esa-sceva/satcom-chunk-collection huggingface dataset:
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 2 Materials and Methods  
### Orah Sorting Line  
The Orah sorting line used in this study measures 30 m in length and 4 m in width and integrates four conveyor chains. The mechanical structure of the Orah sorting line is a critical component of the overall design, as it determines the operational principles of the entire system [26; 27; 28; 29]. The mechanical section of the Orah visual sorting system consists of a feed conveyor, a single-line conveyor, an image acquisition chamber, a grading execution device, and grading output channels, as shown in Figure 1.  
The feeding conveyor is connected to an external Orah washing machine, which is a necessary step in the post-harvest processing of Orah to extend its storage life. During this process, most dust and debris are removed, ensuring that clean images of Orah surface are captured. The single-line conveyor ensures that Orah enters the sorting process in an orderly and continuous manner. This design prevents Orah from accumulating and ensures the smoothness of the sorting process. The conveyor mechanism is equipped with fruit cups in which the Orah is placed. As shown in Figure 2, these fruit cups come into  
Figure 1: Diagram of the mechanical model for the Orah sorting line.  
contact with a belt upon entering the image acquisition chamber. The fruit cups are driven to rotate because the belt speed (V2) is slightly higher than the conveyor chain speed (V1). Consequently, the Orah undergoes horizontal transport while continuously rotating with the fruit cups. Through repeated sampling, multiple images from different angles of the Orah can be captured. When defects are detected, the grading result from adjacent angles can be cross-verified. This reduces the likelihood of misgrading due to limitations in model accuracy. These multi-angle sampled images are also utilized for diameter detection.  
When the fruit cup blocks the photoelectric switch (model: Panasonic CX-411E, Figure 3a), it triggers the industrial camera (Figure 3b) to capture images. This ensures that each fruit cup occupies a fixed position in the image, and consequently, the position of the Orah within the image is also stable. The Hikvision Robot industrial camera (model: MV-CA032-10 GC) features a resolution of 2048 \(\times\) 1536 and employs a global shutter, avoiding the distortion typically caused by rolling shutters when photographing moving objects [30]. The image acquisition chamber is equipped with high-powered LED lighting, providing sufficient brightness at a shutter speed of 1/1000 s to ensure clarity for moving objects. Together, these components ensure that the Orah's surface is captured from multiple angles against a clean and uniform background, producing high-quality images suitable for grading and data collection. An overview of the entire sorting line is shown in Figure 4a,b.  
Figure 3: Component diagram of the Orah sorting line. (**a**) Panasonic photoelectric sensor; (**b**) Hikvision Robot industrial camera.  
Figure 2: Movement and rotation of Orah on the conveyor system.
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## 3 Results
### Finite Element Analysis  
Von-Misses stress distributions of the deformed final state of the auxetic stent under three-point bending, 60\({}^{\circ}\) of twist, 160\({}^{\circ}\) of twist, and buckling, are shown in Figure 5a-d. Von-Misses stress distribution of auxetic stent under three-point bending given in Figure 5a shows the final stage where the loading pin almost touches to supporting pins. As the loading pin moved downward, Von-Misses stress intensified around the top part of the stent symmetrically right under the loading pin. Interestingly, there was almost no compressive stress region on the stent. The bottom side of the stent deformed, and the stent width expanded in the folded form. The deformed state predicted by FEA agrees perfectly with the direct mechanical three-point bending test results, the stent in the final stage expanded in width, and an inside opening was preserved even in the folded state.  
Von-Misses stress distributions for 60\({}^{\circ}\) and 160\({}^{\circ}\) twisting, shown in Figure 5b,c respectively, show maximum Von-Misses stress value remained almost constant with a slight fluctuation around 350 MPa, which confirms that the auxetic stent undergoes plastic deformation at approximately 14\({}^{\circ}\) of the angles of twist and stress redistributed as the large deformations took place in the structure. Initially, the circular structure remained circular while the re-entrant cell structure opened and became rectangles around the edges of the stent. Then, the warping of the structure and local contractions were evident throughout the stent for 60\({}^{\circ}\) of rotation. These results match well with direct mechanical torsion test results where an enlargement takes place in-stent for a 160\({}^{\circ}\) angle of rotation. However, a local narrowing was not evident in the FEA torsion test, which may stem from the difference in boundary conditions: in the actual mechanical test, 3 mm at the edges was fixed while for simulations there were edge circles.  
Von-Misses stress distributions of the auxetic stent in response to longitudinal compression in Figure 5d show that stress increased as the stent folded and reached the ultimate stress of the base material (785 MPa). Stress became concentrated around the buckling region as the stent deformed further. Buckling started after approximately 0.66 mm dis  
Figure 5: Von-Misses stress distributions for stents at failure stage based on finite element analysis: (**a**) three-point bending; (**b**) 60\({}^{\circ}\) angle of twist; (**c**) 160\({}^{\circ}\) angle of twist; and (**d**) longitudinal compression.  
placement as determined from direct mechanical tests and simulations also resulted in unstable deformation after 0.6 mm deflection of the upper plate. After 4 mm deflection, simulations predicted a failure where the stent loses unity of shape. A much narrower cross-section for the simulation was observed compared to the mechanical buckling test.
## 3 Results
### Functional Connectivity Analysis  
#### 3.2.1 Global Connectivity  
Figure 3 illustrates the distribution of PLVmean from all subjects in the AD and HC groups in all five frequency bands. The result of the Mann-Whitney U-test indicated that the average PLVs from all channel combinations were significantly higher in the theta band for the AD participants (\(Mdn=0.31\)) when compared to the HCs (\(Mdn=0.26\)), \(U=326\), \(p=0.0004\). This was not the case for the delta (\(Mdn=0.30\) vs. \(Mdn=0.28\), \(U=258\), \(p=0.12\)), alpha (\(Mdn=0.26\) vs. \(Mdn=0.24\), \(U=264\), \(p=0.09\)), beta (\(Mdn=0.18\) vs. \(Mdn=0.18\), \(U=226\), \(p=0.50\)), and gamma bands (\(Mdn=0.18\) vs. \(Mdn=0.18\), \(U=181\), \(p=0.62\)).  
Figure 3: The average PLVs obtained from all connectivity pairs for the five frequency bands (global connectivity). Plots marked with * indicate a statistically significant difference (\(p<0.05\)) between AD patients and HCs.
## 8 Conclusions  
This research studied the impact of unconventional proposed phase-shift selection on EMI filter optimization for both Band A (9-150 kHz) and Band B (>150 kHz). The results obtained in Band A revealed that the interleaved topology provided has more advantages which gives the possibility of using no filter if the switching frequency is higher than 75 kHz for two units, 50 kHz for three units, and 37.5 kHz four units. Additionally, in Band-B, the application of conventional phase-shift between the units was not effective for all switching frequency ranges. Thus, various phase-shifts (unconventional) were employed to get a higher corner frequency and smaller filter size in Band B based on the EMI estimation technique.  
Notably, in order to design a DM EMI filter, the proposed technique was used to model the noise level with higher accuracy at different phase shifts. In addition, this research highlighted the benefits of using the conventional phase shifts in Band-A to suppress the odd-order harmonics in order to optimize the attenuation requirement for EMI filter design. At the end, filter volume optimization was utilized to get a minimized component size by using an analytical estimation method and selective proper phase shifts. Therefore, a general method based on the analytical equation considering phase shift was employed to make EMI filter volume optimization. Experimental results verified the EMI estimation method with different phase shifts in Band A and Band B, and their maximum errors are below 1 dB.  
[PERSON] conceived and designed the paper and wrote the Introduction and Materials and Methods. Discussions and Conclusions were the collective work of all authors. The writing review and editing were performed by [PERSON], [PERSON], [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{\(f_{sw}\) = 20 kHz (CCM Operation)} \\ \hline
**Phase** & \(\mathbf{\uptheta}\) = **0** & \(\mathbf{\uptheta}\) = **180** & \(\mathbf{\uptheta}\) = **90** \\ \hline
**Band** & **A** & **B** & **A** & **B** & **A** & **B** \\ \hline \multirow{2}{*}{Method(dB\(\mu\)V)/frequency(kHz)} & 20 & 160 & 40 & 160 & 20 & 160 \\  & kHz & kHz & kHz & kHz & kHz & kHz \\ Estimated & 137.1 & 115.8 & 129 & 116 & 133.5 & 115 \\ Simulated & 136.3 & 115.1 & 128 & 115.3 & 133.3 & 115.2 \\ Experimental & 137.3 & 115.4 & 129.3 & 115.75 & 134.2 & 115.8 \\ E\({}_{\text{ss-e}}\)(dB) [1] & 0.2 & 0.4 & 0.3 & 0.25 & 0.7 & 0.8 \\ E\({}_{\text{ss-e}}\)(dB) [2] & 1 & 0.3 & 1.3 & 0.55 & 1.1 & 0.6 \\ \hline \hline \end{tabular}  
* \({}^{1}\): Error between estimated and experiment: [2]: Error between simulation and experiment.  
\end{table}
Table 6: Comparative DM Noise Results for the Case Studies.
## 2 Theoretical Framework
### Propagation through the Rain  
Rainfall through drops of liquid water is one key dynamic natural occurrence that undesirably attenuates the amplitudes of propagated microwave radio frequency signals in the atmosphere. The attenuation is initiated via scattering and absorption when the propagated electromagnetic waves encounter the showered liquid water droplets of different intensities, natures, and concentrations [53]. While the scattering disseminates the propagated microwave signals, the absorption result rises in molecular energy in correspondence with the atmospheric temperature, which causes a comparable loss of electromagnetic signal energies, particularly at higher frequencies.  
The universal power-law model that describes attenuation due to rain is given by Equation (6) [54]:  
\[\gamma_{att}(r)=\gamma_{r}L_{path} \tag{6}\]  
\[\gamma_{r}=\beta(R_{re})^{\alpha} \tag{7}\]  
where \(L_{path}\) (in km) and \(\gamma_{r}\) express the propagation path length and rain rate in Equation (7), respectively. The real channel length (\(l\)) is related with the distance parameter \(d_{s}\) in Equation (8) [55; 56]:  
\[d_{s}=\frac{1}{0.4771^{0.633}R_{re}^{0.073v}f_{c}^{0.123}-10.579(1-\exp(-0.024 l))^{\alpha}} \tag{8}\]  
The power-law parameters \(\beta\) and \(\alpha\) can be obtained by Equations (9) and (10), respectively:  
\[\log\beta=\sum_{j=1}^{4}\left(x_{j}\exp\left[-\left(\frac{\log f_{c}-y_{j}}{z _{j}}\right)^{2}\right]\right)+m_{k}\log f_{c}+z_{k} \tag{9}\]  
\[\log\alpha=\sum_{j=1}^{5}\left(x_{i}\exp\left[-\left(\frac{\log f_{c}-y_{i}}{ z_{i}}\right)^{2}\right]\right)+m_{n}\log f_{c}+z_{n} \tag{10}\]  
Implementation of rain and gaseous \(R_{re}(\mathrm{mm}/\mathrm{h})\) expresses the received depth of total water droplets due to rainfall (in mm) divided by the entire shower duration (in hours). It is given by Equation (11):  
\[R_{re}(\mathrm{mm}/\mathrm{h})=\frac{\mathrm{Total\ depth\ of\ rainfall}}{\mathrm{Entire\ rainfall\ duration}} \tag{11}\]
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
## 2 Materials and Methods
### Data Acquisition and Processing  
Swimmers were recorded with two cameras placed behind the underwater windows (JVC GY-DV500E, 50 Hz, 1/1000 s) and with their optical axis perpendicular to the sagittal plane of the swimmers. Both cameras were located 12 m at the side of the swimmers' lane, the first camera being at 7.5 m and the second camera at 15 m from the starting wall. Swimming recordings were manually digitized and temporally coded by an experienced observer using biomechanical model of four points (shoulder, hip, knee and ankle). Two-dimensional direct linear transformation (2D-DLT) algorithms [28] were employed to transform the two dimensional screen coordinates (in pixels) to real meter coordinates. A rectangular structure of PVC (2 m high and 5 m wide) with known coordinates located in the swimmer's plane of movement was used for the camera's calibration prior to recordings in each camera view. The origin of the reference system was placed on the starting wall of the pool at the water surface level [29], with the horizontal and vertical axes aligned towards the starting direction and the above water direction, respectively.  
The transitioning from underwater to surface swimming was divided in two phases: (1) pre-transition--the last underwater kicking action (downward kick plus upward kick movements, [30]) before the beginning of arm propulsion and (2) transition (breakout)--the first complete arm stroke cycle after pre-transition (Figure 1). The pre-transition was defined from the swimmers' feet in the highest position (or lowest position in backstroke), whereas the transition phase was defined from the swimmers' hands separation to the first or second water entry, respectively, for the butterfly or alternate strokes. In breaststroke, both pre-transition and transition phases were defined from the maximum flexed knee position on the first and subsequent breaststroke kick after the underwater arm pullout.
# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Region II (NDS)**} & \multicolumn{6}{c}{**Region II (DS)**} \\ \cline{2-9}
**Item** & **CTT1** & **CTT2** & **DTT1** & **DTT2** & **CTT1** & **CTT2** & **DTT1** & **DTT2** \\ \hline Execution time (min) & 63.35 & 61.08 & 64.17 & 63.55 & 76.58 & 70.05 & 66.73 & 72.27 \\
2F cable (drop cable) (km) & 79.93 & 63.83 & 54.15 & 27.82 & 141.20 & 114.68 & 84.78 & 73.03 \\
12F cable (distribution cable) (km) & - & - & 19.66 & 29.07 & - & - & - & 23.32 & 42.17 \\
48F cable (feeder cable) (km) & 13.95 & 16.16 & 6.69 & 11.09 & 17.73 & 19.64 & 14.16 & 13.01 \\ Total used optical cables (km) & 93.88 & 79.99 & 80.50 & 67.98 & 158.93 & 134.31 & 122.26 & 128.21 \\ Total used 1 st level splitters (unit) & 32 & 20 & 16 & 24 & 59 & 44 & 51 & 39 \\ Total used 2 nd level splitters (unit) & - & 40 & 54 & 115 & - & 87 & 161 & 233 \\ Total used FOSC (unit) & 32 & 40 & 13 & 24 & 59 & 87 & 43 & 36 \\ Total used PON ports (unit) & 32 & 20 & 16 & 24 & 59 & 44 & 51 & 39 \\ Network total cost (RS) & 1,005,588.35 & 945,542.69 & 889,545.10 & 971,691.88 & 2,394,071.30 & 2,237,337.33 & 2,329,664.76 & 2,343,312.70 \\ Increased cost (\%) & 11.54 & 5.92 & - & 8.45 & 2.69 & 0.32 & - & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the main information generated by the genetic algorithm for the four possible topologies, considering non-dense and dense scenario, for Region II. **Funding:** This research was partially supported by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors thank CNPq for scholarships and grants, and UFBA, Concordia University, University of Bristol (Smart Internet Lab)--UK, Alan Turing Institute--UK, UFRB, UFPE, UFCG and UnB for their educational support. **Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 4 Experimental Evaluation and Results
### Results on Our Self-Collected Dataset  
Generally, IK requires multiple iterations to find the appropriate joint orientation for the pose (seen in Figure 8), while the advantage of neural networks is that they can perform the same action in a single iteration, reducing the overall computation time. The ground truths calculated by the _Blender_ IK solver were used to train our deep-learning-based IK solver.  
The deep-learning-based approach allowed for predicting the joint orientation with high precision. To evaluate the error, the _MPJPE_ metric was used, where the resulting mean square error of all skeleton joints is 0.251 with a standard deviation of 0.067, this implies high predictive capacity; a breakdown of joint position error for all poses by exercise can be seen in Figure 9.  
Additionally, while predicting the human body pose in space is not a necessary task for human posture evaluation, it is an important feature to preview the action performed in a 3D environment from multiple points of view; the breakdown of error in 3D space offset can be seen in Figure 10, while the breakdown by exercise can be seen in Figure 10. Exercise breakdown (a) shoulder flexion; (b) shoulder flexion and internal rotation; (c) shoulder flexion and internal rotation, elbow flexion; (d) shoulder extension and internal rotation; (e) shoulder flexion; (f) full shoulder flexion; (g) shoulder adduction; and (h) exercises from the unsorted MoVi dataset.  
Figure 8: Skeleton inverse kinematics histogram.  
Figure 9: Skeleton inverse kinematics joint MSE by exercise.  
The achieved skeleton inverse kinematics joint MSE was stable accross the whole averaged video-frame sequence as is displayed in Figure 11.  
The average OKS value achieved is 0.989, while its breakdown by exercise can be seen in Figure 12.  
The PCK values thresholded at various detection confidence rates are given in Table 2. Generally, our method allows to achieve better results for joints, which have a lower amplitude of movement and are located at an upper part of the body.  
Figure 11: Skeleton IK joint MSE accross video-frame sequence of an exercise.  
Figure 12: Skeleton inverse kinematics OKS values.  
Figure 10: Skeleton inverse kinematics Offset MSE by exercise.  
Table 3 displays the RMSE and ROME for all keypoints during all exercises. In all joints, the mean values of RMSE and ROME were less than 5\({}^{\circ}\), which is considered clinically valid.  
To demonstrate the agreement between recorded and reconstructed signals, we used the Sprague and Geers' (_S&G_) metric. The overall error is presented by combined _S&G_ error between recorded signals and the IK-reconstructed signals have a more than 97% Pearson's correlation, indicating that the recorded and reconstructed signals are very similar. Table 4 summarizes the _S&G_ error as well as the [PERSON]'s correlation for various exercises performed by all subjects.
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity
#### 3.2.4. Pollututant Reduction  
Carbon emission intensity is closely linked to power consumption, with COD and TN being the primary oxygen-consuming pollutants. Figure 8 illustrates the relationship between COD removal, TN removal, and carbon emission intensity. After excluding outliers with limited sample sizes, the overall trend indicates that as COD and TN removal increases, carbon emission intensity rises correspondingly. Thus, while ensuring effluent compliance with discharge standards, minimizing COD and TN removal as much as possible is recommended to reduce carbon emission intensity and promote environmental sustainability. Additionally, the study revealed that when influent COD concentrations were between 200-250 mg/L, STPs exhibited higher carbon emission intensities than when COD concentrations were in the 250-300 mg/L range. This observation may stem from the combined effects of factors such as treatment capacity, load rate, operational mode, management practices, and process parameters.
## 3 Experiment and Results
### Experiment Theory  
As shown in Figure 5, during the SHPB experiment, the stress wave and the marked incident wave \(\varepsilon_{I}(t)\) were produced by the impact transmitting along the axial direction of the bar [53]. Some stress waves are reflected at the S1 interface and propagate in the opposite direction to form reflection waves \(\varepsilon_{R}(t)\), and the residual stress waves continue to propagate through the specimen to form transmission waves \(\varepsilon_{T}(t)\) in the transmission bar. The incident waves \(\varepsilon_{I}(t)\), reflection waves \(\varepsilon_{R}(t)\), and transmission waves \(\varepsilon_{T}(t)\) were collected by strain gauges A and B, which were attached to the incident rod and transmission rod, respectively.  
The average stress, strain rate, and strain of the specimen could be calculated by Equation (17) [54].  
\[\left\{\begin{array}{l}\sigma_{\mathrm{s}}(t)=\frac{AE}{2A_{\mathrm{s}}}[ \varepsilon_{I}(t)+\varepsilon_{R}(t)+\varepsilon_{T}(t)]\\ \dot{\varepsilon}(t)=\frac{C_{0}}{I_{\mathrm{s}}}[\varepsilon_{T}(t)- \varepsilon_{I}(t)+\varepsilon_{R}(t)]\\ \varepsilon(t)=\frac{C_{0}}{I_{\mathrm{s}}}\int_{0}^{t}[\varepsilon_{T}(t)- \varepsilon_{I}(t)+\varepsilon_{R}(t)]dt\end{array}\right. \tag{17}\]  
where \(A\), \(E\), and \(C_{0}\) are the cross-sectional area, the Young's modulus, and the wave velocity of the bar material, and \(I_{\mathrm{s}}\) and \(A_{\mathrm{s}}\) are the length and cross-section of the specimen.