esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Model Evaluation Index  
The accuracy rate (ACC) and F1 value are the evaluation metrics of pattern recognition models. Tables 2 and 3 and the following formula describe the calculation process of ACC and F1. The F1 score is a statistical measure of the accuracy of a classification model that takes into account both the precision and recall of the class model. In this paper, the F1 score is the average of the F1 scores obtained by building a dichotomy for each class.  
\[ACC=\frac{\sum_{g=1}^{G}n_{gg}}{n}\]  
where \(n\) is the number of all samples in the training set or validation set.  
\[precision=\frac{TP}{TP+FP}\]  
\[recall=\frac{TP}{TP+FN}\]  
\[F1=\frac{2\times precision\times recall}{precision+recall}\]  
where \(TP\) is true positive, \(TN\) is true negative, \(FP\) is false positive, \(FN\) is false negative.  
The evaluation indexes of the quantitative regression models are correlation coefficient (\(R\)), and root mean square error (\(RMSE\)). \(R\) is used to measure the degree of correlation between variables, and the closer \(R\) is to 1 the better the effect. \(RMSE\) is used to measure the deviation between the predicted value and the true value, and the smaller its value, the better the effect.  
\[R=\sqrt{1-\frac{\sum_{i=1}^{n}\Bigl{(}y_{i,actual}-y_{i,predicted}\Bigr{)}^{2}} {\sum_{i=1}^{n}\Bigl{(}y_{i,actual}-\overline{y}_{actual}\Bigr{)}^{2}}}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{i,actual}-y_{i,predicted} \Bigr{)}^{2}}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & & \\ \hline \multirow{4}{*}{Actual class} & \multirow{4}{*}{
\begin{tabular}{c} \(1\) \\ \(2\) \\ \(3\) \\ \(4\) \\ \(5\) \\ \end{tabular} } & \(1\) & \(2\) & \(3\) & \(\cdots\) & \(\mathrm{G}\) \\  & \(1\) & \(n_{11}\) & \(n_{12}\) & \(n_{13}\) & \(\cdots\) & \(n_{1\mathrm{G}}\) \\  & \(2\) & \(n_{21}\) & \(n_{22}\) & \(n_{23}\) & \(\cdots\) & \(n_{2\mathrm{G}}\) \\  & \(3\) & \(n_{31}\) & \(n_{32}\) & \(n_{33}\) & \(\cdots\) & \(n_{3\mathrm{G}}\) \\  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\  & \(\mathrm{G}\) & \(n_{G1}\) & \(n_{G2}\) & \(n_{G3}\) & \(\cdots\) & \(n_{GG}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multiple classification confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & & \\ \cline{3-4}  & & & & & \\ \hline \multirow{2}{*}{Actual class} & Positive & TP & FN \\  & Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for two types of discriminant analysis.  
The software used in this paper is programmed through PyCharm Community Edition, 2021.3, except for the anomalous sample rejection part, which is done in TQ software.
# Pyramid Feature Attention Network for Speech Resampling Detection
## 2 Proposed Method
### Changes in Spectrogram Features in Different Network Layers  
To visually observe the ability of the proposed model to extract features and capture resampling changes, we analyzed the feature variations in the spectrogram at different network layers. Figure 5 shows the feature visualization process of the spectrogram through the proposed model, which includes down-sampled speech, original speech, and up-sampled speech. To observe the changes more intuitively, the resampling is performed by the zero-complementing interpolation method. When \(\text{p}/\text{q}=0.6\), the time-domain length decreases, the frequency-band length stretches, and the energy of the high-frequency component in the spectrogram increases. And zero-padding is carried out before inputting the model for training. When \(\text{p}/\text{q}=2\), the length doubles in the time domain. As the frequency resolution increases in the low-frequency region, the frequency bandwidth decreases. Therefore, the frequency domain exhibits the characteristics of compression, resulting in the energy reduction of the high-frequency components in the spectrogram.  
When the spectrogram enters the first convolution layer, the proposed model can capture the resampling characteristics of the above analysis. After the maximum pooling layer, the model focuses on the pixels with large energy and reduces the dimensions of the feature map. The output of Bottleneck2 layer indicates that the model is still able to capture the characteristics of up-sampling, original speech, and down-sampling in the time and frequency domains. The model extracts semantic information of the spectrogram through Bottleneck4, and the features of the down-sampling frequency domain stretching and up-sampling frequency domain compression can still be observed. It is noted in the figure that the SE layer helps to assign greater weight to channels that are more effective for model detection tasks, and the pixel values of the feature map corresponding to channels are enlarged. Finally, after the lateral connection operation, due to the fusion of the edge texture information captured by the upper layer, it can be seen that the final feature map restores the characteristics of the resampled spectrogram in the time and frequency domains and contains rich semantic information. Thus, the subsequent resampling classification task has better performance.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion
### Laser Spot Position Error
\[\Delta\beta_{\rm mR}=\beta_{\rm mR2}-\beta=\arcsin(\sin(\beta)\cdot(1+\lambda ))-\beta \tag{18}\]  
Figure 9: Laser spot position error analysis. (a) Schematic diagram of laser spot position deviation; (b) Schematic diagram of partial enlargement; (c) Velocity relationship. The curves of \(\Delta\beta_{\text{mR}}\) changing with \(\lambda\) and \(\beta\) are shown in Figure 10. The figure shows that \(\Delta\beta_{\text{mR}}\) increases with the increase in \(\lambda\) and \(\beta\). To meet the engineering requirements, it is necessary to select an appropriate \(\lambda\). For angles \(\beta_{\text{CAL}}\), when \(\lambda_{\text{CAL}}=0.026\), \(\text{abs}(\Delta\beta_{\text{mR}})\leq 0.05^{\circ}\) and \(\lambda_{\text{CAL}}\) represent the maximum position deviation ratio allowed by \(\beta_{\text{CAL}}\). Then, assuming the expected radius \(r=47.5\) mm during the experimental test, the allowable laser spot position error radius \(R_{0}=r\cdot\lambda_{\text{CAL}}=1.235\) mm. Therefore, in this experiment, it is only necessary to control the laser spot to be within a circle with a radius of 1.235 mm centered on the ideal position point P to meet the experimental measuring requirements. The results of the above theoretical analysis show that the position error of the laser spot affects the measurement results of the angle \(\beta\). To verify the influence of the laser point position error, we can adjust the displacement platforms 1 and 2 to shorten the distance between points P\({}^{\prime}\) and P. However, since the geometric size of the laser spot is not zero, we currently have no clear technical means to determine the actual positions of these two points. In addition, the difference between dynamic and static measuring results is caused by a combination of multiple factors. Changing the position of the laser spot alone may not lead to the desired results.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Other Considerations
#### 5.3.3 Crew Resource Management  
While not necessarily the case, most BVLOS operations require multiple crew members. In such instances, it is important that all crew members are empowered to perform their roles effectively. This process is termed Crew Resource Management (CRM) within aviation and is a compulsory subject area for the issuance of pilot's licenses. Good CRM means that the tasks associated with a particular flight are organised and distributed such that all of the crew's resources are applied in a safe and efficient manner [88]. For this reason, it is important that organisations doing BVLOS operations in forest environments have ensured that CRM training is provided, either internally or externally, to staff to ensure that multi-crew operations are effectively managed. While limited, there is already some literature that supports the need for CRM training for large and complex unmanned aircraft operations [96; 97; 98].
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 4 Microfluidic Platforms Applied to Axonal Guidance
### Mechanical Guidance  
Mechanical guidance method is to guide the growth direction of the axon by applying precise forces on the axon, such as pressure and suction. [PERSON] [82] put forward a microfluidic platform taking advantages of flow-controlled axonal guidance techniques to direct axon regrowth by micro-syringe nano-pump and micro-tubing. As shown in Figure 6B, the platform can direct cell culture medium towards neurons to fasciculate one advancing axon onto another. The linear microfluidic flow is induced by a nano-pump into sample chamber at a flow rate of 2.5 \(\upmu\)L/min through a micro-tube of 50 \(\upmu\)m inner diameter. The lower part of Figure 6B shows the distribution of radial forces on the axon at different locations of microfluidic flow. The highlight of this microfluidic device is using precisely controlled culture medium flow to promote formation of neuronal fasciculation and guide orientation of axonal and growth cone. [PERSON] [83] established a miniaturized microfluidic platform that can induce parallel axonal growth of neurons in specific directions along well-defined geometries. The device consists of a growth chamber for placing cell bodies and storing growth medium; a vacuum chamber for applying suction to impart tension; and an array of micro-channels connecting the growth chamber to the vacuum chamber. The suction force generated by vacuum chamber is applied to multiple neurons to induce neurite growth. This microfluidic platform uses vacuum stress to guide axon growth.
## 3 Calculation Results and Analysis
### The Influence of Temperature Change on Frequencies  
Figures 8-10 show the influence of temperature change \(\Delta T\) on the natural frequencies of the nano-annular plates. \(\overline{\Omega}\)= 5, \(s=0.1\), \(\tau=0.1\). It can be seen from the figure that when the boundary condition is C-C, with the positive increase in \(\Delta T\), that is, the temperature gradually increases, the first three order dimensionless natural frequencies of the annular plates gradually decrease. When the boundary condition is C-F, the fundamental frequencies of the annular plates increase with the increase in temperature, and the second and third order dimensionless natural frequencies decrease with the increase in temperature. When the value of rotational velocity is fixed, the change in temperature changes the internal forces in the plane, so that the natural frequencies of the nano-annular plates change. When the value of \(\Delta T\) increases in the positive direction, the absolute values of the radial forces \(N^{T}\) generated by the temperature change can be analyzed through expression (28), which gradually compresses the radial cross sections of the annular plates. This change increases the equivalent stiffness under the first-order vibration of the nano annular plates with the C-F boundary condition and increases the fundamental frequencies. For the nano-annular plates, with inner and outer rings clamped, the change trend is just the opposite. Therefore, the influence of temperature change on the fundamental frequencies of the nano-annular plates during the rotation is related to the boundary conditions.  
Figure 8: The relationship between dimensionless natural frequencies and the temperature change under two different boundary conditions (Kirchhoff plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.  
Figure 7: The relationship between dimensionless natural frequencies and the nonlocal parameter under two different boundary conditions (Reddy plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.
## 2 The University Project \"Morocco\" and Project Aims
### The Project Aims  
Based on the cooperation of the above-mentioned university professional workplaces, the aim was to perform and test modern, fast, and accurate 3D documentation of objects of interest. Today's documentation of historic buildings presupposes the use of modern technologies that will significantly speed up the documentation process [23; 24; 25].  
The main aims of this expedition were:  
1. To define accuracy of new modern easy to use and transportable laser scanners,
2. To determine the possibility of economical and effective technology for documentation of this specific cultural heritage; the target group: Cooperation with historians from the Faculty of Philosophy, the Charles University in Prague and Moroccan historians from the state monument institution (CERKAS),
3. On case projects, to research usability of PLS, TLS, and close-range photogrammetry,
4. To show the quickness and accuracy of results on some case projects, which were very difficult to measure with a traditional approach,
5. To give a recommendation for future work in this area.
## 4 Discussion  
In this study, a field survey was conducted with the aim of investigating how the visual environment at a tourist destination in a regional city is evaluated by different languages and to investigate the impact of signage on the evaluation of the tourist destination. In addition, a questionnaire survey was conducted with the aim of investigating the effects of different national languages on the evaluation of signs such as differences in attention to and impressions of signs, and to collect findings for a universal design taking into account the different characteristics of tourists.  
The main findings of this study on tourist information signage is listed below.  
* When designing signage, not only visibility and clarity, but also the layout and consideration of the surrounding environment should all be of equal importance.
* The difference between Japanese and non-Japanese speakers was that Japanese speakers tended to focus on the details of the sign, while non-Japanese speakers focused on whether the sign was clearly depicted with diagrams and pictures.
* In common with Japanese speakers and non-Japanese speakers, signs with smaller letters were evaluated lower in the factors: \"Visibility\", \"Clarity\", and \"Harmonicity\".
* Compared to Japanese speakers, non-Japanese speakers placed more importance on accessibility, and wayfinding or having difficulty walking affected their evaluation.  
As mentioned in the introduction, the Japanese government is currently promoting multilingual signs, but the above results suggest that it may be better to avoid the easy shift to multilingual signs. It would be better to use other methods such as creating separate signs in Japanese and foreign languages, using pictograms effectively to make signs understandable without foreign language notation, and using ICT technology to enable non-Japanese speakers to view additional information, rather than making all signs multilingual. In some cases, it may be possible to improve signs for non-Japanese speakers while maintaining clarity and visibility. In the future, based on these findings, it will be necessary to conduct a demonstration experiment to verify whether there is a change in the impression of tourists by actually proposing improvements to the signage as shown in Figure 14. Moreover, although this study focused on outdoor signs, indoor signs are considered to be different from outdoor signs in various aspects such as the size of the letters used and the information provided. Therefore, it is necessary to investigate indoor signs in a similar way.
## 6 Simulation Results
### Simulation of Inverse Dynamics + PD Controller  
The Inverse Dynamics + PD controller is designed as shown in Section 4. Here, the coefficient matrices of proportional gain \(\mathrm{K_{P}}\) and derivative \(\mathrm{K_{V}}\) are selected in accordance with (46) and (47).  
\[\mathrm{K_{P}}=\mathrm{diag}\{22500,22500,22500,22500,16900,16900,16900,16900\} \tag{46}\]  
\[\mathrm{K_{V}}=\mathrm{diag}\{300,300,300,300,260,260,260,260\} \tag{47}\]  
In order to illustrate this, suppose that in the output block (robot block) of the Inverse Dynamics + PD controller in Figure 3, the cutting forces are precisely determined in accordance with the cutting force coefficients in case 1 of Table 5. The simulations are carried out for two cases where the cutting force in the input control block is calculated according to the corresponding coefficients in the two cases of Table 5, respectively. For more convenience, the simulation results that correspond to the above two cases are called PD1 and PD2.  
The simulation results are shown in Figure 11. In particular, Figure 11a represents the position error of first joint in comparison with the desired trajectory. Similarly, Figure 11b-h represent the position errors of the second, third,..., eighth joints respectively. The graphs that represent the errors of joint positions show the different errors corresponding to each joint. The errors of the joints that are near the end effector are greater than that of the far ones. Summarizing the joint position errors, we get the errors of the operational point of the cutting point of the tool with respect to the toolpath. Figure 12 shows the error of trajectory of the cutting point of the tool in comparison with the set trajectory as the toolpath. We realize that the trajectory error of PD1 is smaller than that of PD2 because PD1 is corresponding to the assumption of calculating cutting forces precisely.  
Figure 11: _Cont._Thus, with the analysis and the choice of different cutting force coefficients, the control results will be different and affect the machining precision of the robot significantly.
## 3 Time-Domain Analytical Solution of the Received Signal
### Time-Domain Analytical Solution of the Received Magnetic Induction Intensity Signal
#### 3.2.2 Time-Domain Analytical Solution for the Rising Half-Cycle  
Next, we consider the analysis of the rising edge of the actual square wave current signal applied to the excitation coil. By verifying the above method, the reflection coefficient is obtained as:  
\[ \begin{split}\Gamma_{B}(p)&=I(p)R(p)=\Gamma_{BL0}(p )+\Gamma_{BR0}(p)-\sum\limits_{m=1}^{\infty}[\Gamma_{BLm}(p)+\Gamma_{BRm}(p) ]\\ &=\bigg{(}\frac{I}{p}-\frac{\tau_{0}I}{p\tau_{0}+1}\bigg{)} \Bigg{[}\frac{\mu_{r}\lambda_{i}+\gamma^{\prime}}{\mu_{r}\lambda_{i}-\gamma^{ \prime}}-\frac{4\mu_{r}\lambda_{i}\gamma^{\prime}}{\mu_{r}{2}^{\prime}{\lambda _{i}^{2}}-\gamma^{\prime 2}}\sum\limits_{m=0}^{\infty}\bigg{(}\frac{\mu_{r}\lambda_{i}-\gamma^{ \prime}}{\mu_{r}\lambda_{i}+\gamma^{\prime}}\cdot e^{-\gamma^{\prime}d}\bigg{)} ^{2m}\Bigg{]}\end{split}  \tag{33}\]  
By dividing \(\Gamma_{B}(p)\) into terms \(\Gamma_{BL0}(p)\) and \(\Gamma_{BLm}(p)\) with \(\frac{I}{p}\), and terms \(\Gamma_{BR0}(p)\) and \(\Gamma_{BRm}(p)\) with \(\frac{\tau_{0}I}{p\tau_{0}+1}\), the inverse Laplace transform is applied to each separately. For the former, \(\frac{I}{p}\) is transformed into \(\frac{I\tau}{2\lambda_{i}}\bigg{[}\frac{1}{\sqrt{p\tau+\lambda_{i}^{2}-\lambda _{i}}}-\frac{1}{\sqrt{p\tau+\lambda_{i}^{2}+\lambda_{i}}}\bigg{]}\). For the latter, \(\frac{\tau_{0}I}{p\tau_{0}+1}\) is transformed into \(\frac{I}{2}\sqrt{\frac{\tau}{\tau}}\bigg{[}\frac{1}{\sqrt{p\tau+\lambda_{i}^{2} -\sqrt{\tau\tau}}}-\frac{1}{\sqrt{p\tau+\lambda_{i}^{2}+\sqrt{\tau\tau}}}\bigg{]}\), where \(\
u=\frac{\lambda_{i}^{2}}{\tau}-\frac{1}{\tau_{0}}\). At this point, the Laplace inverse transform formula can be applied to continue the solution, denoted as:\[\phi_{n}(a,b,k,t)=\frac{1}{2\pi i}\int_{0}^{+\infty}\frac{e^{-k\sqrt{p}}}{\sqrt{p} \left(\sqrt{p}+a\right)^{n}\left(\sqrt{p}+b\right)}\mathrm{e}^{pt}\mathrm{d}p \tag{34}\]  
where \(\phi_{n}(a,b,k,t)=\frac{1}{(a-b)^{n}}\Bigg{[}\varphi_{0}(b,k,t)-\sum\limits_{j =0}^{n-1}{(b-a)^{j}\varphi_{j}(a,k,t)}\Bigg{]}\). \(\Gamma_{BL0}(p)\) and \(\Gamma_{BLm}(p)\) are obtained by using the frequency shift and scale transformation properties of the Laplace transform [29], combined with the standard Laplace inverse transform formula [25], the Laplace inverse transform form can be obtained as:  
\[\Gamma_{BL0}(t)=2\sqrt{\frac{\lambda_{i}^{2}t}{\pi\tau}}e^{-\lambda_{i}^{2}t/ \tau}-\left(1+\frac{2\lambda_{i}^{2}t}{\tau}\right)\mathrm{erfc}\left(\sqrt{ \frac{\lambda_{i}^{2}t}{\tau}}\right)+1-\varepsilon(t) \tag{35}\]  
\[\Gamma_{BLm}(t) =\Psi\left(\mu_{r},1,2m\lambda_{i}d,\frac{\lambda_{i}^{2}t}{ \tau}\right)+\left[\sum\limits_{n=1}^{2m}{C_{2m}^{n}(-2)}^{n+2}\eta_{n}\left( \mu_{r},2m\lambda_{i}d,\frac{\lambda_{i}^{2}t}{\tau}\right)\right. \tag{36}\] \[+\left.\sum\limits_{n=0}^{2m-1}{C_{2m-1}^{n}(-2)}^{n+2}\eta_{n+2} \left(\mu_{r},2m\lambda_{i}d,\frac{\lambda_{i}^{2}t}{\tau}\right)\right]\]  
where \(\eta_{n}(a,k,t)=\frac{e^{n}e^{-t}}{2}[\phi_{n}(a,-a,k,t)-\varphi_{n}(a,k,t)]\).
## 2 Result and Discussion
### Changes in Chemical Classes of Volatiles Produced during Flower Development  
HS-SPME-GC-MS analysis of flower volatiles indicated that the percentage of each chemical class making up the total volatiles varied among genetic types and cultivars. It was found however, that the differences in the major classes of volatiles were not significantly changed during blooming in most cultivars (Figure 2).  
Figure 2: Evolution of each group of flower volatile compounds during blooming in the Ponkan (**a**); Satsuma (**b**); Qingjia (**c**); Liubencheng (**d**); Eureka (**e**); Bergamot (**f**); Yuhuanyou (**g**); Zaoxiangyou (**h**); and Huyou (**i**); Citrus flowers were sampled at unopened flower “U”, half opened “H”, and fully opened flower “F” blooming stages.  
Monoterpenoids were the major volatile classes in flowers, regardless of stage and cultivar, which accounted for at least 60.20% of total volatiles, and was as high as 92.78% in Bergamot due to the high percentage of monoterrebor carbons and oxygenated monoterenes (Figure 2), which was known by the name \"finger citron\", a citrus with flower and fruit for ornamental as well as medicinal purposes. In addition, sesquiterpenoids were presented in large numbers but in low amounts in almost all cultivars, while few ketones and esters were evident in citrus flower volatiles. The trend in evolution of major chemical classes was not consistent among all cultivars, and fluctuated due to changes in content of major compounds. A similar observation has been reported for the volatile constituents of _Rosa canina_ L. during flower development [41] and also from leaf volatiles of different citrus during leaf development [21].