esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation Analysis
#### 3.4.4 The Combination Effects  
Referring to Figures 8 and 11, the responses of PALSAR L-band and Sentinel-1A C-band towards AGB are different in terms of strength and variation. L-band observations penetrate more into the forest canopy and between branches and spaces, compared to the C-band, which only interacts with top canopy layers before it is scattered back or extinct. In tropical forests, volume and volume-surface scattering dominated the HH in while volumetric scattering due to dense vegetation cover dominated the HV by [38]. It is also likely that forest has a higher amount of canopy variability influencing scattering due to significant surface roughness as observed by the cross-pol (HV) term. Since structure influences the cross-pol term, forest areas that undergo selective harvesting are theoretically observable by PALSAR-2 HV, but not by Sentinel-1A, unless there is excessive timber extraction from the forest. SAR sensors can receive a relatively higher amount of surface scattering in low-density forest rather than a majority of scattering from trunks and trees or branches and crowns. Therefore, stand density, basal area, and AGB influence these relationships; although variability remains low regardless of height of the stands.  
Since most of the sample plots were located in dense and mature forest (of AGB \(\geq\) 200 Mg ha\({}^{-1}\)), the variation of backscatter from both PALSAR-2 and Sentinel-1A polarizations are within the saturation threshold. Except for a number of sample plots that were located inside the secondary and logged forests, which contained relatively lower AGB than that inside the dense forest. Consequently, these factors have influenced the scatterplots in the correlations. The presence of C-band in the combination has complement the L-band at lower part of AGB (<200 Mg ha\({}^{-1}\)) forest thus produce a better prediction overall. Taking the best linear correlations from PALSAR-2 HV and Sentinel-1A VV, with \(R^{2}\) 0.276 and 0.091 respectively, the \(R^{2}\) increased to 0.356 when combined. This has increased the explained variance by about an average of 17.25%. Although a single PALSAR-2 HV polarization from the non-linear correlation can predict the AGB with an \(R^{2}\) of 0.3502, the multiple linear correlations remain stronger even with an increase of explained variance by about 0.58%. In addition, the combination of L-band polarizations, as well as L- and C-band fusion has proven to be successful, thus confirming the hypothesis of the study.
###### Abstract  
Mental fatigue can significantly impair physical performance during sports practice. However, previous studies have not analyzed the impact of combining High-Intensity Interval Training (HIIT) with a cognitive task on physical performance. In this line, we developed the present research to investigate the specific aspects of mental and physical fatigue as well as the potential impact of combining HIIT with a cognitive task. Therefore, the objective of this study was to compare mental and physical fatigue indicators between single HIIT and an incongruent Stroop task added to the HIIT protocol. A cross-sectional study was conducted with 32 tennis players (25 men and 7 women, mean age = 21.40). Two HIIT sessions were performed: (1) one with the incongruent Stroop task presented during rests, and (2) another without the Stroop task during rests. Mental fatigue, mental load, and wellness were measured. Additionally, mean and maximum heart rate, mean and peak speed, and heart rate variability through RMSSD were recorded using a heart rate monitor. Mann-Whitney U tests and Wilcoxon tests were performed to explore differences between during-session loads and pre- and post-HIITs. The results showed that mixed HIIT with the incongruent Stroop task caused higher increases in mental fatigue (\(p\) = 0.04) and the ratio of perceived exertion (\(p\) = 0.001) from pre- to post-HIIT. However, no significant differences in external (speed) and internal (heart rate) factors were observed between the protocols. This suggests that subjective feelings of physical exertion increased significantly due to mental fatigue. However, it did not decrease the physical workload during HIIT for tennis players. Therefore, incorporating short periods of the incongruent Stroop task during HIIT could be used to develop tolerance to mental fatigue without a decrease in the workload.  
strop; cognitive fatigue; HRV; physical fatigue Article
## 2 Materials and Methods
### Image Analysis  
Terrain-corrected masked images were used as inputs for the image analysis. To derive the risk zones, Principal Component Analysis followed by Forward MNF rotation statistics were computed to reduce noise and to the resultant MNF datasets, MTMF was applied (Figure 4). The post-fire ortho-imagery was used to collect forty ground control points to classify the image. Lighter colored areas represent higher fractional cover on a scale of 0-1. These datasets were used as inputs for dNBR (Figure 4), computed from NBR developed by the National Park Service and the U.S. Geological Survey [50]. Near infrared (band 4) and short-wave infrared band (band 7) can provide the best contrast to isolate burned vegetation and healthy vegetation [51]. The formula for NBR is very similar to that of NDVI except that it uses NIR and the short-wave infrared (SWIR-2) wavelengths [11; 50; 52] as shown in Equation(3).  
\[\text{NBR}=(\text{NIR}-\text{SWIR})/(\text{NIR}+\text{SWIR}) \tag{3}\]  
There is usually evidence of change in vegetation and soil properties post-fire so measuring this change in surface conditions is a better estimation of burn severity, hence a dNBR was computed by subtracting the post-fire NBR from the pre-fire NBR as shown in Equation(4) [17]. RdNBR was computed using Equation (5) [18].  
\[\text{dNBR}=\text{NBRpre}-\text{NBRpost} \tag{4}\]  
\[R\text{{}NBR}=\left(\frac{d\text{{}NBR}}{\sqrt{Absolute\Big{(}\frac{Prefix \text{{}NBR}}{1000}\Big{)}}}\right) \tag{5}\]  
Figure 3: Elevation map of Palo Verde National Park in Guanacaste showing the relief of the area.  
Ground data of the areas with high dNBR and RdNBR were acquired as ROIs from the high resolution ortho-imagery.
## 6 Performance Evaluation
### Network Lifetime
Therefore, the best path and shortest path can be used, lowering the probability of dropping a packet. CKN using \(k=3\) shows the second-best delivery ratio, following the same idea; when we use \(k=3\), each node must have at least 3 awake neighbors. This increases the probability of finding the best node to forward the packet. Following the same idea, CKN using \(k=2\) presents a worse delivery ratio than CKN using \(k=3\) and CKN using \(k=1\) has the worst delivery ratio. ECKN presents a similar performance to CKN using \(k=3\), this is due to the fact that nodes closer to the sink have larger values for \(k\), increasing the probability of reaching the sink. Therefore, due to the different values of \(k\) according to the distance to the sink, ECKN performs as well as CKN using \(k=1\) in terms of the network lifetime, while it maintains a good delivery ratio, as good as the one presented by CKN using \(k=3\). Figure 16: (**a**) Number of sleep epochs using random numbers and residual energy as rank. (**b**) Number of alive nodes using random numbers and residual energy as rank.
## 5 Simulations, Results, and Discussions
### Normal-Cycle Scenario  
This scenario represents the normal cycle (no attack is detected) in which the system receives low requests such as in rural areas.  
We consider the following parameters:  
* An LTE system with 3 PRB (\(C=3\)).
* An average arrival rate of \(R_{R}\) (\(\lambda_{1}=1\)).
* An average arrival rate of \(M_{R}\) (\(\lambda_{2}=1\)).
* A service rate of \(R_{R}\) (\(\mu_{1}=3\)).
* A service rate of \(M_{R}\) (\(\mu_{2}=3\)).  
The results of the normal cycle scenario are shown in Table 2:  
In Figure 9, the results and percentages of different phases for the normal cycle scenario:  
* Initial phase probability \(=\pi_{(0,0)}=52\%\).
* Authentic phase probability \(=\pi_{(0,1)}+\pi_{(1,0)}+\pi_{(0,2)}+\pi_{(1,1)}+\pi_{(2,0)}=45\%\).
* Suspicious phase probability \(=\pi_{(0,3)}+\pi_{(1,2)}+\pi_{(2,1)}+\pi_{(3,0)}=3\%\).
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 2 Structure and Working Principle  
### Structure  
According to the technical requirements of the vacuum central drum seed-metering device (take \(2\pm 1\) rice seeds per suck hole as the qualified standard), a vacuum central drum seed-metering device, which can be applied to the direct-seeding of hybrid rice with high quality and small quantity and has the central drum character (singular device sowing multiple rows), was designed.  
Comparatively few studies have been conducted domestically and overseas about vacuum central drum hybrid rice precise-seeding, and the existing hybrid rice metering devices are dominated by round disc devices. Therefore, this study designed a vacuum central drum seed-metering device, As shown in the Figure 1, in which the central drum seed-metering device is mainly composed of 1. a seed box, 2. an outer drum, 3. ventilation housing (connect the positive and negative pressure pipes, respectively), 4. seeding tubes (a seed discharge channel), and 5. an air delivery port (for blowing seeds to different rows).
## 4 Experimental Verification  
Based on the above analysis and optimization, a 37 kW,160 rpm prototype was fabricated, as shown in Figure 19.  
Figure 19: Prototype of 37 kW,160 rpm motor.  
Figure 18: Comparison of output torque and maximum torque before and after motor optimization. (**a**) Curve of output torque. (**b**) Curve of maximum output torque.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Initial Value** & **Predicted Value** & **Simulative Value** \\ \hline Output torque (kNm) & 2.21 & 2.22 & 2.23 \\ Torque ripple (\%) & 1.4 & 0.8 & 0.68 \\ Power factor & 0.915 & 0.93 & 0.925 \\ Efficiency (\%) & 95.64 & 95.7 & 95.68 \\ Copper loss (W) & 852 & 840 & 848 \\ Core loss (W) & 614 & 590 & 600 \\ Solid loss (W) & 31 & 29 & 30 \\ No-load back EMF (V) & 327 & 334 & 331 \\ Maximum torque (kNm) & 9.9 & \(-\) & 9.45 \\ Overload multiples & 4.48 & \(-\) & 4.28 \\ PM weight (kg) & 12.2 & 11.2 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The summary and comparison of the results before optimization, optimization results and finite element calculation results of the motor.  
Under the rated load, the motor was tested for load. Figure 20 shows the calculated and measured line back EMF curves and current curves under rated load. The measured line back EMF was 338 V, which was slightly lower than the simulated 343 V. The two curves were in good agreement. In addition, the measured current was 70 A, and the simulated current was 68.8 A. The error between the calculated value and the measured value was within the acceptable range. The summary and comparison of simulation calculation, measured data and optimized prediction results are shown in Table 6. On the whole, the errors between these three values are acceptable, which further proves the good accuracy of the LHS-Kriging surrogate model.
## 4 Quantitative Assessment Method  
The segmentation of blood vessels is a binary classification problem. In classification problems, we usually use some indicators to evaluate the pros and cons of the algorithm. In this article, we use the values of Accuracy (AC), Precision, Recall, and Area under the ROC curve (AUC) and \(\text{F}_{\beta}-score(\text{F}_{\beta})\) evaluated the performance of FAU-net. We will use True Positive (TP), True Negative (TN), False Positive (FP), recall rate and False Negative (FN) to calculate the above indicators. Performance metrics calculation is as follows:  
\[AC=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
We assume that the positive samples are pixels marked as blood vessels and the negative samples are background. We assign different weights to recall and precision to indicate the preference for classification models. The calculation equation is as follows:  
\[F_{\beta}=\frac{\left(1+\beta^{2}\right)\cdot\ Precision\cdot Recall}{\beta^{2} \cdot\ Precision+Recall} \tag{10}\]  
It can be seen that when \(\beta=1\), then \(\text{F}_{\beta}\) changes back to FI; when \(\beta>1\), the model prefers to improve recall, which means that the model pays more attention to the ability to recognize positive samples. when \(\beta<1\), the model pays more attention to the ability to distinguish negative samples. In this article, we do not want the model to produce the wrong results to cover more samples. It will affect the doctor's diagnosis of the patient's condition. Therefore, we will adjust the parameter \(\beta\) to 0.75.  
Considering the class imbalance of the data set, we also introduce Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. The ROC curve is drawn with True Positive Rate (TPR) as the y-axis and False Positive Rate (FPR) as the x-axis. With the accuracy as the y-axis and the recall as the x-axis, we get the PR curve. The calculation equations of TPR and FPR are as follows:  
\[TPR=\frac{TP}{TP+FN} \tag{11}\]  
\[FPR=\frac{FP}{FP+TN} \tag{12}\]
## 3 System Reliability Evaluation Method Considering Member Failure Credibility
Equation (16) is adopted in this paper to improve the system reliability assessment method previously proposed based on the failure credibility, and the system reliability evaluation method considering member failure credibility under failure criterion I, II, and III is formed, which are respectively derived as Equations (17)-(19). In the case of failure criterion I,  
\[ \begin{array}{ll}P_{f_{I},Bel}=P\{(Z_{1}(X)\leq 0,Bel_{1}),(Z_{2}(X)\leq 0,Bel_ {2}),\cdots,(Z_{n}(X)\leq 0,Bel_{n})\}\\ \approx\sum\limits_{i=1}^{n}P\{(Z_{1}(X)\leq 0,Bel_{1})\}-\sum\limits_{1\leq i <j\equiv n}^{n}P\Big{(}(Z_{i}(X)\leq 0,Bel_{i}),\big{(}Z_{j}(X)\leq 0,Bel_{j} \big{)}\Big{)}\\ \approx\sum\limits_{i=1}^{n}P_{f_{Z_{i}},Bel_{i}}-\sum\limits_{1\leq i <j\leq n}^{n}C\Big{(}P_{f_{Z_{i}},Bel_{i}},P_{f_{Z_{i}},Bel_{j}}\Big{)}\end{array}  \tag{17}\]  
where \(P_{f_{I},Bel}\) expresses the reliability of the system under failure criterion I considering member failure credibility. In the case of failure criterion II,  
\[ \begin{array}{ll}P_{f_{I},Bel}=P\{(Z_{11}(X)\leq 0,Bel_{1}),(Z_{12}(X)\leq 0,Bel_ {2})\}\cup P\{(Z_{22}(X)\leq 0,Bel_{2}),(Z_{23}(X)\leq 0,Bel_{3})\}\cup\\ \cdots\cup P\{(Z_{n-1,n-1}(X)\leq 0,Bel_{n-1}),(Z_{n-1,n}(X)\leq 0,Bel_{n})\} \\ =1-\prod\limits_{i=\cdot}^{n-1}\Big{(}1-P\Big{\{}(Z_{i,j-1}(X)\leq 0,Bel_{j-1}),(Z_ {ij}(X)\leq 0,Bel_{j})\}\Big{)}\\ =1-\prod\limits_{i=\cdot}^{n-1}\Big{(}1-C\Big{(}P_{f_{Z_{i},j-1},Bel_{j-1}},P_ {f_{Z_{ij}},Bel_{j}}\Big{)}\Big{)},j=i+1\end{array}  \tag{18}\]  
where \(P_{f_{I},Bel}\) expresses the reliability of the system under failure criterion II considering member failure credibility. In the case of failure criterion III,  
\[\begin{array}{ll}P_{f_{I},Bel}=1-\prod\limits
## 2 Descriptions of Conventional Histogram-Based Reversible Data Hiding
### Histogram-Modification for Reversible Data Embedding and Extraction  
Histogram-modification scheme for data embedding is adopted from [8], which can be described with the following Steps.  
Step 1. _Histogram generation._ We generate the histogram of \(\mathbf{X}\) in Figure 2(a) first. The luminance with the maximal occurrences in histogram is labeled as \"max point,\" while that with no occurrence is labeled as \"zero point.\" Without loss of generality, we assume that the luminance of the zero point is larger than that of the max point. The luminance values of \"max\" and \"zero\" points, each is represented by 1 byte (or 8 bits), are treated as overhead or side information, \(K\). Hence, a total of 16 bits should be transmitted to the receiver for data extraction. Step 2. _Range selection_. The range of luminance values between max and zero points is recorded in the histogram. Step 3. _Luminance addition in selected range._ In the region between max and zero points recorded in Step 2, luminance values in the selected range are all increased by 1, and we can regard the selected region is shifted to the right by 1. The resulting histogram is depicted in Figure 2(b), and it has high correlations with Figure 2(a), which has the correlation coefficient of 0.9957. Step 4. _Data embedding._ For the embedding of user-defined bitstream \(S\) in Figure 1(a), if the watermark bit is '1,' the luminance value keeps unchanged; if the watermark bit is '0', it is decreased by 1. Figure 2(c) depicts the histogram with this step, and it is also highly correlated with Figure 2(a), with the correlation coefficient of 0.9958.  
Figure 2: **(a)** Histogram of Lena. **(b)** After performing Step 3. Correlation coefficient = 0.9957. **(c)** After performing Step 4. Correlation coefficient = 0.9958.  
In extracting both the hidden data and cover image, the following steps should be executed accordingly:  
1. _Locating of range._ With side information \(K\) in Figure 1(b), luminance values between the max and zero points are compared.
2. _Data extract._ Every pixel in \(\mathbf{X}^{\prime}\) in Figure 1(b) is scanned and examined sequentially to extract the data bits. Performing the reverse procedure to Step 3 of the embedding procedure, extracted data \(\widetilde{S}\) is obtained.
3. _Original image recovery._ By moving the histogram into its original form, the content \(\widetilde{X}\) is recovered. Only the max point is required.
4. _Reversibility validation._ After data extraction, we need to validate whether the outputs of the decoder and the inputs of the encoder are exactly the same. That is, \(\widetilde{\mathbf{X}}=\mathbf{X}\), and \(\widetilde{S}=S\).  
Performing data hiding is simply by shifting certain parts of the histogram of the image, and no calculation is required. Besides, the luminance values of the max and zero points play an important role for making reversible data hiding possible.
## 4 Proposed Method
### Two-Step Learning Model  
The two-step learning model comprises a two-class model for classifying normal and disease images and a three-class model for classifying COVID-19, lung opacity, and viral pneumonia among the diseases. Additionally, 3D rotational augmentation is used to match the number of data points in each class uniformly before training. A label file is created using the lung detection model. The position and class information of the left and right lungs are included in the label file. In the two-class model, the normal class ID is set to 0 and the IDs of all other classes are set to 1. In the three-class model, the class IDs are set to 0, 1, and 2 for COVID-19, lung opacity, and viral pneumonia, respectively. The two- and three-class models are trained using YOLOv4. A block diagram of the proposed two-step learning model is shown in Figure 7.  
In the detection step, the two- and three-class models are used sequentially. First, normal and disease images are classified using the two-class model, and among the diseases classified in the two-class model, COVID-19, lung opacity, and viral pneumonia are classified in the three-class model. Figure 8 shows a block diagram of the detection process, and Figure 9 shows the resulting images yielded by the proposed two- and three-class models. In the resulting images, both the left and right lung positions are accurately detected;  
Figure 6: Results of 3D rotational augmentation. (**a**) Direction of rotation (first row is counterclockwise and second row is clockwise); and (**b**) result images for each direction. Dotted red and black lines indicate positions of image center and esophagus, respectively. Black arrows indicate movement range for each image.  
Figure 7: Block diagram of the two-step learning model.  
meanwhile, normal and disease images are detected accurately in the two-class model, in addition to the class of each disease in the three-class model.  
Multiple objects are detected during the detection stage of the two-step learning model. The maximum class sum (MCS) method was used to classify one symptom from the multiple detection results. MCS is a method of selecting the class with the largest sum after calculating the confidence sum for each detected class. The maximum class sum is expressed as follows:  
\[\text{MCS}=\max_{i}\sum_{j=1}^{n}C_{ij}, \tag{1}\]  
Figure 8: Block diagram of the detection process.  
Figure 9: Result images for the two- and three-class models.  
where MCS represents the class selected based on the sum of the maximum classes; \(i\) and \(j\) are the classes of the detected bounding box and the number of detected bounding boxes, respectively; and \(C\) is the confidence level of the bounding box. If the detected bounding box did not exist, it was denoted as \"\(N\)/\(A\).\"
## 4 Simulation and Experiment Results
### Experiment Results  
A test device is established to verify the proposed method, and is shown in Figure 8. The test deice consists of controller, motor driver, PMSM. The controller is STM32F103, FOC, and SVPWM are implemented by C-program, the clock frequency of controller is 72 MHz, the sampling time of speed loop is 10 ms, the PWM frequency is 16 kHz, current sensing by isolated current sensors (ICS), an encoder of 1000 lines is used. The max speed of motor is 3000 rpm and the rated current is 3.13 A. The serial port communicating baud rate between the STM32F103 and computer is 115,200.  
In order to verify the effectiveness of the proposed method, the proposed method is compared with other controllers. The speed curve under PI speed controller is shown in Figure 9, the proportional gain and integral gain of speed PI controller are 200 A/rad/s and 50 A/rad, respectively. The proportional gain and integral gain of current loop PI controller are 572 V/A and 121 V/A s, respectively. The parameters of sliding mode speed controller are as follows: \(c=21\), \(\varepsilon\)= 50, \(k=0.0075\). The parameters of ESO are as follows: \(\alpha_{1}=3000\), \(\alpha_{2}=3000\). The speed curve under speed loop sliding mode controller is  
Figure 8: Experimental test device.  
Figure 7: Curves of transition process and speed curves using transition process: (**a**) speed curve and acceleration curve of transition process, (**b**) speed curve under \(c\) = 15, (**c**) speed under \(c\) = 30, (**d**) speed under \(c\) = 50.