esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Numerical Simulation Results
### Analysis and Discussions  
It can be seen from the above simulation results that SC-1 and SC-2 appear as shear cracks between pre-existing parallel flaws at the beginning stage of secondary crack initiation. However, from the velocity fields analysis of the particles, the upper part of the SC-1 are composite tensile-shear cracks and the lower part are tensile cracks; the upper part of the SC-2 are tensile cracks, and the lower part are shear cracks. In addition, the newly generated cracks contain tensile micro-cracks and tensile-shear micro-cracks, in which the number of tensile micro-cracks is dominant. As the axial stress is increased, SC-1 continues to extend to the tip of T4, and the newly generated cracks are tensile cracks. When SC-2 continues to extend to the tip of T3, the newly generated cracks are shear cracks. However, based on the velocity field analysis, it can be seen that the newly generated cracks near the flaws T3 and T4 tips are shear cracks, indicating that the types of macro-cracks are not totally determined by the types of partial cracks. Meanwhile, the type of macro-crack does not depend on the dominant type of micro-cracks. Since the tensile strength of the particles is much smaller than the shear strength, when the tensile micro-cracks initiate, a shear band is gradually formed under the action of the shearing force. Similarly, after the particles suffer the shear failure in the compressive zone, the stress concentration effect promotes the initiation of tensile cracks. Therefore, it can be concluded that macro tensile cracks and shear cracks partially contain shear cracks and tensile cracks respectively. SC-1 and SC-2 appear as shear cracks between pre-existing parallel flaws at the beginning stage of secondary crack initiation, and then extend up and down toward the tip of the flaw, and finally T2 and T4, as well as T1 and T3, are connected in the form of arcs. The particles between the pre-existing flaws form a confined compressive member under uniaxial compression. The type of particle velocity on both sides of the secondary crack between the fractures is most complicated, so the velocity field types of the particles are distinctive at different stress loading stages. For example, the velocity fields of the particles near the crack on the T4 tip of the flaw evolve from the initial Type IV into Type III, and finally remain in Type II. Thereafter, the cracks will not propagate any more, indicating that the type of newly generated cracks near the flaw T4 gradually evolves from shear cracks into composite tensile-shear cracks. Finally, because the velocity fields of the particles on both sides of the crack are Type II, the velocity direction and value of the particles are not much different, which can suppress the crack propagation to some extent.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 4 Results and Discussion
### Polarization Calibration Accuracy Inspection and Analysis
Figure 11: Theoretical characteristic diagram of trihedral CR. (**a**) Co-polarization response. (**b**) Co-polarized response. Figure 12: Comparison of CR-1 polarization characteristics before and after calibration. (**a**) Co-polarization response before calibration. (**b**) Co-polarized response after calibration. (**c**) Cross-polarization response before calibration. (**d**) Cross-polarization response after calibration.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 3 CPS Testbed Architecture
### Power System Layer  
The power system models and monitoring and control devices are modeled on the power system analysis platform HYPERSIM from OPAL-RT Technologies. It offers a cost-efficient, scalable, and flexible real-time platform with a highly incorporated Linux-based real-time operating system for extreme performance [45]. The advantage of using the real-time simulator is that it guarantees hard real-time constraints, synchronization accuracy, minimal overhead, and maximal data throughout [46]. It can run test scenarios that are simply impossible to perform with physical test benches. With this tool, the modeled system can interact with external devices through the simulator's I/O and communication protocols. Different electrical system topologies were developed based on different use cases. The tested networks are modeled with passive and active power system elements, and different points are mounted with current transformers (CTs) and potential transformers (PTs) to measure electrical quantities that are sent to external hardware through DNP3 and MODBUS communication protocols and hardware connections.
# Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountains Expressways
## 1 Introduction
reported that drivers were most significantly affected within the range between 250 m before the tunnel entrance and 50 m before the tunnel exit by collecting electrocardiogram (ECG) and eye movement data from 25 drivers during simulated driving [30]. [PERSON] et al. evaluated drivers' perception of curvature through indoor simulation tests [31]. [PERSON] et al. commented that the cubic spline interpolation function model could better fit the dynamic changes in the mean pupil diameter and heart rate [32]. [PERSON] et al. concluded that the drivers' visual load increased with the increasing rates of pupil diameter [33]. [PERSON] et al. conducted a real-vehicle experiment with participants in different tunnels. They used visual characteristic parameters to study the variations in the drivers' mental workload when exiting an extra-long tunnel on an expressway [34]. In summary, previous research focuses on tunnels or tunnel groups, with a predominant use of simulation models and limited real-vehicle experiments. Researchers have collected and analyzed data on the driving speed, visual perception, and ECG indicators of drivers in tunnel or tunnel group environments, providing valuable references for this paper. Studies on driver behavior characteristics have been conducted in simulated environments or in less realistic environments. Researchers have found that the perception of mental workload differs between simulator-based driving experiments and real-world scenarios [35]. On this basis, this study performed real-vehicle experiments on mountainous expressways. A total of 20 drivers participated in the driving tasks in the bridge-tunnel groups. During the experiments, eye-tracking devices were used to collect indicators such as fixation points, and pupil diameter of the drivers. In addition, information such as vehicle speed, locations, and driving videos were collected simultaneously. This paper aims to investigate the aspects further as follows: the distribution of drivers' fixation points in different sections of the bridge-tunnel group; the influence of factors such as tunnel length, distance between adjacent tunnels, and natural lighting on drivers' pupil diameter; the driving workload in the tunnel entrance and exit sections of the bridge-tunnel group. The results can provide a reference for the design and operation of bridge-tunnel groups on expressways.
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 2 Call Model and Test Result for Core Equipment  
A PS-LTE project is driven by introducing a goal-based approach, where business operators establish all environments necessary for services from the core equipment to the radio access network (RAN), command devices, and terminals. Business operators need objective test/verification methods to understand the progress toward achieving the goal for the core equipment after establishing DSCNs. However, as the performance test method for existing LTE services defines a call model that only includes voice over LTE (VoLTE) and data service, defining a new call model including MCPTT services is necessary. In the call model for MCPTT, the definition of a user-based call model should include group voices, group images, and message services to preemptively identify problems that may occur in the field. PS-LTE is established to provide VoLTE, PTT voice and videos, and data services combined in a single LTE network based on all-IP. To provide such services, the core equipment utilizes virtual network function (VNF) technology to ensure a fast and stable establishment of services by installing software on standard hardware platforms.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model
#### 3.3.5 Trajectory Requirement  
It is required for the receiver drone to film each sensor node for sufficient time duration to receive the transmitted data. Let \(\tau_{i}\) denote the time length where (5) is satisfied. To ensure receiving the maximum data size from the \(i\)th sensor node, \(\tau_{i}\) must satisfy  
\[\tau_{i}\geq T_{i}. \tag{10}\]  
The time required to receive the maximum data size from a single virtual sensor node can be formulated as  
\[\tau_{i}\geq\frac{M_{i}}{S_{i}D\log_{2}N_{i}}. \tag{11}\]  
The trajectory of the receiver drone must be determined to satisfy (11).
## 2 Materials and Methods
### Batch Biosorption Experiments  
Batch biosorption experiments were conducted under different initial concentrations to observe the effects of algae species, acid/base treatment, initial metal concentration, reaction time, and pH on the removal of heavy metals. All batch biosorption experiments were performed by reacting a bioadsorbent (0.05 g) with 30 mL of metal solution at 25 \(\pm\) 0.5 \({}^{\circ}\)C with agitation at 100 rpm in a shaking incubator (SJ-808 SF, Sejong Scientific Co., Bucheon, Korea) unless otherwise stated. The stock metal solution (1000 mg/L) was prepared by dissolving Cd(NO\({}_{3}\))\({}_{2}\), Cu(NO\({}_{3}\))\({}_{2}\), Ni(NO\({}_{3}\))\({}_{2}\), and Pb(NO\({}_{3}\))\({}_{2}\) (Samchun Chemical Co., Pyeongtaek, Korea) in deionized water. The initial pH was adjusted to 4 using 0.1 M NaOH and 0.1 M HNO\({}_{3}\).  
For the comparison of different algal species, including _H. fusiformis_, _G. gracilaria_, and _C. fragile_, 0.05 g of each dried algae was reacted with 30 mL of 100 mg/L of mixed-metal solution at pH 4 for 24 h. The biosorption experiments for _H. fusiformis_ treated with different concentrations of HCl or NaOH were performed under the same conditions as the algae comparison experiments.  
The biosorption characteristics of 1 M NaOH-treated _H. fusiformis_ (HN_1M) were analyzed by varying the reaction time (30-1440 min), initial heavy metal concentrations (10-1000 mg/L), and solution pH (2-9). Kinetic biosorption experiments were performed with pH 4 and 10 mg/L of metal solution and 0.05 g of bioadsorbent under different reaction times (30, 60, 120, 180, 360, and 1440 min). Equilibrium biosorption experiments were conducted with different initial metal concentrations (10, 50, 100, 300, 500, 700, and 1000 mg/L) for a fixed reaction time of 1440 min. The influence of pH on heavy metal biosorption was investigated under different pH values from 2.0 to 9.0 at 10 mg/L of initial metal concentration for 1440 min of reaction time.  
After biosorption experiments, the heavy metal solution was separated from the bio adsorbent using filter paper (0.45-\(\upmu\)m polypropylene filter; Whatman, Clifton, NJ, USA). HNO\({}_{3}\) (2%) was applied in drops to the filter solution, to prevent the precipitation of heavy metals prior to analysis. The metal concentration in the solution was measured using an inductively coupled plasma spectrometer (ICP-OES 5100 Series, Agilent Technologies Inc., Carpinteria, CA, USA). The amount of metal absorbed was calculated from its initial and final concentrations. The removal percentage was obtained from the ratio of the removed amount to the initial concentration.  
\begin{table}
\begin{tabular}{c c c c c c} Biomass & Untreated & NaOH 0.1 M & NaOH 1 M & HCl 0.1 M & HCl 1 M \\ \hline _H. fusiformis_ & HR & HN\_0.1 M & HN\_1 M & HH\_0.1 M & HH\_1 M \\ \hline \end{tabular}
\end{table}
Table 1: Conditions for modifying _H. fusiformis_ using HCl and NaOH.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 1 Introduction
The initial information of the person, e.g., the demographic information of a person, will be kept in a restricted part of the memory at the time of manufacturing or in the one-time erasable memory of the device. Generally, this are very ordinary personal information, e.g., name, parent's name, address, gender, etc. All of this information will be encrypted and stored in that stated memory. The details of the architecture are given at Section 3. Our secondary target is to meet the demand of storing a large volume of data, even if it is an ever-growing dataset. As we are working on smart devices where memory size is a concerning issue, we have presented a method of repeatedly implanting secrets in a small-sized cover. As a medium, we have chosen a small DNA/RNA sequence. That technique has helped us in both managing large data and securing them. The proposed scheme has several distinct features: (i) It presents a simple architecture for cyber devices that will be a boon for production in the industry; (ii) The architecture would be useful for managing its memory, a signaling unit and sensors effectively and building a perfect synchronization among these units; (iii) Uses of restricted memory will allow the devices to be used for other purposes, e.g., in banking, owner identifying, etc. ; (iv) As far as we know, no other study has used a small chunk of nucleotides for embedment of a large volume of data, e.g., a chunk of the ten nucleotides is enough to conceive any volume of information; (v) A multi-time implantation method is proposed for the first time in the field of its kind; (vi) There is no other scheme that generalizes its functionality to work with any size of message, e.g., message size of some multiple of the length of DNA; (vii) Shuffling elements in the stego key by the proposed scheme is an innovative idea that is effective for improving the security of data. For this, we think that the proposed method will be a striking one in the field of cyber physical manufacturing, as well as the DNA-based data hiding arena. The study involved experiments on real medical data, as well as on four separate RNA and three DNA sequences to justify its robustness. Four RNA sequences of COVID-19 and one DNA sequence were taken from the NCBI repository. The RNA sequences of nCoV-19 and DNA sequences are open to all [4; 5; 6; 7] and, therefore, freely available. Two additional sequences were prepared by us from real data. The proposed method worked successfully in all the RNA/DNA sequences and provided promising results. The article comprises seven sections. Section 2 is provided to give a description of the background studies. Section 3 narrates the proposed cyber physical manufacturing architecture. The proposed multi-time data embedment method is given in Section 4. Section 5 analyses the proposed scheme. We discuss the results in Section 6. Section 7 concludes the article.
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 3 Improved BSGA* Algorithm
### Improved Heuristic Functions
(**a**) Map size is 10 \(\times\) 10. (**b**) Map size is 20 \(\times\) 20. (**c**) Map size is 30 \(\times\) 30.  
path, so \(f(n)\) is even higher than \(f(n)\) that corresponds to the nodes at the beginning of the search process. Therefore, in the later stage of the A * algorithm, many nodes near the starting point will be repeatedly searched. As shown in Figure 7b, this phenomenon has been improved somewhat in the GA* algorithm but still exists. To address the above problem, this study, using a BS structure to improve the GA* algorithm, which is named the BSGA* algorithm. The BSGA* algorithm searches from the starting point and the target point simultaneously, where the nodes to be searched in the forward search process beginning from the starting point are stored in \(OpenList\_forward\), and the nodes to be searched in the reverse search process beginning from the target point are stored in \(OpenList\_backward\). The forward and reverse search processes are roughly the same as that of GA*. The difference is that the target point of the forward search is \(Current\_back\), which is the current node of the reverse search, and the cost function is shown in Equation (9). The target point of the reverse search is \(Current\_forward\), which is the current node of the forward search, and the cost function is shown in Equation (10). The two search processes are carried out alternately and both adopt the eight neighborhood search strategy until the current nodes of the two search processes meet. \[\left\{ \begin{array}{l}f_{forward}(n_{f})=g_{forward}(n_{f})+\frac{h_{ forward}(n_{f})}{D_{forward}(n_{f})}\\ D_{forward}(n_{f})=\frac{1}{\sigma\sqrt{2\pi}}e^{\frac{-(h_{forward}(n_{f} )-p)^{2}}{2\sigma}}\end{array} \right. \tag{9}\]  
\[\left\{ \begin{array}{l}f_{backward}(n_{b})=g_{backward}(n_{b})+\frac{h_{ backward}(n_{b})}{D_{backward}(n_{b})}\\ D_{backward}(n_{b})=\frac{1}{\sigma\sqrt{2\pi}}e^{\frac{-(h_{backward}(n_{b })-p)^{2}}{2\sigma}}\end{array} \right. \tag{10}\]  
where \(n_{f}\) is the current node of forward search; \(n_{b}\) is the current node of the reverse search; \(g_{forward}(n_{f})\) is the actual cost from the starting point to \(n_{f}\); \(h_{forward}(n_{f})\) is the Euclidean heuristic cost of forward search from \(n_{f}\) to \(n_{b}\); and \(D_{forward}(n_{f})\) is the dynamic attenuation coefficient of the forward heuristic function. \(g_{backward}(n_{b})\) is the actual cost of the target point to \(n_{b}\); \(h_{backward}(n_{b})\) is the Euclidean heuristic cost of the next reverse search from \(n_{b}\) to the \(n_{f}\); and \(D_{backward}(n_{b})\) is the dynamic attenuation coefficient of the backward heuristic function. The pseudo code for the BSGA* algorithm is as follows (Algorithm 1). Figure 7: Comparison of the cost during the calculation process. (a) A*. (b) GA*. (c) BSGA*.
# Designing and Conducting an Injury Study in Amateur Badminton Players
## 3 Results
### Tissue of Injuries during Competition and Training  
The results of tissue injuries during competition are shown in Table 4, and the results from training are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total (_n = 504_)** & **Male (_n = 302_)** & **Female (_n = 202_)** \\ \hline Skin & 48 (6.28) & 22 (4.69) & 26 (8.81) \\ \hline Muscle & 272 (35.60) & 166 (35.39) & 106 (35.93) \\ \hline Tendon & 92 (12.04) & 58 (12.37) & 34 (11.53) \\ \hline Bone & 39 (5.10) & 19 (4.05) & 20 (6.78) \\ \hline Ligament & 224 (29.32) & 149 (31.77) & 75 (25.42) \\ \hline Cartilage & 63 (8.25) & 41 (8.74) & 22 (7.46) \\ \hline Joint & 26 (3.40) & 14 (2.99) & 12 (4.07) \\ \hline Total & 764 (100) & 469 (100) & 295 (100) \\ \hline \multicolumn{3}{c}{Case (\%)} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tissue of injuries during competition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total (_n = 504_)** & **Male (_n = 302_)** & **Female (_n = 202_)** \\ \hline Knee & 112 (12.43) & 79 (14.23) & 33 (9.54) \\ \hline Calf & 56 (6.22) & 27 (4.86) & 29 (8.38) \\ \hline Ankle & 107 (11.88) & 69 (12.43) & 38 (10.98) \\ \hline Foot & 15 (1.66) & 9 (1.62) & 6 (1.73) \\ \hline Toe & 10 (1.11) & 3 (0.54) & 7 (2.02) \\ \hline Total & 901 (100) & 555 (100) & 346 (100) \\ \hline \multicolumn{3}{c}{Case (\%)} \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total (_n = 504_)** & **Male (_n = 302_)** & **Female (_n = 202_)** \\ \hline Skin & 69 (7.74) & 31 (5.71) & 38 (10.89) \\ \hline Muscle & 356 (39.91) & 208 (38.31) & 148 (42.41) \\ \hline Tendon & 118 (13.23) & 84 (15.47) & 34 (9.74) \\ \hline Bone & 29 (3.25) & 13 (2.39) & 16 (4.58) \\ \hline Ligament & 238 (26.68) & 161 (29.65) & 77 (22.06) \\ \hline Cartilage & 65 (7.29) & 45 (8.29) & 20 (5.73) \\ \hline Joint & 17 (1.91) & 1 (0.18) & 16 (4.58) \\ \hline Total & 892 (100) & 543 (100) & 349 (100) \\ \hline \multicolumn{3}{c}{Case (\%)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tissue of injuries during training.  
The tissues most frequently injured during competition were muscles (35.60%), ligaments (29.32%), and tendons (12.04%). In males, the most frequently injured tissues were muscles (35.39%), ligaments (31.77%), and tendons (12.37%), whereas in females, they were muscles (35.93%), ligaments (25.42%), and tendons (11.53%).  
During training, the tissues most commonly injured were muscles (39.91%), ligaments (26.68%), and tendons (13.23%). For males, the injuries predominantly affected muscles (38.31%), ligaments (29.65%), and tendons (15.47%). For females, the most commonly injured tissues were muscles (42.41%), ligaments (22.06%), and skin (10.89%).
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Quantitative Performance Evaluation  
Table 2 provides comprehensive quantitative analysis of system performance across varying SNR conditions using Pearson correlation coefficients as the primary metric for signal fidelity assessment. The correlation values compare the recovered signal with the differentiated input signal, as the demodulation process inherently recovers the derivative rather than the original input signal. The negative correlation coefficients, ranging from \(-0.1505\) under severe noise conditions (SNR = \(-8.4387\) dB) to \(-0.8908\) under noise-free conditions, indicate that the recovered signal exhibits a phase shift relative to the differentiated signal while maintaining strong correlation magnitude, demonstrating successful derivative signal recovery.  
Figure 16: PPM generation and demodulation performance under noise-free conditions. (**a**) Input sinusoidal signal (5 Hz, 1 Vpp); (**b**) Differentiated input signal serving as VCO control; (**c**) VCO output with frequency modulation based on derivative amplitude; (**d**) PPM signal generated by monostable multivibrator processing of VCO output; (**e**) Demodulated signal after monostable processing of received PPM; (**f**) Final recovered signal following low-pass filtering, representing the reconstructed derivative of the original input.  
The data reveals that the system maintains reasonable performance even under challenging noise conditions, with correlation coefficients improving systematically as SNR increases. Notably, at SNR = 6.1815 dB, the correlation coefficient reaches \(-0.8802\), approaching the noise-free performance level of \(-0.8908\), indicating effective noise tolerance within practical operating ranges.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 3 Channel Model  
The following sections present the modeling of attenuation factor \(h_{1}\) and \(h_{2}\) corresponding to terrestrial and underwater links as well as the joint FSO-TU channel modeling.