Underwater sites may be inaccessible to researchers due to difficult conditions, i.e., depth or currents, or for conservation reasons or legal reasons [12; 73; 100; 105]. They may also have not been preserved in their previous form because all physical artifacts were permanently removed from their original position and context, e.g., due to the threat of destruction or looting [13]. In such cases, virtual reconstruction based on previous mapping and data registration may be the only option for repeated analysis [13]. It is also worth noting that even if an underwater site is available for another visit, this visit, unlike land-based excavations, is most often associated with additional preparations and most often with difficulties and limited time [15]. VR and AR technologies can also increase interest in learning from both students and the wider public. Unlike land areas, underwater archaeological sites and the monuments they contain, e.g., ancient ports or shipwrecks, are usually not accessible to the general public due to the specialized diving skills required to view them. Most often, photos or individual artifacts exhibited in maritime museums provide only fragmentary access to these cultural goods [31; 105]. In this case, the use of VR technology seems to be particularly desirable, because it will play an important role in the dissemination of cultural heritage and not only provide access to a given archaeological site, but also create a unique experience related to it [106]. These activities will take place without any associated risks with exploration of the underwater world [100]. An additional advantage may be the availability of this type of station without distance or time limits, if they are designed to be used also outside the walls of the museum, e.g., as mobile AR applications [105]. It is worth noting that these types of activities, apart from being \"spiritual\", also have a material dimension. Public access to underwater and marine cultural heritage has been proven to have a very positive impact on the local economy [76].
## 4 Experimental Methods  
A novel image extraction method is proposed to process the wildlife monitoring images captured by the WMSN monitoring system, as depicted in Figure 3. The target region that contains the wildlife is the major object of interest, whereas the background regions only provide reference information. The steps of the algorithm are as follows:  
1. Color information is extracted by constructing a color space, which is utilized to solve the problem of uneven illumination in the monitoring images;
2. Texture information is extracted based on Hermite transform to ensure the target region; in other words, the extracted texture information is not affected by illumination or the shooting angle, even under conditions including a complex background and uneven illumination;
3. To guarantee the extraction efficiency, the adaptive mean-shift algorithm is utilized to extract the foreground region when the color and texture information is received.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Monitoring Node** & **Parameter** \\ \hline Camera & OV7725 QVGA 30 fps \\ Pixel & 640 \(\times\) 480 \\ Memory card & SD 16G \\ Controller & STM32 \\ Monitoring range & 120\({}^{\circ}\)/Radius 10 m \\ Maximum transmission distance & 1200 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the wireless multimedia sensor network (WMSN) node.  
Figure 2: Wildlife monitoring images: (**a**) _Capeolus pygargus_; (**b**) _Sus scrofa_; (**c**) _Cervus elaphus_.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 6 Results  
To estimate the channel occupancy efficiency, we employed the model proposed in Section 4.3. In particular, we used Equation (1). This equation depends on Equation (12), which models the transmission times of the SCHC messages defined in Figure 5. To determine the times of Equation (12), we used the values of Table 3, which shows the parameters applied to obtain the numerical results. In this table, the SCHC Fragment processing time and SCHC ACKeq processing time represent the additional time \(t_{proc}\) used to send a LoRa message to the network, which is defined as a vector. The first element  
Figure 6: LPWAN architecture used in testbed.  
of the vector is the processing time for spreading factor 7; the second element corresponds to the spreading factor 8, and so on.  
Figure 7 shows the channel occupancy efficiency for the eight data rates defined in Table 3. Each graph shows the experimental vs. theoretical efficiency for a particular spreading factor. Note that, for the theoretical efficiency, we have used Equation (1), and for the experimental efficiency, we employed the testbed described in Section 5. In general, the results show that the efficiency decreases when the probability of the loss of a SCHC message increases. Figure 8b shows the theoretical channel occupancy efficiency for each spreading factor. As can be seen, the spreading factors with the lowest and highest efficiency are the spreading factors 7 and 12, respectively. For any given probability, the efficiency increases when the spreading factor is increased. The above is valid except for spreading factor 10, for which the efficiency was observed to be between the spreading factors 7 and 8.
## 4 Discussion  
This study began with a challenge to develop a discriminative SAR system that compares with conventional memory-based or DDS-based waveform generators. Previously, a multiplier was used to generate broadband, but this study illustrates that the waveform generator can generate a stable  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Range (\(\mu\)s)** & \begin{tabular}{c} **Minimum** \\ **(degree)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **(degree)** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **(degree)** \\ \end{tabular} & \
\begin{tabular}{c} **Variation** \\ **(degree)** \\ \end{tabular} &
\begin{tabular}{c} **Standard Deviation** \\ **(degree)** \\ \end{tabular} \\ \hline \hline
**(a)** & **No PD** & \(\mathbf{-13.58}\) & \(\mathbf{13.73}\) & \(\mathbf{-1.80}\) & \(\mathbf{29.79}\) & \(\mathbf{5.45}\) \\ \hline
**(b)** & 0–6 & \(\mathbf{-16.85}\) & \(9.28\) & \(\mathbf{-3.66}\) & \(19.17\) & \(4.37\) \\ \hline
**(c)** & 0–6 + 7–8 & \(\mathbf{-14.18}\) & \(12.36\) & \(\mathbf{-1.66}\) & \(15.47\) & \(3.93\) \\ \hline
**(d)** & 7–8 & \(\mathbf{-16.75}\) & \(12.35\) & \(\mathbf{-3.09}\) & \(22.20\) & \(4.71\) \\ \hline
**(e)** & 7–9 & \(\mathbf{-11.61}\) & \(14.00\) & \(1.75\) & \(14.79\) & \(3.84\) \\ \hline
**(f)** & **0–6 + 7–9** & \(\mathbf{-14.98}\) & \(\mathbf{8.01}\) & \(\mathbf{-3.12}\) & \(\mathbf{10.13}\) & \(\mathbf{3.18}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of pre-distortion (PD) and analysis.  
Figure 13: Results of pre-distortion for each section.  
wideband waveform at baseband with a varied shape by applying the proposed method. When the baseband is up-converted to a high frequency, RF linearity is distorted and may cause spectrum and phase noise. Several studies have been performed using RF linearity analysis of high-power amplifiers in SAR and telemetry systems [21; 23; 24; 25]. The use of the multiplier may amplify the various errors generated at intermediate frequencies, but the proposed method in this study can directly generate a high-performance wide bandwidth waveform. Furthermore, the proposed method should be able to acquire high-resolution images of less than 30 cm by generating 500 MHz of bandwidth. Concerning the direct coding of the FPGA, variables for many locations and observation environments can be used and verified.  
In addition to verifying the reliability of the waveform generator, a pulse-to-pulse correlation analysis was performed. Generating a coherent waveform can reduce azimuth distortion and increase the reliability of the reference signal [26; 27]. Signals with phase error, signals with compensated error, and pre-distorted signals were correlated, with the results demonstrating that a difference of 0.4% exists in 90 signal samples.  
Based on the results of this study, further studies are expected to develop and experiment with RF demodulators, X-band ground-based SAR, and high-resolution images using UAV SAR.