esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Performance of Individual Bands  
Out of the final 28 top-performing models, the green band is included in 25 models, and the blue band in 21. Both bands feature prominently in retrieval indexes, for example, I\({}_{3}\), I\({}_{18}\), and I\({}_{24}\). Generally, atmospheric correction processors tend to yield lower uncertainty with the green and blue bands than for the red to near-infrared range, likely because of better signal strength and SNR [19; 23]. Green and blue also often produce promising results when paired with the red band, such as in Mg\({}_{\text{2-All}}\), Mg\({}_{\text{2-AW}}\), and Mg\({}_{\text{2-EH}}\) (see also [89]). The red band is utilized in 15 of the top-performing models and appears in various retrieval indexes, including I\({}_{6}\), I\({}_{7}\), and I\({}_{12}\). Despite the wide range of performance outcomes, the red band proves particularly effective in some models, for example Mg\({}_{\text{2-8S}}\).  
The near-infrared band, which is only used in I\({}_{4}\), appears in two models: one for Landsat 5 the other for Landsat 7. However, both models show unsatisfactory performance (R\({}^{2}\) of 0.19). Another band that appears in just two models is the ultra-blue, featured in I\({}_{1}\) and I\({}_{2}\) for M\({}_{\text{LB-WLO}}\) and Mg\({}_{\text{2-HH}}\), with a better performance for M\({}_{\text{LB-WLO}}\) (RMSLE of 0.18 and an R\({}^{2}\) of 0.39). Contrary to expectations, the red-edge bands of Sentinel-2 do not appear in any of the retrieval indexes. The most sensitive bands for modeling low concentrations of Chl-_a_ are red, green, and blue (RGB). Only at higher Chl-_a_ concentrations does a reflective peak in the red-edge become more distinguishable. The relatively low Chl-_a_ concentration training data likely explain why the red-edge bands do not appear in the Sentinel-2 models: the highest Chl-_a_ concentration for Sentinel-2 matchups is 17 \(\upmu\)g/L.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 6 Performance Indicators  
### Channel Estimation Performance Indicators  
In order to evaluate the performance of the proposed OMP-SDRNet frameworks in OFDM systems, we choose the mean square error and the bit error rate to measure. Firstly, the MSE performance of channel estimation is analyzed as follows:  
\[MSE=\frac{1}{N}\sum\limits_{i=1}^{N}{{{{({{H_{{esti}}}-{H_{{real}}})}^{2}}}}} \tag{45}\]  
where \({H_{{esti}}}\) represents the estimated channel information, i.e., \({H_{{SDNet}}}\) and \({H_{{SDRNet}}}\), and \({H_{{real}}}\) represents the actual channel information. The BER performance of channel estimation is analyzed as follows:  
\[BER=\frac{{{Bit_{err}}}}{{{Bit_{sum}}}} \tag{46}\]  
where \({Bit_{err}}\) represents the number of incorrect bits and \({Bit_{sum}}\) represents the total number of bits.
# Progress in Construction Robot Path-Planning Algorithms: Review
## 3 Global Path-Planning Algorithms  
### A* Algorithm  
The A* algorithm is a map-based search method widely used in various robots. In the construction field, it can be applied to construction robots working in places with weak or even no mobile communication signals, such as material transport robots and earthmoving robots working in mining areas. The algorithm was initially proposed and elaborated by researchers [PERSON], [PERSON], and [PERSON] in 1968 [22]. The core concept of the algorithm is its heuristic evaluation strategy, where the heuristic function is defined as \(f(n)=h(n)+g(n)\), \(g(n)\) represents the actual cost incurred from the start node to the current node, and \(h(n)\) denotes the heuristic estimated cost from the current node to the goal node. The total estimated cost from the start node to the goal node is represented by \(f(n)\). By evaluating the potential cost of each node on the path, the search direction is guided to reach the goal node with the shortest path or optimal cost. The estimated cost is commonly calculated using Euclidean distance or Manhattan distance. This article uses Manhattan distance as an example, and Figure 5 illustrates its working principle.  
Figure 4: Introduction to evaluation criteria chart.  
Figure 5: A* algorithm diagram.  
However, the A* algorithm faces limitations such as long computation times, low efficiency, and rough path planning. To address these challenges, researchers have proposed various improvements.  
[PERSON] adopted the Octile Distance formula, replacing traditional Manhattan or Euclidean distances, which better adapts to complex paths [23]. [PERSON] utilized the Jump Point Search method to identify and skip unnecessary nodes, reducing the number of nodes expanded during the search process and improving the algorithm's efficiency [24].  
[PERSON] combined the A* algorithm with a greedy algorithm, achieving a multi-objective path-planning strategy. This approach also enables the algorithm to find multiple high-quality paths that meet the problem requirements in complex environments [25].  
[PERSON] proposed the EBS-A* algorithm by incorporating extended distance, bidirectional search, and path smoothing techniques, significantly improving search speed [26]. [PERSON] integrated the A* algorithm with local path-planning technology, enabling dynamic path adjustment and obstacle avoidance, thereby enhancing the practicality and safety of path-planning [27].
###### Abstract  
Solar tracking systems allow an increase in the use of solar energy for its conversion with photovoltaic technology due to the alignment with the sun. However, there is a compromise between tracking accuracy and the energy required to perform the movement action. Consequently, the wear of the tracker components increases, reducing its useful lifetime and affecting the profitability of these systems. The present research develops a novel tracking strategy based on real-time measurements to increase the lifetime without reducing the energy productivity of the tracking systems. The proposed approach is verified experimentally by implementing the real-time decision-making algorithm and a conventional tracking algorithm in identical tracking systems under the same weather conditions. The proposed strategy reduces energy consumption by 14.18% due to the tracking action, maintaining a practically identical energy generation between both systems. The findings highlight a 53.33% reduction in the movements required for tracking and a 60.77% reduction in operation time, which translates into a 6.8-fold increase in the lifetime of the solar tracking system under the experimental conditions applied. The results are promising, so this research initiates and motivates the development of more complex models to increase the useful life of the tracking systems and their profitability and environmental impact concurrently.  
energy productivity; lifetime optimization; photovoltaic system; solar energy; solar tracker; tracking strategy +
Footnote †: journal: Applied Sciences
## 3 Solution Algorithm  
Let an ellipse \(E\) with semi-axes \(a\) and \(b\) be the projection of the spheroid \(\mathbf{E}\) on the plane \(XOY\). It is assumed that the centers \(v_{k}=(x_{k},y_{k},z_{k})\) of the spheres \(S_{k}\) of variable radii \(r_{k}\) belong to the axis \(OX\), i.e., \(v_{k}=(x_{k},0,0)\).  
The parametric description of the ellipse \(E\) presented in [28] is used:  
\[E=\big{\{}(x(s,t),y(s,t)):0\leq s\leq b,0\leq t\leq 2\pi\big{\}}, \tag{3}\]  
\[x(s,t)=\frac{\cos t}{a}(a^{2}-b^{2}+s\cdot b),\ y(s,t)=s\cdot\sin t. \tag{4}\]  
Note that the point \(x(s,t)=\frac{\cos t}{a}(a^{2}-b^{2})\) belongs to the axis \(OX\) for \(s=0\). Denote by \(\Lambda(\varepsilon,M)\) the cover that is the projection of \(\boldsymbol{\Lambda}(\varepsilon,M)\) on the plane \(XOY\). By the definition, the cover \(\Lambda(\varepsilon,M)\) is an outer approximation of the ellipse \(E\), i.e., \(\Lambda(\varepsilon,M)=\bigcup\limits_{k=1}^{M}C_{k}(x_{k},0)\). Suppose that the _odd number_ of the circles is used for the cover, and the first circle has its radius \(b+\varepsilon\) and is centered at the point \((0,0,0)\), as shown at Figure 0(a). Then, due to the symmetry, the ellipse \(E\) can be covered by a collection of \(M=2m+1\) circles. Here, the right-hand side of the \(E\) can be covered by the circles \(C_{k}(x_{k},0),k=0,1,\ldots,2m\) with \(x_{k+1}>x_{k},x_{k}\geq 0\), and similarly the left-hand side.  
For the case of an _even number_ of circles, the ellipse \(E\) be covered by a collection of \(2m\) circles \(C_{k}(x_{k},0),k=1,\ldots,2m\). Let the first circle be centered at the point \((x_{0},0,0)\), as shown at Figure 0(b). To find the parameters \(x_{0},r_{0}\) of the first circle for \(0\leq t\leq\frac{\pi}{2}\), the following optimization problem is solved:  
\[t_{0}=\min_{(r,t)\in V}t\]  
\[\left\{\begin{array}{l}r^{2}-(\frac{a_{\varepsilon}}{\cos t}\cdot({a_{ \varepsilon}}^{2}-{b_{\varepsilon}}^{2}))^{2}-b^{2}\geq 0\\ \frac{b_{\varepsilon}}{a_{\varepsilon}}\cdot\bigvee{b_{\varepsilon}}^{2}\cos^ {2}t+a_{\varepsilon}^{2}\sin^{2}t-r\geq 0\end{array}\right..\]  
For the given \(t_{0}\) corresponding parameters \(x_{0},r_{0}\) are defined in the form of  
\[x_{0}=\frac{a_{\varepsilon}}{\cos t_{0}}\cdot({a_{\varepsilon}}^{2}-{b_{ \varepsilon}}^{2}),\ r_{0}=\sqrt{x_{0}^{2}+b^{2}}.\]  
Using the parametric description of the ellipse [28], the parameter \(x_{0}\) can be obtained analytically as follows (we would like to thank the anonymous referee for pointing out the possibility of getting the explicit solution):  
\[x_{0}=\frac{\sqrt{({a_{\varepsilon}}^{2}-{b_{\varepsilon}}^{2})({b_{ \varepsilon}}^{2}-b^{2})}}{a_{\varepsilon}}.\]  
The details are presented in the Appendix A.  
Then, due to the symmetry, the right-hand side of the ellipse \(E\) can be covered by a collection of \(m\) circles \(C_{k}(x_{k},0),k=1,\ldots,m\), for \(x_{k+1}>x_{k}\), \(x_{k}>0\) and similarly for the left-hand side.  
In what follows, the solution algorithm for the OSC problem is presented only for the odd number of spheres, since the difference between the odd and even cases is only in the parameters of the first sphere.
# Value of Information Based Data Retrieval in UWSNs
## 4 Path Planning Algorithms
### Hybrid Path Planner with Intermediate-Node-Visitation-HIPP  
HIPP has the same logic behind it as HPP but the only difference is that it uses GIPP instead of GPP. The algorithm first discovers the hot-spots, i.e., catastrophe reporting nodes and then it finds the intermediate nodes that can be visited. Afterward, these nodes are visited using GIPP while the rest of the nodes are visited using LPP.  
The algorithm for HIPP is given as Algorithm 5. The complexity of this algorithm is \(\mathcal{O}(h^{3}+l)\) where \(h\) is the number of high-priority sensor nodes while \(l\) is the count of low-priority sensor nodes. Similarly, as in the case of HPP, \(h\leq n\) and \(l\leq n\), therefore, the complexity of HIPP is determined as \(\mathcal{O}(n^{3})\).  
```
1:procedureHPP(\(S\), \(s_{o}\))
2:\(S\leftarrow\{s_{1},s_{2},...,s_{n}\}\)\(\triangleright\) Set of sensor nodes
3:\(V\leftarrow\emptyset\)\(\triangleright\) Visitation sequence
4:\(S_{HP}\leftarrow\{s_{m}\mid s_{m}\in S\wedge s_{m}\text{ is high-priority }\}\)\(\triangleright\) Set of high-priority sensor nodes
5:\(i\gets s_{o}\)\(\triangleright\) Sensor node to start tour from
6:while\(S_{HP}\
eq\emptyset\)do
7:\(j\leftarrow\textsc{getNodeMaxVol}(S_{HP})\)
8:\(T\leftarrow\emptyset\)
9:\(T\leftarrow\textsc{tourIntermediate}(i,j,S)\)
10:\(V\gets V+T\)
11:\(S_{HP}\gets S_{HP}-T\)
12:\(i\gets j\)
13:endwhile
14:\(S_{LP}\gets S-V\)\(\triangleright\) Set low-priority sensor nodes
15:\(V\leftarrow\textsc{V}+\textsc{LPP}(S_{LP})\)
16:return\(V\)
17:endprocedure
```  
**Algorithm 5** Hybrid Path Planner with Intermediate-Node-Visitation-**HIPP**.
# Hand Gesture Recognition on Edge Devices:
## 8 Efforts to Bring HGR to the Edge
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Algorithm** & **Data type** & **Accuracy** & **Size** & **Inference Time** & **Effort for Edge Applicability** \\ \hline Beamforming, peak detection for distance estimation [38] & Ultrasound & FOV: 1 m, 90\({}^{\circ}\); angular error 40 mm, 4\({}^{\circ}\) & Runs on a FPGA & Real time & Specific FPGA implementation \\ \hline Traditional signal processing [46] & Ultrasound & Radial distance error: \(\pm\)1.5 mm, 2\({}^{\circ}\), arc error \(\pm\)3.5 mm, \(\pm\)3\({}^{\circ}\) & — & 0.033 s & No ML/specific implementation \\ \hline Tree-ensemble [47] & Ultrasound & 96.7\% & — & 11.7 ms, incremental sample process & Requires laptop, farhter adaptations suggested by authors \\ \hline TCN, beamforming [44] & Ultrasound & 92–100\% & 10k parameters & 270 k MAC operations per inference & Lower frame rate suggested \\ \hline Amplitude envelope, B-Scan (horizontal sectional image), 3D-Scan (3D volume image) & Ultrasound & 11\({}^{\circ}\), axial: 171.5 mm & Processing on a GPU & 270 k MAC operations per inference & Lower frame rate suggested \\ \hline Amplitude envelope, B-Scan (horizontal sectional image), 3D-Scan (3D volume image) & Ultrasound & 11\({}^{\circ}\), axial: 171.5 mm & Processing on a GPU & 43 FPS (hemispherical pulse), 0.76 FPS (transmit beamforming) for 3D-Scan frame rate & Requires FPGA \\ \hline Threshold based [1] & Image data & 96\% & \begin{tabular}{c} Cognex 4400 \\ vision processor and Sun 4/330 \\ \end{tabular} & 0.1 s & \begin{tabular}{c} Special-purpose image processing \\ computer used \\ \end{tabular} \\ \hline Traditional signal processing for 1D tracking [83] & Ultrasound & Up to 2.5 mm & \begin{tabular}{c} Implemented on a \\ phone \\ \end{tabular} & 2.76 ms & \begin{tabular}{c} Specific \\ implementation \\ \end{tabular} \\ \hline Convolution-based [76] & Radar & 78–99\% depending on topology & \begin{tabular}{c} Implemented on \\ Intel Neural \\ Compute Stick 2; 386,614 parameters \\ \end{tabular} & \(<\)1.013 s & \begin{tabular}{c} Model Optimizer \\ Open VINO, \\ FPS32, FPS16 \\ \end{tabular} \\ \hline Convolution-based [81] & Radar & \(>\)99\% & 0.14 MB & \begin{tabular}{c} 0.147 ms with \\ caching, 0.909 ms \\ with caching \\ \end{tabular} & \begin{tabular}{c} Life Runtime \\ (LiteRT), formerly \\ TensorFlow Lite \\ \end{tabular} \\ \hline Convolution based [63] & WiFi & 96.03\% & \begin{tabular}{c} 363,655 \\ parameters, \\ 195,239 parameters \\ in compressed \\ model \\ \end{tabular} & 0.19 s & \
\begin{tabular}{c} Linear mapping \\ instead of \\ convolutional \\ layers \\ \end{tabular} \\ \hline State space representation, angle-range features, clustering [73] & Radar & 98.2\% & — & 3.84 s per gesture &
\begin{tabular}{c} Lower number of \\ features by \\ avoiding \\ redundant ones \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 2 Governing Equations
 \tag{23}\]  
\[(-D_{11}+E_{31}^{1}-E_{31}^{2})\frac{\partial^{4}w}{\partial x^{4}}+(-D_{11}+E _{31}^{1}-E_{31}^{2})\frac{\partial^{4}w}{\partial y^{4}}+(-2D_{12}+2E_{31}^{1 }-2E_{31}^{2}-4D_{66})\frac{\partial^{4}w}{\partial x^{2}\partial y^{2}}+N_{x }\frac{\partial^{2}w}{\partial x^{2}}+N_{y}\frac{\partial^{2}w}{\partial y^{2}} -kw=q \tag{24}\]  
When the piezoelectric rectangular plate vibrates linearly, its displacement can be expressed in the form of space-time separation:  
\[w=\widetilde{w}e^{i\omega t} \tag{25}\]  
Therefore, the free vibration Equation (23) can be further represented as  
\[ \begin{array}{l}(-D_{11}+E_{31}^{1}-E_{31}^{2})\frac{\partial^{4}\widetilde{w }}{\partial x^{4}}+(-D_{11}+E_{31}^{1}-E_{31}^{2})\frac{\partial^{4}\widetilde{ w}}{\partial y^{4}}+(-2D_{12}+2E_{31}^{1}-2E_{31}^{2}-4D_{66})\frac{\partial^{4} \widetilde{w}}{\partial x^{2}\partial y^{2}}+N_{x}\frac{\partial^{2}\widetilde {w}}{\partial x^{2}}+N_{y}\frac{\partial^{2}\widetilde{w}}{\partial y^{2}}\\ -k\widetilde{w}=\omega^{2}(I_{0}\widetilde{w}-I_{2}\frac{\partial^{2}\widetilde {w}}{\partial x^{2}}-I_{2}\frac{\partial^{2}\widetilde{w}}{\partial y^{2}}) \end{array}  \tag{26}\]  
The DQM is adopted in this paper to discretize the differential Equations (24) and (26) to algebraic equations. Then, to solve the obtained algebraic equations, the eigenvalue process will be applied. According to the idea of the DQM [29], the \(r\)-th derivative order of function \(f(x)\) can be expressed as  
\[\left.\frac{\partial^{r}f(x)}{\partial x^{r}}\right|\ _{x=x_{i}}\ =\sum_{j=1}^{n}c_{ij}^{r}f(x_{i}) \tag{27}\]  
where \(n\) is the number of grid points along \(x\) direction and \(c_{ij}^{r}\) can be expressed as  
\[\left\{ \begin{array}{l}c_{ij}^{r}=r\left[c_{ii}^{r-1}c_{ij}^{1}-\frac{c_{ij} ^{r-1}}{(x_{i}-x_{j})}\right]\ \ (i,j=1,2,...,n,\ \ i\
eq j\text{ and }2\leq r\leq n-1)\\ \\ c_{ij}^{1}=\frac{M(x_{i})}{(x_{i}-x_{j})M(x_{j})}\ \ \ (i,j=1,2,...,n,\ \ i\
eq j)\\ \\ c_{ii}^{r}=-\sum\limits_{j=1,j\
eq j}^{n}c_{ij}^{r}\ \ \ (i=1,2,...,n\ \,\ r=1,2,...,n-1)\\ \\ M(x_{i})=\prod\limits_{j=1,j\
eq j}^{n}\left(x_{i}-x_{j}\right)\ \ \ \ \ \ (i=1,2,...,n)\\ \end{array} \right. \tag{28}\]  
According to the method of picking points, the characteristic points of \(N_{1}\) and \(N_{2}\) are placed discretely along the \(X\)-axis and \(Y\)-axis, respectively, in the domain of a functionally graded piezoelectric plate, and the coordinates of the discrete points are  
\[\left\{ \begin{array}{l}x_{i}=\frac{1}{2}\left\{1-\cos\left[\frac{\pi(i-1)}{N _{1}-1}\right]\right\}l_{a}\ \ \ \ \ \ \ \ (i=1,2,...,N_{1})\\ y_{j}=\frac{1}{2}\left\{1-\cos\left[\frac{\pi(j-1)}{N_{2}-1}\right]\right\}l_{b} \ \ \ \ \ \ \ (j=1,2,...,N_{2})\\ \end{array} \right.
# Investigation of OFDM-Based HS-PON Using Front-End LiFiSystem for 5G Networks
## 4 Conclusions  
We propose a symmetric and bidirectional HS-PON based on LiFi using four 50 Gbps channels. A hybrid fiber-LiFi link transmits four 16-QAM OFDM-modulated signals in the downlink and four in the uplink. Results show that a hybrid HP-PON/OFDM-based LiFi system can offer a maximum fiber range of 100 km at a \(-\)17 dBm receiver sensitivity. Also, it enables indoor LiFi communication with a range of up to 20 m over a fixed 10 km fiber. When fiber-LiFi channel impairments and noise are taken into account, a maximum irradiance and incidence half-angle of 500 can provide reliable transmission at BER of \(10^{-3}\). In addition, the system offers BERs between \(10^{-9}\) and \(10^{-20}\) over LiFi communication links with detection areas between 1.5 and 4 cm\({}^{2}\). Successfully obtaining a high SNR of 7.76 dB, high SNR of 20.05 dB, and high OSNR of 16.79 dB can be achieved with a gain of 20.05 dB, a noise figure of 3.24 dB, and a noise factor of 3.24 dB. In addition, a comparative analysis indicates that our proposed work outperforms existing ones in terms of its high transmission rate, long-haul hybrid fiber-LiFi communication, and cost-effectiveness.  
This field offers numerous opportunities for future research. To address the security issues in the outdoor environment, pulse amplitude modulation is one of the modulation techniques that has been developed to support multi-access users and devices [42].  
[PERSON] and [PERSON] discussed the plan and agreed on it. [PERSON], [PERSON], [PERSON] and [PERSON] drafted designs for the manuscript. The original manuscript was written by [PERSON] and [PERSON]. The manuscript was edited by [PERSON] and [PERSON]. They all reviewed and commented on the original draft of the manuscript. All authors have read and agreed to the published version of the manuscript.  
There is no funding involved for this research. To conduct this research, [PERSON] would like to acknowledge CTTC Barcelona Spain.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Related Work  
Study [1] presented an approach that used pre-trained CNNs to detect COVID-19 cases using chest X-ray images. Three pre-trained CNNs (AlexNet, GoogLeNet, and SqueezeNet) using binary and multi-classification were applied to three publicly available X-ray databases. The results obtained in this study ranged between 98% and 99.8% for binary classification and between 95% and 98% for multi-classification. [PERSON] et al. [10] used CoroNet to detect COVID-19 using chest X-ray images. This study relied on a basic CNN model called Xception with an infiltration layer and two connected layers. This model was used to classify three classes (COVID-19, pneumonia, and normal) with 89.6% accuracy. It also achieved 99% precision for binary classification (COVID-19 and normal) and classifying four categories (COVID-19, bacterial pneumonia, viral pneumonia, normal) with 89% accuracy. [PERSON] et al. [11] provided a DenseNet201-based model with a quadratic SVM classifier for diagnosing coronavirus cases based on a chest X-ray obtained from a publicly available COVID-19 radiography dataset. This study had two main axes: feature extraction and classifier learning. On the first axis, the researchers used a CNN model trained on ImageNet, a dataset containing 1.2 million images from 1000 categories. The outputs of the first axis were used as inputs for the second axis. This study achieved an accuracy of 98.16%, a sensitivity of 98.93%, and a specificity of 98.77%. [PERSON] et al. [12] proposed CovidGAN, a network built on a VGG-16 network that had previously been trained to detect COVID-19. This study also used the GAN approach to generate artificial chest X-ray images. This study achieved 85% accuracy on actual data and 96% accuracy using synthetic additives. [PERSON] et al. [13] suggested using a pre-tested AlexNet network to classify COVID-19 using chest X-rays, a binary classification in which the model was trained to classify COVID-19 versus bacterial pneumonia and COVID-19 versus normal pneumonia. The triple classification was also used to classify COVID-19, normal pneumonia, and bacterial pneumonia. The quadruple classification was used to classify COVID-19, bacterial pneumonia, viral pneumonia, and non-COVID-19. The proposed model achieved 94.43% accuracy, 98.19% sensitivity, 95.78% specificity, 91.43% accuracy, 91.94% sensitivity and 100% specificity for COVID-19 pneumonia. [PERSON] et al. [14] relied on the ResNet-101 network, which was trained on 1500 \(\times\) 1500 X-ray images to diagnose COVID-19 using binary classification between virus-infected and normal X-ray images. The lung was segmented using a pre-trained U-Net network. This study achieved a classification accuracy of 98%.  
As can be understood from the studies in the literature, it has been seen that COVID-19 based on X-ray images can be detected with high accuracy with various deep learning algorithms. Similarly, machine learning methods often give high success rates. Table 1 shows a comparison between our study and the related work studies.