esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A New Approach for Optical Encoders
### Proof of Principle  
To prove the optical principle, a first set-up was realized using a High Dynamic Range complementary metal-oxide semiconductor (CMOS) (HDRC(r)) image sensor and a glass lid on top of it. Figure 7 shows the demonstrator for the proof of principle. To mount the laser diode with a lens holder on it, the lid was processed with traces made of Cr/Au using a laser-cut mask and physical vapor deposition (PVD). The laser diode and the lens were assembled directly on the glass lid. The glass lid itself was assembled on top of the image sensor. The grating with a grating constant of 1.7 \(\upmu\)m was attached at the rotatable shaft with a specific mounting. After joining the housing with the shaft and the image sensor, the characteristics of the spots could be observed using the image sensor.  
For fabrication of the lenses, ultra-precision machining (UPM) was used. This allowed fabricating custom lenses in polymethyl methacrylate (PMMA) using a diamond tool. In this way, a dimensional accuracy of 1-2 \(\upmu\)m and a surface roughness of 10 nm were achieved for the lens. A commercially available blazed grating [18] was used and integrated into a ball-bearing encoder chassis that was mounted on top of the image sensor.  
With this equipment, it was possible to visualize and to investigate the spot shape of the two concepts with two different lenses. Measurements of this arrangement with the two lenses were performed--the first one for the segmented photo detector with two elliptical spots with different intensity, and the second one for the Gray code pattern. With the additional toroid lens, it was possible to expand the diffracted beam in the radial direction to realize two linear spots. The measured spot shapes are shown in Figure 8a,b for both lenses. Figure 8c shows the relative intensity of the spots from the second lens.  
Figure 8: Spot shape measured with log-scale HDRC® image sensor.  
Figure 7: Demonstrator for proof of principle. (**a**) Glass lid with laser diode; (**b**) High Dynamic Range complementary metal-oxide semiconductor (CMOS) (HDRC®) image sensor with the glass lid and lens on top of it; (**c**) blazed grating on a rotatable shaft; (**d**) fully assembled demonstrator.  
Analysis of the images obtained using the log-scale HDRC(r) image sensor showed good agreement within the theoretically calculated optical laser spots. Figure 9a shows the simulated spot for the segmented photo detector, while Figure 9b shows the spot for the Gray code detector. For the simulation, the optical ray-tracing software OSLO(r) was used. For the light source, a Gaussian beam profile was assumed. By turning the encoder shaft, the laser spots of the diffracted beams circulated around the laser diode. That means that both lenses were suitable for the optical path with each characteristic spot shape.
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 4 Discussion  
In this section, we will discuss the results we obtained. By comparing our results with previous literature, we get the price of selfishness and the impact of node density on the throughput capacity of WANETs.  
Firstly, our results can unify previous works [4; 5], when we set \(p(n)=1\) and \(\zeta=1\) or \(\zeta=n\).  
Secondly, As shown in Figure 5, Figure 5a depicts the relation of throughput capacity with the probability \(p(n)\) and node density \(\zeta\). From Figure 5a, we find that selfish nodes can severely impactthe achievable per-node rate. In particular, when the square length \(l(n)=\Omega(1)\), a degradation factor of \(p^{\frac{1}{2}}(n)(\zeta p(n))^{\alpha}\) emerged, while for the case of \(l(n)=O(1)\), the degradation factor is \(p^{\frac{1}{2}}(n)\). In addition, node density has a trivial impact on the throughput. If the node density \(\zeta>\frac{1}{p(n)}\), there is no impact on the throughput, while for the case of \(\zeta>\frac{1}{p(n)}\), the throughput loss caused by node density is \(\zeta^{\alpha}\). The reason for the performance degradation is that, due to the selfish nodes, all communications operate on partial nodes, and the selfish nodes cannot be utilized. Thus, it is hard for a node to find a relay node. With the extent of selfish nodes increasing, a transmitter needs to enlarge its transmission radius to find a relay node. From [4], we know that the network capacity is decreased with increasing transmission radius, since the number of simultaneous transmissions is decreased. We can enhance the transmission power to offset the impact of node density, which also provides sufficient conditions to guarantee the connectivity of the network [20]. In addition, we also give a comparison with previous works in Figure 5b. Figure 5b demonstrates that there exists a throughput loss caused by selfish nodes and node density. This insightful result is quite important because it provides valuable insight on the desirable operating point that balances selfish nodes and node density with throughput. We need to increase \(p(n)\) in order to get more altruistic nodes, but as a node itself, it is quite the opposite. Hence, for future work, we will use Game Theory to solve the benefit between nodes and network performance.
## 3 Results and Discussion
### Residual Stress Distribution Characteristics According to Sheet Thickness
Figure 13 presents the residual stress distribution from the nugget center to the edge in the x-, y-, and z-directions. In the presence of the spacer, the tensile residual stress at the nugget, resulting from spot welding, overlapped with the tensile stress caused by the spacer in all directions, leading to a larger distribution of tensile stress at the nugget center. In the z-direction, which plays a critical role in crack opening, compressive stress was observed at the nugget edge in the without of a spacer. Conversely, in the x- and y-directions, significant tensile residual stress was concentrated at the nugget center when a spacer was present. The stress distribution in the z-direction at the nugget center remained nearly unchanged regardless of the with or without of the spacer. However, tensile stress was observed near the electrode edge when a spacer was used, whereas compressive stress was induced at the nugget edge when no spacer was present [27; 28; 29]. These findings indicate that the spot weld joint with a spacer exhibits lower stability compared to the joint without a spacer [30]. This is likely due to the compressive stress induced by the electrode edge at the nugget edge. Therefore, it can be concluded that the joint with the spacer is less stable than the joint without it.
## 4 Conclusions  
It can be concluded that greater retention of vitamin C, color and volatiles were observed in samples processed by HHP than heat-treated samples during storage. Particularly, Hexanal, _(E)_-2-hexenal and aldehydes, which characterized as the major contributor to the 'fruit, fresh, grassy and green' notes of kiwifruit, were retained stable after HHP treatment at 500 MPa for 15 min. HHP is possible to be effective and promising as an alternative to traditional heat treatment in juice production industry.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2304-8158/9/4/485/s1](http://www.mdpi.com/2304-8158/9/4/485/s1)]([http://www.mdpi.com/2304-8158/9/4/485/s1](http://www.mdpi.com/2304-8158/9/4/485/s1)), Table S1: Volatiles in kiwifruit pulp beverage treated with HHP after 10-day storage, Table S2: Volatiles in kiwifruit  
Figure 8: Changes of volatile groups in heat-treated and HHP-treated kiwifruit pulp beverage during storage at 4 °C for 40 days. (**A**) Total aldehydes content; (**B**) total ketones content; (**C**) total alcohols content; (**D**) total esters content; (**E**) total acids content. Heat treatment (85 °C for 10 min) (**\(\blacksquare\)**), HHP (high hydrostatic pressure, 400–600 MPa for 5–15 min): 400 MPa/5 min (\(\blacktriangledown\)), 400 MPa/10 min (\(\blacktriangle\)), 400 MPa/15 min (\(\blacktriangle\)), 400 MPa/15 min (\(\blacktriangle\)), 500 MPa/5 min (\(\blacklozenge\)), 500 MPa/10 min (\(\blacklozenge\)), 500 MPa/15 min (\(\blacklozenge\)), 600 MPa/5 min (\(\blacklozenge\)), 600 MPa/10 min (\(\blacklozenge\)), 600 MPa/15 min (\(\square\)). All data were expressed as means \(\pm\)SD, \(n=3\).  
pulp beverage treated with HHP after 20-day storage, Table S3: Volatiles in kiwifruit pulp beverage treated with HHP after 40-day storage.  
**Author Contributions:** Experiment running, data collection, manuscript writing: Y.C. Experimental design, manuscript editing and revision: F.L. Methodology: H.R. Data analysis: X.F. Statistical Analysis: H.Y. Writing editing: Y.L., and Z.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Keypoint Research and Invention Program of the Thirteenth (2017 YFD0400700, 2017 YFD0400702 and 2017 YFD0400702-2), Open Project Program of China-Canada Joint Lab of Food Nutrition and Health, Beijing Technology and Business University (BTBU) (KFKT-ZJ-201907) and the Key Program for Shaanxi Science and Technology (No. 2020 NY-146).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analysis of Compound Stained Cervical Cell Images Using Multi-Spectral Imaging
## 2 Design and Principle  
### Design of Microscopic Multi-Spectral Imaging System  
The overall structural design of the microscopic multi-spectral imaging system is shown in Figure 1. It mainly consists of the automatic microscope, imaging system and LED-composite light source.  
The automatic microscope is the Olympus BX41, which we transformed in this system. Through being driven by a screw mandrel and three stepping motors instead of manual operation, the microscope stage was reconstructed for automatization. An optoelectronic switch was installed at the edge of the microscope stage for automatic locating to the origin. These transformations realized the automatic focusing and scanning of cervical smears. The bright-green absorption is the largest under the 620 nm band, the Fulgen absorption is the largest under the 570 nm band, while the eosin absorption is the largest under the 520 nm band. In light of this, the coefficient of Forgen peeling under a specific light source can be calculated according to the curve.  
The original illumination source of the microscope was replaced by a set of combined LED light sources. The wavelength accuracy of these LEDs is 5 nm. Light source 1 consists of three different wavebands of LEDs, the central wavelength of them being 620 nm (red), 520 nm (green), and 460 nm (blue); these three colors are easy to utilize for color imaging, and light source 2, emitting monochromatic light at 570 nm, can improve the accuracy of DNA quantitative analysis.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
###### Abstract
Section 5 discusses key technologies in cooperative and cognitive HSTNs. Section 6 discusses open issues, and Section 7 provides a conclusion. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Latency** & **Speed** \\ \hline (Satellite) Starlink & 20 ms & 80–150 Mbps \\ \hline (Satellite) Oneweb & 70 ms & 7 Gbps per satellite \\ \hline (Satellite) Konnect VHTS & 600–700 ms & 500 Gbps \\ \hline (Terrestrial) 4G & 20–30 ms & 300 Mbps \\ \hline (Terrestrial) 5G & \textless{}10 ms & 10–50 Gbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of satellite and terrestrial networks.
## 4 Experiments
### Experiment Setup  
In the experiments, we used the PyTorch framework and Adam [44] optimization to implement Op-Trans method. Our experiments ran on a single Teals P100 GPU with 16 GB RAM. The ranges of several hyperparameters are listed as follows: the embedding dimension \(d\) = {50, 80, 150}, learning rate \(\eta\) = {0.00001, 0.0001, 0.001}, the margin value \(\gamma\) = {3, 4, 5, 6}, the mini-batch size \(b\) = {500, 1000, 2000}, the number of clustering centers \(k\) = {20, 50, 80}, the number of clustering iterations \(s\) = {10, 30, 50}, equilibrium coefficient \(\alpha\) = {1.5, 2, 3}, and the epoch \(epoch\) = {2000, 3000, 5000}. The optimal hyperparameters on the FB15K237, WN18 RR, FB15K, and WN18 were obtained after several experiments, and the optimal hyperparameters are shown in Table 3.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 3 Results
### Modal Analysis  
The natural frequencies of the structure from 0 to 660 Hz are shown in Table 1, while the first three modes are represented in Figure 13.  
The first mode, at a frequency of 10.8 Hz, shows that the endoscope oscillates following the tangential axis. This is due to the non-symmetrical design of the frame in the ZX plane. The second mode at 15.4 Hz highlights the cantilever of the endoscope. The third mode at 20.9 Hz is a combination of both, a second order for the endoscope (vibration antinode visible at the middle) and a bending for the frame.  
The effective mass participation factor (EMPF) represents the percentage of the system mass that participates in a particular mode in each direction. It provides a measurement of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **Frequency (Hz)** & **Mode** & **Frequency (Hz)** & **Mode** & **Frequency (Hz)** \\ \hline
1 & 10.8 & 25 & 215.4 & 49 & 417.5 \\
2 & 15.4 & 26 & 219.7 & 50 & 423.7 \\
3 & 20.9 & 27 & 223.6 & 51 & 441.4 \\
4 & 41.8 & 28  
the energy contained within each resonant mode. A mode with a large EMPF is usually a significant contributor to the dynamic response of a system.  
The effective mass-to-total-mass ratio is between the excited effective mass and the total mass of the body. By incrementing this value over the modes for each direction, the cumulative percentage of the excited mass was post-treated. Moreover, in order to actually validate the modal analysis, it is necessary to have at least 80% of participation mass in each direction.  
Figure 14a shows that the structure resonates at 41.8 and 47.9 Hz with a high mass participation factor in the radial axis. This result was generated by the flexibility of the rear part of the frame (given in Figure 15a). In Figure 14a, two modes appear at the frequency of 67.7 and 151.3 Hz in the vertical axis. These two modes are harmonics of the second mode at 15.4 Hz. In addition to the mass of the cantilever beam and the front of the endoscope, the mass of the frame also contributes to the resonance (Figure 15b).  
The natural frequencies of the structure around working frequencies of external components and plasma have negligible mass fraction participation (Figure 14a,b).  
Figure 14: (a) Evolution of the effective mass participation factor; (b) cumulative ratio effective mass-to-total-mass.  
Figure 15: (a) Fourth mode representation—F\({}_{4}\) = 41.8 Hz; (b) Seventeenth mode representation—F\({}_{17}\) = 151.3 Hz.
## 2 Materials and Methods
### Post-Processing for Artifact Removal  
The key idea of the proposed post-processing method is finding the correction factor \(\Gamma\), namely, the ratio between the two constants, as  
\[\Gamma\equiv F/G, \tag{12}\]  
from an already-reconstructed image. The optimal hardware adjustment gives a value of \(F\) equal to \(G\), and thus \(\Gamma\) becomes unity in an ideal case. Otherwise, \(\Gamma\) is not unity, in which case we can equalize the two constants in Equations (8) and (9) by simply multiplying \(\Gamma\) into Equation (9), which allows us to reconstruct a better en face tomogram.  
Despite the short coherence length of the light source used in an FF-OCT system, when the sample plane has some up-and-down variations within the coherence length, the image taken at a certain depth includes interference fringes, due to the varying phase \(\Phi\). If the phase variation of the sinusoidal functions, like those in Equations (8) and (9), is random, the ensemble averages of their squares become one-half. With this idea, \(\Gamma\) can be obtained by comparing the averages of the squares of the two intermediate signals, as follows  
\[\Gamma^{2}\ =\ E_{ana}{\left[{I_{\cos}}^{2}\right]}/{E_{ana}{\left[{I_{\sin}}^{2} \right]}}, \tag{13}\]  
where \(E_{ana}{\left[{X}\right]}\) is defined as the area average of signal \(X\). The constants \(F\) and \(G\) can then be adjusted numerically with the obtained \(\Gamma\), which allows us to achieve an image reconstruction, without residual fringes in the following manner:  
\[\sqrt{{I_{\cos}}^{2}+{\Gamma}^{2}{I_{\sin}}^{2}}\ =\ AF\ =\ A\Gamma G. \tag{14}\]  
However, it is necessary to check whether the phase variation is random enough within the area of interest, or select the area providing the random phase variations.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 6 Experiments With Simulated Data
### 3-D Space Localization with Probabilistic Model
#### 6.2.2 Localization Performance with Different Training Environment  
As discussed in Section 5.2, different training conditions also affect the quality of the model, which leads to different localization performance. In this section, the localization performances with different training conditions are presented and compared in Figure 7.  
In general, the localization accuracy for all models decreases with increasing noise level. As predicted in Section 5.2, the model trained with moderate noise conditions performs best, and slightly outperforms the one trained with no noise data. This is because the additive zero-mean Gaussian noise increases the variance of the training data, which introduces diversity in the splitting values for all features and forces trees to select the features that can still provide valuable information under interference. However, the localization result of the model trained in a strong noise environment entirely degrades because the model cannot capture the details of feature characteristics from noisy data, as demonstrated in the third column of Figure 5. According to this comparison result, in the following simulations, moderate noise data are used as training data for both the proposed and reference method for comparison.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm
### SOC Estimation Accuracy Verification
#### 3.3.1 Uncertainty of Measurement Accuracy  
Owing to the limitation of sensor acquisition accuracy, there are some acquisition errors in the current and voltage data acquired during vehicle driving. At present, the acquisition error of the mainstream current sensor is within 1%, and the voltage acquisition accuracy is within \(\pm\)5 mV. In order  
\begin{table}
\begin{tabular}{l} \hline \hline \multicolumn{1}{c}{**The Specific Calculation Process Is as Follows:**} \\ \hline Building a nonlinear system: \(\left\{\begin{array}{l}x_{k}=f(x_{k-1},u_{k-1})+a_{k-1}\\ y_{k}=g(x_{k},u_{k})+v_{k}\\ \end{array}\right.\) \\ Initialization: \(\hat{x}_{0}=E(x_{0}),P_{0}^{+}=E\bigl{[}(x_{0}-\hat{x}_{0})(x_{0}  
to fully ensure the reliability of the verification, this study evaluates the estimation effect of the HIF in the event of inaccurate measurement by adding colored noise interference to the current and voltage data.  
Figure 9 shows that when colored noise is added to the current and voltage data, the SOC estimation results obtained by HIF and EKF have different degrees of deviation. Since the robustness of HIF algorithm is better than EKF, the SOC estimation results based on HIF are slightly better than that based on EKF. The estimation error of the former is within \(\pm\)0.02, while that of the latter is within \(\pm\)0.04.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Mathematical Modeling of the ISD
#### 2.3.4 Energy Balance of the Cover in the Drying Area  
According to Figure 4, the energy balance for the cover in the drying area results from the convective heat exchange between the cover and the heat transport by the drying air, which flows between the cover and the paddy rice in the drying area:  
\[T_{cov}=\frac{\alpha_{cov}\cdot\left(1+\tau_{cov}\cdot\rho_{pd}\right)\cdot I_ {t}+h_{w}\cdot T_{amb}+h_{c,cov-air}\cdot T_{air}+h_{r,cov-sky}\cdot T_{sky}+h _{r,pad-cov}\cdot T_{pad}}{h_{w}+h_{c,cov-air}+h_{r,cov-sky}+h_{r,pad-cov}} \tag{10}\]  
where \(T_{pad}\) is the temperature of paddy rice and \(\rho_{pad}\) is the reflectance of paddy rice.  
As found in a previous study [34], the reflectance is dependent on the zenith angle (\(\theta\)):  
\[\rho_{pad}=0.2704+1.816\cdot 10^{-5}.e^{0.1761\cdot\theta} \tag{11}\]
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Ocean Scenarios  
The site of experiment in the ocean scenarios was selected at a dock in Qingdao. During the experiment, the buoy equipped with hardware of sensor node was pulled down to a fixed location by the fishing boat and placed in the sea for testing. The sink node was placed on the shore to receive data. Two local convergence nodes of the sink node were 20 m apart. The antennas were fixed by tripods. The height of antenna was about 1.5 m, and any two antennas were more than one meter apart.  
In the third experiment, the sensor node was placed 400, 600, 800, 1000, 1300, and 1600 m offshore for testing. Then, in order to analyze the performance of different antennas placement strategies in the ocean scenarios, we conducted the fourth experiment within a radius of 1100 m. A total of four test locations were selected for the measurement, and the comparison measurement was performed at different locations in the area to be monitored. The experiment tested 3000 frames at each location. The locations of buoys and local convergence nodes in the ocean scenarios are shown in Figure 11. The scene of the sink node and the sensor node in the ocean scenarios are shown in Figure 12. The weather on the day of the experiment was fine, with a southeast wind of levels 3-4.  
Figure 10: Performance comparison of diversity scheme with different antenna configuration schemes. (**a**) Comparison of PER between between macrodiversity reception scheme with two clusters using four antennas and single cluster using eight antennas reception scheme. (**b**) Comparison of FER between between macrodiversity reception scheme with two clusters using four antennas and single cluster using eight antennas reception scheme.