esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 5 Analysis of Proposed Method
### Execution of Data Extraction Module  
Data extraction rules work at receiver end. The receiver end has all the necessary information to extract the implanted data and to reconstruct the cover DNA/RNA. It has \(\bar{N}_{x}=\) TTTGTTGAGT-CACGCGGGTA- CGTGAAACGA-GCTTTTATGA;  
Secret key, \(S=00011111011000001\).  
Then, the receiver generates \(\bar{R}_{x}\), where \(\bar{R}_{x}=11\)-11-11-10-11-11-10-00-10-11 \(\parallel\) 01-00-01-10-10-10-11-00 \(\parallel\) 01-10-11-10-00-00-01-10-00 \(\parallel\) 10-01-11-11-11-00-11-10-00 \(\parallel\) The execution steps are shown below:  
1. Execution cycle 1: Using Equation (3): \(R_{1}=\) 10-01-11-11-11-10-01-11-00-0 (last chunk of \(\bar{R}_{x}\)). Using Equation (4): \(R_{2}=\) 01-10-11-10-00-00-00-01-10-00 (second chunk of \(\bar{R}_{x}\) from the last). Using Equation (5), we can compute new \(R_{1},R_{2},S\) and \(M_{1}\). Here, \(M_{1}\) is the value of \(M_{3}\) in the data implantation module. The result is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(R\) & 01 & 00 & 01 & 10 & 01 & 10 & 10 & 10 & 11 & 00 \\ \hline \(S\) & 00 & 00 & 11 & 11 & 00 & 01 & 11 & 00 & 00 & 01 \\ \hline \(M_{2}\) & 00 & 10 & 01 & 11 & 01 & 11 & 01 & 11 & 01 & 01 \\ \hline \(\bar{R}\) & 01 & 10 & 11 & 10 & 00 & 00 & 00 & 01 & 10 & 00 \\ \hline \end{tabular}
\end{table}
Table 4: Results of execution cycle 2 in data implantation module.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(\boldsymbol{N_{r}}\) & \(\boldsymbol{\mathrm{T}}\) & \(\boldsymbol{\mathrm{T}}\) & \(\boldsymbol{\mathrm{T}}\) & \(\boldsymbol{\mathrm{G}}\) & \(\boldsymbol{\mathrm{T}}\) & \(\boldsymbol{\mathrm{T}}\) & \(\boldsymbol{\mathrm{G}}\) & \(\boldsymbol{\mathrm{A}}\) & \(\boldsymbol{\mathrm{G}}\) & \(\boldsymbol{\mathrm{T}}\) \\ \hline \(R\) & 11 & 11 & 11 & 10 & 11 & 11 & 10 & 00 & 10 & 11 \\ \hline \(S\) & 00 & 00 & 11 & 11 & 00 & 01 & 11 & 00 & 00 & 01 \\ \hline \(M_{1}\) & 10 & 11 & 01 & 11 & 10 & 00 & 11 & 10 & 01 & 10 \\ \hline \multicolumn{8}{c}{Using Equation (1): \(\bar{R}=\bar{R}\oplus S\oplus M_{i}\)} \\ \hline \(R\) & 01 & 00 & 01 & 10 & 01 & 10 & 10 & 10 & 10 & 11 & 00 \\ \hline \end{tabular}
\begin{tabular}{c} 2. Execution cycle 2: \(\bar{R}_{x}=\) 11-11-11-10-11-11-10-00-10-11 \(\parallel\) 01-00-01-10-10-10-10-11-00 \(\parallel\) 01-10-11-10-00-00-00-01-10-00 \\ \(\bar{N}_{r}=\) GCTTTTATGA \\ \(\bar{N}_{x}=\) TTTGTTGAGT-CACGCGGA-CGTGAAACGA -GCTTTTATGA \\ The result is tabulated in Table 5.  
\end{table}
Table 3: Results of execution cycle 1 in data implantation module.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(R\) & 01 & 10 & 11 & 10 & 00 & 00 & 00 & 01 & 10 & 00 \\ \hline \(S\) & 00 & 00 & 11 & 11 & 00 & 01 & 11 & 00 & 00 & 01 \\ \hline \(M_{3}\) & 11 & 11 & 11 & 10 & 00 & 10 & 11 & 10 & 00 & 01 \\ \hline \(\bar{R}\) & 10 & 01 & 11 & 11 & 11 & 11 & 00 & 11 & 10 & 00 \\ \hline \end{tabular}
\end{table}
Table 5: Results of execution cycle 3 in data implantation module.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Automatic Generation of Streams
#### 4.1.3 Algorithm for Generation of Incremental Drifts  
Incremental drift is a type of drift that occurs slowly and incrementally, unlike the sudden and more noticeable abrupt drift. The key difference between gradual and incremental drift is the rate at which the underlying concept being learned changes over time. Gradual drift occurs slowly and progressively, while incremental drift occurs suddenly and abruptly. In our experimental data streams, a change in concept does not occur in a binary fashion. Incremental drift may not be immediately apparent, but it presents a unique challenge for detecting and adapting to changes in data streams, as the shift in concept may be subtleand occur over a longer period of time. Detecting and adapting to incremental drift may require sophisticated algorithms and monitoring techniques to identify and respond to changes in the data distribution in a timely and effective manner [22].  
Algorithm 3 is designed to generate streams with incremental drift, and takes as input the variable of duration, which is defined by a random number of bits. In addition, the start and end positions of each drift are randomly generated. For each sample of the stream generated by this algorithm, there is only one drift that increments until it reaches a value of 1 bit, simulating a concept drift that changes incrementally over time.  
```
Input :\(n>0\), \(dur>0\) Output :Array stream \(\leftarrow[1:n]\); duration \(\leftarrow\) random\((1:dur)\); whiletrue do \(init\)\(\leftarrow\) random\((1:n)\); duration \(\leftarrow\) init + duration; ifduration \(>n\)then \(init\)\(\leftarrow\) random\((1:n)\); duration \(\leftarrow\) random\((1:dur)\); continue end  else \(signal\)\(\leftarrow\) 0.1; start \(\leftarrow\) init; \(r\)\(\leftarrow\) 0; whilesignal \(<\) 1 and \(r\)\(\leftarrow\) duration do \(r\)\(\leftarrow\) random\((start+1:dur)\); \(stream[start:r]\)\(\leftarrow\) signal; \(start\)\(\leftarrow\) \(r\); \(signal\)\(\leftarrow\) signal + 0.1; \(stream[r+1:n]\)\(\leftarrow\) 1;  end while break  end while  end while
```  
**Algorithm 3** Incremental drift type
# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
###### Abstract  
Text detection of container numbers is essential in logistics for the efficient tracking and management of containers. However, the wear and damage that container numbers often endure in harsh environments make detection particularly challenging. We propose a Dual-Branch Adaptive Multi-Scale Network (DAMNet) to address these issues. The DAMNet integrates Efficient Multi-Scale Attention (EMA) into the Differentiable Binarization (DBNet) model to address the challenges of improving accuracy and efficiency in the current text detection networks. We also incorporate an auxiliary information branch that processes images using the Otsu thresholding method to extract local features. This additional branch enhances the model's ability to learn the features of container number text. Furthermore, we improve the feature pyramid structure by introducing Adaptive Spatial Feature Fusion (ASFF). This method retains rich detailed information while capturing global context, improving the model's robustness. We also propose a dual-loss strategy. It employs BCE loss for classification tasks and Dice loss to tackle imbalanced positive and negative samples. Both losses are combined via weighting. The experimental results demonstrate that the improved DBNet model achieves a precision of 89.7%, a recall of 80.1%, and an F-score of 83.5% on the ICDAR2015 dataset. The recall increased by 3.9% compared to the original model.  
text detection; multi-scale attention; dual branch; feature fusion +
Footnote †: journal: Applied sciences
### Atomic and Electronic Structure of Composites Based on [M(Salen)] and Carbon Nanomaterials  
A detailed examination of the relevant literature [3; 8; 9; 10; 11; 12; 134; 135; 136; 137; 138; 139] showed that TEM, XRD, EDS, Raman, and IR spectroscopy, alongside thermogravimetric analysis and electrochemical methods, are the most commonly used techniques to characterize the structure of composites based on [M(Salen)] and poly-[M(Salen)] with various carbon materials, such as carbon nanotubes, single-walled carbon nanohorms (SWNHs), graphene, graphene oxide (GO), reduced GO (rGO), nitrogen-doped graphene, few-layer graphene (FLG), etc. At the same time, XPS is not widely used for the above-mentioned composites despite the fact that it is the most powerful method for identifying the chemical state of functional atoms in nanostructured composite materials. The currently available XPS data for [M(Salen)]/CNT, [M(Salen)]/GO, poly-[M(Salen)]/CNT, and poly-[M(Salen)]/FLG, etc., are presented in Table 4.  
In works [103; 104; 134; 135; 137; 138], XPS data were published on monomeric complexes [M((OH)\({}_{2}\)-salen)] and [M(Salen)] with various complexing metals, Mn, Co, Cu, and Ni, as well as with vanadium oxide (VO), which were deposited on the surface of MWCNTs functionalized by chlorination [103; 134; 135; 137; 138] or by adding NH\({}_{2}\) amino groups [104]. In other studies, the XPS measurements on composites based on poly-[Co-Tos-(Salen)] (Tos-p-toluenesulfonic anion, CH\({}_{3}\)C\({}_{6}\)H\({}_{3}\)SO\({}_{2}\)) [105] and [Ni(salphen)] polymers deposited on a CNT surface [8] as well as poly-[Ni(3-CH\({}_{3}\)-salen)] electrochemically deposited on N-doped FLG (N-FLG) [37] are presented. XPS studies of GO-based composites with deposited [M(Salen)] complexes with the metals Co, Cu, Fe, and Ni and also with a VO are described by [PERSON] et al. [108; 110], [PERSON] et al. [109], and [PERSON] et al. [111]. The composites investigated in these works were synthesized using preliminary functionalization of GO surface with amino NH\({}_{2}\) (NH\({}_{2}\)-GO) [108; 109; 110] and with 3-(Aminopropyl) trimethoxysilane (APTMS) [111]. For clarity and subsequent comparison of changes, all XPS data measured in [8; 37; 101; 103; 104; 105; 108; 109; 111; 134; 135; 137; 138] are summarized in Table 5.  
Figure 16: N 1\(s\)**(a)**, C 1\(s\)**(b)**, and O 1\(s\)**(c)** NEXAFS spectra of the H\({}_{2}\)(Salen) ligand (purple line) and the [Ni(Salen)] complex (blue line). In the N 1\(s\) and O 1\(s\) spectra, the capital letters denote X-ray absorption bands due to the N 1\(s\) and O 1\(s\) electron transitions to unoccupied electronic states, respectively. In the case of C 1\(s\) spectra, the capital letters with dashes and asterisks denote X-ray absorption bands due to the transitions of C 1\(s\) electrons of ethylenediamine and phenolic groups to unoccupied electronic states, respectively [46]. In the above-mentioned works, the authors used different approximating functions and approaches to fit the C 1\(s\) and N 1\(s\) PE spectra, which resulted in a different number of composites.
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 3 Materials and Experimental Setup  
### Datasets  
In our study, the Xiongan dataset was used to evaluate and compare the performance of the proposed method with that of other existing methods [47]. Xiongan is located in Matiwan village of the Xiongan New Area in Hebei Province, China. The Xiongan dataset is a state-of-the-art hyperspectral remote sensing image dataset (Figure 1). The Xiongan New Area is the recently developed special economic zone established by the Chinese government in 2017. The area mainly consists of plains with a warm semi-humid continental monsoon climate [48].  
The Xiongan dataset was obtained through a flight campaign experiment, which was completed by the Shanghai Institute of Technical Physics (SITP) and the Institute of Remote Sensing Applications (IRSA). SITP and IRSA are both affiliated with the Chinese Academy of Science (CAS). In this flight campaign experiment, data were collected by using an airborne multi-modular imaging spectrometer (AMMIS), which is the latest airborne hyperspectral imaging system developed by SITP [49; 50; 51]. The AMMIS possesses three modules: visible near-infrared (VNIR), shortwave infrared (SWIR), and long-wave infrared (LWIR). Each module was equipped with three spectrometers and integrated into a hyperspectral imaging system [10]. Based on this system, remote sensing images with high spectral and spatial resolutions can be acquired by using AMMIS.  
The hyperspectral images in the Xiongan dataset were collected during the period from 15:40 to 16:03 on 3 October 2017, by using the VNIR module of AMMIS carried by an aircraft. The aircraft was operated at an altitude of 2000 m, with a coverage area of 1320 km\({}^{2}\). After implementing high-precision geometric correction, band registration, radiometric calibration, and residual strip-noise removal [10; 49], 1-level hyperspectral image data with 256 bands and a spectral resolution of 2.4 nm were obtained. As the hyperspectral images were acquired in clear, cloud-free weather conditions, they were less affected by clouds and the atmosphere.  
Simultaneously, to manually acquire labels of the Xiongan dataset, IRSA conducted ground-truth investigations and accurately mapped the geometry of each site. In the Matiwan village of Xiongan, the research team investigated 57 sample areas and obtained 39 pictures. Finally, 12 categories were defined based on the ground investigations, as shown in Table 1 and Figure 1. After geographic registration, the ground-truth labels and hyperspectral images constituted a complete Xiongan dataset with a pixel resolution of 3750 \(\times\) 1580 and spatial resolution of 0.5 m.
## 3 Results
### TPD at 20 atm of PZI30 and PAI10-40 Sorbents  
TPD experiments of PAI10-40 sorbents were conducted to examine the regeneration properties under 20 atm, as shown in Figure 6. In addition, a test of PZI30 sorbent was conducted to identify the decomposition temperature of the KHCO\({}_{3}\) structure at 20 atm. The CO\({}_{2}\) desorption of PAI-\(x\) sorbents started at 150 \({}^{\circ}\)C, and the desorbed CO\({}_{2}\) peak was observed in the temperature range from 150 \({}^{\circ}\)C to 400 \({}^{\circ}\)C. The TPD results of PAI10 and 20 sorbents showed one peak between 250 \({}^{\circ}\)C and 350 \({}^{\circ}\)C, and the TPD results of PAI30 and 40 sorbents showed two peaks between 150 \({}^{\circ}\)C and 400 \({}^{\circ}\)C. In the case of low K\({}_{2}\)CO\({}_{3}\) loading such as PAI10 and 20, the CO\({}_{2}\) peak was produced by the decomposition of the KAl(CO\({}_{3}\))(OH)\({}_{2}\) structure. In the case of high K\({}_{2}\)CO\({}_{3}\) loadings such as PAI30 and 40 sorbents, the CO\({}_{2}\) peak was produced by the decomposition of the KHCO\({}_{3}\) and KAl(CO\({}_{3}\))(OH)\({}_{2}\) structures. Particularly, the KHCO\({}_{3}\) decomposition of PAI30 and 40 sorbents appeared at the same temperature range as the PZI30 sorbent, and the CO\({}_{2}\) peak caused by the decomposition of KAl(CO\({}_{3}\))(OH)\({}_{2}\) increased with increasing K\({}_{2}\)CO\({}_{3}\) loading. The CO\({}_{2}\) peak caused by KHCO\({}_{3}\) decomposition also increased due to increased K\({}_{2}\)CO\({}_{3}\) loading. Based on the TPD results, the alumina-based CO\({}_{2}\) sorbent was confirmed to be capable of regeneration at temperatures below 400 \({}^{\circ}\)C at 20 atm. This study concluded that the potassium-loaded alumina-based sorbent had a higher CO\({}_{2}\) capture capacity than the zirconium-based sorbent due to the high surface area of alumina and the KHCO\({}_{3}\) and KAl(CO\({}_{3}\))(OH)\({}_{2}\) conversion. This means that these sorbents could be used as a support and additive to prepare a sorbent with the potential for CO\({}_{2}\) capture and regeneration under high and moderate temperature conditions.  
Figure 5: TG **(a)** and DTG **(b)** curves of PZI30 and PAI10–40 sorbents after CO\({}_{2}\) sorption according to K\({}_{2}\)CO\({}_{3}\) loading.  
Figure 6: TPD results at 20 atm of PZI30 and PAI10–40 sorbents after CO\({}_{2}\) sorption at 20 atm.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 4 Results and Discussion
### Evaluate FSOs Design
#### 4.2.2 Analysis of BER  
The BER analysis, conducted through OptiSystem's built-in BER analyzer using pseudo-random bit sequences at various rates (8, 12, 16, and 20 Gbps), shows differences between with-EDFA and without-EDFA FSOs systems. One of the most notable characteristics of FSOs systems using EDFA is their performance, as demonstrated in Table 2, where transmission distances from 15-25 km at both 8 Gbps and 12 Gbps show BER values of 0, indicating that the analyzer could not detect any errors during the simulation period. Conversely, FSOs systems without EDFA exhibit significantly higher BER values with increasing transmission range. As evidenced in Table 3, at 16 Gbps, the BER increases from \(6.83964\times 10^{-75}\) at 5 km to \(1.41034\times 10^{-6}\) at 25 km. Similarly, at 20 Gbps, the BER deteriorates from \(2.42704\times 10^{-12}\) to \(0.0011338\) over the same distance range, significantly reducing the system's transmission quality. These specific BER values are obtained from OptiSystem's BER analyzer and can be verified using the relationship \(\text{BER}=0.5\times\text{erfc}(Q/\sqrt{2})\)[46], where \(Q\) represents the measured Q-factor from this study simulation. While these extremely low BER values show the theoretical capabilities of the system, they would be challenging to verify experimentally due to real-world constraints such as environmental conditions, hardware limitations, and the need for longer data sequences.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 6 Promising Bio-Energy Solutions, Lessons Learned  
Our review of bio-energy harvesters focuses on three promising green energy resources. Tables 1-3 depict summaries of the key research components in the fields of  
Figure 16: Rack-pinion gear based biomechanical electricity generation used in backpacks for utilizing up and down movements of the center of mass, redrawn from the schematic shown in [315].  
MFC, enzyme-based fuel cells and biomechanical energy harvesters, respectively, over the last decade.  
\begin{table}
\begin{tabular}{c c c} \hline
**MFC Focus Topic** & **Sub-Topic(s)** & **Studies** \\ \hline \hline \multicolumn{2}{c}{MFC Biofilm} & \multicolumn{1}{c}{[53,68,77,159,217,227,229,323-327]} \\ Dual chamber MFC & & [175,200,229,323,328-341] \\ Single chamber MFC & & [57,194,217,342-348] \\ \multicolumn{2}{c}{MFC Anodes} & \multicolumn{1}{c}{[20,49,61,63-65,349-372]} \\ MFC Cathodes & & [73,75,76,90,99,106,107,116,161] \\ Air cathodes & & [63,65,70,74,77,126,344,348] \\ Bio cathodes & & [68,77,145,159,179,183,192,202,224,254] \\ Algae/micro-algae bio cathodes & & [72,177,193,195,196,217] \\ Plant bio cathodes & & [2,140,158,176,184,186-189,370,373] \\ Membranes & & [16,27,63,80,88,98,374,375] \\ Cation exchange membranes & & [85,86] \\ Anion exchange membranes & & [91–93,336] \\ Porous, ceramic membranes & & [87,95–97,373,376-379] \\ Supported liquid ion membrane & & [26,88,232] \\ PMS & & [23,62,109,211,380] \\ Remote power & & [6,11,23,24,61,65,68,107,110,123,127-129,135-137,140-142,151,172,197,199] \\ generation & & [19,29,58,60,67,73,114,121,132,149,155,194,202,203,216,315,322,325, \\ Waste processing & Waste-water processing & & 328,330,336,359,360] \\ constructed-wetland & & [111,190,191,219,221-225] \\ textile and dye processing & & [140,217–220] \\ solid waste processing & metal recovery & [242–246] \\ Biosensing & & [30,231–233,240,241,369] \\ Powering robots & & [11,28,31,111,129,143,154,194,201,202,247–255,354] \\ \hline \end{tabular}
\end{table}
Table 1: Summary of reviewed works on MFCs.  
\begin{table}
\begin{tabular}{c c c} \hline
**EBC Focus Topic** & **Sub-Topic(s)** & **Studies** \\ \hline \hline \multicolumn{2}{c}{Review articles} & \multicolumn{1}{c}{[33,261–264,386–389]} \\ EBFC cell components & Enzyme immobilization & [25,259,390,391] \\  & Anodes & [265–267,392–397] \\  & Micro-fluid structure & [398–402] \\ Tested with plants & & [270–273] \\ Tested with bio-hybrid organisms & Insects & [9,12] \\  & Molluscs & [8] \\  & Lobsters & [34] \\  & Mammals & [32,35,277,278] \\ Targeted towards external human use & Contact lens & [280–282] \\  & Skin patches & [13,279,285,403–405] \\  & Wearable fabric & [397,406] \\ Powering biosensors & & [12,33] \\ Powering organ on chip & & [407] \\ \hline \end{tabular}
\end{table}
Table 2: Summary of reviewed works on enzyme-based fuel cells.
## 4 Discussion
Consequently, five compounds that were relatively more abundant in healthy phloem as determined by HS-SPME were selected as stimuli to test EAG responses of weevils. Due to the limitation of weevils, we did not test more individual compounds or blends. Although both weevil species could be attracted by volatiles from healthy phloem of _A. altissima_, there were different responses to tested compounds between them. Chemosensory genes were identified in _E. scrobiculatus_ and _E. brandti_ by antennal transcriptome sequencing, and the odorant binding proteins of _E. scrobiculatus_ and _E. brandti_ show different expression patterns [28]. It is plausible that each species shows different abilities to recognize volatiles that are involved in feeding and reproduction. Other studies haveshown that insect behavioral responses to host volatile blends can exceed the responses to individual components [20]. Usually, blends of compounds are needed to elicit adequate behavioral responses by a foraging insect [29; 30; 31]. Consequently, we need to explore the response of weevils to host volatile blends in the future. The morphological characteristics and physical conditions of plants may influence the feeding behavior of phytophagous insects. [PERSON] and [PERSON] [32] studied the effect of potato glandular trichomes on host acceptance behavior of Colorado potato beetle (_Leptinotarsa decemlineata_) larvae. They found that removal of the trichome barrier by wiping leaflets with tissue paper lead to increased incidence of feeding by larvae. [PERSON] and [PERSON] [33] studied the feeding and boring behaviors of _lps paraconfusus_ Lanier on the bark of a host (pine) and non-host tree (fir) species, and they found that the beetle preferred the pine to fir phloem when the outer bark was removed and the phloem was retained intact. However, no preferences were apparent for the intact pine or fir outer bark when the phloem was removed. Under natural conditions, _E. scrobiculatus_ and _E. brandti_ often feed on the tree-of-heaven together, so releasing them together in the large-arena experiment was meant to simulate the field condition. _E. scrobiculatus_ adults feed on annual branches, perennial branches and petioles in the field [12]. Here, we found that a few weevils responded to volatiles emitted by bare phloem of healthy and injured trees, and adults significantly preferred phloem from the trunk in large-arena experiment, whereas female and male weevils were significantly attracted by all the tested materials in the Y-tube bioassay. This suggests that the responses observed in the field may include factors other than plant volatiles. In the case of _E. brandti_, the observed behavior of adults in the field matched more closely the olfactory-based responses documented in this study.
## 4 Simulation and Analysis of Transmission Lines Foreign Object Detection
### Simulation Results and Analysis
#### 4.2.3 Comparison of Different Feature Extraction Networks  
In order to determine the CNN model for feature extraction, experiments on five networks including DenseNet-201, GoogLeNet, AlexNet, ResNet-101, and EfficientNet-B0 were conducted to compare their learning performance. After the simulation experiment, the performance indexes including the accuracy, _macro_P_, _macro_R_ and _macro_F_1 of the above models are obtained, as shown in Table 5.  
Figure 9: The accuracy under different feature extraction layers of different networks.  
It can be seen from Table 5 that GoogLeNet has the highest accuracy and \(macro\_F_{1}\), which are 94.84% and 94.31%. The accuracy of EfficientNet-B0 is 93.81% and the \(macro\_F_{1}\) is 93.59%, which is the second place behind GoogLeNet. The network with the lowest performance is AlexNet, with the accuracy of 90.72% and the \(macro\_F_{1}\) of 90.22%. The above results are sufficient to show that GoogLeNet is the optimal network in feature extraction, followed by EffecentNet-B0 and DenseNet-201.