esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Approach
#### 2.3.2 Regression Gaussian Process  
There have been recent significant strides in ML using the GPR, which is a nonparametric Bayesian regression technique [57; 58]. This technique provides numerous benefits, including the potential to function effectively with minimal data as well as provide contemporaneous estimates of the output and related uncertainty.  
The main advantage of employing GPR is the simultaneous estimation of the output value and the associated uncertainty. By using GPR, the formulated hybrid model can generate a probability density function (PDF) over the range FTs that embodies both the predicted Ttf and the corresponding uncertainty associated with various sources.  
GPR works by taking into account a training set of data (\(x_{i}y_{i}\)); \(i=1,2,\ldots,n\), where \(x_{i}\)\(\epsilon\)\(\mathbb{R}\)d and \(y_{i}\)\(\epsilon\)\(\mathbb{R}\) are drawn from an undefined distribution. Predicting the output parameter \(y^{\star}\) from a fresh input vector \(x^{\star}\) as well as the training data is the purpose of a GPR model. The preceding is the basic form of a linear regression model:  
\[y=x^{T}\beta+\epsilon \tag{2}\]  
where the \(\beta\) vector is the estimated coefficient of model fitting and \(\epsilon\) is the error associated with the model. It should be noted that \(\epsilon\) is normally distributed with a mean value of zero mean and a variance value denoted as \(\sigma^{2}\). In general, a Gaussian process (\(GP\)) is determined through its associated mean function \(m(x)\) and covariance function (or kernel function), \(k(x,x^{\prime})\), where \(x\) and \(x^{\prime}\) are two instances in the input features matrix \(x\). Therefore, the predicted values of \(y^{\star}\) can be formulated as a \(GP\):  
\[y^{\star}\sim GP(m(x),k\big{(}x,x^{\prime})) \tag{3}\]  
It should be noted that the covariance function must be chosen or made correctly because it is a key factor in how well GPR works. In reality, a lot of alike covariance functions in the GPR are available; however, reaching a particular choice is a case-by-case decision. One of the famous kernels that are commonly utilized with GPR is the radial basis function, which is written as [57]:  
\[K\big{(}x_{i},x_{j}\big{)}=\exp(-\frac{\big{\|}x_{i}-x_{j}\big{\|}^{2}}{2 \sigma^{2}}) \tag{4}\]  
where \(\big{\|}x_{i}-x_{j}\big{\|}\) represents the Euclidean distance among the two subject vectors and \(\sigma\) represents the dispersion of the kernel function's distribution.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 2 CVDP Network Design
### Network Structure
#### 2.2.1 CNN Module  
In [30], combining CNN with self-attention modules has been proposed to improve the network performance.VIT network introduces a self-attention mechanism and pays more attention to the location of frequency hopping signals, which has excellent feature extraction ability, but it needs a large amount of data for training to converge. To address this limitation, we modify the VIT network, replacing the image segmentation module with a CNN network. The CNN module processes the time-frequency spectrograms and outputs the corresponding feature maps, which can reduce the size of the time-frequency spectrograms and improve the inductive bias of the network, enabling it to converge quickly even when trained on small data sets. Specifically, the CNN module's structure, as presented in Table 1, where the dimension of the time-frequency spectrograms is 256 \(\times\) 128. The input to the network is a four-dimensional tensor with a size of 64 \(\times\) 3 \(\times\) 256 \(\times\) 128 and a batch size of 64.  
Figure 2: CVDP network structure diagram.
## 4 Results  
### Discharges, Flow Velocity, Grain Size and DEM  
During the field campaign (31 January 2018, 1 February 2018 and 2 February 2018), measurements were carried out at cross-section P1 (Figure 7, Table 3). On these days, discharges were 226 m\({}^{3}\)/s, 261 m\({}^{3}\)/s and 373 m\({}^{3}\)/s, respectively. The mean flow velocity varied from 1.1 m/s to 1.3 m/s, mean water depth varied from 2.0 m to 2.3 m and cross-section width was approximately 110 m. Bedload consists of fine sand (125 \(\upmu\)m \(<\) d \(<\) 250 \(\upmu\)m) and medium sand (250 \(\upmu\)m \(<\) d \(<\) 500 \(\upmu\)m). Figure 8 shows depth-averaged velocities mapped along the river reach after ADCP surveys on two consecutive days.  
Figure 9a illustrates a DEM (1 February 2018) of the region where the modified ISS-DOTv2 method was applied. By the plot difference of the two DEMs (2 February 2018-1  
Figure 8: Depth-averaged velocities measured with ADCP and interpolated along the river reach. (a) 1 February 2018; (b) 2 February 2018.  
Figure 7: Flow velocity measured with ADCP at cross-section P1 on 1 February 2018. Vertical black lines are the positions of moving-bed tests and where bedload samples were collected.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **31 January 2018** & **1 February 2018** & **2 February 2018** \\ \hline Water Level (cm) & 148 & 163 & 177 \\ Discharge (m\({}^{3}\)/s) & 226 & 261 & 373 \\ Mean Flow Velocity (m/s) & 1.13 & 1.18 & 1.33 \\ Cross-Section Area (m\({}^{2}\)) & 200.3 & 221.4 & 280.4 \\ Width (m) & 101.3 & 109.4 & 120.8 \\ Mean Depth (m) & 1.98 & 2.02 & 2.32 \\ Hydraulic Radius (m) & 1.85 & 1.81 & 2.30 \\ Water Level Slope (cm/km) & 15 & 25 & 25 \\ d\({}_{50}\) (mm) (bedload) & 0.268 & 0.274 & - \\ d\({}_{50}\) (mm) (bedload) & 0.397 & 0.406 & - \\ d\({}_{50}\) (mm) (bed material) & 0.302 & - & - \\ d\({}_{50}\) (mm) (bed material) & 0.418 & - & - \\ Transport Stage & 11.4 & 12.5 & 15.7 \\ Manning’s n (s/m\({}^{1/3}\)) & 0.016 & 0.020 & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured and estimated hydraulic parameters at Taquari River, cross-section P1.  
February 2018), the areas with aggradation and degradation during the time interval between the surveys were identified (Figure 9b). Analyzing the vertical variation at all grid points, 96% of the points presented erosion/deposition heights in the range \(\pm\)50 cm, and 81% in the range \(\pm\)25 cm. The distribution of the morphological variation was reasonably symmetrical, with 48% of the points undergoing aggradation and 52% undergoing degradation.
## 3 Results
### Autofluorescence Spectra of Lung Tissue Samples  
On the basis of the obtained fluorescence images, the areas of interest were measured by the spectrometer with a 420 nm long-pass filter. The spectra were normalized to 100% at the peak maximum and then calculated with curve-fitting. The wide spectra were contributed by several endogenous fluorophores, such as free NADH, protein-bound NADH, free FAD, basic form, and neutral form of porphyrins [36], tryptophan, collagen, and elastin. The fluorescence of protein-bound FAD is very weak [37] and not considered in this study. According to the reported emission spectra of the endogenous fluorophores listed in Table 1, it can be found that tryptophan, collagen, and elastin need to be excited by ultraviolet light [38]. With the excitation light of 405 nm in the present study, five fluorophores should be considered including free NADH, protein-bound NADH, free FAD, basic form, and neutral form of porphyrins. The normalized spectra of lung tissue samples were fitted with five peaks, as shown in Figure 2. Each peak was of a Gaussian function and representing the emission of a pure fluorophore. The goodness of fit was evaluated through an adjusted \(R\)-square, and all were more than 0.98 in our study.  
Figure 2: Autofluorescence 3D images of (**a**) normal (Video S1) and (**b**) cancerous (Video S2) lung tissues at different depths. Scale bar is 50 μm.  
Typically, compared with the normal tissues, the fluorescence intensity of free FAD in cancerous tissues increased, while the free and protein-bound NADH contributed less for the spectra. The optical oxidation reduction (redox) ratio is a measurement of cellular metabolism, which is defined as the ratio between NADH and FAD [41; 42]. In this study, the optical redox was calculated as the ratio of NADH (free and protein-bound) to free FAD, \(I_{\text{NADH}}/I_{\text{FAD}}\). In this case (Figure 3, which is Patient 1 in Figure 4), \(I_{\text{NADH}}/I_{\text{FAD}}\) were calculated as 1.76 and 1.04 for normal and cancerous tissues, respectively. We also found that the fluorescence of porphyrins changed in cancerous tissues. The intensity ratio of neutral form of porphyrins (with peak at 630 nm) to the basic form (with peak at 590 nm), \(I_{630\text{ nm}}/I_{590\text{ nm}}\), were calculated as 1.10 and 1.59 for normal and cancerous tissues, respectively. The change of fluorescence intensity ratios between normal and cancerous tissues may indicate changes of cellular metabolism and cancer development.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### Mechanical Tests  
The space remote sensing payload will experience a short but severe variation during the launch phase [20]; in order to ensure the stability of the mirror component during the on-orbit operation phase, mechanical tests are required for the component during the ground phase, including a small-scale feature-level sinusoidal scanning test, a sinusoidal vibration test, and a random vibration test.  
During the mechanical test, it is necessary to paste two test signal points on the mirror component, which are placed at the center of the mirror and at the center of the base. The test signal points can be used to monitor the changes of the participating products during the mechanical test, and at the same time, after the end of the mechanical test, through the computation of all the data, we can locate the component's weak position and evaluate the level of weakness. Two control signal points are pasted at the edge position of the vibration tooling to control the excitation of the mechanical test. By obtaining the numerical changes of the test points, it can be judged whether the designed component can withstand the influence brought by the mechanics.  
The tests procedure in this paper starts with small-scale feature-level sinusoidal scanning tests with a frequency between 10 and 500 Hz and an excitation of 0.2 g, followed by an 8 g sinusoidal vibration test with a frequency between 0 and 100 Hz and an excitation of 8 g, then a feature-level sinusoidal scanning test with a frequency between 10 and 500 Hz and an excitation of 0.2 g, followed by a random vibration test (2 min) with a frequency between 20 and 2000 Hz and an excitation of 5.66 g, and finally carry out the frequency between 10-500 Hz, the excitation is 0.2 g in the feature-level sinusoidal scanning test. During the test preparation stage, firstly, connect the vibration tooling with the shaking table, then connect the mirror component with the vibration tooling; finally, paste the test points and control points. The schematic diagrams for the mechanical tests of the mirror component are shown in Figure 9.  
Figure 9: Mechanical test diagram of mirror component.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Condition** & **Forced Displacement (5 \(\upmu\)m)** & **Forced Displacement (5 \(\upmu\)m)** \\ \hline \hline \end{tabular}
\end{table}
Table 8: RMS of mirror shape error under Forced Displacement of 5 \(\upmu\)m.
## 4 Discussion
### Inclinations  
Data presented in Figure 8 and Table 3 indicate that the exploitation of the 1/II longwall in the 405/1 hard coal seam in the time period of 2 months changes the primal values of inclinations.  
A maximum change for the ground points had a place in the case of the 'z10d-z11d' section (a section located closest to a linear discontinuous deformation and perpendicularly to it). This, consecutively, amounted to 1.2 mm/m, 2.1 mm/m, and 5.1 mm/m in the subsequent cycles. This means that an operation of the 1/II longwall in the 405/1 seam caused an increase of 12.5% in the inclination value.  
A minimum change for the points stabilized in the ground had a place for the 'z57d-z58g' section (a section located parallel to a ground step). An increase in the inclination value in the subsequent measuring cycles of: 0.5 mm/m, 0.3 mm/m, and 0.2 mm/m (a diminishing increase) has been consecutively noted. That means that the exploitation of the 1/II longwall in the 405/1 hard coal seam caused an increase of 1.7% in the inclination value.  
For the wall points, a maximum change took place in case of the's09-s10' section (a perpendicular section to a linear discontinuous deformation). An increase in the values (1.3 mm/m, 0.5 mm/m, and 1.5 mm/m) has been generally observed. Extraction of a hard coal from the 1/II longwall in the 405/1 seam caused an increase of 3.4% in the value of inclination.  
In turn, the smallest change in the inclination values in the building occurred in case of the's57-s58' section located parallel to a ground step (0.2 mm/m, 0.0 mm/m, and 0.2 mm/m in sequence), which means an increase of 3%.  
It can be generally said that a change in the inclination values observed on the sections located in the ground (a maximum value of 12.5%) is greater than it was noted in the wall sections (a maximum value of 3.4%). This means that the ground and the building work in a different way because the building walls have a greater stiffness than the subsoil and are less susceptible to deformations.  
The inclinations of the sections (in the ground and on the walls) arranged perpendicularly to the ground step are bigger than the inclinations of the sections lying parallel to the deformation. It can be noted that a discontinuous linear deformation causes an increase in inclination values in a perpendicular direction to its longitudinal axis and a decrease in the inclination values along this axis.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 5 Results and Discussion
### Pareto Set Evolution (the Design Space Perspective)  
In Figure 5, the found Pareto set for [_Prob1_] is presented. The graphs show relative (top) and absolute (bottom) ship velocity. In the charts, it can be seen that the final solution makes use of tidal streams, which is especially visible in the second half of the route, in the lower area of the map. As a result, the relative speed can be lower, which contributes to saving fuel and reducing travel time.  
Figure 4: The second optimisation problem under consideration ([_Prob2_])—it is presented as [_Prob1_] in Figure 1 but with the tidal stream given by Equation (A5). (**left**) the tidal stream at time \(t_{A}+3h\). (**right**) the tidal stream at time \(t_{A}+9h\). The colour bar shows absolute values \(|\mathbf{v}_{t}|\) of the tidal stream.  
Figure 3: \(T_{d}\) function (see Equation (6)) shown for s = 1, 10, 20,...; \(s_{\text{ MAX}}=200\).  
Figure 6 shows the evolution of the Pareto set in consecutive steps of the algorithm (1, 30, 100, and 200) for [_Prob1_]. The graphs show that in subsequent steps, the algorithm generates an increasingly larger Pareto set. It is especially visible in the lower part of the map area. This agrees with the results presented in Figure 5, where these routes made better use of the tidal stream, especially in the second half of the route. It is worth noting that none of the Pareto set snapshots contain the straight line solution (which seems to be an obvious one), which is caused by the action of the tidal stream.  
Figure 7 shows the same process but for [_Prob2_].  
Figure 5: [_Prob1_]: Pareto set (the paths in the design space) obtained with the proposed algorithm. (**top**) the colour of the path illustrates the relative ship velocity (STW, in m/s). (**bottom**) the colour of the path illustrates the absolute ship velocity (SOG).  
Figure 6: [_Prob1_] Pareto set (the design space) in consecutive steps of the algorithm. (**top left**) step is 1, Pareto set size is 50. (**top right**) step is 30, Pareto set size is 267. (**bottom left**) step is 100, Pareto set size is 438. (**bottom right**) step is 200, Pareto set size is 512. The colour illustrates the relative speed (in m/s) of the ship.
## 3 Methods
### Long Short-Term Memory  
As a descendent of RNN, [50] proposed an approach called long short-term memory (LSTM) to overcome the vanishing gradient problem in RNN. In LSTM, the ordinary unit cell repeats the input-output sequence; in RNN, this is replaced by a memory cell. LSTM contains three gates: the input gate \(i_{t}\), forget gate \(f_{t}\), and output gate \(o_{t}\). In addition to these gates, there are two different parts: cell state \(c_{t}\), which keeps information from previous states and transfers it to the next, and the hidden state \(h_{t}\), which is the output of the LSTM cell. The equation of input gate, forget gate, and output gate is defined as  
\[i_{t}=\sigma(w_{i}[h_{t-1},x_{t}]+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\Big{(}w_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{2}\]  
\[o_{t}=\sigma(w_{o}[h_{t-1},x_{t}]+b_{o})\text{,} \tag{3}\]  
where \(w_{i}\), \(w_{f}\), and \(w_{o}\) are the weight matrix, \(x_{t}\) is input, \(h_{t-1}\) is the hidden state from previous time step, \(b_{i}\), \(b_{f}\) and \(b_{o}\) are bias vector and \(\sigma\) is the sigmoid activation function for the gates. The activation functions introduce nonlinearity by transforming inputs to targeted outputs with a nonlinear regression procedure, making the model capable of learning and performing more complex tasks. After the calculation of gates, the cell state and hidden state can be defined as  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot tanh(w_{c}[h_{t-1},x_{t}]+b_{c}) \tag{4}\]  
Figure 3: The overall process chart of the study, starting from data sources and ending with the final-user output.  
\[h_{t}=o_{t}\odot tanh(c_{t}), \tag{5}\]  
where \(w_{c}\) is the weight matrix, \(c_{t-1}\) is the cell state from the previous time step, \(b_{c}\) is the bias vector, \(tanh\) is the hyperbolic tangent activation function and \(\odot\) is the element-wise multiplication. The size of the weight matrix is determined according to the unit size and hidden layer size of the LSTM model, feature vector dimension, and feature sequence length. It should be noted here that the weight matrix of LSTM does not change through timesteps. For detailed information please refer to [69].
## 2 Related Work
### Land Cover Classification  
The task of land cover classification is an important task for the RS community, which aims to observe the Earth and monitoring its changes [5]. Early works mostly relied on low-resolution RS images to generate information about the Earth [23]. To extract information, each pixel in RS images was classified by a patch around each pixel [24]. However, this method is computationally inefficient. Another disadvantage was that boundary information in RS images was fuzzy, leading to incorrect information [25]. To monitor a target study area, it is difficult to extract accurate information from low-resolution RS images. Recently, the development of RS technology has made capable high-resolution images for land cover classification, such as disease detection [26], yield estimation [27], and weed detection [28]. With high-resolution RS images, semantic segmentation methods were applied to classify whether each pixel in vineyards is healthy or diseased [25]. Likewise, various information can be extracted by the semantic segmentation methods at a pixel-level depending on what researchers aim to classify. In this study, we take a first approach to classifying cabbage fields in the highlands.
###### Abstract  
Introduction: The rapidly contagious process of respiratory viruses such as SARS-CoV-2 makes it urgent to multiply testing for diagnostics to identify the active viral shedding cases (current infection, carriage state or, residual viral RNA) and decrease the risk of transmission to other patients and healthcare professionals. Although nasopharyngeal swabs (NPSs) are the most common specimen type used for COVID-19 diagnosis, they require supervision by a professional, and concerns have been raised regarding healthcare personnel exposure, difficulty in collection, and patient discomfort. Viral RNA can also be detected in specimens such as saliva, blood, bronchoalveolar lavage fluid, sputum, faeces, and urine. This study aimed to provide updated information about the most suitable biological material to diagnose SARS-CoV-2, considering the risk assessment, specialization needed, test cost, complexity of the collection, and sample treatment associated with the different types of specimens. Methods: An extensive search of scientific review articles was made to collect information about the biological specimens to identify SARS-CoV-2 in the urine, sputum, nasopharyngeal, oropharyngeal, bronchoalveolarolar (BAL), saliva, faeces, and blood. For this purpose, an index score was developed based on seven categories: Materials and Equipment; Infection Risk for the Health Professional; Infection Risk for the Patient; Collection; Cost; Specialized HR; and RNA Extraction Type. Results and Discussion: Each criterion from the index score was quoted from 1.0 to 5.0, and a sum was made to classify which specimen is the best choice to diagnose SARS-CoV-2, according to the chosen parameters. Data indicated that urine specimens are the most elementary biological sample to access. Regarding RNA extraction, NPSs, OPS, and BAL presented the maximum score. However, BAL has the lowest score regarding associated costs. Concerning sputum and saliva, all the aspects were evaluated with a score of 5.0 except for the RNA Extraction Type in sputum. Regarding the total scores of the multiple specimens, the lowest corresponds to BAL with a score of 1.7, followed by blood with 3.1 and NPSs and OPS with 3.6 and 3.7, respectively. Urine and faeces have the same value, 4.4, sputum has 4.9, and the highest and maximum possible value corresponds to saliva with 5.0, making this last specimen the most suitable for all considered parameters. Conclusion: Although OPS and NPSs are the most used specimens, there are better alternatives. Among all the specimens of the respiratory system, saliva is the most cost-effective specimen for performing SARS-CoV-2 diagnosis. Even though these infections are usually diagnosed clinically based on symptoms and local epidemiology, the identification of the specific pathogen may affect clinical management and be crucial for containing potential outbreaks.  
SARS-CoV-2; COVID-19; biological material; score; risk +
Footnote †: journal: Applied Sciences