esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Summary  
(1) This paper presented a formula for calculating the transmission efficiency of a power-split planetary gearbox. The transmission efficiency was calculated for a herringbone planetary gear transmission system with different numbers of planetary gears. The transmission efficiency was found to gradually decrease as power was transmitted along the transmission path; however, the rate at which the transmission efficiency decreased varied.  
Figure 19: Power loss comparison with literature experimental results. (a) Tooth surface friction power loss; (b) total power loss.  
(2) The influence of power loss and transmission efficiency on the herringbone planetary transmission system was studied under different lubrication conditions. The results suggest that lubrication state has a large influence on transmission efficiency and selecting the optimal lubrication method can greatly reduce gear tooth frictional power loss and increase the transmission efficiency.  
(3) The input speed was varied and the influence of power losses and transmission efficiency of the herringbone planetary transmission system was studied. When the rotational speed increased, the gear meshing efficiency decreased more slowly than the windage efficiency. Moreover, input speed was found to have a larger influence on windage power loss than on gear tooth frictional power loss. Thus, windage power loss should not be neglected in transmission efficiency calculations, particularly when gears operate at higher speeds.  
(4) Finally, the input power was varied and the influence of power losses on the transmission efficiency of the herringbone planetary transmission system was analyzed. Frictional power loss at the tooth surface was found to rapidly increase with increasing input power, whereas the windage power loss hardly changed at all. Furthermore, the transmission efficiency of the gear transmission system increased with the increasing input power.
## 5 Experiments
### Simulation Results
As a consequence of this reduction, the knowledge generated can reach a larger number of vehicles in all densities analysed, resulting in a higher coverage rate, close to 98%, on average, as shown in Figure 17d. Furthermore, the broadcast suppression mechanism implemented has helped reduce the number of packet collisions (Figure 17b). The average reduction reached more than 75%, 70% and 50% compared to the probabilistic approach, catch-up and DA2 RF, respectively. At the end, the EBM system also presented the lowest average delay, among all systems analysed, being around 0.15 s (Figure 17c). Two main lessons learned from the experimental results are as follows. The first one is that there is a need for a mechanism to select the most relevant vehicle in VANET applications. By using this type of mechanism, it is possible to make the solution more scalable. The second one refers to the egocentric betweenness measure being a viable option for the VSM in highly dynamic networks. Although our case study focused on an urban scenario, the vehicle selection mechanism is applicable to any scenario where there are vehicles on the road.
## 3 Results
### Ablation Experiment  
To comprehensively evaluate the efficacy of GCAFF-Net, we conducted ablation experiments utilizing the DeepLabV3+ network as a baseline. Table 6 lists the average water detection indicators for three testing scenes. It is worth noting that DeepLabV3+ has a PA of 84.67% and an IoU of 79.81% in three scenes. When only replacing the GCAASP in the original DeepLabV3+, we observed a 3.02% and 4.62% increase in PA and IoU, respectively. By integrating the AFFM proposed in this article, we achieved a 3.48% increment in PA and a 5.76% increment in IoU. Similarly, after introducing the AMM, the performance of PA and IoU improved by 2.79% and 5.92%, respectively. After adding all three modules, we saw a significant increase of 7.45% in PA and 7.15% in IoU. These results clearly indicate that the network proposed in this paper can significantly improve the accuracy of water detection in SAR images, producing impressive water detection results.
## 4 Discussion and Conclusions  
In summary, a digital holographic method allowing automatic digital refocusing of an in vivo brain OCT data set obtained at the de-focal plane was demonstrated. An ASM algorithm was utilized to simulate the wave propagation, and the optimum propagation distance was automatically determined by iteratively evaluating the contrast of the digitally focused B-scan OCTA images. Then, the propagation distance providing maximum contrast to the OCTA image was chosen and re-entered to reconstruct the best OCT and OCTA images. The performance of the proposed method was demonstrated by visualizing cross-sectional and en face OCTA images of the mouse brain.  
Figure 5: Comparison of en face maximum intensity projections (MIPs) of the original and recovered OCTA/OCT images obtained at specific depths (0–280 \(\upmu\)m for OCTA image, and 180, 460, 740, and 1020 um for OCT images). (**a**) En face MIPs and an enlarged view of original and recovered OCTA images (recovered by OCTA or OCT images), respectively. (**b**) Line profiles of white dotted lines in (**a**). (**c**) Recovered and original OCT images at specific depths. (**d**) Contrast to noise ratio (CNR) of the OCT images in (**c**). Yellow triangles show noticeable the contrast change between images. Scale bar is 500 \(\upmu\)m.  
Furthermore, we have compared the refocusing results using OCT/OCTA images as guiding stars. Using OCTA images demonstrated better performance on the mouse brain tissue. Our processing strategy (using OCTA images as guiding stars) enables software based automatic refocusing with high accuracy in the mouse brain tissue when using SD-OCT for measurements. In this experiment, one dimensional (B-scan direction) focusing was performed. Two dimensional (2D) focusing has not been used due to the instability of the OCT system in the C-scan direction. In future, utilizing another reference arm in the system or a cover glass on top of the sample surface [14] could allow tracing and removal of phase fluctuation along the C-scan direction. 2D refocusing of OCTA images shall be tried.  
Conceptualization, K.S.P. and J.W.K.; methodology, K.S.P. and J.W.K.; software, K.S.P. and J.W.K.; validation, K.S.P., J.W.K., B.H.L., and T.J.E.; formula analysis, K.S.P. and J.W.K.; investigation, K.S.P., resources, T.J.E.; data curation, K.S.P.; writing--original draft preparation, K.S.P. and J.W.K.; writing--review and editing, T.J.E. and B.H.L.; visualization and review, K.S.P.; supervision, T.J.E.; project administration, T.J.E.; funding acquisition, T.J.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the \"Research on Advanced Optical Science and Technology\" grant funded by the GIST, the Industrial Technology Innovation Program, (N0002310, 10063364) of the Ministry of Trade, Industry and Energy of Korea, and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare (HI18C0858).  
The authors declare no conflict of interest.
## 3 Results and Validation
### Experimental Validation
#### 3.2.3 Experimental Results and Discussion  
The dynamic response of the system was investigated. To this end, the refined finite element model shown in Figure 11, reproducing by symmetry one half of the test specimen, was used. At first, a normal mode analysis was implemented. The first 4 modes are shown in Figure 12. Then, the dynamic frequency response was investigated and compared with the experimental one. The plot in Figure 13 compares the electrical signals produced by the outer and inner piezo transducers, numerically estimated by considering the expanding piezo forces (solid magenta and cyan lines) and the pin forces alone (dot-dashed magenta and cyan lines) and the experimental data (red and blue lines). The pin force magnitude was computed by multiplying it by a factor of 2.89 \(\times\) 10\({}^{-3}\) (see Table 8) the tangential and resultant loads were applied by Nastran to simulate the thermal expansion effect. The difference between the configurations with and without the piezo device becomes more evident as the frequency increases for the impact of the piezoelectric patch on the overall dynamic response, both in terms of the mass and stiffness. Some considerations could be made for the results presented in the plot of Figure 13:  
* A direct comparison should be done between the solid lines referring to the experimental (red and blue) and numerical (magenta and cyan) data, since all of them refer to the beam with the piezo actuator mounted on it. The numerical model seems to be adequate not only in the static condition but also for a certain dynamic range, as suggested by the good agreement found for the first two peaks at 84 and 274 Hz; amplitude differences were noticed, although they can be attributed to the system damping not having an impact on the proposed model;
* The deviation between the experimental outcomes with piezo forces (red and blue solid lines) and numerical predictions with pin forces replacing the piezo forces (magenta and cyan dashed lines) are due to the remarkable effect of the piezo patch on the overall dynamic response, both in terms of the mass and stiffness; it is, however, worth noting that once again a good agreement is found for the two peaks. The remaining  
Figure 10: The instrumented beam, showing an outer view (**left**), inner view (**right**), and layout of the piezo patches on the outer side (**middle**).  
peaks in the range do not differ so much in terms of frequency but they do in terms of magnitude.  
Figure 11: Finite element model of the test specimen.  
Figure 12: First four normal modes of the test specimen.  
For this configuration, the ratio of the radial component over the tangential component was 22.6% in a condition of static actuation. This value is in line with the one highlighted by the red marker in the parameterization illustrated in the plot of Figure 21.
## 4 Discussion  
### Evaluations of the MOD17A2H Products over Diversity Ecosystems in the Arid Region  
The MODIS Collection 6 GPP products improved the spatial resolution of GPP estimation, which means the estimated GPP is more comparable with the footprint in the areas with heterogeneous landscapes in the desert-oasis-alpine ecosystems in the arid region. Meanwhile, the MOD17A2H products updated the meteorological forcing data, FPAR data and land cover data, which highlight the better spatial resolution of 500 m. However, compared to the flux tower-based GPP data, the MOD17A2H GPP products still illustrate some limitations in the simulations of magnitude and spatial-temporal variation of GPP in the desert-oasis-alpine ecosystems in the arid region. From the slope of linear regression for the scatter plot in Figures 3a and 4a, the slope values were only 0.49, which revealed significant underestimation of the GPP in the study region. When compared to the site level of flux tower-based GPP (Figure 6), the slope values in most sites of the study regions were also less than 1.0, except for some desert ecosystem sites. This showed that the MOD17 product underestimated GPP in most high productivity sites of cropland, grassland, and forest ecosystems in the arid region, but overestimated GPP at some low productivity sites of desert ecosystems compared with tower-based GPP, consistent with the results of Reference [12] and Reference [27].
## 2 Materials and Methods  
### Materials  
Table 1 summarizes the chemical composition of the binders by X-ray fluorescence (XRF) analysis. OPC (ASTM type I), HFS_OPC, and EPC (ASTM type III) were used as the concrete binders. For OPC, the density and fineness were 3150 kg/m\({}^{3}\) and 330 m\({}^{2}\)/kg; for HFS, they were 3130 kg/m\({}^{3}\) and 380 m\({}^{2}\)/kg; and for EPC, they were 3160 kg/m\({}^{3}\) and 488 m\({}^{2}\)/kg, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{8}{c}{**Chemical Composition (\%)**} & \multirow{2}{*}{**L.O.I. (\({}^{\text{4}}\)**)**} \\ \cline{2-2} \cline{4-9}  & **CaO** & & & & & & & & \\ \hline OPC [1] & 60.34 & 4.85 & 19.82 & 3.83 & 3.30 & 2.90 & 1.08 & 0.86 & 3.02 \\ HFS [2] & 61.00 & 4.51 & 19.22 & 4.14 & 3.35 & 3.13 & 1.04 & 0.79 & 2.82 \\ EPC [3] & 61.44 & 4.72 & 20.33 & 2.95 & 3.42 & 3.73 & 0.95 & 0.79 & 1.67 \\ \hline \hline \end{tabular} \({}^{(1)}\) OPC: Ordinary Portland cement; [2] HPS: High Fineness and SO\({}_{3}\) ordinary Portland cement; [3] EPC: Early Portland cement; [4] L.O.I.: Loss on ignition.  
\end{table}
Table 1: Chemical composition of the used binders by X-ray fluorescence (XRF) analysis.  
Figure 1 shows the results of the sieve analysis curves on the aggregates. Washed sea sand and crushed sand were used as the fine aggregates. Further, the fineness modulus of the fine aggregates was 2.84.  
For washed sea sand, the fineness, density, and absorption were 2.01, 2600 kg/m\({}^{3}\), and 0.79%, respectively. For crushed sand, the fineness, absorption, and density were 3.29, 0.87%, and 2570 kg/m\({}^{3}\), respectively. Crushed coarse aggregate with a size of 25 mm, absorption of 0.76%, and density of 2600 kg/m\({}^{3}\) was used as the coarse aggregate. For the chemical admixture, a PC superplasticizer and a TEA-based PC superplasticizer were used.
## 4 Result and Discussion  
### Single Dynamic Impact Test  
#### 4.1.1 Stress-Strain Curve  
The strain rate ranging from 30 s\({}^{-1}\)-190 s\({}^{-1}\) was included on the normal UHPC specimen. Resulting from calculations according to Equations (2) and (3), the stress-strain curves of the specimens with different fiber contents are shown in Figure 4, and the results  
Figure 3: Schematic diagram of SHPB device. However, whether the fiber was oriented or not had little effect on the increase of the peak stress in the single-impact compression test, and only had a significant effect on the increase of the maximum strain. It can be seen from Table 3 that the specimens with 6% steel fiber content showed the highest stress peak and had the best impact resistance. When the incident bar speed was 12.1 m/s, the strain rate of the SF6F specimen was 189.85 s\({}^{-1}\), and the peak stress reached 196.4 MPa. The strain rate of the SF6D specimen was 191.56 s\({}^{-1}\), and the peak stress reaches 192.5 MPa. However, the contribution of fiber in this aspect covered up the problem of reducing the compactness of the matrix, which was caused by the high fiber content. [PERSON] [22] also conducted impact tests on UHPC with different fiber contents. He found that with the increase of the steel fiber content, the number of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Static**} & \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Inclence** \\ **Bar Speed** \\ **(m/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Strain Rate** \\ **(s\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak Strain** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Peak Stress** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Energy** \\ **Absorption** \\ **Property** \\ \end{tabular} } \\ \hline \multirow{4}{*}{D-SF2} & \multirow{4}{*}{115.36} & 5.4 & 31.18 & 0.306 & 33.2 & 0.29 & 0.12 \\  & & 7.4 & 54.59 & 0.531 & 57.3 & 0.50 & 0.43 \\  & & 9.1 & 96.75 & 0.848 & 89.8 & 0.78 & 0.83 \\  & & 11.3 & 131.07 & 1.598 & 159.4 & 1.38 & 2.23 \\  & & 11.8 & 152.97 & 1.898 & 175.9 & 1.52 & 4.23 \\ \hline \multirow{4}{*}{D-SF4} & \multirow{4}{*}{145.7} & 6.2 & 52.61 & 0.590 & 56.2 & 0.39 & 0.34 \\  & & 7.3 & 94.68 & 0.881 & 85.8 & 0.59 & 0.77 \\ \cline{1-1}  & & 10.2 & 107.42 & 1.167 & 116.7 & 0.80 & 1.43 \\ \cline{1-1}  & & 11.4 & 133.02 & 1.198 & 137.2 & 0.94 & 2.60 \\ \cline{1-1}  & & 12.3 & 186.56 & 1.879 & 207.1 & 1.42 & 5.47 \\ \hline \hline \multirow{4}{*}{D-SF6} & \multirow{4}{*}{130.63} & 5.7 & 45.19 & 0.954 & 46.0 & 0.35 & 0.38 \\ \cline{1-1}  & & 8.5 & 76.15 & 0.964 & 84.8 & 0.65 & 0.85 \\ \cline{1-1}  & & 10.4 & 109.94 & 1.480 & 128.8 & 0.99 & 1.88 \\ \cline{1-1}  & & 11.8 & 144.67 & 1.309 & 172.7 & 1.32 & 3.51 \\ \cline{1-1}  & & 12.1 & 191.56 & 1.366 & 192.5 & 1.47 & 4.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Influence of directional fiber content on impact compression performance.
# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 4 Results and Discussion
### The Result of Principal Component Analysis  
The results of the correlation analysis of the influencing factors are shown in Figure 6. There is a strong correlation between ship length, ship width, ship weight, and ship approved load. Referring to the hierarchical structure model proposed by [PERSON] about the definition of influencing factors of the ship [17], ship dimension is used to represent ship length and ship width, while ship loading represents ship weight and ship approved load. Therefore, the final analysis variables are ship lock sill, operation direction, ship dimension, ship draft, loading capacity, and ship actual load.  
PCA was conducted on the above six variables. The eigenvalues and corresponding contribution rates and cumulative contribution rates are presented in Table 2, with a scree plot shown in Figure 7. From the scree plot, it can be seen that the cumulative contribution rate of the first three variables reaches 92%. From the fourth
## 4 ASK Modulation and Demodulation
### ASK Modulation for General Implant Applications  
To reduce the size of the implanted devices, a low-efficient RF induced capacitor-less, _i.e._, C-less ASK demodulator was developed to regulate the stable output voltage. The detailed characteristics of the proposed demodulator are shown in [28]. In order to further reduce the size of implantable devices, [PERSON] _et al._[48, 49] developed C-less and R-less without trigger envelope detectors. These demodulators improve the noise margin and also reduce the quantity of transistors. [PERSON] _et al._[50] also designed a ASK demodulator without passive elements and with only 15 CMOS transistors. The demodulator has a high bandwidth of 300 MHz for lower ISM band frequencies, which is enough for almost all kinds of biomedical implantations. [PERSON] _et al._[51] developed their previous work [46] further, providing the ability of working over a small modulation index without any passive elements using a self- sampling scheme to reduce the chip area and cost as well as to receive higher efficiency. [PERSON] _et al._[52] designed an ASK demodulator based on a maximum modulation index up to 2.86% and a 50% maximum modulation rate. The size of the CMOS is comparatively smaller in this general category of biomedical implants. The modulation index and rate are designed as:  
\[Modulation\;Index=\frac{{{v_{H}}^{-}}(-{{V_{L}}})}{{{v_{H}}^{+}}(-{{V_{L}}})} \times{\bf{100\%}} \tag{4}\]  
\[Modulation\;Rate=\frac{{Data\;Rate}}{{operated\;carrier}}\times{\bf{100\%}} \tag{5}\]  
where \({{V_{H}}}\) and \({{V_{L}}}\) represent the maximum and minimum amplitudes of the modulated signal wave form in which the modulation index increases the efficiency of the system and reduces the size. Data rate is the rate at which information is being transferred and operating carrier is the suitable operating frequency in the modulation rate or signaling speeds that contributes to transfer data rate with high efficiency.  
To reduce the power consumption in medical implanted devices an ASK demodulator without using any passive elements has been designed [53]. It consists of a rectifier, an envelope detector, digital shaper and load driver. In general, non-coherent ASK modulators are sensitive to the carrier frequency and modulation index. To avoid this disadvantage, a new circuit based comparator is designed in an ASK demodulator including a full wave rectifier, a voltage reference and a small capacitor to filter the high frequency components and a signal formatter circuit [54].
# Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey
## 4 Optimizations for Healthcare and Biomedical Devices
### Geometry Optimization  
For implantable medical devices, wireless energy transferring is developing exponentially and the most famous solution is that of inductive powering. The inductive coils misalignment is the main drawback in wider wireless powering and is solved by the geometry optimization presented in [70]. A new algorithm for the coil couple design is presented where it provides a performance metric where the maximum and minimum targeted values of the load power are defined. This method ensures the stability and suitable output specifications, and it is formal procedure that can be employed in the computer-aided design (CAD) tool. The presented geometry optimization can optimize the transmitting coil external radius, transmitting coil turns number, coils internal radii, and receiving the coil turns number.  
In [71], geometrical coil optimization was performed, which is employed for optimizing planar spiral coils with integrated capacitive elements for enhancing the power transfer efficiency while reducing the size of transmitter and receiver configurations. The proposed method results in a maximum efficiency of 31% with the existence of biological tissues where a 30 mm primary and a 10 mm secondary coil is sized (see Figure 18).  
Figure 16: Aim of the optimization process.  
Figure 17: Perspective representation of the MCC technique used in healthcare [69].
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.7 Eeru-Ca [30]  
Clustering is commonly used in WSN, but its introduction in UWSN is very recent. In 2015, Ref. [30] introduced energy efficient routing for UWSNs-a clustering approach (EERU-CA), an energy-efficient routing clustering method. Here, the author proposed using a customized node that only serves as the CH. All the CHs are connected to a receiving unit, while other nodes are distributed across the area in clusters. The cluster heads are always at the shortest distance with respect to the cluster members. The authors claim that using the specialized nodes increases the system's energy efficiency. However, it is impractical to place specialized nodes in practice and the desired efficiency.
## 4 Results and Discussion
### Influence of Panel Tilt Angle on System Performances
On the other hand, the larger tilt angle leads to more monthly useful thermal energy in winter and less monthly useful thermal energy in midsummer (July and August) and the transition season (May, June, and September). Therefore, the system with the larger tilt angle needs more auxiliary heat in May, June, and September. Taken together, there exists a medium tilt angle resulting in minimum annual auxiliary heat. Figure 11: Monthly useful thermal energy with various panel tilt angles. Figure 10: Annual thermal efficiency, annual electrical efficiency, annual primary energy-saving efficiency, and annual auxiliary heat with various panel tilt angles. Comprehensively considering thermal, electrical, and shading performances, the suggested tilt angle is 20\({}^{\circ}\)-28\({}^{\circ}\), which results in relatively lower auxiliary heat demand and relatively higher shading coefficient. The average thermal, electrical, and primary energy-saving efficiencies are respectively 38.25%, 11.95%, and 64.97% when the tilt angle ranges from 20\({}^{\circ}\) to 28\({}^{\circ}\). A too small tilt angle will cause relatively much more auxiliary heat, although the shading performance can be improved. A too large tilt angle makes against not only thermal performance, but also shading performance.