esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Mineral Composition on Micromechanical Properties  
According to the previous XRD quantitative analysis, the coal samples mainly consist of amorphous organic matter and contain accessory minerals, including quartz, kaolinite, calcium carbonate, and iron carbonate. To investigate the influence of different mineral compositions on the mechanical behavior of coal, indentation responses under both the initial and hygrothermal coupled treatment conditions were examined.  
As shown in Figure 14a,c, under the initial condition, the indentation at Point 6 is located entirely within the organic matrix of the coal. In contrast, the indentation tip at Point 2 penetrates a dark-gray crystalline mineral, while the indentations at Points 5 and 7 penetrate light-gray crystalline minerals. Compared to Point 6, the projected indentation area at Point 2 is larger, whereas those at Points 5 and 7 are smaller. The peak indentation depth at Point 6 is 3434.58 nm, while that at Point 2 is 4392.87 nm, indicating a significantly lower hardness at this location. In contrast, the peak indentation depths at Points 5 and 7 are 3129.61 nm and 3289.04 nm, respectively, suggesting these regions are mechanically harder than the organic matrix. Additionally, the hardness at Point 6 is 290.77 MPa; at Point 2, it is substantially lower at 163.81 MPa; and at Points 5 and 7, it is markedly higher, at 366.60 MPa and 369.31 MPa, respectively. It can be inferred that Point 2 most likely corresponds to kaolinite, a mineral characterized by a relatively low strength, whereas Points 5 and 7 are likely associated with quartz, which has a comparatively high strength.  
Figure 13: Hardness and elastic modulus fitting curves. (**a**) Various state fitting curves. (**b**) Summary of the fitted curve.  
As shown in Figure 14b,d, under hygothermal coupling conditions, the indentation at Point 1 is evenly distributed within the organic matrix of the coal sample. The indentation tip at Point 5 extends into a dark-gray crystalline mineral, while the tips at Points 3 and 8 are embedded in light-gray crystalline minerals. Relative to Point 1, the projected indentation area at Point 5 is larger, whereas the areas at Points 3 and 8 are smaller. The peak indentation depth at Point 1 is 3762.64 nm, while the depth at Point 5 reaches 4161.67 nm, indicating a significantly deeper penetration. In contrast, the depths at Points 3 and 8 are 3357.19 nm and 3306.39 nm, respectively, both notably shallower than at Point 1. Furthermore, the hardness at Point 1 is 247.27 MPa, while that at Point 5 is 182.79 MPa, indicating a significantly lower hardness. The hardness values at Points 3 and 8 are 315.14 MPa and 322.25 MPa, respectively, both significantly higher than at Point 1. These findings suggest that Point 5 is most likely associated with a lower-strength kaolinite mineral, whereas Points 3 and 8 are most likely associated with higher-strength quartz minerals.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 2 Control Equations  
To calculate water hammers in pipes, the following momentum and continuity equations can be employed [1].  
\[\bar{s}\frac{\partial h}{\partial x}+\frac{\partial v}{\partial t}+v\frac{ \partial v}{\partial x}+\frac{f}{2D}v|v|-g\sin\theta=0 \tag{1}\]  
\[\frac{\partial h}{\partial t}+v\frac{\partial h}{\partial x}+\frac{c^{2}}{g} \frac{\partial v}{\partial x}=0 \tag{2}\]  
where \(h\) is the head of the pressure head, \(v\) is the average flow velocity of the pipe section, \(f=8 gn^{2}(4/D)^{1/3}\) is the resistance coefficient along the pipe, \(n\) is the roughness, \(D\) is the pipe diameter, \(x\) is the distance along the pipe axis, and \(c\) is the wave speed in the pipe.  
Equations (1) and (2) can be expressed in the following linear form:  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{A}\frac{\partial\mathbf{u}}{\partial x}=\bm {s} \tag{3}\]  
where  
\[\mathbf{u}=\begin{bmatrix}h\\ v\end{bmatrix}\quad\mathbf{A}\mathbf{=}\begin{bmatrix}v&c^{2}/g\\ g&v\end{bmatrix}\quad\mathbf{s}=\begin{bmatrix}0\\ g\sin\theta-\frac{f[v]v}{2D}\end{bmatrix}\]  
Equation (3) can be written in the following approximate form, which can be solved by solving the Riemann problem [28].  
\[\frac{\partial\mathbf{u}}{\partial t}+\frac{\partial\mathbf{F}(\mathbf{u})}{\partial x}= \mathbf{s}(\mathbf{u}) \tag{4}\]  
where \(\mathbf{F}(\mathbf{u})_{x}=\overline{\mathbf{A}}\mathbf{u}\), \(\overline{\mathbf{A}}=\begin{bmatrix}\overline{v}&c^{2}/g\\ g&\overline{v}\end{bmatrix}\), \(\overline{v}\) is the average flow velocity. If the effect of convective terms is ignored, it can be taken as \(\overline{v}=0\). This paper takes the arithmetic average proposed by [PERSON] [29] as \(\overline{v}=(v_{i}+v_{i+1})/2\). The eigenvalues of the coefficient matrix \(\overline{\mathbf{A}}\) are \(\lambda^{\pm}=\overline{v}\pm c\).  
Figure 1 shows the schematic diagram of pipeline meshing, and the finite volume method is utilized to attain the discrete solution. \(\mathbf{F}_{i-1/2}\) is the value of \(i-1/2\) interface flux and \(\mathbf{U}_{i}\) is the mean value of \(\mathbf{u}\) in cell \(i\). Taking the control cell \(i\) as an example, Equation (4) can be integrated along the x-direction from the control cell interface \(i-1/2\) to the interface \(i+1/2\) (The range of \(i\) is 0 to \(N\), and \(N\) is the number of pipe grids).  
\[\frac{\partial}{\partial t}\int_{i-1/2}^{i+1/2}udx+\mathbf{F}_{i+1/2}-\mathbf{F}_{i- 1/2}=\int_{i-1/2}^{i+1/2}sdx \tag{5}\]  
Equation (5) is the conservation form of the momentum and continuity equations in the control unit \(i\). Let \(\mathbf{U}_{i}=\frac{1}{dx}\int_{i-1/2}^{i+1/2}udx\); if both sides of the Equation (5) are divided by \(\Delta x\), the following equation can be obtained (\(n\) is a known time step and \(n+1\) is an unknown time step).  
\[\mathbf{U}_{i}^{n+1}=U_{i}^{n}+\frac{\Delta t}{\Delta x}\int_{i-1/2}^{i+1/2}sdx- \frac{\Delta t}{\Delta x}(\mathbf{F}_{i+1/2}-\mathbf{F}_{i-1/2}) \tag{6}\]  
Figure 1: Schematic diagram of pipeline meshing.
## 4 Construction of the Safety Passage Redundancy Evaluation Model for the Taxiway Bridge
### Analysis of the Maximum Allowable Load of the Taxiway Bridge Based on the Maximum Number of Passes  
According to the analysis in the previous section, the taxiway bridge can operate at least 255,500 times in association with the maximum landing weight in the specified life cycle. Considering the difference between the basic weight of the aircraft and the full weight of the aircraft, as well as the influence of the aircraft seating rate and other factors on the size of aircraft load, it is therefore necessary to explore the relationship between different load sizes of standard aircraft type B74-400 and the maximum deflection value in the middle span of the taxiway bridge under the maximum operation times to make the load reduction measures appropriate for the safe operation of the taxiway bridge.  
The standard aircraft type B74-400 operates 255,500 times in the FEM with a basic weight of 1827.21 kN, a maximum landing weight of 2857.63 kN, a maximum take-off weight of 3968.93 kN, and a maximum ramp weight of 3978.00 kN. The data of the maximum deflection value in the middle of the span after operation are derived, and the correlation between the maximum deflection value in the middle of the span and the load is analyzed. The relationship between the maximum deflection value in the span and the load is obtained as follows:  
\[Y=0.0079Q_{t}+Y_{c} \tag{8}\]  
\(Y\) (mm) is the maximum deflection value of the bridge deck mid-span; \(Q_{t}\) (kN) is a load of the standard aircraft type; \(Y_{c}\) (mm) is the mid-span deflection caused by the weight of the taxiway bridge. The curve is shown in Figure 9, and the dotted line represents the fitting curve.  
With the increase in load, the maximum deflection at the mid-span increases linearly, and the correlation coefficient \(R^{2}\) = 0.9864, indicating that the correlation degree of the curve to the data is as high as 98.64%. According to this relationship, the allowable maximum deflection value \(Y\) = 26.03 mm, the load \(Q_{t}\) = 3121.33 kN can be obtained, indicating that the standard aircraft type needs a load reduction, and the maximum can only operate 255,500 times with the load value of 3121.33 kN to ensure the safe passage of the aircraft.  
For the above relationship, the following curve can be obtained as shown in Figure 10 by increasing the aircraft load value to 4500 kN without considering the limitations of the code and taking ten verification nodes for verification purposes.  
Figure 8: Data validation curve.  
The trend of the relationship value is highly consistent with the simulation value of the FEM, and the relative error is controlled within 5%, which can verify that the formula is accurate and reliable.
## 4 Jasmonates: Biosynthesis, Signaling, and Functions
### Deeper Mechanistic Insights: How MeJA Modulates Trichome Development and Cannabinoid Biosynthesis
#### 4.2.2 Specific Regulators in Cannabis  
Recent investigations in cannabis highlight CsMYC4, a bHLH transcription factor that responds to exogenous MeJA, as a major regulator of glandular trichome density and cannabinoid [49]. Overexpression studies suggest that CsMYC4 can drive the initiation and expansion of capitate-stalked trichomes, which are the primary sites of cannabinoid production [41]. By relieving JAZ repression, MeJA permits CsMYC4 and other transcription factors, such as MYB or HD-ZIP proteins, to trigger genes involved in trichome cell division, cell expansion, and resin secretion [50]. This includes genes encoding cuticle biosynthetic enzymes, cell-wall modifiers, and transporters for secreted metabolites, all of which may be upregulated in tandem to enhance trichome functionality. Although short-term MeJA treatments do not always cause dramatic increases in the final cannabinoid synthases (e.g., THCA or CBDA synthase), there is consistent enhancement of upstream enzymes and trichome regulatory genes [51]. As these regulatory factors expand trichome capacity, any baseline expression of THCA synthase or CBDA synthase can process a larger flux of precursors, ultimately raising the total cannabinoid output.  
Figure 3: Exogenous methyl jasmonate (MeJA) induces the COII–JAZ–MYC signaling pathway, leading to the activation of jasmonate-responsive genes, the expression of defense and secondary metabolism pathways, and the development of glandular trichomes in _Cannabis sativa_ L. Figure created using Biorender.com.
## 4 Experiments and Analysis
### Impact on the Orbital Accuracy
Some scholars have recently pointed out that when the hardware delay errors in SLR observations are estimated as constants or satellite-station pair-related errors, the accuracy of the satellite orbits and Earth parameters can be further improved by about 30% [42]. Similarly, if the hardware delay errors in SLR observations can be accurately processed in the future, the accuracy improvement of the SLR-aided autonomous orbit determination will be better than the existing conclusions.
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 3 Results and Discussion  
### Hydration Heat  
The cement hydration process may be divided into five stages, including the dissolution period, the induction period, the acceleration period, the deceleration period and the slow reaction period [32]. Figure 3 shows the isothermal hydration heat curves of mixtures with and without CNA; the hydration heat emission rate and hydration heat in Figure 3 are equal to heat flow and heat, respectively. It can be found that the exothermic peak of the specimen with CNA appeared first, which is advanced by approximately 1.5 h than the blank specimen. It was also found that the cumulative heat released by the specimens with CNA is significantly higher than that without CNA. This indicates that CNA may effectively promote the early hydration process of cement. According to the instrument measurement, the theoretical maximum hydration heat (\(Q_{max}\)) of cement slurry with CNA (356.4 J g-1) is slightly higher than that without CNA (343.3 J g-1). This indicates that CNA may slightly increase the total heat release from cement hydration.  
Figure 4 shows the fitting process of the hydration kinetic parameters of the CNA group and the blank group. The reaction exponent \(n\) and reaction rate constant \(K_{1}^{\prime}\), \(K_{2}^{\prime}\), \(K_{3}^{\prime}\) of the mixtures with and without CNA are summarized in Table 2.  
The kinetic parameters in Table 2 are substituted into the differential equations of Equations (1)-(3), the kinetic curves of reaction rate \(F_{1}(\alpha)\), \(F_{2}(\alpha)\), \(F_{3}(\alpha)\) and reaction degree \(\alpha\) for NG, I and D processes are obtained, respectively, as shown in Figure 5.  
Figure 4: Fitting curves of kinetics parameters in NG stage (**a**), I stage (**b**) and D stage (**c**) of blank sample; Fitting curves of kinetics parameters in NG stage (**d**), I stage (**e**) and D stage (**f**) of cement mixture with CNA.  
Figure 3: Cement hydration rate and heat of hydration curve.  
From Figure 5, both of the dynamic differential curves of mixtures with and without CNA can accurately fit the d\(\omega\)dt curve obtained from the hydration heat test, which verifies that the hydration process of cement mixtures with and without CNA is dominated by the NG-I-D mechanism. In Figure 5, \(\alpha_{1}\) represents the transition point from the NG stage to the I stage, and \(\alpha_{2}\) represents the transition point from the I stage to the D stage. The mixture with CNA obtains a higher value of \(\alpha_{1}\) (0.20) than that without CNA (0.15), which indicates that CNA effectively promotes the hydration process in the NG stage. This phenomenon may be explained by that CNA sol particles with small particle size may provide a crystal nucleus for the generation of the C-S-H gel when silicate ions are saturated in the NG stage, which reduces the potential energy of C-S-H gel generation and promotes the generation of C-S-H gel. Compared with the blank group, in stage I, the CNA group's hydration degree increased by 0.02, which also promoted cement hydration.
## 5 Conclusions  
In the present study, inspired by the advantages and applications of directed complex network, association rules are integrated with complex network to develop the risk interaction network of seafarers' unsafe acts. Based on complex network theory, the developed network is quantitatively analyzed, and the risk evolution characteristics and laws of seafarers' unsafe acts are clarified. The main contributions of the present study are as follows:  
1. The present study introduces association rule into complex network and develops a directed weighted network of seafarers' unsafe acts and their influencing factors with 53 nodes and 260 edges, which avoids the defect that the edge weights are over-reliant on expert experience in the development of traditional complex networks, providing a new way to investigate the risk interaction of seafarers' unsafe acts involved in maritime accidents.
2. The risk propagation characteristics and paths of seafarers' unsafe acts are identified and demonstrated, which could provide control priorities for interrupting the risk propagation of seafarers' unsafe acts so as to improve maritime transportation safety.  
Some limitations of the present study should be noted. Epistemic uncertainty in the initial data may affect the reliability of the results [56]. Maritime accident databases often suffer from data errors and missing data. Therefore, the present study selected accident reports issued by official maritime investigation authorities as research data, which have a certain degree of reliability. It is important to continue collecting and collating maritime accident reports, and mining new datasets may produce new research findings. Then, with the rapid development of emerging technologies such as big data and Internet of Things, ship intelligence has become an inevitable trend in the development of the shipping industry, and new seafarers' unsafe acts and their influencing factors may appear in maritime transportation activities. Therefore, it is necessary to update the dataset of seafarers' unsafe acts and their influencing factors in future research. Additionally, the combination of GISIS database, AIS data etc., with maritime accident investigation reports should be considered to further enrich the research data sources, which may generate interesting findings.  
Methodology, H.L.; Software, H.L.; Formal analysis, H.L.; Resources, X.M.; Writing--original draft, H.L.; Supervision, X.M. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support provided by the National Key Research and Development Program of China (grand No. 2019 YFB1600602).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 3 Mathematical Model Development
### Motion Profile Analysis of the Characterization
The total time for each completed characterization process is \(2t_{T}\). In a single characterization process, the total travel distance \(X_{0}\) equals twice the sum of the descent distance \(X_{DEC}\) and the ascent distance \(X_{ASC}\). A mathematical analysis of the trapezoidal pattern for downward travel requires motion with a constant acceleration function \(\Delta X=V_{0}\Delta t+\frac{1}{2}a\Delta t^{2}\) (\(constant\)\(a\)), where \(X\) is the final position, \(V_{0}\) is the initial velocity, \(\Delta t\) is the time interval, \(V\) is the final velocity, and \(a\) is the acceleration to evaluate each segment [34]. Tables 3 and 4 show the equations of the intervals covered and accelerations obtained. The total distance (\(X_{T}\)) required to complete a test corresponds to the sum of four distances traveled on the Z-axis. Each vertical run covers a distance of 78.994 mm. This comprises a 75.819 mm path from the Z-axis origin to the top surface of the ZIF socket, along with a 3.175 mm path for inserting or removing the SOIC package into the ZIF socket. The distance covered at maximum speed is the sum of distances \(X_{A}\), \(X_{B}\), and \(X_{C}\), while the ZIF socket operating distance is the sum of distances \(X_{D}\) and \(X_{E}\). The equation for the total distance traveled is as follows:  
\[X_{T}=4\cdot[X_{A}+X_{B}+X_{C}+X_{D}+X_{E}] \tag{18}\]  
Replacing the values obtained from the motion trapezoidal pattern, we obtained a total distance traveled as a function of time-speed, as shown in Equation (19):  
\[X_{T}=2V_{max}(\Delta t_{A}+2\Delta t_{B}+\Delta t_{C})+2V_{ZIF}(\Delta t_{C}+2 \Delta t_{D}+\Delta t_{E}) \tag{19}\]  
The total time required to complete a test is equal to the sum of the partial times \(\Delta t_{DES}\), \(\Delta t_{ASC}\), and \(\Delta t_{OUT}\), as shown below:  
\[t_{T}=2\Delta t_{DEC}+2\Delta t_{ASC}+3\Delta t_{OUT} \tag{20}\]
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 4 Experiments Settings and Results
### Main Results  
In this section, we will compare the G&G attack with several mainstream attacks. These methods generate adversarial point clouds in a black-box setting. For simplicity, in the comparative experiments, we use PointNet [34] as the white-box surrogate model and introduce four classification models for black-box attacks on the ModelNet40 dataset: PointNet++ [36], CurveNet [32], DGCNN [56], and PointCNN [57]. The Sensitivity Map effectively describes the vulnerabilities of classification models and the confidence of a single point under perturbation, making the perturbation search more targeted. We compare the ASR of different attacks under no defense, SOR (Statistical Outlier Removal), and DupNet [58]. We assume the baseline does not have access to the training point clouds or classifiers. For SOR, we use the same parameters proposed in the original paper (\(k=2\), \(\alpha=1.1\)). For training DupNet on ModelNet40, we use an upsampling rate of 2. Based on empirical observations, as the number of dropouts increases, the natural accuracy of the classifier decreases further, as shown in Table 1.  
Overall, our black-box transfer attack poses a significant threat to various defenses. As shown in Tables 2 and 3, under the same conditions, the method can reduce the original success rate of PointNet++ [36] from 90.7% to 19.6%. Compared to the listed methods, our method consistently achieves the highest ASR. It is worth noting that, compared to DGCNN [56], PointNet++ [36], and PointCNN [57], PointCNN [57] shows higher robustness, with a lower ASR for various attacks. More significant perturbations are required to launch attacks successfully. Part of the reason is that DGCNN [56] is weaker in handling sparse graph data. Additionally, DGCNN [56] uses pairwise dynamic distance counting to search for \(k\)-nearest neighbors, which includes semantic features in multi-layer systems but ignores some vector directions between adjacent points. Therefore, models that incorporate advanced grouping operations (such as PointNet++ [36] and PointCNN [57]) tend to have higher robustness.
## 2 System Design and Principle
### Principle
#### 2.2.3 Principle of Starting Position Determination  
According to the data frame structure designed above, the timing diagram of how to find the starting position is shown in Figure 5. The SPAD outputs discrete random electrical pulses after detecting the light signal. We designed a counter to count the single photon pulse output by SPAD, and a timer counts the silent time of the signal output by SPAD. When the counter count value reaches a certain value and the silent time count value also reaches a certain value, we consider this moment as the starting position. At this time, pulling up the sampling enable signal starts the sampling module. The sampling module  
Figure 4: Frame structure.  
Figure 3: Principle of recovering time slot synchronous clock based on deep learning.  
Figure 2: Principle of neural network trained to extract the hidden phase.  
starts sampling the single photon pulse signal output by SPAD and converts the sampled sequence into binary sequence. Then, the \(Nf_{S}\)/\(b_{r}\) sampling points in front of the binary sequence are fed into the trained neural network, so that the time slot synchronous clock phase value can be restored, and the time slot synchronous clock can be restored according to the phase value, where \(f_{S}\) represents the sampling clock frequency, and \(b_{r}\) represents the baud rate of the modulated signal.