esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
MotionMonitor (Innovative Sports Training, Inc., Chicago, IL, USA) software was used to analyze the EMG and ground reaction force data. A threshold of 20 N ground reaction force was used to define the instants before and after ground contact. Previous studies have shown that the muscle pre-activation begins 100 to 150 ms before ground contact in the drop jump [15; 16; 17; 18]. Therefore, the extraction period in this study was set to 100 ms before ground contact for analysis of muscle pre-activation. The latent periods for lower limb muscle activation were set to 30-60 ms after ground contact for analysis of the short-latency responses [17; 18; 21]. Muscle pre-activation and short-latency responses are indicators of the neuromuscular function of the lower limb muscles.  
The EMG signals of each muscle were processed by a band-pass filter at 10-500 Hz, and the root mean square (RMS) calculation was then performed. The window used for the RMS was 50 ms. The average RMSs from 0.5 to 2.5 s during the MVICs of each muscle over 3 s were taken as the reference values for normalization of the EMG. The RMS values of three drop jumps in each condition were averaged for the analysis and then normalized to the condition with the wood surface and platform height of 40 cm in each respective period of muscle activation for comparison.
## 5 Conclusions  
In this paper, a new heat transfer mathematical model--HWHE--was developed based on analytical solutions. Validation of the proposed model was conducted by comparing the calculated results with literature values under four conditions. It is found that the deviations of heat flux on the heat exchanger are 2.53%, 0.99%, 2.12%, and 1.96%, proving the accuracy of the proposed model. By the mathematical model, it is much faster and more convenient for engineering applications to predict the heat exchanger's surface temperature distribution, thermal resistance, and heat flux compared to existing numerical models of HWHE.  
Study on heat transfer performance of HWHE with the proposed model shows that, ratio of thermal resistance caused by the pipe is only 4.02% and the main thermal resistance of the heat exchanger exists on the plate which accounts for 95.98% of the whole thermal resistance. Our results also show that thermal resistance of the heat exchanger can be reduced 5-8% by adding aluminum foil around the pipes.  
The influence of other factors such as pipe diameters, distance between pipes, pipe's conductivity, plate's thickness and conductivity, and convective heat transfer coefficient of the air on the heat transfer performance of HWHE were studied. The wall's conductivity and air's convective coefficient are two important factors influencing the heat exchanger's thermal property and should be enhanced in practice. The thickness of the plate should be considered as an important factor in strengthening heat exchanger's heat transfer performance, especially when the pipe distance is large. The pipe's distance plays a more important role in strengthening heat exchanger's heat transfer performance than the pipe's diameter and contact angle. Due to the small proportion of pipe thermal resistance to the whole thermal resistance, the contact area, pipe diameter, and conductivity have little effect on the heat exchanger's heat transfer performance. On the other hand, replacing copper pipe with pipe with a lower heat conductivity will not significantly weaken the heat transfer performance of HWHE.  
All authors contributed to the research in this paper. [PERSON] put forward the problem, deducted the equations of this paper which form the analytical solution and wrote the original version of the paper. [PERSON] was responsible for the MATLAB program development and validation of the model. [PERSON] analyzed key factors affecting the thermal property of HWHE and revised the paper. [PERSON] and [PERSON] gave some critical suggestions on the influence factors affecting the thermal property of HWHE.  
Figure 20: The heat exchanger’s thermal resistance vs. pipe contact angle and distance.  
**Funding:** The research was funded by [The National Natural Science Foundation of China] grant number [51508016] and [The Key Project of National Natural Science Foundation of China] grant number [51338006] and [The Natural Science Foundation of Beijing Municipality] grant number [Grant No. 3152007].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 5 Experimental Verification  
In this experiment, the proposed baseline analysis model was used to simulate different baseline-intensity images using Matlab software. The experiment was based on the simulation parameters for SMS-TomoSAR systems given in Table 1. The length of the synthetic antenna was 500 m. From Table 1, we know that the experiment simulated 21 baselines. In Figure 1, we define the same-track vertical baseline length \(L_{same}\) as the length of vertical baseline interval of an adjacent track in the satellite formation, and the repeat-track vertical baseline length \(L_{repeat}\) as the vertical baseline interval of an adjacent track of the same satellite. Compared with an SMS-TomoSAR system, monostatic SAR does not demonstrate same-track vertical baseline length, which directly limits the baseline span of monostatic SAR. In addition, as previously stated, we can consider SMS-TomoSAR as monostatic SAR when an SMS-TomoSAR system employs only one satellite. However, when we specify the number of flights as \(M=7\), the monostatic SAR has only six baselines, which is insufficient to obtain high-precision height resolution images. In practice, we need to obtain enough data to complete an urgent mission within a fixed time, so monostatic SAR is not adequate. Overall, we sought to identify a baseline optimization method for SMS-TomoSAR instead of monostatic SAR, for optimal height-dimensional resolution from one flight of a multi-satellite formation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Main image height (m) & \(H=600\),000 & Target height (m) & \(S_{0}=8\) \\ Number of formation & \(N=3\) & Target complex & \\ satellites & \(M=7\) & scattering coefficient & \(\gamma(s)=\exp(j\pi/4)\) \\ Number of flights & \(M=7\) & Target height span (m) & \([s_{\min},s_{\max}]=[-180,180]\) \\ Repeat-track vertical & \(50\leq L_{repeat}\leq 100\) & Same-track vertical & \\ baseline length (m) & & baseline length (m) & \(100\leq L_{same}\leq 200\) \\ Baseline span (m) & \(B_{spun}=500\) & Unambiguity height (m) & \(S_{unambiguity}=360\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
In this paper, we consider the applicability of the optimal baseline design, discrete Fourier transform (DFT), non-uniform discrete Fourier transform (NDFT) and compressive sensing (CS, here in the BP algorithm) imaging algorithms. Further, we compared and analyzed the imaging results in the height direction of those three imaging algorithms for SMS-TomoSAR.
## 3 Applications and Limits of ATPs in Water and Wastewater Treatment
### Applications of ATPs in Water and Wastewater Treatment  
During the last decade, a dramatic rise in ATPs research, development, and applications was observed, due to increasing concerns about CECs' presence in water and wastewater, and to the increasing diffusion of water reuse projects for high-quality use [8]. While ozonation, activated carbon adsorption, and membrane filtration processes are, at the moment, the most common consolidated processes for advanced wastewater treatment [61], none of them are technically defined as a proper ATP. In fact, ozone alone in pure water does not generate radicals, unless combined with UV or peroxide, although studies on the side reactions of ozone with phenols or amine groups show its -OH generation potential in the presence of dissolved organic matter (DOC). Ozone doses \(\geq\) 0.25 g O\({}_{3}\)/g DOC were shown to be sufficient to remove at least 80% of some CECs with high ozone reactivity in different wastewater treatment plant effluents [62]. For this reason, wastewater effluent ozonation could be classified as an AOP under specific circumstances [63]. Ozone-resistant compounds include pesticides (e.g., propenton, aldicarb), aromatic compounds, and chlorinated solvents [64]. Since UV irradiation alone is, as mentioned, the least effective AOP process in CECs removal, its combination with photocatalysis was tested, using TiO\({}_{2}\) as the catalyst at a concentration between 0.2 and 2 g TiO\({}_{2}\)/L, to enhance free hydroxyl radical formation. This approach removed endocrine-disrupting compounds between 12 and 99%, requiring contact times between 0.5 and 8 h [65]. Photocatalysis also showed bisphenol A (BPA) removal up to 99% with a process time up to 140 min, with a mineralization ratio of just 40% (the other 60% consisting of intermediate by-products of an undetermined nature). Reducing the contact time to 20 min, conversion was reduced to 35%, with a 10% mineralization fraction only [66]. UV can also be combined with other oxidants, such as O\({}_{3}\) and H\({}_{2}\)O\({}_{2}\), to increase the process effectiveness. [PERSON] and [PERSON] carried out a wide review of AOP applications in the treatment of water from various sources, as summarized in Table 6[18]. An updated review was recently published by [PERSON] et al. [67].
## 4 Discussion
The study found a strong correlation between digital image indices and physiological indices of wheat during the flowering stage. The mean correlation coefficients were flowering (6) (0.638) \(>\) filling (7) (0.626) \(>\) maturity (8) (0.548) \(>\) jointing (3) (0.523). In contrast, [PERSON] [7] found that the correlation coefficients for winter wheat digital image indices with biomass and yield at different fertility periods followed the order of jointing, flowering, tasseling, and pre-overwintering. The difference in the above results may be due to variations in wheat planting times. The ANOVA results indicate that the various fertilization treatments had significant differential effects only on the digital image indices G, B, and INT. The colors in the UAV images appeared more saturated during the wheat flowering stage compared to the jointing stage; this is mainly due to the fact that the indices lose sensitivity at this stage [22; 23; 24]. During the later stages of wheat fertility, the digital image index showed a significant correlation with potassium content in all parts of the plant. However, there was no significant correlation with nitrogen and phosphorus. These findings are consistent with a study conducted by [PERSON] and [PERSON] [25; 26]. While several representative and complementary imaging indices and indicators were screened in this study, the current study has certain limitations in its selection of digital image indices and physiological indicators. Future research will seek to expand and enrich the relevant aspects examined in this study.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 3 Materials and Methods
### Experimental Data  
To evaluate the efficacy of file encryption, image files featuring Lenna, Mandrill, Peppers, and Airplane were employed, each possessing dimensions of 512 pixels by 512 pixels.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 3 Method Description
### Boundaries' Detection  
The position of the signal boundaries of \(x_{n}\), defined as the limit points of a logic state, is directly obtained from the indices of the zeros detected. With the aim of accurately detecting the edges of \(x_{n}\), the position of each boundary is adjusted according to the definition of signal rise time, characterizing the required time for the signal to switch from one state to another. Indeed, considering the absolute amplitude difference between boundaries from the same edge as \(bnd\_diff\), the boundary of the low state is defined as the first value greater than or equal to the detected low boundary + 10% of \(bnd\_diff\), while the boundary of the high state is defined as the first value less than or equal to the high boundary \(-\) 10% of \(bnd\_diff\).  
As illustrated with green diamonds in Figure 11, zeros of the derivative in association with the rise/fall time detected in the signal provide an accurate detection of the signal state boundaries, especially when a state's amplitude is fluctuating.
## 5 Discussion
### Performance of VS and ER Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Profile ID 1** & **Time-Lapse 2** & **AV ER 3** & **SD ER 3** & **CV ER 4** & **AV EC 3** & **RD ER 5** \\ \hline ERT1 & March & 44.06 & 23.06 & 0.52 & 300 & 0.043 \\  & June & 43.37 & 22.78 & 0.53 & 310 & 0.028 \\  & September & 42.15 & 22.83 & 0.54 & 320 & 0 \\  & December & 38.18 & 19.96 & 0.52 & 340 & \(-\)0.104 \\ ERT2 & March & 48.85 & 33.18 & 0.68 & 250 & 0.034 \\  & June & 49.00 & 31.51 & 0.64 & 250 & 0.037 \\  & September & 47.17 & 37.12 & 0.79 & 270 & 0 \\  & December & 45.69 & 30.78 & 0.67 & 270 & \(-\)0.032 \\ ERT3 & March & 37.34 & 20.65 & 0.55 & 580 & \(-\)0.027 \\  & June & 38.40 & 22.69 & 0.59 & 580 & 0.002 \\  & September & 38.33 & 22.87 & 0.60 & 590 & 0 \\ \hline \hline \end{tabular}  
* ID and location as in Figure 1d. 2 Dates are referred to year 2014 as in Table 2. 3 AV and SD are average and standard deviation of ER in \(\Omega\) m and of EC in \(\mu\)S cm\({}^{-1}\). 4 CV is dimensionless coefficient of variation of ER (SD-to-AV ratio) as a fraction. 5 RD = (\(z-z^{\star}\))/\(z^{\star}\) is dimensionless relative difference of nodal ER data in a time-lapse ERT (\(\preceq\)) regarding nodal ER data in the September 2014 reference time-lapse ERT (\(z^{\star}\)), as a fraction.  
\end{table} 
Table 6: Time-lapse ER models statistics. comparable quotas of the relative error between models having different number of data. For this purpose, CV must be additionally corrected by applying the factor \(n/n^{\prime}\), where \(n\) is the total number of data of a given model and \(n^{\prime}\) is the total number of data of the biggest explored model: MASW3 for VS models (Figure 5) and ERT3 for ER models (Figure 6); \(n\) values are in Figure 7 and in Table 7. Normalized MAE (CVMAE), RMSE (CVRMSE), and STD (CVSTD) were used (Table 7).
## 3 Results  
### Calibration of the Basin  
Before uploading to the SUPer system, the model was calibrated using the SWAT model. It is noted that the calibration control points that the model presented were r\({}^{2}\) above 0.67, as shown in Figure 2. Different studies have already been developed for the basin with the aim of contributing to the better management of water resources in the basin [30].  
The average annual water balance of basin 73 presents an average annual precipitation of 661 mm, surface runoff of 73 mm, lateral flow of 20 mm, return flow of 67 mm, evaporation and transpiration of 459 mm and potential evapotranspiration of 1788 mm, as seen in Figure 3. The statistics of the average annual water balance is shown in Table 1. Note that 69% of precipitation is due to actual evapotranspiration. The baseflow is 55% of the total flow. The baseflow is the lateral flow and return flow. The total flow is 161.34 mm. In the first layers of the soil, infiltration/plant uptake/soil moisture redistribution occurs, which transforms in the lateral flow. The streamflow is the total flow. The streamflow was 24% of the precipitation. That is, 24% of the precipitation is transformed into runoff. The percolation to shallow aquifer is 16% of the precipitation. The baseflow is 13% of the precipitation. The surface runoff is 11% of the precipitation. When we simulate climate change, the water balance is greatly altered. There is a significant decrease in the baseflow, as shown in Figure 4. I draw attention to the baseflow because it is very important in semi-arid areas.  
\begin{table}
\begin{tabular}{l c} Streamflow/Precip & 0.244 \\ Baseflow/total flow & 0.545 \\ Surface runoff/total flow & 0.455 \\ Perc/precip & 0.163 \\ Deep recharge/precip & 0.008 \\ ET/precipitation & 0.695 \\ \end{tabular}
\end{table}
Table 1: Water balance ratios.  
Figure 2: Relationship between runoff observed and simulated for the Pajeu basin.
# Notch Fatigue Life Research Based on Critical Distance Theory
## 2 Critical Distance Theory
In fatigue analysis, the same type of consistent relationship can be derived by simply replacing the corresponding static load parameters with fatigue-related parameters. The fatigue equivalent of fracture toughness \(K_{\text{c}}\) is the fatigue crack expansion threshold \(\Delta K_{\text{th}}\), so the relation of critical distance in fatigue analysis can be expressed as (6). \[L=\frac{1}{\pi}\left(\frac{\Delta K_{\text{th}}}{\Delta\sigma_{0}}\right)^{2} \tag{6}\]  
where \(\Delta K_{\text{th}}\) is the crack expansion threshold; \(\Delta\sigma_{0}\) is the fatigue limit of the material.
## 3 Gear Ratio Optimization Problem
### Optimization Results and Analysis  
The integrated NSGA-II approach described above is used to generate Pareto-optimal solutions for the gear ratio optimization problem. Setting the population size to 100 and the Pareto fraction to 0.3, the Pareto front between the acceleration time from 0 to 100 km/h and the 100 km electricity consumption is shown in Figure 6. From the graph, the 100 km electric energy consumption of the Pareto-optimal solutions have no obvious difference and the range is less than 0.2 kWh/100 km, while the range of the acceleration time corresponding to these solutions is about 1.5 s. Therefore, the point indicated by the pentagram in Figure 6 is chosen as the compromise solution (\([i_{1},i_{2}]=[11.07,7.09]\)) in this paper, where the 100 km electric energy consumption is 12.72 kWh/100 km and the acceleration time is 7.03 s.  
In order to reveal the effect of the selected gear ratio combination, Table 3 provides a comparison of the acceleration time and electricity consumption between the two-speed transmission and fixed-ratio transmission. The acceleration time of the EV with two-speed transmission is reduced by 1.15 s compared to the EV with fixed-ratio transmission. In addition, in both the NEDC and WLTP drive cycles, the 100 km electric energy consumption can be reduced by more than 4%.  
Figure 5: The boundary range of SOC with the initial SOC at 0.6 for New European Drive Cycle (NEDC) drive cycle.
## Materials and Methods
### Determination of the eIF4E-Cap Complex Dissociation Constants \(K_{\text{D}}\)  
A previously described pyrene fluorescence intensity binding method was used to determine the dissociation constants of the nonfluorescent ligands binding to the eIF4E protein in a competitive binding experiment [16]. The experiments were performed in 96-well, black, non-binding assay plates with point fluorescence measurements (\(\lambda_{exc}\) = 345 nm; \(\lambda_{em}\) = 378 nm). Each well contained a buffer (50 mM Hepes/KOH pH = 7.2 containing 100 mM KCl and 0.5 mM EDTA), 10 nM of pyrene-labeled 7-methylguanosine pentaphosphate probe, ligand **1-5** or m\({}^{7}\)G\({}_{\text{P3G}}\) (half-log dilutions of C\({}_{\text{lig}}\) from 100 \(\upmu\)M to 0.003 \(\upmu\)M), and 75 nM of eIF4E protein. The reagents and added protein were pre-incubated for 15 min at 30 \({}^{\circ}\)C and stirred at 300 rpm. Measurements were performed in three different temperatures: At 20, 30, and 37 \({}^{\circ}\)C. A previously derived Equation (1) [35] was used to calculate the inhibition (dissociation) constant \(K_{\text{I}}\) of a competitive ligand according to the dependence of the recorded fluorescence intensity on the inhibitor concentration.  
\[K_{I}=\frac{IC_{50}-[P_{t}]+[PL]_{50}\bigg{(}\frac{K_{D}}{[L]_{50}}+1\bigg{)}}{ \frac{[L]_{50}}{K_{D}}+\frac{[P_{t}]_{0}}{K_{D}}+1} \tag{1}\]  
where \(IC_{50}\) is the inhibitor concentration required to replace 50% of the fluorescent probe from the protein binding site, \([P_{t}]\) is the total protein concentration, \([PL]_{50}\) is the protein-probe complex concentration at 50% inhibition, \([L]_{50}\) is the free probe concentration at 50% inhibition, and \([P]_{0}\) is the free protein concentration without inhibition.  
In order to determine IC\({}_{50}\) value, we used Origin(r) 2017 (OriginLab Corp., Northampton, MA, USA) software to fit the curve described by the derived Equation (2) to the measured fluorescence intensity values for various inhibitor concentrations.  
\[F=F_{0}\frac{F_{max}-F_{0}}{1+10^{(log(IC_{50})-log(C_{lig.}))p}} \tag{2}\]  
where \(C_{lig.}\) is the concentration of non-fluorescent ligand.  
The plots were created in GraphPad Prism v. 7.00 (GraphPad Software, La Jolla, CA, USA).
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 5 Motivation and Contribution of the Article  
This article clearly described the proposed technique, which is one of the best beamforming techniques to reduce the interference in the MIMO-NOMA-based CRN. The MMSE method, along with the NMSE iterations, were used to limit the power transmission for the better exposure of the proposed performance. The number of antennas at the BS was not limited because the signal alignment matrix was used at the BS to align the channel matrix. The motivation of an article was to enhance the secure transmission of the data packets by reducing the interference as effectively as possible. The simulation results show the effectiveness of the proposed technique.
## 3 Results and Discussion
### Bias-Variance Analysis
This high variance suggests that AB predictions are more susceptible to fluctuations in the training dataset, potentially leading to overfitting. The high variance significantly resulted in its highest overall \(MSE\) score, indicating that AB struggles with the bias-variance trade-off, leaning towards higher variability in prediction. Based on this analysis, CB appears to be the most effective model in minimizing the total prediction error and achieving a favorable bias-variance trade-off, hence providing the most consistent generalization performance, followed by the ET model. RF exhibited high bias with high total prediction error, indicating less flexibility in fitting the training dataset and potential for underfitting. AB, conversely, exhibited high variance with high total prediction error, indicating a greater sensitivity to noise and potential for overfitting. These findings align with the cross-validation results, where RF and AB showed an unstable performance in Figure 7 and a large discrepancy between training and validation scores in Table 3.
## 4 Proposed Path Planning Techniques
### RRT (Rapidly exploring Random Tree) and RRT* Algorithms  
The secondly implemented algorithm is the rapidly exploring random tree (RRT) algorithm [36]. It is used for single-query applications. The algorithm builds a tree of feasible trajectories rooted at the initial vertex. The algorithm is explained in pseudocode 2 and can be summarized as follows:  
```
Input:\(q_{1}\), n of nodes, step size \(\alpha\) Output: Tree T = (V, E)  initialize V = \(|q_{S}|\), E = \(\emptyset\)
```
1:for i = 0: n do
2:\(q_{i}\)\(\leftarrow\) random sample from C
3:\(q_{near}\)\(\leftarrow\) nearest neighbor of \(q_{i}\) in C
4:\(q_{new}\)\(\leftarrow\)\(q_{near}\)\(+\frac{\alpha}{|q_{i}-q_{new}|}\)\((q_{i}-q_{near})\)
5:if\(q_{near}\in\) C\(\subset\)\(C_{free}\)then V \(\leftarrow\) V \(\cup\)\(\{q_{new}\}\), E \(\leftarrow\) E \(\cup\)\(\{(q_{near}\), \(q_{new})\}\)
6:endfor ```  
Many derivatives of RRT algorithms are present and sought after. The most common derivative is the RRT* [37], which will be implemented on the 4 OWMR.