esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupling Motion and Energy Harvesting of Two Side-by-Side Flexible Plates in a 3D Uniform Flow
## 1 Introduction
To our knowledge, the coupling motions and energy conversion performance of two side-by-side flexible plates in a 3D viscous flow have not been modeled and studied numerically yet. The primary purpose of this work is to correlate the fluid-induced flapping dynamics and energy conversion performance of the flexible plates with their aspect ratios and transverse gap distances in 3D. Towards this end, we apply a hybrid algorithm combined with the lattice Boltzmann method (LBM) [35; 36] and immersed boundary method (IBM) [37] to solve the fluid flow and fluid-structure interactions. To solve the passive deformation of the flexible body, a finite element method based on a triangular mesh [38; 39] is adopted. To validate this coupled algorithm, the dynamics of flow past a square flexible plate were simulated at low Reynolds number. Then, numerical simulations based on various parameters were carried out, and the coupled flapping modes and energy conversion performance were analyzed systematically. The rest of this paper is organized as follows. In Section 2, we detail the physical model and numerical method, including the algorithm validation. In Section 3, we perform the simulations and discuss the results. Finally, a brief conclusion is drawn in Section 4.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches
### 3D-Information-Based Approaches  
Although there are many kinds of 2D information, it is difficult to use 2D information to measure the 6D pose under some special conditions, or the method requires complex algorithms to obtain precise results. With the development of hardware, more and more devices that can record 3D scene information are appearing, such as depth cameras [67] and 3D scanners [68]. Compared with 2D information, 3D information preserves the original appearance of the object, which is more useful for measuring 6D pose [44; 69]. Combined with 3D information, the method is more robust and can obtain more accurate results. Mainstream methods can be broadly divided into the following two categories.
# Omni Wheel Arrangement Evaluation Method Using Velocity Moments
## 3 Omni Wheel Arrangement Evaluation Method Using Velocity Moments
### Derivation of \"Easy-to-Operate Omni Wheel Arrangement\"  
Suppose that in an n-wheel Omni wheel robot, the position and orientation of \(n-1\) wheels are fixed. When moving straight ahead in the x and y directions, the velocity moment generated by the remaining wheel must satisfy the following equations. \(V_{x}\) and \(V_{y}\) are common values for all wheels so that they can be removed from both sides.  
\[\cos(\theta_{n})l_{i}\sin(\theta_{n}-\beta_{n})=-\sum^{n-1}\cos(\theta_{i})l_ {i}\sin(\theta_{i}-\beta_{i}) \tag{4}\]  
\[\sin(\theta_{n})l_{i}\sin(\theta_{n}-\beta_{n})=-\sum^{n-1}\sin(\theta_{i})l_ {i}\sin(\theta_{i}-\beta_{i}) \tag{5}\]  
From the above equations, \(\theta_{n}\) and \(l_{n}\sin(\theta_{n}-\beta_{n})\) are obtained as follows:  
\[\theta_{n}=\arctan\frac{-\sum^{n-1}\sin(\theta_{i})l_{i}\sin(\theta_{i}-\beta _{i})}{-\sum^{n-1}\cos(\theta_{i})l_{i}\sin(\theta_{i}-\beta_{i})} \tag{6}\]  
\[l_{n}\sin(\theta_{n}-\beta_{n})=\frac{-\sum^{n-1}\cos(\theta_{i})l_{i}\sin( \theta_{i}-\beta_{i})}{\cos\theta_{n}}=\frac{-\sum^{n-1}\sin(\theta_{i})l_{i} \sin(\theta_{i}-\beta_{i})}{\sin\theta_{n}} \tag{7}\]  
By expanding \(l_{n}\sin(\theta_{n}-\beta_{n})\), we see that the omni wheel position \((x,y)\) is obtained as follow:  
\[l_{n}\sin(\theta_{n}-\beta_{n}) =l_{n}\cos\beta_{n}\sin\theta_{n}-l_{n}\sin\beta_{n}\cos\theta_{n} \tag{8}\] \[=x\sin\theta_{n}-y\cos\theta_{n}\]  
It can be seen that the velocity moment balance can be satisfied by placing the Omni wheel on the straight line that satisfies the Equation (8).  
Figure 3: Pattern diagram of the Omni wheel robot: The parameters of each wheel of an Omni wheel robot are shown when a wheel of radius \(r\) is placed at position (\(x\),\(y\)) and direction \(\theta\). \(\omega\) and \(V\) are the rotational and ground speeds of the wheel, and \(\dot{l}^{\prime}\) are the distance to the center of rotation of the robot relative to the velocity vector of each wheel.  
Therefore, Equations (6) and (8) can be used to determine the nth Omni wheel and its position and orientation. As can be seen from the fact that \(V_{x}\) and \(V_{y}\) were removed in determining Equations (4) and (5), the wheel arrangement obtained satisfies Equation (3) at all velocity \(V=(V_{x},V_{y})\).
# Querying on Federated Sensor Networks
## 4 Results
### The Comparison of the Layered and Flat FSN Models
#### 4.2.3 The Querying Latency Results  
As shown in Figure 6, Rumor Routing has a large latency compared to other protocols because of the occurrence of packet forwarding loops on the flat FSN model. We also observe that this query resolving latency increases as the distance between query and data source increases. This result  
Figure 5: Energy consumption on FSN.  
indicates a need for an efficient loop detection mechanism for querying protocols running on a flat FSN model.
## Appendix A
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{4}{c}{**Least Squares Mean (SE) for Group x Time Interaction**} & \multicolumn{2}{c}{**Effect**} & \multirow{2}{*}{**\(p\)-Value**} \\  & \multicolumn{2}{c}{**OEP Plus Usual Care**} & \multicolumn{2}{c}{**Usual Care**} & \multicolumn{2}{c}{**Size**} & \multicolumn{1}{c}{**F**} & \multirow{2}{*}{**\(p\)-Value**} \\  & **Baseline** & **6 Months** & **Baseline** & **6 Months** & & \(\mathbf{n_{p}}^{2}\) & \\ \hline
**TUG Single-Task** & & & & & & \\ Duration (s) \(\vee\) & 31.60 (5.58) & 27.93 (5.65) & 23.56 (3.14) & 21.73 (3.14) & 0.0068 & 0.47 & 0.51 \\ Sit-to-Sand Lean Angle (\({}^{c}\)) \(\wedge\) & 39.39 (4.21) & 34.34 (4.61) & 37.70 (3.87) & 34.40 (3.89) & 0.0020 & 0.10 & 0.76 \\ Turn Angle (\({}^{c}\)) \(\vee\) & 124.23 (12.37) & 141.19 (12.10) & 136.51 (10.80) & 143.38 (10.81) & 0.0174 & 0.98 & 0.34 \\ Turn Duration (s) \(\vee\) & 3.15 (0.29) & 3.42 (0.28) & 3.35 (0.16) & 3.23 (0.16) & 0.0256 & 1.62 & 0.23 \\ Turn Velocity (\({}^{c}\)) \(\wedge\) & 81.25 (11.96) & 105.24 (11.80) & 87.88 (7.97) & 100.14 (7.99) & 0.0259 & 3.10 & 0.10 \\
**TUG Dual-Task** & & & & & & & \\ Duration (s) \(\vee\) & 40.86 (5.80) & 29.32 (5.88) & 41.13 (5.18) & 31.68 (5.03) & 0.0027 & 0.15 & 0.70 \\ Sit-to-Sand Lean Angle (\({}^{c}\)) \(\wedge\) & 40.40 (5.66) & 34.57 (6.02) & 37.40 (3.82) & 35.31 (3.67) & 0.0069 & 0.35 & 0.56 \\ Turn Angle (\({}^{c}\)) \(\vee\) & 127.99 (15.38) & 153.26 (15.38) & 119.12 (10.79) & 135.27 (10.85) & 0.0025 & 0.25 & 0.62 \\ Turn Duration (s) \(\vee\) & 3.53 (0.28) & 3.98 (0.28) & 3.23 (0.24) & 3.35 (0.24) & 0.0076 & 0.59 & 0.45 \\ Turn Velocity (\({}^{c}\)) \(\wedge\) & 71.19 (9.21) & 90.15 (9.14) & 75.97 (6.60) & 86.46 (6.63) & 0.0071 & 1.78 & 0.20 \\
**Sx Sit-to-Stand** & & & & & & & \\ Duration (s) \(\vee\) & 28.82 (6.07) & 32.57 (6.16) & 20.40 (2.82) & 19.96 (2.83) & 0.0227 & 1.66 & 0.22 \\ Sit-to-Stand Lean Angle (\({}^{c}\)) \(\wedge\) & 29.74 (4.39) & 26.16 (4.82) & 33.12 (4.52) & 29.13 (4.54) & 0.0002 & 0.00 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Least Squares Mean (SE) for Group x Time Interaction**} & \multicolumn{2}{c}{**Effect**} & \multicolumn{1}{c}{**\(p\)-Value**} \\
**Variable** & \multicolumn{2}{c}{**OEP Plus Usual Care**} & \multicolumn{2}{c}{**Usual Care**} & \multicolumn{2}{c}{**Size**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{\(p\)-Value**} \\  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{1}{c}{**6 Months**} & \multicolumn{2}{c}{**Baseline**} & \multicolumn{1}{c}{**6 Months**} & \multicolumn{1}{c}{} & \\ \hline
## 4 Results  
Aiming to validate the correctness of the proposed sensorless scheme for the MPCC, a 2.2 kW PMSM experimental prototype with 300 V dc-link voltage was employed in the test, as illustrated in Figure 7. All the parameters of the experimental machine are the same as the simulated setup, as listed in Table 2. A dSPACE1104 digital controller was applied for the real-time regulation via connecting to the S-function of Matlab/Simulink2009a with a PC, realizing the control system with a sampling period of 200 \(\upmu\)s. Meanwhile, the drive circuit was considered as the connection module of software and hardware to realize the control of the PMSM by the software, including signal acquisition and transfer circuit, inverter drive circuit, 3L-NPC inverter module, and so on. Specifically, the 3L-NPC inverter module was derived from three F3L300R07 PE4 IGBT modules from INFINEON with a maximum voltage stress of 650 V and a current flow of 600 A, and each module  
Figure 6: Diagram of the position observer of the rotor.  
consisted of four IGBT switches and two diodes for clamping purposes. The F3L300R07 PE4 modules were equipped with the proven PSPC 432-EP4 driver, which could open/close each IGBT switch in isolation. The snubber circuit in the PSPC 432-EP4 was designed simply as a single capacitor type, with a capacitance of 470 pF. Additionally, phase current and capacitor voltage were measured by current sensors HAS50S and voltage transducers LV25-P, respectively.
## 4 Dry and Wet Ambient Conditions  
The fatigue life of the referential RC deck in dry conditions and fully submerged was investigated in previous research [17; 26; 31]. However, this study targets the effect of wetting locations only at the upper entire surface layer of the RC deck. Figure 8 shows the relation of load cycles and the central live load deflection of three cases: dry, upper mesh layer \"wet\", and full submerged conditions, where their remaining fatigue lives are 221.49, 8.16, and 2.93 million cycles, respectively, as shown in Table 2.  
The pore-mechanics based simulation can indicate the profile of erosion by K factor (Equation (1)) at the central zone of the referential RC deck (upper layer \"wet\" in Table 2). Concrete erosion is severe around the path of the moving wheel as shown in Figure 9, and the concrete is fully eroded at 70,000 cycles as shown in Figure 10. The computed erosion profile matches the reality of the RC decks in the laboratory. Figure 11 shows the rise and decay of the pore water pressure. When the concrete composite is more or less sound as solid, pore pressure may rise according to the external load. However, when the erosion evolves much, the pore water pressure cannot rise because the water may easily pass through an assembly of cracks. In accordance with the erosion, we have varying compressive stresses of concrete at the top layer central elements and tensile stresses of reinforcement at the bottom ones as shown in Figures 12 and 13, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & \
\begin{tabular}{c} **Remaining Fatigue Life** \\ **(Million Cycles)** \\ \end{tabular} &
\begin{tabular}{c} **Reduction in Life** \\ **(Compared to Dry Case)** \\ \end{tabular} \\ \hline Dry Condition & 221.49 & 1.0 \\ Upper Layer (Wet) & 8.16 & 1/27 \\ Submerged & 2.93 & 1/75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Remaining fatigue life of dry and wet ambient conditions.  
Figure 8: Load cycles and central live load deflection of dry and wet ambient conditions.  
Figure 7: Standardized states for stagnant water studied cases.  
Figure 11: Load cycles and the pore water pressure of the central zone of the RC deck.  
Figure 10: Load cycles and the erosion factor of the central zone of the RC deck.  
Figure 9: Multi-scale simulation of RC deck under fatigue loading in upper layer “wet” case.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 7 Challenges  
While VLC has many advantages over RF communication, it also faces unprecedented challenges that need to be mitigated. One of these challenges is commercialization, in which lighting companies and phone manufacturers have to develop their future devices to accommodate the current VLC technologies for use in future applications. Another challenge is creating new VLC standards and developing the current ones by considering the latest technological evolutions in the field [126]. In addition, the performance of VLC can be degraded or distorted due to background noise. Manchester coding mitigates this background noise [6]. Moreover, to cater for dual-functionality LD- or SLD-based SSL-VLC applications, the quality of the produced white light still needs to be improved using stable light converters that can be used for prolonged periods of time. The use of LDs as data transmitters also faces issues with misalignment and outages because there are many ways in which the line-of-sight link between the transmitter and the receiver might be lost or blocked. A critical concern is the hazards related to eye safety, as long-term exposure to high-intensity light is potentially harmful to human vision and circadian rhythm. In the case of using conventional LEDs as transmitters, the limitation in allowable bandwidths would potentially limit the transmission rate in Li-Fi systems; thus, micro-LEDs and LDs are more promising alternatives, offering higher bandwidths and transmission rates. Another updated modulation technology, quantum communication, which is also being applied to the VLC system, will introduce the principle of quantum communication.  
The following section discussed the state of the art of OWC systems and potential techniques for improving the performance or the data rate. The possible solutions for solving problems of the current VLC system mention in [136] are discussed, such as:  
1. Bandwidth limitation of the light source.
2. Si-based detectors are mainly sensitive to infrared waves.
3. Point-to-Point communication based on a single transmitter and detector.
4. Transmission and reception antennas require a large lens group.
# Technologies for the Production of Fertilizable Mammalian Oocytes
## 1 Introduction
### Main Causes of Intertility in the Female
#### 1.2.1 Disease-Related Infertility  
Premature ovarian insufficiency (POI), polycystic ovary syndrome (PCOS) and endometriosis may all play a role in inducing female infertility [2]. In addition, treatments for cancer and other systemic pathologies are very important determinants of female infertility due to their high gonadotoxic potential. In detail, POI occurs in about 1% of women. It is defined as the cessation of menstrual cycles in women younger than 40 years of age in the presence of an elevated serum Follicle Stimulating Hormone (FSH). The causes of POI may be genetic (Turner's syndrome, in particular), environmental, infective or metabolic. Other forms of POI are associated with autoimmune conditions, or may have a-iatrogenic origin, occurring as a consequence of surgical interventions or cancer treatments (see below). Inflammatory aging, an intriguing process that induces inflammatory reactions in various tissues and organs, may also play a role in the pathogenesis of POI [5]. However, in the majority of cases, the origin of POI remains unknown [2]. Instead, PCOS is an endocrine disorder affecting 5-10% of the female population. It is a heterogeneous condition characterized by ovulation impairment, a typical ultrasound morphology, and hyperandrogenism [6]. Socioeconomic status, lifestyle, cultural factors and race may influence PCOS determinants and variations in phenotype [2]. PCOS is also frequently associated with obesity. Obese women affected by PCOS may show even more severe metabolic and reproductive alterations than the not obese counterpart [7]. Endometiosis is a pathological pelvic inflammatory process usually associated with infertility. Endometiosis-related infertility may be due to anatomical alterations of the female genital tract in presence of adhesions and fibrosis; endocrine and/or immunological alterations may also contribute to impairment of fertility in women of reproductive age affected by endometriosis. Even if fertilization occurs, embryo implantation may be affected in the presence of endometriotic lesions [2]. The strict relation between endometriosis and infertility is proven as well by the fact that, if lesions are removed, the condition of infertility may undergo reversal [8].
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 7 Conclusions  
It is challenging to accurately acquire the defective areas with different depths on PVC materials with minimum human intervention. In this paper, we developed an effective approach to accurately detect defects with different depths on a PVC specimen. The whole process was made automatic, and no human intervention was required. We firstly quantitatively analyzed the relationships between defect depth, temperature, and time. Then, we proposed the maximum of standard deviation of the sensitive region method (MSDSMM) to decide the best frame to calculate defect boundaries, which answered a key question in thermographic defect detection. By integrating blob analysis and the Otsu method, we further proposed a fully automated process to extract defect areas. As a result, our study should offer a great help in non-destruction defect detection and quality assurance.  
Further effort will be focused on extracting defects in ordinary temperature fields. For example, it may be possible to combine the MSDSRM with a temperature field processing technique such as FWHM to determine the defect size through the analysis of the surface temperature field. Furthermore, our study assumes that the infrared images have enough contrast for image processing processes such as defect contour extraction and binary segmentation, and we are aware that such a condition might not always be feasible in practice. Therefore, some pre-processing will be needed, which might affect the detection accuracy.  
Conceptualization, L.Y., H.L. and Y.L.; Methodology, L.Y. and P.Y.; Software, L.Y., Q.S. and H.L.; Validation, P.Y.; Formal Analysis, X.Z.; Investigation, L.Y.; Resources, L.Y.; Data Curation, X.Z.; Writing-original draft preparation, L.Y., X.Z., Q.S., P.Y. and Y.L.; Writing-review and editing, L.Y., P.Y., Q.S., and Y.L.; Supervision, L.Y., Y.L. and P.Y.; Funding acquisition, L.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Nation Natural Science Foundation of China, grant number 51865038\", \"Chinese Government Scholarship for studying abroad, grant number 201708360122\" and \"Graduate Innovation Fund of Jiangxi Province, grant number YC2020-S547\". The APC was funded by \"Nation Natural Science Foundation of China, grant number 51865038\".  
This work was supported by the National Natural Science Foundation of China (Grant No. 51865038), the Chinese Government Scholarship for studying abroad (Grant No. 201708360122) and the Graduate Innovation Fund of Jiangxi Province (Grant No. YC2020-S547).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Theoretical Assessment
#### 2.2.3. Composst Production Potential and Synthetic Fertilizer Replacement  
The carbon-to-nitrogen (C/N) ratio is one of the most important parameters for composting. In this study, compost was considered a mixture of livestock manure and rice straw to obtain the optimum C/N ratio. For the carbon source, rice straw is considered because rice is regularly cultivated all over the country. The C/N ratio in a composting pile needs to range from 20:1 to 40:1. This study considered the C/N ratio of compost to be 30:1. The amount of manure and rice straw needed for the desired C/N ratio is estimated using the Pearson Square procedure [15]. A detailed worksheet for the calculation of the total amount of compost based on the C/N ratio is provided in Appendices A and B (Figure A  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\begin{tabular}{c} **Livestock** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Manure** \\ **Generation Rate** \\ **(kg/Head/Day)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Availability** \\ **Coefficient** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **TS of** \\ **Manure (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Biogas Yield** \\ **(m\({}^{3}\) kg\({}^{-1}\) TS)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **DM (\% of** \\ **Manure)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **VS (\% of** \\ **DM)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **CH\({}_{4}\) Content** \\ **(\% of Biogas)** \\ \end{tabular} } \\ \hline Large Animal & 19 & 50 & 25 & 0.60 & 25 & 80 & 60 \\ Small Animal & 0.8 & 13 & 25 & 0.40 & 18 & 80 & 45 \\ Poultry & 0.045 & 90 & 29 & 0.80 & 10 & 70 & 60 \\ \hline \end{tabular}
\end{table}
Table 1: Values used to estimate the biogas potential in this study.  
and Table A1). The reduction of total mass after the composting of feedstock ranged from 10-50% of the initial mass [39]. This study assumed a higher value of mass reduction percentage to calculate the total compost generation from livestock manure.  
The percentage of synthetic fertilizer replaceable by livestock manure compost (SR) was calculated by Equations (4) and (5) [40]. The average values used for N, P, and K content in livestock manure (Table 2), the loss of respective nutrients during the composting process, and the bioavailability of nutrients are shown in Table 3. The use of synthetic fertilizer in the districts was collected from the allocation of fertilizer in 2024 by the Department of Agriculture in Bangladesh [41].  
\[\text{SR (\%) = 100 \times Estimated yearly nutrient supply from manure compost \div \tag{4}\]  
\[\text{Used or allocated synthetic fertilizer in a year} \tag{5}\]  
\[\text{Nutrient supply = (\% of N, P, and K content in manure) \times (yearly available} \tag{6}\]  
\[\text{ manure generation) \times (100 - loss of N, P, and K during composting) \times 0.01 \times (Bioavailability of N, P, and K by agricultural plants)
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 4 Validation with Real Data
### Validation with the Interferogram over the Etna Volcano
We can see the minimum residues, the minimum PSD value and the minimum SPD can be achieved by the proposed filter. The proposed method can reduce 92.09% residues, indicating that the proposed filter has better performance in phase denoising. It can also be seen that the proposed method has the smallest RMSE. From this point of view, the proposed filter shows better denoising and edge preserving ability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Filter**} & **Positive** & **Negative** & **Residue** & \multirow{2}{*}{**PSD (\(\times\)10\({}^{5}\))**} & \multirow{2}{*}{**SPD (\(\times\)10\({}^{5}\))**} & \multirow{2}{*}{**RMSE**} \\  & **Residues** & **Residues** & & & & \\ \hline Unfiltered & 10,005 & 10,008 & - & 3.4254 & 2.5439 & - \\ Goldstein & 5430 & 5431 & 45.73 & 2.5304 & 1.9945 & 0.2820 \\ Wavelet & 2817 & 2824 & 71.81 & 1.9773 & 1.7258 & 0.1824 \\ Lee & 2339 & 2342 & 76.61 & 1.8647 & 1.6119 & 0.1705 \\ Proposed & 790 & 792 & 92.09 & 1.3952 & 1.3223 & 0.1693 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation with the real data over Etna volcano, PSD: the phase standard deviation; SPD: the sum of phase difference, RMSE: the root mean square error. Figure 9: Deformation results of (**a**) true deformation phase; (**b**) the Goldstein filter; (**c**) the wavelet filter; and (**d**) the Lee filter; (**e**) the proposed filter. (Unit: rad).