esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Inter-Annual Variability of the Relationships between EVI2 and Crop Yields  
Vegetation indices are highly correlated with crop photosynthetic capacity [10; 48], and can be used to model biomass accumulation and to estimate crop yield [13; 49]. They are standard products (or can be derived) from measurements of many satellite sensors, particularly for the coarse resolution operational satellites [50; 51; 52; 53]. This provides a basis for regional-scale crop yield modeling using regression models [1; 21; 42]. It was observed that crop yield was best correlated with EVI2 derived from a time period corresponding to peak growth stage of the crops (Figures 3 and 4), which has been confirmed by many studies. For instance, it was reported that MODIS data acquired 65-75 days after green-up for maize and 80 days after green-up for soybean provided the best yield forecast in Central United States [42]. [PERSON] et al. [21] observed that MODIS NDVI from late June to early July can provide preliminary yield forecasts across the Canadian Prairies. The study by [PERSON] [1] on soybean and corn yield estimation in the corn belt region of the central United States also showed that MODIS NDVI was best from late July to early August.  
However, empirical models based on vegetation indices may be spatially or temporally dependent [19]. The inter-annual variability was shown by either variations of the best time for yield estimation or the difference in linear regression models (Figure 5). This was determined by year-specific meteorological conditions, crop growth calendars, as well as management practices (e.g., areal proportions of crops and rotational decisions). EVI2 at peak growth stage can capture the cumulative growth conditions before this time but cannot capture important yield limiting events onwards. As observed from this study, the exact time for the highest \(R^{2}\) between yield and EVI2 varied within a wide time window, in which the linear correlation was still significant but was lower than the largest \(R^{2}\). A regression model could be established by averaging vegetation indices over this time window [21; 54]. This would lead to a more stable regression model applicable to multiple years and might be useful for yield forecasting, but will induce an inferior performance for a particular year when specific yield limiting events in that year are not captured.  
Figure 10: Monthly precipitation for July–September from the weather station at London, Ontario (Station identifier: 6144475), versus correspondent long term normals from 1985 to 2016 shown with the same color but in dashed lines.
## 2 Verification of Simulation Methods
### Numerical Method
\begin{table}
\begin{tabular}{l c} \hline
**Parameters** & **Values** \\ \hline Volume of Atmospheric Computational Domain \(V_{Air}/\mathrm{mm}^{3}\) & \(5.50\times 10^{8}\) \\ Initial Pressure of Atmospheric Computational Domain \(P_{B}/\mathrm{kPa}\) & 100.34 \\ Initial Temperature/k & 300.15 \\ \hline \end{tabular}
\end{table} 
Table 2: Main parameters of the atomsphere domain. Figure 6: The schematic diagram of the cavity–tube model mesh partitioning. 0.31 million, further increases in grid count have a minimal impact on the simulation results. Thus, this study selects 0.31 million as the grid count for subsequent research. Using the 0.31 million grid, a time step independence analysis is conducted, with simulations carried out at time steps of 1 \(\times\) 10\({}^{-4}\) s, 5 \(\times\) 10\({}^{-5}\) s, 2 \(\times\) 10\({}^{-5}\) s, 1 \(\times\) 10\({}^{-5}\) s, 5 \(\times\) 10\({}^{-6}\) s, and 2 \(\times\) 10\({}^{-6}\) s. The pressure variations at the monitoring points are similarly compared with the results presented in Figure 7. The time step has a minimal impact on the simulation results, and this study adopts 1 \(\times\) 10\({}^{-5}\) s, consistent with the experimental conditions, as the time step for subsequent research. The model was simulated using various turbulence models under conditions of 0.31 million grids and a time step of times 10\({}^{-5}\) s. The simulation results were compared with experimental data, as illustrated in Figure 7. The maximum errors were 2.7% for the k-e model, 2.2% for the SSG model, and 2.1% for the SST model. The different turbulence models exhibited minimal impact on the pressure variation at the monitoring points. Based on the findings in reference [20], this paper selects the SST model as the turbulence model for subsequent research. The amplitude of pressure oscillations in the simulation was relatively greater than that observed in the experiments. Considering the presence of friction on the wall surfaces of the flow path during the experiments, while the simulated sudden change boundary represents an ideal boundary without time variation, these factors would contribute to a reduction in the amplitude of pressure oscillations in the experiments, aligning with the comparison results. Therefore, this validates the accuracy of the three-dimensional  
Figure 7: **(a)** Test point and end faces; **(b)** Grid independence analysis; **(c)** Time step independence analysis; **(d)** Turbulence model analysis. simulation and establishes a solid foundation for further in-depth research based on this simulation method.
## 4 Discussion
### Chl Concentration and Broad Band Vegetation Indices Performance from Leaf to ESU Level  
According to our results, the relationship between broad band VIs and CC increased as we move from leaf level to ESU level (Figure 7). Although the correlation between CC and broad band VIs was significant at leaf level (Table 2), this correlation was relatively weak (\(R^{2}\) \(\sim\) 0.4, p \(<\) 0.001). The relatively weak relationship is explained by the variability in the data. Differences in leaf structure among mangrove species affect the leaf spectral reflectance. Therefore, the response of VIs to CC varies among mangrove species (Tables 3-6). Conversely, at ESU level, high significant correlation was observed between six Landsat 8 broad band VIs and CC (N = 12; \(R^{2}\) \(>\) 0.7; p \(<\) 0.001). At this stage, the CC measured at each ESU was averaged and compared with the Landsat 8 broad VI pixel value. In this study, Landsat 8 NDVI green was able to explain \(\sim\)80% of the variation in CC at the ESU level. The linear model that produced this large correlation was the basis for upscaling CC at landscape scale.  
The main difference between broad band and hyperspectral algorithms is the width of the spectral band used for the computation of the index. Broad band indices use information from wide regions of the spectrum such as blue, green, red, near infrared, and short wave near infrared regions. On the contrary, hyperspectral indices include narrow regions of the spectrum. It is possible to derive information about the structure and biochemical composition of vegetation from both types of indices, however, hyperspectral indices that include bands in areas of the spectrum of high absorbance by chlorophyll a and b (particularly between 650 and 690 nm) perform best at estimating chlorophyll concentration. The transition region between the red and the near infrared part of the spectrum, the so-called red edge (680-750 nm) tends to shift towards longer wavelengths at high chlorophyll concentrations [41]; therefore, those indices that include narrow bands in the red edge are more sensitive to variations in chlorophyll.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 5 Experiments Results
### Detection Dataset Construction  
The target detection task based on deep learning requires a large amount of training data, and the quality of the dataset also determines the final training effect. The target detection network in this section needs to detect the fused images in Section 3, so the dataset in this section comes from the fusion results obtained in Section 3. Some images in the dataset are shown in Figure 16.  
The targets to be detected in this dataset include pedestrians, cars, buses, street lights, motorcycles, and large trucks, which are all common objects in the field of civilian monitoring. The backgrounds of the images include high-rise buildings, tunnels, underground parking lots, the sky, lawns, forests, warehouses, and other backgrounds. The sampling angles of these images also include vehicle-mounted head-up, static head-up, and bird's-eye monitoring, which fully considers the needs of the research background.  
Bounding boxes are manually labeled using Labellmg by drawing rectangles around the target objects, as shown in Figure 17. For YOLO format, each line in the annotation file specifies an object class, the normalized center coordinates \((x,y)\), and the normalized width and height of the bounding box.  
Figure 16: Parts of fusion image dataset.  
Figure 17: Image annotation using LabelImg.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 3 Methodology
```
1:Initialize:
2:quantum_cost_function()
3:classical_cost_function()
4:network_fluctuation()
5:optimization_step()
6:space_independent_processing()
7:For each simulation in n_simulations:
8:Initialize quantum_time, classical_time to 0
9:Initialize quantum_p_history, classical_p_history,
10:quantum_expected_cost_history,
11:classical_expected_cost_history,
12:network_fluctuation_history
13:For each spatial_config in n_spatial_configs:
14:Initialize quantum_p and classical_p as uniform distributions
15:For each tin n_timesteps:
16:Quantum Model:
17:Start timer
18:Calculate quantum_costs using quantum_cost_function
19:Stop timer and add to quantum_time
20:Classical Model:
21:Start timer
22:Generate random images
23:Calculate classical_costs using classical_cost_function
24:Stop timer and add to classical_time
25:Calculate network_fluctuation for current time step
26:For n_iterations:
27:Update quantum_p using optimization_step
28:Update classical_p using optimization_step
29:Apply space_independent_processing to quantum_p and classical_p
30:If t > 0:  
Update quantum_p, classical_p on step and network fluctuation
31:Normalize probabilities
32:Store current quantum_p and classical_p in respective histories
33:Calculate and store expected costs for quantum and classical models
34:Return quantum_time, classical_time, quantum_expected_cost_history,
35:classical_expected_cost_history ```  
**Algorithm 1**Quantum vs. Classical 6G Network Simulation
## 3 Deep-Learning-Based Channel Estimation Methods
### Data-Driven Deep-Learning-Based Channel Estimation Methods
#### 3.3.1 Application of DNN in Data-Driven Channel Estimation
[54] investigated a DNN-based scheme for underwater acoustic channel estimation in OFDM systems. The DNN utilizes transmitted pilots and received symbols in this scheme to reconstruct the UWA channel. Encouragingly, the scheme shows outstanding signal detection performance on the estimated channel. Simulation results demonstrate its efficacy, reducing the BER by over 40% in comparison to the LS algorithm. Furthermore, as the pilot number grows, the BER performance approaches that of the MMSE algorithm. To improve the estimation performance in impulse noise environments, [PERSON] et al. [55] developed a method based on the denoising autoencoder-DNN (DAE-DNN). The proposed method consists of three steps: first, utilizing DAE for preprocessing, namely learning the impaired data and restoring clean received signals under conditions where impulse noise is present; second, employing the preprocessed data from DAE to train the DNN offline; and finally, the DNN estimates the CSI online. Experimental results demonstrate that this method has strong robustness under impulse noise conditions, outperforming the MMSE algorithm, the orthogonal matching pursuit (OMP) algorithm, and the LS algorithm in terms of MSE and BER.
## 3 Test Results and Discussion
### Behavioral Analysis
#### 3.3.3 Compression Behavior  
In order to better observe the compression behavior of the mixtures, the parameters of the global and granular compression indices (\(C_{c}\) and \(C_{c\text{-}s}\)) were utilized. The physical meaning of granular compression index (\(C
## 5 Experimental Study
### Experimental Setup  
The data used to measure the efficiency of GGA in MiniGrid are nine C-type Solomon benchmark problems (C101 to C109) [55], and 10 Gehring and Homberger benchmark problems (C1_10_1 to C1_10_10) [15]. The Solomon benchmark problems have 100 customers, each of whom always places an order and has a maximum of 25 vehicles. Gehring and Homberger benchmark problems use 1000  
Figure 8: Population produced by the combination of mutated segments in the MiniGrid processes.  
Figure 7: Sending mutated segments of the population to all processes in MiniGrid.  
customers. GGA tests use a population of 100 individuals, the evolutionary process is conducted through 120 generations, the crossover rate is 100%, and the mutation factor is defined using Equations (12) and (13). In this experimental study, GGA is run 30 times, and the average fitness value of these runs is used as the performance value. This value is compared with those obtained to other approaches described in the existing literature.  
Furthermore, two non-parametric statistical tests are applied to carried out a statistical analysis of the results produced by the GGA method when comparing them with those obtained by other algorithms. Non-parametric statistical tests are used in this work since it is known that the experimental studies involving evolutionary algorithms do not fulfill the necessary conditions (independency, normality, and homoscedasticity) to apply a parametric test such as ANOVA [56]. In accordance with [PERSON] et al. [57], two post-hoc tests are carried out: The Wilcoxon test [58] is conducted to compare two algorithms, and the Friedman test [59] and the Bergmann-Hommel post-hoc procedure [60] are used to compare several algorithms.  
One non-parametric statistical test evaluates the statistical significance of the average rank of the experimental results through computing the \(p\)-value without making any assumptions about the distribution of the analyzed data. This \(p\)-value is used to accept or to reject the null hypothesis of the experiment, which holds that the performance of the compared algorithms does not present significant differences. If the \(p\)-value does not exceed a predefined significance level (0.05 in this work), the null hypothesis is rejected, indicating that the algorithms are statistically different. Wilcoxon test is designed to compare the two experiments, and two tests must be conducted to compare three or more algorithms: First, the Friedman test is used to detect differences between multiple experiments. Next, if statistical differences exist, a post-hoc test must be conducted to detect the differences between all existing pairs of algorithms. In this work, the Bergmann-Hommel test is used as a post-hoc test since it analyzes the differences of each possible pair of algorithms and whether to reject each of them or not. These statistical tests are applied using the scmamp R library [61].
## 5 Asymptotic Secrecy Outage Performance Analysis
### Insights on Optimal Solution  
In Figure 4, we study the relationship between the power allocation coefficient and SOP of secondary users with various TAS schemes in \(P_{t}=20\) dBm and \(P_{t}=30\) dBm. From Figure 4, as \(\alpha_{1}\) continues to increase, overall SOP presents a trend of first decreasing and then increasing in the MCC and ST schemes. Table 1 provides the optimal transmit allocation coefficient found by the SA algorithm to minimize the overall SOP with different \(P_{t}\). We set the initial temperature \(T_{0}=100\), the final temperature \(T_{1}=1\), the cooling coefficient \(\varepsilon=0.99\), and the inner loop has 200 samples to iterate at the same time. This setting is conducive to finding more accurate results, but the iteration time will be slightly longer. Interestingly, different \(P_{t}\) correspond to different optimal power allocation factors. This is because \(P_{t}\) affects signal and interference strength, resulting in a non-linear change in SINR. Combined with the analysis of Table 1 and Figure 4, the SA algorithm we proposed to find the optimal power parameters is reasonable and correct.  
Figure 4: _Cont._  
Figure 3: The secrecy outage performance of secondary users with various TAS schemes and interference temperature. (**a**) SOP of \(U_{1}\); (**b**) SOP of \(U_{2}\); (**c**) SOP of \(U_{3}\); and (**d**) overall SOP.  
For more valuable insights, we compared the overall SOP of the system for fixed power allocation and optimal power allocation with \(P_{t}=20\) dBm, as Table 2. We found that the secrecy outage performances of max-\(U_{1}\) and max-\(U_{2}\) of the MCC scheme are better, and the SOPs of max-\(U_{3}\) in the MCC and ST scheme are higher in the fixed power allocation, which is not conducive to secrecy communication. The overall secrecy outage performance with different TAS schemes obtained after optimization based on the SA algorithm has relatively improved, and the optimization effect of the ST scheme is the best.  
Figure 5 shows the convergence rate of the overall SOP of the secondary network optimized by the SA algorithm for different TAS schemes. We observe that the MCC scheme converges faster than the ST scheme, among which max-\(U_{1}\) is the fastest.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\mathbf{\mathrm{M1}}\) & \(\mathbf{\mathrm{M2}}\) & \(\mathbf{\mathrm{M3}}\) & \(\mathbf{\mathrm{ST}}\) \\ \hline SOP with fixed power allocation & 0.3460 & 0.3342 & 0.4257 & 0.5915 \\ SOP with optimal power allocation & 0.3360 & 0.3307 & 0.3983 & 0.3463 \\ optimization percentage & 2.9\% & 1\% & 6.4\% & 41.4\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of optimized secrecy outage performance.  
Figure 4: The SOPs of secondary users with various TAS schemes based on SA algorithm. (**a**) Overall SOP with max\(-U_{1}\) of MCC scheme; (**b**) overall SOP with max\(-U_{2}\) of MCC scheme; (**c**) overall SOP with max\(-U_{3}\) of MCC scheme; and (**d**) overall SOP with ST scheme.
## 3 Results
(95\% Cb)**} \\ \hline Age at & \(\leq\)54 & 108 & 18 & 83.3 (74.9–98.8) & 34 & 68.1 (83.9–77.1) & 31 & 77 & 74.8 (88.9–00.4) & 9 & 69.3 (48.6–83.3) & 0.678 & 1.147 (0.594; 221.4) & 0.683 \\ disease & \(\sim\)54 & 80 & 7 & 91.3 (82.8–96.4) & 10 & 87.1 (82.8–93.8) & 33 & 5 & 84.8 (64.5–3.0) & 77 & 75.6 (11.4–91.0) & 0.252 & 0.958 (0.244; 146.0) & 0.261 \\ Molecular & \(\sim\)54 & 80 & 7 & 91.3 (82.8–96.4) & 10 & 86.7 (71.3–95.3) & 20 & 1 & 99.0 (57.1–99.3) & 3 & 83.1 (82.1–96.8) & 0.600 & 1.391 (0.401; 841.87) & 0.603 \\ subtype & Luminal A & 76 & 6 & 92.1 (83.6–97.1) & 10 & 86.7 (71.3–95.3) & 20 & 1 & 99.0 (57.1–99.3) & 3 & 83.1 (82.1–96.8) & 0.600 & 1.391 (0.401; 841.87) & 0.603 \\  & Luminal B & 42 & 5 & 88.1 (74.4–96.0) & 9 & 77.8 (63.2–89.7) & 17 & 7 & 88.8 (82.9–81.6) & 7 & 98.8 (82.9–81.6) & 0.091 & 0.47 (0.139; 116.2) & 0.103 \\ HER2 pos & 23 & 5 & 78.3 (56.3–92.5) & 10 & 54.9 (34.5–76.8) & 15 & 3 & 66.7 (34.4–88.2) & 3 & 66.7 (34.4–88.2) & 0.758 & 1.279 (0.339; 485.2) & 0.717 \\  & Triple negitve & 31 & 30 & 50.3 (73.4–98.0) & 9 & 71.0 (50.8–85.9) & 19 & 91 & 93.3 (51.8–9.97) & 2 & 84.8 (40.0–97.2) & 0.522 & 1.220 (0.339; 430.70) & 0.761 \\ Histological & Ducial & 168 & 23 & 86.3 (75.0–90.6) & 40 & 75.6 (84.8–81.9) & 51 & 17 & 72.6 (56.9–89.2) & 7 & 72.0 (57.5–83.2) & 0.707 (0.502; 155.57) & 0.902 \\  & Other & 20 & 20.0 (86.3–88.8) & 49 & 74.4 (56.3–94.3) & 11 & 90.9 (58.7–99.8) & 2 & 80.8 (30.9–90.4) & 0.438 & 1.971 (0.343; 11.32) & 0.447 \\ Tumor size & T1 & 135 & 10 & 93.3 (87.7–96.9) & 29 & 77.9 (69.8–84.5) & 34 & 7 & 79.4 (62.1–91.3) & 10 & 66.9 (49.5–82.6) & 0.187 & 6.69 (0.388; 1248) & 0.195 \\  & T2 & 53 & 11 & 29.5 (59.9–89.2) & 15 & 71.5 (57.8–83.2) & 30 & 5 & 83.6 (55.3–44.4) & 6.79 (71.4–91.3) & 0.099 & 1.212 (0.86 eThere were no significant differences in survival curves for BCS patients and mastectomy patients in each of the considered histological characteristics subgroups. No differences were detected even when the patient's age at first diagnosis was considered (Table 2). The only exception was for tumor dimensions ranging from 20 mm to 50 mm as well as for Luminal B tumors. In these cases, a significant difference close to the acceptable threshold of 10% was detected (\(p\)-values of 0.109 and 0.103, respectively). In more detail, it seemed that in T2 tumor patients, the recurrence probability could rise by 101.2% in BCS-treated patients compared to mastectomy-treated patients (HR = 2.102). The same probability of recurrence seemed to be reduced by 63.6% in the case of the Luminal B tumor patients (HR = 0.474).
## 2 Materials and Methods
### Channel Reduction
#### 2.7.4 Normalized Deviation  
The normalized deviation (\(z\)) has proven to be more robust than the simple standard deviation, which is also applied across all channels. Each value (\(x\)) is reduced by the respective mean value (\(\bar{x}\)) between the channels and then normalized by the standard deviation (\(\sigma\)) of the values between the channels [20]:  
\[z(\sigma)=\frac{x-\bar{x}}{\sigma}. \tag{3}\]  
The result, shown in Figure 12c, clearly shows the difference from simple standard deviation.
## 3 Results and Discussion
### Preparation of Aliquat 336/PolyHIPE Membrane  
Due to the high viscosity of Aliquat 336, the impregnation of the extractant requires the aid of suitable solvent. In this study, ethanol was selected as the solvent, since ethanol is a good solvent for extractant. Solvent absorption in PolyHIPE is important, since it governs the subsequent extractant impregnation and quality of the final membrane. Figure 4a shows the uptake curve of ethanol in PolyHIPE. The curve exhibits a high initial uptake rate followed by a slower absorption of solvent in the later stage. The pattern of solvent uptake in PolyHIPE membrane suggests a possible two-step process. For each stage of solvent uptake, the shape of the weight gain curves resembles that of other accumulation phenomena with an asymptotic equilibrium [33]. Therefore, the kinetic of solvent uptake behavior can be described by two-stage Peleg model (Equation (4)) [33; 34].  
\[R(t)=\frac{\frac{\mathbb{I}_{11}}{t}t}{1+\frac{\mathbb{K}_{11}}{\mathbb{K}_{1 1}}}+\frac{\frac{\mathbb{I}_{12}}{\mathbb{K}_{12}}[(t-t_{b})+|t-t_{b}|]}{2+ \frac{\mathbb{K}_{22}}{\mathbb{K}_{12}}[(t-t_{b})+|t-t_{b}|]} \tag{4}\]  
where \(R(t)\) is the weight ratio (g of solvent uptake/g of PolyHIPE), \(t_{b}\) is the break point time of associated two-stage curve, \(K_{ij}\) stands for the related constant (\(i\) = 1 for the Peleg rate constant and \(i\) = 2 for the Peleg capacity constant), \(j\) for different stages of absorption.  
The fitted parameters of two-step Peleg model show that the solvent uptake rate of the first stage (1/\(K_{11}\) = 11.765) is significantly higher than that of the second stage (1/\(K_{12}\) = 0.043). The higher initial uptake rate in the first stage is due to the fast diffusion of ethanol into the large cavities of PolyHIPE, while the second stage of absorption is attributed to the slower diffusion of ethanol into the small cavities. The amount of ethanol uptake in the first and second stage of absorption is 1.94 and 0.22 g/g PolyHIPE, respectively. It is interesting to note that the amount of ethanol uptake in the second stage of absorption is about 11.6% of the total amount, corresponding to the relative pore volume of small pore size as determined by mercury porosimetry.  
For the impregnation of Aliquat 336, the amount of immobilized Aliquat 336 increases with increasing Aliquat 336 concentration in ethanol (Figure 4b). At about 60 vol % of Aliquat 336, the highest amount of impregnated Aliquat 336 in PolyHIPE membrane is 2.64 \(\pm\) 0.10 g/g of PolyHIPE membrane, indicating that the PolyHIPE is filled with Aliquat 336.  
Figure 4: **(a)** Absorption of ethanol by PolyHIPE. **(b)** Effect of Aliquat 336 concentration in ethanol on the mount of immobilized Aliquat 336.