esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Brief Description of the Strain Energy Density Approach  
Considering the SED criterion [19] statements for dynamic loadings, fatigue failure occurs when the strain energy range \(\Delta\overline{W}\) averaged in a control volume of radius \(R_{c}\) ahead of the crack tip reaches its critical value \(\Delta W_{c}\). Simple analytical formulations have been obtained in Ref. [19] for the assessment of the critical value of the strain energy density range \(\Delta W_{c}\) (Equation (1)) and of the critical radius \(R_{c}\) (Equation (2)):  
\[\Delta W_{C}=\frac{\Delta\sigma_{A}^{2}}{2E} \tag{1}\]  
\[R_{C}=\left(\frac{\sqrt{2e_{1}}\Delta K_{1A}}{\Delta\sigma_{A}}\right)^{\frac {1}{1-\lambda_{1}}} \tag{2}\]  
where \(\Delta\sigma_{A}\) is the fatigue limit of the material without geometric singularities, \(E\) is the Young's modulus of the material, \(\lambda_{1}\) is [PERSON] eigenvalue for mode 1 [18], \(\Delta K_{1A}^{N}\) is the amplitude of the Notch Stress Intensity Factors (NSIF) fatigue threshold, and \(e_{1}\) is a parameter dependent either on geometrical parameters (the notch opening angle \(2\alpha\)) or on material properties (the Poisson's ratio \(\
u\)). The reader should refer to Ref. [19] for \(e_{1}\) formulation. Dealing with rollers, the control volume is a circle centered at the weld root tip (blue circle in Figure 3), and its radius, as well as the critical strain energy density range value, is only dependent on the material [11].  
Figure 2: Typical roller assembly.  
The value of the critical radius has been obtained by leveraging on previously published data either for failures at the weld root or at the weld toe. Concerning the former, considering the value of 211 MPa mm\({}^{0.326}\) for the amplitude of the Notch Stress Intensity Factors \(\Delta K^{N}_{1A}\) obtained by [PERSON] and [PERSON] [41] for the transverse non-load carrying fillet welded joints, and that of 153 MPa for the fatigue strength \(\Delta\sigma_{A}\) of butt ground welds obtained by [PERSON] at al. [42], the critical value \(R_{c}\) for steel welded joints corresponds to 0.28 mm. Dealing with the case of failure to occur at the weld root, instead, considering \(\Delta K^{N}_{1A}=180\) MPa mm\({}^{0.5}\), Equation (2) gives a critical value of the radius equal to 0.36 mm. However, being on the safe side, an \(R_{c}\) value of 0.28 mm has been used in this work also for failures occurring at the weld roots.
## 3 Velocity Obstacle Model for MASS
### Velocity Obstacle
#### 3.1.4 VO for Multiple Mobile Objects  
When multiple objects (including mobile and static) are detected surrounding the MASS, the VO model can be calculated by the overlapping area of VOs of all surrounding objects. Suppose there are \(k\) objects detected in the sensing range of an MASS \(A\), whose VOs are denoted by \(\{VO_{B_{1}},\cdots,VO_{B_{k}}\}\), respectively. Then, the joint VO for multiple objects is:  
Figure 2: (a) VO of static objects; (b) illustration of the VO model of multiple mobile objects. \(B_{1}\) and \(B_{2}\) are two objects. The shaded areas show \(VO_{B_{1}}\) and \(VO_{B_{2}}\), respectively.  
\[VO_{[B_{1}:B_{1}]}=VO_{B_{1}}\cup VO_{B_{2}}\cup\cdots\cup VO_{B_{k}} \tag{5}\]  
Figure 2b illustrates an example when there are two objects. \(B_{1}\) and \(B_{2}\) are two objects. The shaded areas show \(VO_{B_{1}}\) and \(VO_{B_{2}}\), respectively. Only when the velocity vector \(v_{A}\) is outside both \(VO_{B_{1}}\) and \(VO_{B_{2}}\), collision avoidance from \(B_{1}\) and \(B_{2}\) can be guaranteed.
## 3 Secondary Optical Design
### TIR Lens Design
#### 3.1.1 Intermediate Aspheric Refractive Surface Calculation  
Since the lenses are symmetrical to each other, the right half will be used for the design, and the design schematic is shown in Figure 6.  
First, the starting point \(P(x_{i}y_{i})\) is determined on the central aspherical plane, which is the highest point of the refractive surface of the lens. The angle of incidence is (\(\theta_{1}+\beta_{1}\)) and the angle of emergence is \(\beta_{2}\). Assuming that the incident medium is air, whose refractive index is expressed by \(n_{0}\), and the emergent medium is the lens material \(n_{1}\), according to the law of refraction, it is obtained that,  
\[n_{0}\sin(\beta_{1}+\theta_{1})=n_{1}\sin\beta_{2} \tag{4}\]  
The curve is an accumulation of countless points, so the coordinates of the next point are derived from the initial coordinates. According to the trigonometric transformation:  
\[\frac{dy_{i}}{dx_{i}}=\frac{y_{i}-y_{i-1}}{x_{i}-x_{i-1}}=\tan(90-\theta_{1}) \tag{5}\]  
Figure 5: Optical design working principle diagram.  
Figure 6: Central non-sheik surface optical design.  
where \((x_{i-1},y_{i-1})\) are the coordinates of the next point. Combining the above two formulas, the iterative relationship between the coordinates of a point and the initial coordinates and the angle of incidence is shown in the following equation.  
\[y_{i-1}=\frac{y_{i}-k_{i}x_{i}}{1-\tan\theta_{i-1}k_{i}} \tag{6}\]  
\[x_{i-1}=\frac{\tan\theta_{i-1}(yi-k_{i}x_{i})}{1-\tan\theta_{i-1}k_{i}}\]  
The calculation method proposed in this design can be studied according to the demanded size of the lens. Firstly, the size of the intermediate non-surface is given, and the horizontal coordinate \(x_{i}\) is assumed. The vertical coordinate is obtained according to the trigonometric transformation, and the point is the critical point between the intermediate non-spherical surface and the edge total reflective surface. Finally, according to the above formula, the discrete point of this surface can be found by constant iteration.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
###### Abstract  
Accurate prediction of air demand in free-surface flows through high-head spillway tunnels with multiple vents represents a critical design challenge. Existing empirical formulas for estimating air demand, derived from studies of single vents using experimental and prototype data, are not directly applicable to multi-vent configurations. This study investigates the combined effects of key parameters on ventilation requirements: (1) flow characteristics (velocity range of 6-12 m/s and depth varying between 0.06 and 0.1 m); (2) vent geometry (total vent area from 28 to 140 cm\({}^{2}\) and spatial distribution). Through an experimental analysis, an empirical formula is derived to correlate wall roughness with interfacial shear stress, enabling an improved method for estimating air demand in spillway tunnels with multiple air vents. The resulting predictive model achieves \(\pm\)25% agreement with two prototype case studies and model tests. These experimentally validated relationships provide quantitative guidelines for optimizing ventilation system designs.  
spillway tunnel; free surface; air demand; interfacial shear stress Article
# Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys
## 3 Results and Discussion
### Chip Cross-Section Area (S) and Chip Width (\(b\))  
Figure 10 shows the mean values of \(S\) as a function of \(f\), for both alloys tested. As it can be observed, \(S\) increases with \(f\), as expected. The evolution is almost linear with \(f\), for both alloys. This is logical, taking into account the assumed plane strain hypothesis where \(b\) is considered constant. Assuming this hypothesis, \(b\) can be calculated by Equation (4). So, the obtained theoretical value is \(b=1.13\) mm.  
\[b\ =\ a_{p}/sin\kappa_{r}, \tag{4}\]  
This hypothesis is in good agreement with the experimental results for \(b\), as can be seen in Figure 11.  
Figure 10: Chip cross-section area (\(S\)) as a function of feed.  
Figure 9: Shear angle (\(\varphi\)) as a function of feed.  
The mean value of \(b\) remains almost constant when \(f\) increases, and it is very close to the theoretical value. Only slight changes can be observed for the highest feed values, especially for the UNS A97075 alloy, due to the larger deformation of the chip's cross-section with higher feed values, as can be seen in Figure 6. Moreover, the more irregular section makes it more difficult to complete the experimental measurement of \(b\), which may result in inaccuracies in its values. Finally, no significant changes in \(S\) and \(b\) values were observed along the axial machining length (\(L\)). The mean value variation of \(S\) and \(b\) along \(L\), is around 11% and 13%, respectively, which is within the standard deviation of the measurement process.
## 4 Implementation and Results
### Feature Extraction Times  
The feature extraction methods presented in Table 1 were grouped into statistical features, additional features (energy, root mean square, line length, average power, Higuchi fractal dimension, Petrosian fractal dimension, Hjorth, zero crossing, and higher-order crossing), frequency-domain features, time-frequency, and location (spatial)-domain features. The calculation of the approximate time for entropy variants is performed individually. This division was made because the number of characteristics in the time domain is numerous, and the entropy variants have a high computational cost.  
The time it takes to generate the features was calculated via averaging three executions of our feature package for a given signal for a single data point, i.e., a specific user, with 1 and 32 channels.  
Table 4 summarizes the approximate time in seconds it takes to extract features in the DEAP dataset. The \"Additional\" features group has a shorter extraction time despite being composed of several algorithms (Petrosian fractal dimension method, energy, root mean square, line length, average power, zero crossing, higher-order crossing, and the statistical techniques: variance, skewness, and maximum). Next are the time-frequency feature group and statistical feature group.  
On the other hand, frequency-domain features have a longer execution time than the other features, followed by the approximate and sample entropy variants.  
Figure 8: Classification algorithms.
## 5 Conclusions  
In this paper, we constructed an underwater optical-acoustic cooperative communication platform for underwater wireless data transmission and applied it to an ROV. A series of simulations and experiments were designed to verify the performance of the system, such as the output waveform of the visible light communication transmitter circuit, the receiver eye diagram, the outdoor communication distance, and the optical-acoustic cooperative communication data transmission.  
The performance of underwater visible light communication depends on many factors, such as transmit power, underwater attenuation coefficient, transmission rate, and offset angle. For simplicity, we chose the classical OOK modulation scheme in the system. In the data processing, we introduced RS coding and 8B/10B coding to reduce the BER.  
We also proposed an error-free data transmission protocol for optical-acoustic cooperative communication to ensure the accurate transmission of underwater data. High-speed data such as images and video are transmitted through visible light communication, while low-speed data such as control signaling are transmitted through acoustic communication. The performance of the underwater optical-acoustic cooperative communication system was proved to be satisfactory according to the experimental results.  
The angular offset of the transmitter and receiver has a large impact on the performance of the system. The experiment was implemented in a stationary environment with fixed underwater nodes. In a dynamic environment, the link alignment of visible light communication will have a large impact on the system. Further work should investigate the automatic alignment of visible light links in dynamic environments. In addition, further research on optical-acoustic cooperative wireless communication using this system is expected in both shallow and deep seas.  
Conceptualization, X.Z. and F.C.; methodology, X.Z.; software, J.H., J.L., and S.X.; validation, J.H., J.L., and F.C.; formal analysis, X.Z.; investigation, J.L.; resources, S.X.; data curation, J.H.; writing--original draft preparation, J.H.; writing--review and editing, X.Z., J.L., and F.C.; visualization, S.X.; supervision, J.L.; project administration, F.C.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **CSR-ARQ** & **GBN-ARQ** & **SR-ARQ** \\ \hline
6 & 0.1002 & 0.1005 & 0.1030 \\
9 & 0.0143 & 0.0123 & 0.0172 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The packet loss rate in the experiment.  
**Funding:** This work was financially supported by the Science and Technology Program of Guangdong Province, China (Grant No.: 2018B020207002).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Fast Reroute (FRR)
When analyzing FRR mechanisms, it is necessary to consider the following:  
* The level of computational complexity of the algorithm (precomputing);
* Effectiveness of network protection (repair coverage);
* Options for protection against line or router failure;
* Fault detection time and communication recovery time;
* Percentage success rate of saved packets;
* Multicast technology support. Currently, there is no ideal FRR mechanism. The goal of the developed mechanisms is to optimize the performance and reliability of the network and thus minimize the negative impact of failures on the operation itself [56; 57].
## 4 Results and Discussion
### Pressure Fluctuations and Vibration Characteristics Caused by the Slug Flow
#### 4.2.1 Pressure Fluctuations and Comparisons
A numerical simulation was carried out for the slug flow conditions of \(V_{\text{sl}}=0.5\) m/s, \(V_{\text{sg}}=0.5\) m/s and \(V_{\text{sl}}=1\) m/s, \(V_{\text{sg}}=1\) m/s, and the data of the two cases were compared and analyzed. Eight pressure-monitoring points were set at the same positions in the fluid domain as the eight pressure sensors on the experimental pipeline, and the pressure fluctuations within the flow field were monitored during the simulations. The pressure data at the initial time were unstable and transient. Therefore, the last 5 s of the simulated time data was used for comparative analysis when the pressure data at each monitoring point became stable. In Figure 13, a comparison of the time-averaged pressure drop between the numerical simulation data and experimental data at each point is shown. The results show that the pressure data collected during the simulation match well with the experimental data. The relative discrepancy between the simulations and the experiments is within 15%. A similar distribution among the detection points between the simulations and experiments can be observed, indicating the feasibility of the employed numerical model. Figure 12: Pressure fluctuations in the frequency domain.
## 2 Data and Methods
### Methodology
#### 2.2.1 Extended Elastic Impedance (EEI) Inversion  
The EEI inversion is introduced by [PERSON] et al. [9], which is an extended concept of elastic impedance (EI) inversion introduced by [PERSON] [14]. The EEI is formulated as:  
\[\text{EEI}(\chi)=\text{AI}_{0}\left[\frac{AI\cos{(\chi)}}{AI_{0}}\frac{GI\sin{( \chi)}}{GI_{0}}\right], \tag{1}\]  
where \(\chi\) is the amplitude versus offset (AVO) rotation angle, AI is acoustic impedance, GI is gradient impedance and AI\({}_{0}\) and GI\({}_{0}\) are, consecutively, normalized AI and GI [9].  
The EEI formulation Equation (1) suggests that the EEI values for different \(\chi\) angles at any given partial seismic data can be inverted once the AI and GI values are obtained. In this paper, AI and GI are obtained by solving simultaneously the two-term AVO equation [15] for two (near and far) angles of stacks. The pre-stack seismic data simultaneous inversion approach [16] is used to calculate AI and GI.  
The estimated petrophysical properties associated with EEI (EEI petrophysics) are obtained by correlating Equation (1) with the targeted petrophysical log for certain optimum \(\chi\) angles. The optimum \(\chi\) is obtained at the well site location by simulating Equation (1) for scanned \(\chi\) angles from \(-90\) to \(+90\) degrees. The EEI with the highest correlation with the petrophysical log is chosen as the estimated petrophysical properties, and the associated \(\chi\) angle is chosen as the optimum \(\chi\). The optimum \(\chi\) then can be used to estimate the petrophysical properties for any other given calculated AI and GI entire seismic data volume.
## 3 CB-Tree: A B-Tree Employing Cascade Memory Nodes
### Delete Operation  
The delete operation in CB-tree is similar to the insert operation. All record deletions are performed in the memory node. First, CB-tree finds a target leaf node to delete a record by visiting the nodes from the root node to the leaf node. If there are flash nodes in visited nodes, CB-tree performs node switching. After swapping the nodes, the record deletion is performed in the leaf memory node and parent nodes are then also updated with the information of the changed leaf node. When the leaf memory node underflows after the record deletion, the leaf memory nodes retain its status in order to delay the write operation on the flash memory. That is, the underflowed leaf node will not be merged with its neighbor leaf node before node switching is performed. Similar to the B-tree, the underflow node is merged with its neighbor node after node switching.  
As shown in the top of Figure 10, for example, when a record with key 25 is deleted, the record with key 25 in leaf node D' is removed without any flash operation because the deleting record stays in the memory node. After the deletion, if records with keys 20 and 21 are deleted, leaf node D' will underflow. Since leaf node D' is the memory node, CB-tree does not perform the merge operation for leaf node D' until node switching occurs in the leaf level. If node switching happens in the leaf level, the rest record with key 19 will be merged with leaf node C and the parent nodes are updated.  
Figure 9: Example of the insert operation in the case of sequential insertions.  
The search operation in CB-tree finds a record with a specific key by recursively visiting nodes from the top level to the bottom level regardless of the node type as does in the original B-tree. Its basic algorithm is similar to that of the original B-tree except that cascade memory nodes are loaded into the main memory in advance.
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 4 Simulation and Result Analysis
### Satellite-Velocity-Error Analysis  
In this part, the influence of satellite-speed error is considered in single-satellite positioning. We set the simulation time to 3000 s and the single-integration time was 150 s. The INS setting was the same as the pseudorange case. Satellite-speed error was from 0 to 1 m/s and the interval was 0.02 m/s.The remaining errors were all 0. The simulation was repeated 100 times under the same experiment conditions. Figures 6 and 7 show the results of the satellite-velocity error.  
Figure 4: Our algorithm under single pseudorange error of the \(x,y,z\) axis.  
Figure 5: Performance comparison of different algorithms under single pseudorange error.  
Figure 6 shows the mean of the positioning errors of the \(x,y,z\) axis for different satellite-velocity errors. It can be seen that the results were very close to the pseudorange results but the satellite-velocity error was 0.5 m/s, the position error of the three axes was \(120,75,24\) m and was larger than the pseudorange-error results. This means that the effect of the satellite-velocity error was larger than that of the pseudorange error in the single-satellite position. Figure 5 shows the performance comparison of the error absolute values (referred to as RMS) for the three methods under single-satellite-velocity error conditions. It can be seen that the RMS value of every algorithm was approximately linear to the satellite-velocity value and our proposed UKF algorithm was also superior to the other algorithms; the EKF algorithm was better than the least-squares Newton iteration algorithm.
## 4 Bioactive Properties
### Antitumor Effects
[239] reported that galbanic acid (**213**) reduced the number of blood vessels in tumor cells by more than 40%, significantly reduced the proliferation of vascular endothelial growth factor-(VEGF)-induced human umbilical-vein endothelial cells (HUVECs), and inhibited VEGF-induced migration and tube formation in HUVECs. It was shown to have an inhibitory effect on tumor-induced angiogenesis. MMPs are capable of degrading the vast majority of proteins in the extracellular matrix and disrupting the extracellular matrix and basement membrane barriers of tissues, which play a crucial role in the invasive and metastatic process of cancer cells [240]. Thus, inhibiting the activity of MMPs is an effective strategy to block the migration of tumor cells. Studies have demonstrated that galbanic acid (**213**) can inhibit the activity and expression of MMP2 and MMP9 [241]. Hypoxia-inducible factor (HIF) is a transcription factor that regulates the expression of genes involved in the regulation of hypoxic mechanisms (e.g., angiogenesis or apoptosis) as well as tumor growth, invasion, and metastasis [242]. Hypoxia in tumors can stimulate and induce HIF-1\(\alpha\) and HIF-2\(\alpha\) protein expression [243]. EGFR-MAPK is an important signaling pathway with regulatory effects on HIF-1\(\alpha\) expression [244]. [PERSON] et al. [245] found that galbanic acid (**213**) downregulated HIF-1\(\alpha\) and HIF-1\(\beta\) mRNA expression under both hypoxic and normoxic conditions, and it had an inhibitory effect on HIF-1 activation. Under normoxic conditions, it shortened the half-life of the EGFR (HIF-1 downstream genes) and promoted EGFR degradation to inhibit HIF activation. Meantime, it inhibited HIF-1\(\alpha\) accumulation in A549 and OVCAR-3 cells by suppressing the EGFR/HIF-1\(\alpha\) signaling pathway [244]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Names** & **No.** & **Cell Lines** & **ICg (uM)** & **References** \\ \hline
8-O-Acetyl-sinkiangenorin F & **209** & AGS & \(62.7\pm 2.5\) & [81] \\ \hline Coladin & **232** & HCT116 & \(3.7\pm 1.5\) & [75] \\  & & HT-29 & \(5.4\pm 1.2\) & [75] \\ \hline Conferol & **267** & COLO205 & \(11.19\pm 0.68\) & [246] \\ \hline Conferone & **265** & COLO205 & \(27.63\pm 0.69\) & [246] \\  & & MCF-7 & \(34.02\pm 0.68\) & [246] \\ \hline Episamarcandin & **305** & AGS & \(83.8\pm 1.4\) & [128] \\ \hline Farnesiferol A & **228** & HeLa & \(20\pm 0.2\) & [189] \\ \hline Farnesiferol C & **202** & HeLa & \(25\pm 0.8\) & [189] \\  & & AGS & \(101.6\pm 1.3\) & [128] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The IC\({}_{50}\) values of sesquiterpenenes and sesquiterpene derivatives against different cancer cell lines. The anticancer mechanisms of umbelliprenin (**150**) and galbanic acid (**213**) are shown in Figure 10.