esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Wing Corrugation on the Aerodynamic Efficiency of Two-Dimensional Flapping Wings
## 2 Modeling of Flapping Wing
### Reynolds Number and Average Angle of Attack along Wingspan  
The parameters used for calculating the Reynolds number and the lift and drag coefficients are given below:  
\[Re_{r}=\frac{U_{mem}c}{v} \tag{1}\]  
\[U_{mem}=\frac{2 rf\pi\Psi}{180} \tag{2}\]  
\[C_{L}=\frac{L}{0.5\rho U^{2}S} \tag{3}\]  
\[C_{D}=\frac{D}{0.5\rho U^{2}S} \tag{4}\]  
where \(Re_{r}\) is the Reynolds number, \(U_{mem}\) is the average velocity in a flapping cycle, \(c\) is the chord length (26.5 mm), \(v\) is the kinematics viscosity of air at room temperature (\(1.51\times 10^{-5}\) m\({}^{2}\)/s at 20 \({}^{\circ}\)C), \(r\) is the spanwise location, \(\Psi\) is the amplitude of flapping angle (190\({}^{\circ}\)), \(f\) is the frequency (20 Hz), \(C_{L}\) and \(C_{D}\) are lift and drag coefficients, respectively, \(L\) and \(D\) represent the lift and drag of a wing, respectively, \(\rho\) is the air density, and \(S\) is the wing area. For the two-dimensional calculation, the chord length (\(c\)) multiplied by 1 is used as the wing area.  
Figure 4 shows the distribution of the Reynolds numbers and average angles of attack during the translational stage of the flapping motion, along the wingspan from the root to the tip of the KUBeetle. The Reynolds number in the inboard wing (\(r\)/\(R<0.6\)) is less than \(10^{4}\), due to lower flapping-wing speed. Meanwhile, at the outboard wing (\(r\)/\(R>0.6\)), the Reynolds number is of the order of \(10^{4}\), reaching a maximum of 13,000 at \(\tau\)/\(R\) = 0.8 and then decreasing near the wingtip due to smaller chord lengths (\(\tau\)/\(R>0.8\)). The figure shows that during the translational stages, the flapping wing operates mostly at high average AoA, decreasing from (\(\sim\)80 to \(\sim\)35)\({}^{\circ}\) at the wing root to wing tip, respectively, due to passively twisted wings.  
Figure 3: Extracted two-dimensional wing kinematics: (**a**) rotation angles \(\theta_{t}\) and \(\theta_{m}\) at \(r\) = (0.375 and 0.75)\(R\); (**b**) description of wing motion in upstroke and downstroke.
# High-Speed Running and Sprinting Thresholds in Elite Female Team Sports: A Systematic Review
## 4 Discussion
### Practical Applications  
Given the lack of consensus on specific absolute thresholds for defining high-speed running (HSR) and sprinting in female athletes and the absence of an international standard, practitioners may consider using the velocity ranges identified in this review as a practical reference. Understanding the speed characteristics of each sport is essential for designing training programs tailored to the demands of female team sports.  
Establishing appropriate HSR and sprint thresholds in female athletes can support coaches and performance staff in managing weekly training loads and reducing injury risk, particularly for soft-tissue injuries associated with chronic exposure to high-speed efforts. Moreover, accurate threshold determination is especially valuable for guiding return-to-play protocols, ensuring that injured players are progressively and safely reintroduced to high-speed activities.  
Additionally, having sport-specific reference values is critical for practitioners working across different disciplines. For sports scientists, the ability to adjust or reprocess data using shared benchmarks facilitates collaboration and data comparison with clubs, federations, or organizations that may apply different standards in female sports.  
Nevertheless, implementing individualized or sport-specific thresholds may pose challenges, particularly in teams with mixed experience levels or in settings with limited access to advanced tracking technologies and analysis resources. Practitioners may need to balance the ideal application of individualized thresholds with practical constraints, opting for simplified approaches where necessary  
Key points:  
* In the absence of standardized thresholds, coaches and practitioners should apply the velocity ranges identified in this review (e.g., \(\sim\)16 km/h for HSR; \(\sim\)20-21 km/h for sprinting) as reference points while considering the specific demands of each sport and player role.
* Appropriate HSR and sprint thresholds can help manage weekly training loads and minimize injury risk, especially for soft-tissue injuries linked to high-speed running exposure. Tracking these thresholds is also valuable for safe and effective return-to-play protocols.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Summary of Wildfire Manager Needs
#### 2.2.5 Smoke and Air Quality Information  
Although smoke management tools were not identified as critical to tactical decision-making in the survey responses, this information is critical for other emergency management operations. Non-fire management users require smoke-related intelligence for critical operations such as evacuation planning [61; 62; 63], public health forecasting [64], and aviation visibility [65]. Smoke forecasting using tools such as FireWork [30; 66] and BlueSky [29; 67] are dependent on wildfire size and location information. Other smoke monitoring applications (e.g., Global Fire Assimilation System (GFAS); [33]) require FRP [31; 32] as a primary input.  
The delivery of operational smoke and air quality forecast is a highly automated process. As of 2020, Environment and Climate Change Canada will launch a new air quality forecast twice a day (initiated at 00 and 12 UTC), and smoke forecast every 6 h (00, 06, 12, 18 UTC). Each execution is updated with the latest wildfire information available. The scheduling of forecast executions is tied to the availability of new weather and wildfire emission data, and computing resources. Due to this scheduling, the data latency requirements are less stringent than for wildfire management applications, but the requirement to focus on the peak burn overpass period still persists. Additionally, smoke and air quality applications emphasize a strong interest in smaller wildfire detection, an interest in thermodynamic parameters controlling plume rise and height (e.g., [68; 69]), and a larger coverage (e.g., North America).
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 5 Simulation and Experiment
### Discussion  
According to the simulation and experiment, RRT*-offline has better performance and can effectively solve the problem of robot motion planning in live working scenarios.  
1. Real-time performance: By generating a random tree offline, most of the calculation is removed from the real-time calculation process, which effectively improves the efficiency of real-time motion planning.
2. Practicability: The randomness of the planning results is the main limiting factor of RRTs. Dynamic area sampling and tree pruning solve this problem to a certain extent. The expansion and pruning of offline trees are complicated tasks, but they only need to be completed once.
3. Versatility: The research in this paper is aimed at a live working environment, and its characteristic is that most of the obstacles are fixed, the positions of a few obstacles are changed, and the target position is also changed. In similar scenarios, the algorithm in this article can be used. For example, in the flexible manufacturing process, tools and parts will change during the production process, but the overall production environment is fixed. In the research, we also found the following problems: 1. The process of demonstrating the planning results in a simulation environment consumes operating time and reduces operating efficiency.
2. The offline tree cannot be displayed intuitively, and there is no good way to evaluate the offline tree.
# Popular Biochar as an Alternative Substrate for Curly Endive Cultivated in a Soilless System
## 2 Materials and Methods  
### Biochar Treatments and Related Analysis  
The tested biochar was derived from automatically chipped trunks and large branches of poplar (_Populus nigra_ L.) pyrolyzed at 450 or 700 \({}^{\circ}\)C for 48 h. The feedstock was free of pollutants and foreign materials. Main physicochemical traits of the used substrates were analyzed. The physicochemical parameters were determined as described by [PERSON] et al. [15] and [PERSON] et al. [4]. Thus, the pH was determined with a pH-meter (GLP 21, Crison, Carpi (Modena), Italy, ISO 10390) on a 60 g biochar sample mixed with 300 mL of deionized water after shaking the suspension for 60 min at room temperature (22 \({}^{\circ}\)C). The electrical conductivity (EC) was measured in a 1:5 _v/v_ substrate-water extract with a conductivity meter (HI 4321, Hanna Instruments, Ronchi di Campanile, Padova, Italy, ISO 11265).  
Substrate total nitrogen content (N) was assessed by dry combustion using an elemental analyzer (Carlo Erba Instruments, Milano, Italy, ISO 13878); ash content corrections was used by loss on ignition at 600 \({}^{\circ}\)C in an electric muffle furnace.  
Substrate phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg) and sodium (Na) were investigated by using 0.2 g of dry sample (105 \({}^{\circ}\)C for 24 h) after acid digestion in a microwave oven (CEM Mars Xpress, Matthews, NC, USA); substrate digests were filtered, diluted and analyzed by atomic absorption spectrometry (AA Analyst 200, Perkin-Elmer, Shelton, Connecticut, USA).  
Particle density was determined by considering the ratio of organic matter and ash content, taking into account an organic matter density of 1550 kg m\({}^{-3}\) and an ash density of 2650 kg m\({}^{-3}\). Dry bulk density was determined considering the ratio of sample mass and the ring volume. Consequently, the total porosity was determined following the physical relationship: total porosity = 1.1--(bulk density/particle density).  
Nine growing substrate mixtures (Table 1) were arranged by combining volumes of pelletized biochar (sieved at 5 mm-mesh) and sphapnum peat (0-3 mm sized, H3 decomposition degree, pH 4.3). Biochar and peat were accurately amalgamated in order to minimize breakage of biochar particles, then mixed with 2 L of water and air dried.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Petrophysical Testing  
Extensive petrophysical testing was conducted on the specimens. In this section, the testing methods for density, porosity, resistivity, relative permittivity and seismic wave velocities are briefly described.  
Density and PorosityDensity and porosity were determined by two individual operators: The Geological Survey of Finland (GTK) and SP Technical Research Institute of Sweden. The method used was based on Archimedes' principle, and uses three weighings of the specimens (dry in air, saturated in air, saturated in water) to determine the pore space and bulk volumes, which are then used to calculate the density and porosity when the density of the saturation fluid is known.
## 7 Conclusions and Future Work  
In this article, we investigated the in-pipe image transmission based on the visible light relay communication (VLRC) technique. First, the optical transmission channel was introduced in order to explain the influences brought by the scattering and absorption effects in the pipe. Then, two types of image transmission approaches analog image signal relay transmission (AISRT) and digital image frame relay transmission (DIFRT) have been proposed and implemented. The AISRT system was built based on a complete optical transmitter, relay module, and receiver to achieve real-time image transmission. For the DIFRT system, apart from the basic transmission module, discrete cosine transform (DCT) compression and inverse discrete cosine transform (IDCT) decompression methods were adopted to realize more accurate and efficient transmission. Besides, a special data frame was designed to enhance the robustness and precision of in-pipe image transmission. To validate the feasibility and performance of VLRC-based image transmission systems, three typical experiments have been carried out in the empty and water pipe. Especially, for the water pipe, before inspection, the pipe was drained. However, some water remained in the pipe. Three transparent tubes which were filled with nearly 1.5 L water were inserted inside the experimental pipe to simulate the real water pipe environment. Firstly, the waveform test on the analog and digital image signal were conducted. The results revealed that, for the DIFRT method, the digital signals could basically achieve the relay image transmission in the 3 m length pipe channel, rather than the analog signals which had a huge signal distortion during the relay transmission. Secondly, the transmission performance in the different mediums using AISRT methods had been further evaluated. Analysis based on four parameters mean square error (MSE), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and structural similarity index measure (SSIM), we recognized that AISRT could not be selected as a suitable approach for VLRC-based image relay transmission. Finally, DIFRT-based image transmission with different bit error rate (BER) was tested. The conclusion could be achieved that digital image frame relay transmission was able to better overcome the effects brought by the absorption and scattering phenomenon and had relatively higher image transmission quality. In summary, the DIFRT system based on VLRC technique demonstrates advantages in some aspects of the transmission range, the strong image reconstruction capability, and the transmission speed. Firstly, due to the signal relay, the image transmission could reach much longer range than the single point-to-point communication. Theoretically, if these technologies such as suitable re-modulation, signal amplifying, and filtering with relay node could be improved, the system could overcome the attenuation brought by the absorption and scattering effects. As a result, the transmission distance
# Effect of Geometrical Factors on Torsion in Cold Roll Forming of the Lower Side Beam of a Car
## 3 Results and Discussion
### Web Width  
As shown in Figure 8a, the strain differences of different web widths almost coincided. It is difficult to see a significant difference from the figure, but its peak value showed a gradual decrease with the addition of web widths. Regarding the torsion angle obtained from the section of simulation results, it can be seen (as shown in Figure 8b) that with the increase of web width, the degree of torsion eased. For every 20 mm increase of web width on average, the torsion decreased by 0.13\({}^{\circ}\)/m. Therefore, it can be concluded that although the web has little effect on torsion, increasing the web width is still beneficial to reduce torsion defects.  
Figure 8: Simulation results of different web widths. (**a**) Influence of different web width on torsion; (**b**) Torsion decrease about 0.13\({}^{\circ}\)/m with an average 20 mm increase in flange width.  
Figure 7: The node was used to extract the path and direction.  
The reason for this is that, with regard to the webs of the materials involved in the torsional process, with the increased extension of the web surface materials engaged in torsion, the forming angle, thickness, and other factors will face the same circumstances, and after each iteration, the torque difference will also not change. The adverse effects generated by the torque error (i.e., torsional defects) are absorbed by more web material, thus reducing the degree of torsion on the whole.
## 2 MLEs of Parameters in Generalized Poisson with Under-Dispersion and Its Mean Regression Model  
Let \(\{Y_{i}\}_{i=1}^{n}\stackrel{{\text{iid}}}{{\sim}}\text{GP}^{(1)}( \mu,\alpha)\) and \(Y_{\text{obs}}=\{y_{i}\}_{i=1}^{n}\) denote the observed counts. Define  
\[\mathbb{I}_{0} \triangleq \{i\text{: }y_{i}=0,\ 1\leqslant i\leqslant n\},\] \[\mathbb{I}_{1} \triangleq \{i\text{: }y_{i}=1,\ 1\leqslant i\leqslant n\}\quad\text{and}\] \[\mathbb{I}_{2} \triangleq \{i\text{: }y_{i}\geqslant 2,\ 1\leqslant i\leqslant n\}.\]  
Let \(m_{k}\) denote the number of elements in \(\mathbb{I}_{k}\) for \(k=0,1,2\), then we have \(m_{0}+m_{1}+m_{2}=n\). Based on (2), the likelihood function of \(\{\mu,\alpha\}\) is  
\[L(\mu,\alpha) = \left(\prod_{i\in\mathbb{I}_{0}}\text{e}^{-\frac{\mu}{\alpha}} \right)\left(\prod_{i\in\mathbb{I}_{n}}\text{e}^{-\frac{\mu+\alpha-1}{\alpha}}\right)\] \[\times\ \prod_{i\in\mathbb{I}_{2}}\frac{\mu[\mu+(\alpha-1)y_{i}]^{y_{ i}-1}\exp\{-[\mu+(\alpha-1)y_{i}]/\alpha\}}{\alpha^{y_{i}}y_{i}!}\] \[\propto \exp\Bigl{(}-\frac{m_{0}\mu}{\alpha}\Bigr{)}\cdot\Bigl{(}\frac{ \mu}{\alpha}\Bigr{)}^{m_{1}}\exp\Bigl{[}-\frac{m_{1}(\mu+\alpha-1)}{\alpha} \Bigr{]}\] \[\times\ \frac{\mu^{m_{2}}}{\alpha^{\sum_{i\in\mathbb{I}_{2}}y_{ i}}}\exp\biggl{\{}-\frac{[m_{2}\mu+(\alpha-1)\sum_{i\in\mathbb{I}_{2}}y_{i}]}{ \alpha}\biggr{\}}\cdot\prod_{i\in\mathbb{I}_{2}}[\mu+(\alpha-1)y_{i}]^{y_{i}-1},\]  
where \(\sum_{i\in\mathbb{I}_{2}}y_{i}=n\bar{y}-m_{1}\) and \(\bar{y}=(1/n)\sum_{i=1}^{n}y_{i}\). Then, the log-likelihood function of \(\{\mu,\alpha\}\) is given by  
\[\ell(\mu,\alpha) = -\frac{m_{0}\mu}{\alpha}+m_{1}[\log(\mu)-\log(\alpha)]-\frac{m_{1 }(\mu+\alpha-1)}{\alpha}+m_{2}\log(\mu)\] \[-\ (n\bar{y}-m_{1})\log(\alpha)-\frac{m_{2}\mu+(\alpha-1)(n\bar{y}-m _{1})}{\alpha}\] \[+\ \sum_{i\in\mathbb{I}_{2}}(y_{i}-1)\log[y_{i}(y_{i}^{-1}\mu+ \alpha-1)]\] \[= (m_{1}+m_{2})\log(\mu)-n\bar{y}\log(\alpha)-\frac{n(\mu-\bar{y}) }{\alpha}\] \[+\ \sum_{i\in\mathbb{I}_{2}}(y_{i}-1)\log(y_{i}^{-1}\mu+\alpha-1)+ c_{1}, \tag{4}\]  
where \(c_{1}\) is a constant free from \(\{\mu,\alpha\}\).
## 3 Results
### Statistical Patterns of Bedform Features
#### 3.1.3. Crest Height and Trough Depth Variation Characteristics  
The probability density distributions of the crest height and the trough depth fitted by the program are shown in Figure 8. Figure 8a,b represents the distributions of the crest height \(\eta c\) and the trough depth \(\eta t\) of the 2D bedforms, and Figure 8c,d represents the distributions of the crest height \(\eta c\) and the trough depth \(\eta t\) of the 3D bedforms. The results demonstrate that the \(\eta c\) and \(\eta t\) of the 2D bedforms obey the normal distribution, and the \(\eta c\) and \(\eta t\) for the 3D bedforms obey the Gamma distribution. Table 4 outlines the distribution parameters for the dimensionless crest height and trough depth of both the 2D and 3D bedforms. The standard deviation for the crest height of the 2D bedforms, which follows a normal distribution, is 0.334. This value is slightly smaller than that for the trough depth, resulting in a steeper probability density distribution curve for the crest height compared to that of the trough depth. Conversely, the scale parameter for the trough depth of the 3D bedforms following a Gamma distribution is 0.324, which is slightly larger than that for the crest height. This attribute produced a more dispersed probability density distribution curve for the trough depth compared to that of the crest height.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Statistic**} & \multicolumn{3}{c}{**2D Bedforms**} & \multicolumn{3}{c}{**3D bedforms**} \\ \cline{2-7}  & \multirow{2}{*}{**Distribution**} & \multicolumn{2}{c}{**Distribution Parameters**} & \multirow{2}{*}{**Distribution**} & \multicolumn{2}{c}{**Distribution Parameters**} \\ \cline{3-4} \cline{6-7}  & & **Shape** & **Scale** & **Form** & **Log Mean (\(\mu\))** & **Log Standard** \\  & & **Parameter (\(\alpha\))** & **Parameter (\(\beta\))** & & & **Deviation (\(\sigma\))** \\ \hline \(\Delta s\) & Gamma & 15.185 & 0.066 & Lognormal & \(-\)0.084 & 0.420 \\ \(\Delta l\) & Gamma & 15.610 & 0.064 & Lognormal & \(-\)0.063 & 0.358 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of dune height distributions for different bedforms.  
Figure 8: Probability density distributions of crest height and trough depth. (**a**,**b**) represent 2D bedforms, and (**c**,**d**) represent 3D bedforms.
## 3 Results  
### Histological Scoring of AC Degeneration after DMM  
For qualitative analysis of osteoarthritis changes, Safranin-O/Fast Green staining was performed at 2 and 8 weeks after Sham or DMM surgery (Figure 1A), and a modified Mankin scoring system [27] was applied to quantitatively assess the degeneration of the medial and lateral tibial plateau (MTP and LTP) of the knee AC (Figure 1B). Two weeks after surgery, a significant difference (\(p\) = 0.002) in modified Mankin scores was observed in the MTP between the Sham- (1.33 \(\pm\) 0.47) and the DMM-operated mice (4.67 \(\pm\) 0.47). At this early-OA stage, significantly more severe cartilage degeneration of the MTP compared to the LTP was also detected in the DMM group (respectively, 4.67 \(\pm\) 0.47 and 2.33 \(\pm\) 0.47; \(p\) = 0.008). Eight weeks after surgery, focal absence of Safranin-O staining indicative of proteoglycan loss was observed in the MTP of DMM mice (Figure 1A, white arrow) but AC degradation scores were only slightly elevated compared to the 2 weeks post-operation group (Figure 1B), implicating only a mild progression of OA. The significantly different Mankin scores in the medial tibial compartment remained between the Sham- and the DMM-operated group (2.00 \(\pm\) 0.00 versus 5.67 \(\pm\) 1.70, \(p\) = 0.038); however, we only observed a tendency of higher Mankin scores for MTP compared to LTP in DMM mice, which did  
Figure 1: Histological analyses of knee joints 2 and 8 weeks after surgery. (**A**) Representative Safranin-O/Fast Green staining of frontal knee sections of C57B1/6J mice. White arrow indicates cartilage proteoglycan loss in the medial tibial plateau in 8-week-old DMM mice. MTP = medial tibia plateau; LTP = lateral tibia plateau. (**B**) Histological grading of AC degeneration in Sham and DMM mice according to a modified Mankin scoring system. Medial and lateral tibia plateau scores presented as a mean of 3 different animals for each group scored by 3 different observers. Statistical significance calculated by unpaired \(t\)-test (*; \(p\) < 0.05; **:**: \(p\) < 0.01). (**C**) Picrosirius red staining and polarization light microscopy demonstrate discontinuity of the collagen network in the superficial zone (white arrows) of DMM mice 8 weeks post-surgery.  
not reach the level of significance (respectively, 5.67 \(\pm\) 1.70 and 2.67 \(\pm\) 0.47; \(p\) = 0.121). In Sham mice, the modified Mankin scores for MTP and LTP were not statistically different in either age group. Picrosirius red staining combined with polarization light microscopy demonstrated comparable and intact collagen fibril organization in the MTP cartilage of Sham mice at 2 weeks and 8 weeks post-surgery (Figure 1C). However, in DMM mice, at 8 weeks postoperatively, we observed discontinued birefringence at the superficial zone (Figure 1C, white arrows) and reduced signal intensity in the middle and deep zones (Figure 1C), suggesting that the progression of OA is associated with degradation or structural disorganization of the collagenous network.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Key Parameters Influencing Grounding Performance
#### 2.3.2 Electrode Material and Configuration  
Electrode material impacts resistivity and corrosion resistance, influencing grounding performance. Copper is preferred for its low resistivity and high conductivity [12]. Electrode configuration (vertical, horizontal, or mesh) affects grounding resistance, with vertical rods performing better in high-resistivity soil due to greater earth contact.