esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In previous studies, the design of fuzzy PID mainly relies on the experience of experts. In this paper, the genetic algorithm is constantly iterated to improve the fuzzy PID's control ability. In the 150 iterations comparison between the double genetic algorithm and the haploid genetic algorithm, more than 10 negative optimizations, in the first 30 iterations, appear. In the subsequent iterations, the haploid genetic algorithm first shows the optimal optimization result, which is \(2.64\times 10^{6}\). The optimal result of the diploid genetic algorithm is the lowest, \(2.13\times 10^{6}\). The reason for this result is the instability of the genetic algorithm. The haploid genetic algorithm can try to make better selections through unstable accumulation of variation factors, but it fails to retain excellent genes in many iterations. The diploid genetic algorithm has a high stability in the iterative process. It can retain beneficial genes and eliminate harmful genes. Compared with the haploid genetic algorithm, the minimum ITEA index is optimized by 19.3%. In terms of water flow, the increase of water flow is non-linear to the decrease of reactor temperature, and excessive increase of water flow will weaken the cooling effect. However, the temperature difference between the inlet and outlet of the reactor can be reduced by increasing the water flow properly. Comparing the temperature difference between the inlet and outlet of the reactor, under the three control strategies, the diploid genetic algorithm has a lower temperature difference of 2.22 K, higher stability, and 0.011 variance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Maximum Temperature** & **Mean Temperature** & **Variance** \\  & **Difference** & **Difference** & **Variance** \\ \hline DGA & 2.22 & 1.87 & 0.010 \\ HGA & 3.03 & 2.09 & 0.046 \\ FUPID & 3.17 & 2.54 & 0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical table of water temperature difference.  
Figure 17: Three strategies of reactor inlet and outlet temperature variation diagram.  
The shortcoming of this paper is that although the influence of the temperature inside a single cell on the cooling system is analyzed, the change of the temperature inside the battery pack is ignored. In the next study, 10 or more fuel cells can be designed using the existing single-cell model. The fuel cell stack model can obtain the temperature variation of each fuel cell in the simulation calculation, and analyze the high-yield heat regions and help design the thermal management control system with accurate cooling.
## 5 Measurement Results  
The proposed low-voltage hybrid dual-path SSPLL is fabricated in 40-nm CMOS technology. The chip micrograph is shown in Figure 13 and the active core area is 450 \(\times\) 400 \(\upmu\)m\({}^{2}\). The power dissipation breakdown is also captured in Figure 13.  
A Rohde & Schwarz FSWP50 phase noise analyzer and a Rohde & Schwarz FSW50 spectrum analyzer are used to test the phase noise and spur noise of the proposed SSPLL, respectively. The measured output frequency range is 5.3 to 5.9 GHz, thus, the central output frequency is 5.6 GHz. Figure 14 shows the phase noise and spur at the central frequency (divided by 2, at 2.8 GHz). The tested RMS integrated phase noise is 207.9 fs. The in-band phase noise at 1 MHz offset frequency is \(-125.9\) dBc/Hz which is \(-119.9\) dBc/Hz when the output frequency is referred at 5.6 GHz. Moreover, the tested spur is \(-51.8\) dBc at central frequency as shown in Figure 14b.  
Figure 12: Fitted phase noise: (**a**) phase noise of the reference clock and the input buffer; (**b**) voltage noise of the MSSF; (**c**) current noise of the SSCP; (**d**) phase noise of the VCO; (**e**) phase noise of the SSPLL.  
Figure 15 shows the curves of tested phase noise covering the output frequency range from 5.3 GHz to 5.9 GHz. The largest integrated jitter appears at 5.5 GHz and the smallest integrated jitter shows at 5.7 GHz. Combining with the power consumption, the worst and the best FoM (from Equation (1)) of the proposed circuit are \(-249.8\) dB and \(-251.6\) dB, respectively.  
Table 1 shows the results of this study compared with the results of prior art PLLs. The proposed SSPLL achieves good phase noise performance while achieving low power dissipation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Tech.** & **Supply** & **Out Freq.** & **Ref. Freq.** & **Ref. Spur** & **RMS Jitter** & **Power** & **FoM** (dB) \\ \hline
**This work** & **40** & **0.8** & **5.6** & **112** & \(-\)**51.8** & **196.5 (10 k–100 M)** & **1.8** & \(-\)**251.6** \\ \hline
[6] & 65 & 0.9 & 6.8 & 106.25 & \(-\)40 & 190 (10 k–100 M) & 2.25 & \(-\)251 \\ \hline
[8] & 65 & 0.45 & 2.4 & 10 & \(-\)50.1 & 2800 (1 k–100 M) & 0.265 & \(-\)236.8 \\ \hline
[15] & 28 & - & 7.77 & 80 & \(-\)66.4 & 82 (10 k–10 M) & 14.7 & \(-\)250 \\ \hline
[23] & 16 & 0.9/1.8 & 18 & 450 & - & 164 (1 k–10 M) & 29.2 & \(-\)241 \\ \hline
[24] & 65 & 1.2 & 5 & 50 & \(-\)77 & 357 (10 k–10 M) & 3.9 & \(-\)243 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison.  
Figure 14: (a) phase noise and (b) spur noise at central output frequency (divided by 2).  
Figure 13: (a) chip micro-photo and (b) power dissipation breakdown.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 5 Comparison of Transmission Efficiency of Different Structures
### Structural Dynamic Simulation  
For further investigation of the effect of different structural designs on the system, dynamics simulation of the cycloid system was performed with ADAMS. The static friction coefficient was uniformly set to 0.3, while the dynamic friction coefficient was 0.1. Within 0.5 s, 100 steps were set. The corresponding model is shown in Figure 16.  
The structure not utilizing the pin tooth sleeve is the red line in Figure 17. There are three obvious peaks, corresponding to 5492 N, 4077 N, and 2565 N. The addition of the pinion sleeve makes the overall transmission force much smoother according to the first-order derivative of Figure 18. There is no obvious bump. The addition of the pin tooth sleeve can improve the transmission performance of the system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Contact Surface** **Roughness (\(\mu\)m)**} & \multicolumn{2}{c}{**With Sleeve**} & \multicolumn{2}{c}{**Without Sleeve**} \\  & **Transmission** & **Power** & **Transmission** & **Power** \\  & **Efficiency (\%)** & **Consumption (W)** & **Efficiency (\%)** & **Consumption (W)** \\ \hline
0.2 & 99.93 & 49.3403 & 99.84 & 121.8433 \\
0.3 & 99.94 & 47.9409 & 99.84 & 119.1266 \\
0.4 & 99.94 & 46.8202 & 99.85 & 115.9343 \\
0.5 & 99.94 & 47.9757 & 99.85 & 113.0107 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transmission characteristics depending on roughness.  
Figure 16: Two structural simulation models. (**a**) Construction without pin tooth sleeve (red indicates pin teeth). (**b**) Construction with pin tooth sleeve (cyan indicates sleeve).  
Figure 17: Variation curve of meshing force.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
###### Abstract  
Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0-4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL \(\leq\) 1) from OA (KL \(\geq\) 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods.  
knee osteoarthritis classification; 3D MRI; X-ray; 3D convolutional neural network +
Footnote †: journal: Applied Sciences
## 4 Conclusions and Discussion  
In this paper we have analyzed the security of the BB84 protocol against any collective attack using the methods and tools used in proving security against the more powerful joint attack. By doing this we maintain the security proof relatively simple, yet we achieve a far more meaningful result than previously achieved for the collective attack [4]. The basic idea of this paper can also be found in a presentation given by one of us ([PERSON]), at the Technion [7].  
The same theorems (8 and 9) proven in this paper, are also obtained by [5] for the joint attack. This result leads to an asymptotic error-rate threshold of 7.56%,12 the same asymptotic result obtained for the joint attack in [5, 8]. Note that these results are not just asymptotical but also explicit in the sense that for every \(\epsilon\) and every threshold smaller than \((7.56-\epsilon)\), a sufficiently large number \(n\) can explicitly be calculated such that the final key is reliable and secure. Explicit numbers expressing the reliability and security can also be obtained. To the best of our knowledge, such explicit results were not obtained via the methods shown in [9]. The threshold of 7.56% obtained here and in [5, 8] still has a gap from the asymptotical threshold of 11% reported by [9]. This gap can be explained by the different choice of privacy amplification, see for instance [5, 10, 11].  
Footnote 12: We refer the reader to section 5 of [5] for detailed results and further discussion.  
Other researchers also reached very interesting results regarding the collective attack and its relations to the joint attack, via other methods. See for instance [12, 13] in which it is proven that security against collective attacks implies security against joint attacks. However, their definition of the collective attack is not identical to the definition given in [2], which is used in [3, 4] and in the current paper. Furthermore, the conjecture that the strongest joint attack _is_ a collective attack is not addressed by [12, 13] and remain an open problem. We leave the comparison of our result to the results obtained via these other methods for a future research.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Logistic Regression  
Logistic regression was applied using the crop suggestion as a dependent variable and other variables such as area, production rate, and soil temperature as independent variables. Here, the regression based on multinomial or ordinal shall be applied along with the different kernel functions for data analysis. The slope of the rate of crop production using logistic regression is presented in Equation (1).  
\[f(x)=\frac{L}{1+e^{-k(x-x_{0})}}x \tag{1}\]  
where \(L\) is the maximum value in a curve, \(x\) is the real number, \(x_{0}\) is the middle point value in the sigmoid, and \(k\) is the growth rate of the curve.
## 4 Model Area and Data  
The study area is from 116\({}^{\circ}\)22\({}^{\prime}\) E west to 117\({}^{\circ}\)48\({}^{\prime}\) E in the east and from 22\({}^{\circ}\)68\({}^{\prime}\) N in the south to 23\({}^{\circ}\)73\({}^{\prime}\) N in the north, including Shantou's offshore and surrounding waters. We used SMS (surface-water modeling system) software to lay unstructured triangular grids. The density of open boundary nodes decreases gradually from land to sea, which can improve computational efficiency while encrypting the critical research areas. The grid resolution in the coastal part of the open boundary is high, and the grid resolution in the main study area near the coastal area of Shantou City is up to 100 m. The research area grid contains 55,790 computing nodes and 108,354 irregular triangular grids (Figure 3). Considering that the research object belongs to the ocean surface, it is divided into five \(\sigma\) layers vertically.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Constituent**} & \multirow{2}{*}{**Tide Station**} & \multirow{2}{*}{**Measured**} & \multirow{2}{*}{**Simulated**} & \multirow{2}{*}{**Error (m)**} & Measured & \multirow{2}{*}{**Simulated**} & \multirow{2}{*}{**Eprod (\(\diamond\))**} & \multirow{2}{*}{**Error (m)**} \\  & & Amplitude (\({}^{\circ}\)) & Amplitude (\({}^{\circ}\)) & & & Epoch (\({}^{\circ}\)) & \\ \hline \multirow{3}{*}{K1} & Yunaowan & 36.04 & 36.93 & 0.89 & 287.02 & 286.31 & 0.71 \\  & Shantou & 32.13 & 22.89 & 9.24 & 301.74 & 293.57 & 8.17 \\  & Haimen & 33.42 & 32.35 & 1.07 & 290.62 & 288.48 & 2.14 \\ \hline \multirow{3}{*}{O1} & Yunaowan & 25.43 & 25.53 & 0.10 & 241.82 & 241.10 & 0.72 \\  & Shantou & 22.16 & 15.62 & 6.54 & 254.45 & 250.87 & 3.58 \\  & Haimen & 22.72 & 22.03 & 0.69 & 242.10 & 242.80 & 0.70 \\ \hline \multirow{3}{*}{M2} & Yunaowan & 57.94 & 68.79 & 10.85 & 7.27 & 10.70 & 3.43 \\  & Shantou & 32.89 & 49.40 & 16.51 & 26.38 & 20.30 & 6.08 \\  & Haimen & 26.51 & 35.57 & 9.06 & 11.35 & 15.26 & 3.91 \\ \hline \multirow{3}{*}{S2} & Yunaowan & 10.79 & 14.79 & 4.00 & 73.42 & 73.78 & 0.36 \\  & Shantou & 7.66 & 8.52 & 0.86 & 100.68 & 87.19 & 13.49 \\ \cline{1-1}  & Haimen & 4.13 & 9.16 & 5.03 & 95.86 & 86.66 & 9.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Verification of tidal harmonic constant.  
Figure 2: Changes of the bed bottom in the Shantou Port in five years.  
High-precision data are an essential condition for the model to obtain reliable results. The water depth data in this paper adopt the global seabed topographic data of ETOPO1 provided by the NOAA (National Ocean and Atmospheric Administration) and the chart  
Figure 3: Grid setting in Shantou offshore (the red box in the figure is the coastal area of Shantou City where the grid resolution is up to 100 m).
## 2 Study Area and Datasets
### Field Measurements  
The field campaign in the three field sites was conducted simultaneously with the SAR satellites overpass from 17 to 20 September 2019 to collect soil surface temperature, wheat height and soil moisture content for each sampling location in the experimental field. The wheat was in the flowering growing stage with relatively high biomass. Each point was distributed uniformly and typically in each field. Volumetric soil moisture content was taken using a Theta probe and averaged using five measurements for each sampling location (four vertices and center point of the sampling square). Soil and wheat samples were processed in drying oven at 105 \({}^{\circ}\)C for 24 h (soil) and 65 \({}^{\circ}\)C for 48 h (vegetation) to get the gravimetric soil moisture and the vegetation water content. The measured soil moisture contents of the three field sites are shown in Figure 2, where the measured soil moisture contents in Site c# are apparently lower but have a wide value range, while the values in Site b# have a relatively concentrated distribution. The average values of soil moisture are 0.265 (Site a#), 0.247 (Site b#) and 0.114 cm\({}^{3}\)/cm\({}^{3}\) (Site c#), respectively. Site c# has low values of measured soil moisture due to the sandy soil texture, while more loam soils are distributed in site a# and b#. Roughness parameters including surface RMS (root mean square) height and correlation length were observed using a custom-built roughness profiler, a 2-m-long panel with 200 pins digitized at a 0.5-cm interval. Each roughness profile was recorded as photographs and digitalized to obtain a profile contour curve for each location. Values of RMS height and correlation length were extracted from the digitized curve using compiled programs in IDL (Interactive Data Language). Two measurements of roughness parameters (measured in the east-west, south-north directions, alternatively) are obtained as the final value in each sampling point. The RMS height has the values ranging from 0.62 to 1.18 cm with the average value of 0.89 cm. The low and high values corresponded to sown and ploughed fields, respectively. The correlation length had the average value of 24.69 cm with a minimum of 9.55 cm and a maximum of 32.47 cm.
## 2 Principles of the Proposed Method
### CD Estimation Principle Based on FrFT  
According to the inverse transform of the FrFT, the FrFT of the signal can be regarded as its expansion on the function space based on the inverse transform kernel. Meanwhile, the inverse transform kernel function is a set of orthogonal chirp basis on the fractional order domain. Therefore, an input chirp signal is an impulse function after FrFT, that is, it has a good energy concentration characteristic for a given chirp signal. Therefore, FrFT can be used to extract the information contained in the chirp signal.  
The chirp signal and CD have certain similarities. The frequency of the chirp signal changes linearly with time, and the CD indicates that signals with different frequency components have different propagation speeds. Therefore, it can also be understood that the transmission delay of the signal changes linearly with the frequency of the signal. Then, if the chirp signal is a linear frequency modulation in the time domain, the CD is a linear time modulation in the frequency domain, so the chirp and CD have a certain analogy. In the following, FrFT will be used to analyze the chirp signal and the CD, respectively, to obtain the relationship between the three, and then the method of CD estimation will be proposed through the idea of chirp signal parameter estimation based on FrFT.  
The FrFT method is applied to the CD monitoring of optical fiber communication systems. Using the unique advantages of joint analysis in the time-frequency domain, it can better analyze the impact of CD on optical communication signals. On the one hand, it can better estimate the CD and improve the efficiency and accuracy of the overall optical performance monitoring; on the other hand, it can contribute to the efficient compensation of these impairments, improve the overall performance of the communication system, and reduce artificial costs, improve flexibility, and optimize resource utilization in the optical network.  
Figure 1: The rotation transformation diagram of the fractional domain.
## 4 Theoretical Derivation
### Influence of Tunnel Freezing Temperature on Pipeline Settlement  
The statistical relationship between tunnel freezing temperature and pipeline settlement shown in detail in Figure 12 deeply reveals the key role of temperature factor in the stability control of tunnel construction. Specifically, when a lower freezing temperature (such as \(-50\)\({}^{\circ}\)C) is used, this extremely low-temperature environment significantly promotes the formation of frozen soil walls or frozen soil bodies, which not only increase in thickness, but also significantly improve in strength. This strengthening effect greatly enhances the support ability of the frozen soil structure to the surrounding soil, which is like building a solid barrier, effectively limiting the range and degree of formation disturbance during tunnel excavation, thereby reducing the formation settlement caused by construction activities.  
On the other hand, the phenomenon of frost heave induced by low temperature is particularly significant in the soil above the tunnel, showing obvious uplift phenomenon. Although this uplift may seem unfavorable at the early stage of freezing, because it changes the original formation shape, it actually provides a \"pre-compression\" effect for the subsequent excavation process. However, with the gradual excavation of the tunnel, the soil uplifted by frost heave quickly loses its support and is transformed into a significant settlement. It is worth noting that this settlement often has a large magnitude and a certain lag; that is, the initial settlement may not be obvious, but with the passage of time and stress redistribution, the settlement will gradually increase.  
Specific to the numerical value, comparing the pipe settlement under the two freezing conditions of \(-50\)\({}^{\circ}\)C and \(-15\)\({}^{\circ}\)C, we can clearly see the significant impact of temperature difference. At a low temperature of \(-50\)\({}^{\circ}\)C, thanks to the stronger frozen soil support effect, the settlement of the pipeline is effectively controlled above \(-20\) mm, showing a better con  
Figure 11: Influence of tunnel excavation rate on pipeline settlement.  
struction control effect. In contrast, when the freezing temperature is increased to \(-15\)\({}^{\circ}\)C, the strength and thickness of the frozen soil wall or frozen soil body are weakened, and the supporting effect on the surrounding soil is reduced, resulting in a significant increase in pipeline settlement to \(-33.2\) mm, highlighting the important role of low-temperature freezing in controlling the settlement of tunnel construction.