esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 5 Results and Discussions
### Accuracy  
Table 2 shows the final accuracy of spectral maps and PMSFE maps in nine models, and the increased rates after using a parallel mechanism. By adding feature-enhanced maps into the network the accuracy of AlexNet and VGGNet are increased from 88.77% to 90.18%, from 91.46% to 92.77% in 16-layers vision with batch normalization (BN) and from 91.70% to 93.34% in 19-layers vision with BN [39]. Although the mean accuracy of VGGNet performs better than AlexNet, the rate of increase is similar (approximately 1.5%).  
The rate of increase is approximately 1% in ResNet and 0.56% in DenseNet, due to the feature-enhanced map enhancing the structure information using every trail. This phenomenon also reflects the ability of DenseNet and ResNet to extract features better than AlexNet's and VGGNet's.  
The highest accuracy is 93.71% using PMSFE maps in ResNet-152, which increases the final accuracy from 91.1% to 93.71%. Across the rate increase of 2.61%, 1.52% is due to performance improvements of the network, and 1.09% is due to the parallel mechanism of spectral feature-enhanced maps.  
Figure 10 shows the confusion matrix of spectral maps and PMSFE maps in AlexNet, VGGNet-19, ResNet-152 and DenseNet-200. It obvious that load level 2 and load level 3 are easily confused when using original spectral maps. The probabilities of the neural network misclassifying level 3 into level 2 are correspondingly 7%, 11%,11% and 8%. The percentages decrease to 4%, 8%, 9% and 8% after using PMSFE maps.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**Spectral Maps**} & \multicolumn{2}{c}{**PMSFE Maps**} & \multirow{2}{*}{**Increased Rate**} \\ \cline{2-2} \cline{4-6}  & **Mean** & **Standard** & **Mean** & **Standard** & **(\%)** \\ \multicolumn{1}{c}{} & **Accuracy (\%)** & **Deviation** & **Accuracy (\%)** & **Deviation** & \\ \hline AlexNet & 88.78 & 0.0019 & 90.18 & 0.0031 & 1.4 \\ VGGNet-16 BN & 91.46 & 0.0026 & 92.77 & 0.0030 & 1.31 \\ VGGNet-19 BN & 91.70 & 0.0016 & 93.34 & 0.0026 & 1.64 \\ ResNet-50 & 92.50 & 0.0027 & 93.08 & 0.0023 & 0.58 \\ ResNet-101 & 92.61 & 0.0025 & 93.68 & 0.0032 & 1.07 \\ ResNet-152 & 92.62 & 0.0023 & 93.71 & 0.0027 & 1.09 \\ DenseNet-121 & 92.33 & 0.0051 & 9
## 4 Measurement
### Comparison of Torque Ripple Reduction  
The application of a fault-tolerant control strategy has a significant impact on torque ripple during motor operation in phase failure. Torque ripple is closely related to motor noise, and this is an important parameter to consider when designing electric motors for EV and HEV. As a result, it is critical to eliminate it as much as possible when the motor is in a fault state.  
This section shows the torque ripple waveforms for each of the four conditions. At a rated load of 3.5 Nm, the motor speed was 2500 rpm. The ripple was measured using a torque sensor with a range of 20 Nm, while 10 V at the sensor output equals 20 Nm.  
Figures 18-21 depict the torque ripple during normal operation (Figure 18), one-phase fault operation without editing (Figure 19), control editing to unbalanced currents (Figure 20), and editing to symmetrical currents (Figure 21).  
The waveforms show that the torque ripple was approximately \(\pm\)2 Nm during normal operation (Peak to Peak). The torque ripple increased to \(\pm\)8.8 Nm in Figure 18 during the one-phase failure operation. This represents an increase of up to 440%, which is unacceptably high for an engine operating as an electric vehicle. We were able to reduce the magnitude of the torque ripple to unbalanced currents to \(\pm\)5.6 Nm using fault-tolerant control and to approximately \(\pm\)3.8 Nm using symmetrical currents. This represents a more than 50% improvement in the ripple compared to the fault condition.  
Figure 17: Dependence of phase currents on torque, constant motor speed 500 rpm.  
Figure 18: Torque ripple, normal operation, 2500 rpm, 3.5 Nm.  
Figure 21: Torque ripple, edit: symmetric current, 2500 rpm, 3.5 Nm.  
Figure 19: Torque ripple, one-phase fault operation, 2500 rpm, 3.5 Nm.  
Figure 20: Torque ripple, edit: nonsymmetric current, 2500 rpm, 3.5 Nm.  
Figure 22 depicts the torque ripple path for a nominal motor torque of T = 3.5 Nm. The characteristics were covered by a linear line. The torque ripple waveforms show that changing the control to symmetrical currents produces almost identical ripple to normal operation.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline bamital & 97.13\% & 100.00\% & 98.54\% & padcrypt & 73.34\% & 97.72\% & 83.79\% \\ banjori & 88.24\% & 99.22\% & 93.40\% & phorpiex & 41.93\% & 83.95\% & 55.93\% \\ bazzloader & 99.93\% & 99.99\% & 99.96\% & pitou & 67.26\% & 97.67\% & 79.66\% \\ chinad & 82.06\% & 90.32\% & 85.99\% & proslikefan & 22.78\% & 21.86\% & 22.31\% \\ conficker & 23.05\% & 30.27\% & 26.17\% & pseudoman & 34.01\% & 79.88\% & 47.70\% \\ corebot & 88.11\% & 89.41\% & 58.61\% & pushdo & 60.87\% & 77.89\% & 68.33\% \\ cryptblocker & 22.50\% & 26.00\% & 24.13\% & pushdotid & 45.67\% & 72.46\% & 56.03\% \\ darkwatchman & 99.09\% & 99.78\% & 99.44\% & pyskpa2s & 84.06\% & 82.93\% & 83.49\% \\ dircrypt & 1.10\% & 25.92\% & 2.11\% & qadars & 53.53\% & 84.45\% & 65.52\% \\ dyre & 99.96\% & 99.82\% & 99.89\% & qakbot & 71.12\% & 41.28\% & 52.24\% \\ ebury & 94.24\% & 99.33\% & 96.72\% & qsnatch & 97.83\% & 88.47\% & 92.91\% \\ emotet & 65.37\% & 95.44\% & 77.60\% & ramdo & 53.70\% & 97.99\% & 69.38\% \\ flubot & 75.10\% & 53.18\% & 62.27\% & ramnit & 12.71\% & 20.15\% & 15.59\% \\ fobber & 10.75\% & 39.40\% & 16.89\% & rambus & 45.02\% & 50.63\% & 47.66\% \\ gamover & 99.93\% & 98.55\% & 99.23\% & rovnix & 55.93\% & 92.70\% & 69.76\% \\ gameover\_p2p & 32.04\% & 54.46\% & 40.34\% & shifu & 42.82\% & 54.01\% & 47.77\% \\ gozi & 10.12\% & 72.67\% & 17.76\% & simda & 82.71\% & 82.69\% & 82.70\% \\ gramoverio & 73.72\% & 100.00\% & 84.87\% & sphinx & 5.69\% & 64.55\% & 10.45\% \\ locky & 20.03\% & 34.56\% & 25.36\% & suppobox & 52.40\% & 79.42\% & 63.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Model 1, not pre-trained, trained with the same number of few examples used in the incremental method for Darkshell class, precision, recall, and F1 score of each DGA family. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Family** & **Prec. ** & **Rec. ** & **F1** & **Family** & **Prec. ** & **Rec.
# Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets
## 3 Methods and Algorithms
### Isolation and Culture of Adult Rat Cardiomyocytes  
Adult SD(Sprague-Dawley) rats, male or female, weighing between 200-250 g, were provided by the Experimental Animal Center of Xuzhou Medical College. SD rats were injected with 5000 U kg of heparin intraperitoneally. After 10 min, they were anesthetized with 1% pentobarbital sodium. The heart was quickly opened and immediately placed in ice-cold oxygen-saturated calcium free solution. All the blood vessels were cut around the heart except the aorta, connective tissue, and the upper part of the aortic arch. The heart was then hung on the Langendorff perfusion device. The insertion depth of the cannula should not exceed the aortic valve and should be fixed by wire ligation. After the aorta was retrogradely perfused without calcium solution, after 5 min, the enzyme solution was perfused for 20 to 30 min. The temperature around the heart and the temperature of the perfusate were maintained at 37 \({}^{\circ}\)C during the whole perfusion, and the flow rate was 6 mL/min.  
When the heart became soft, it was cut into dissolved 25 mg collagenase in 5-6 mL warm Powell medium with 12.5 \(\upmu\)L CaCl\({}_{2}\)(30 \(\upmu\)M); the atria and basal tissue were cut off, the ventricular tissue was cut into pieces of about 1 mm\(\times\)1 mm\(\times\)1 mm, and was gently blown with a thick open pipette. Single cell and cell clusters fell off, then were filtered with 200 mesh nylon mesh. The filtrate was allowed to stand at room temperature for 20 min, the cells were naturally sediment, the supernatant was then removed, 12 mL of warm Powell medium was further added to 120 \(\upmu\)L of CaCl\({}_{2}\) (1 mM) for natural sedimentation, and the cells were allowed to stand at room temperature for one hour until use. The cells were seeded in a 12-well culture plate, and each well was about \(2\times 134\) cells; cells were cultured in a CO\({}_{2}\) incubator in a serum-free high-dose DMEM.  
Under the inverted light microscope, the cardiomyocytes mainly showed the following forms: The better-performing cardiomyocytes were long rod-shaped cells stretched, the cell membrane was intact, the surface was smooth, the refractive index was strong, and the cells had no obvious pulsation,  
Figure 1: Workflow of automatic cardiomyocyte clustering and counting.  
showing a clear horizontal pattern. The pattern has sharp edges at both ends--the ratio of length to width is \(\{4\leq l\leq 6\}\):1 (\(l\) is the length of cardiomyocytescytes); the cardiomyocytescytes are blurred in horizontal stripes, and there are vesicles in the cells; some of the cardiomyocytescytes are rounded and blunt at both ends. Bending or contracting, the volume becomes larger, and the cell membrane surface is blistered; the other cardiomyocytescytes become round or severely atrophied into dead cells. The purpose of this system is to distinguish between four types of cardiomyocytescytes and counts of cells of type A, B, and C at each time period, as shown in Figure 2.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Network Protocol  
For network protocols, TCP performance over mmWave links can be enhanced by utilizing the mmS-TCP. Because of the extremely fluctuating mmWave channel's low link utilization, the current TCP is unable to offer enough throughput. The design aim of 5G cannot be realized since throughput may improve but \"Round-Trip Time (RTT)\" will also increase depending on the mmWave base station queue management approach used. Therefore, to meet the 5G design target of a rapid delivery rate and extremely low latency simultaneously, the current TCP congestion control technique was adjusted to enhance link consumption, and the AQM approach has been added to lower end-to-end delay. The S-TCP approach to traffic window increase or decrease was first adjusted to improve TCP flow stability while preserving high throughput. Moreover, mmWave base stations were equipped with the CoDel queue management technique to avoid buffer bloat and reduce end-to-end latency.
## 3 POD Results
### Impact of the Number of the Ground Stations  
The number of ground tracking stations is crucial for the quality of POD results. On the one hand, sufficient tracking measurements are the guarantee of the accuracy of POD. On the other hand, redundant data will not improve calculation accuracy but will reduce calculation efficiency. As a rule of thumb, with 50 stations, an orbit with satisfactory accuracy can be determined [33]. Under the premise of high POD accuracy, we select different station networks to analyze whether the addition of ISL data can reduce the dependence of POD on the number of ground stations. These tracking station networks consist of different numbers of stations that are evenly distributed globally. With 10 stations as a unit interval, the number of stations is increased from 10 to 50 for the orbit determination. Different networks of tracking stations marked with different colors are plotted in Figure 6.  
The overlapping orbit errors of BDS-3 satellites are plotted in Figure 7. The 3D errors of three types of satellites show an expected trend, that is, the more stations used, the higher the obtained accuracy. The accuracy of orbits obtained using 50 stations is similar to the accuracy of the results from 80 stations (see Figure 3). By using more stations, visible improvement is found in the along-track and cross-track directions, while the radial accuracy of these three satellites does not show much change. More importantly, according to the slope of the broken line in Figure 7, it can be inferred that the orbit accuracy changes most when the number of stations decreases from 20 to 10.  
Figure 6: Distribution of stations with different station selection schemes. Purple, green, yellow, blue, and red represent different tracking networks.  
Table 4 lists the improvement percentage of orbit accuracy obtained by increasing the number of tracking stations. For MEO satellites, the subsatellite points fall between 55\({}^{\circ}\) N and 55\({}^{\circ}\) S on the ground, thus increasing the number of global tracking stations can continue to improve the orbit accuracy, especially when the number of stations increases from 10 to 20 (19.7%) and from 40 to 50 (7.5%). With regard to GEO and IGSO satellites, since the subsatellite points are only over the Asia-Pacific region, the addition of globally distributed stations contributes to a slight improvement in orbit accuracy. Starting with 20 stations, each additional 10 stations bring a maximum of 3.9% improvement in the 3D RMS. Therefore, with the supplement of sufficient ISL data, an appropriate ground tracking network should be selected for the three types of satellites according to the required accuracy, which can improve the calculation efficiency of POD and ensure accuracy.  
Figure 7: Comparison of BDS-3 orbit accuracy obtained with different station selection schemes for” MEO satellites (**a**); IGSO satellite C38 (**b**); IGSO satellite C39 (**c**); and GEO satellite C59 (**d**).
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 1 Introduction
We demonstrate that the proposed protocol is able to optimize delay, packet loss ratio and throughput. Furthermore, the system security is improved. The main contributions of this paper could be summarized in three aspects:  
1. A novel geographical routing protocol GIN is proposed by combining the GEAR with the inspecting mechanism. It can resist the typical malicious attacks (black hole, wormhole and Sybil attacks) and promote the system performances. 2. We make use of the Subgame Perfect Nash Equilibrium (SPNE) to choose the inspecting nodes. In order to maximize the network lifespan, the analysis is based on a non-cooperative and repeated general-sum game to make the tradeoff between costs and capabilities. 3. The network model and specific routing process are introduced based on the theory analysis. The validation results of NS3 simulator demonstrate that the proposed protocol outperforms the other three protocols in many aspects. The rest of paper is organized as follows: In Section 2, the related work is presented and discussed from three different perspectives. In Section 3, the specific details of GIN are introduced. Three main aspects include the preliminary foundation, model establishment and implementation processes. In Section 4, the simulation scenario based on NS3 and Wireshark tools is given to explore the advantages of GIN. The validation results are analyzed in depth. In Section 5, we summarize the whole paper and describe the future work.
## 3 Results and Discussion
### Investigation of Internal and External Mass Transfer in Different Strategies
Moreover, in research [24] on the convective drying of Cynara cardunculus, the value of the Biot number was 0.09-0.18, the lag factor was in the range of 0.39-0.6, the constant of drying was 3.29 \(\times\) 10\({}^{-4}\)-5.27 \(\times\) 10\({}^{-5}\), and the Dincer number was 5.05 \(\times\) 10\({}^{5}\)-3.15 \(\times\) 10\({}^{6}\). In this research, the plot slope and the intercept value (mass transfer parameters from the Crank model) were obtained at 1.11 \(\times\) 10\({}^{-4}\)-9.19 \(\times\) 10\({}^{-4}\) s\({}^{-1}\) and \(-\)3.65-5.59 \(\times\) 10\({}^{1}\), respectively. In other research [35], the values of the slope of the graph for convective drying of two varieties of apple were obtained in the range of 1.55 \(\times\) 10\({}^{-4}\)-7.67 \(\times\) 10\({}^{-5}\) and 1.21 \(\times\) 10\({}^{-4}\)-6.51 \(\times\) 10\({}^{-5}\), respectively. Figure 4: Convective mass transfer coefficients in different strategies from Crank and Dincer models. Figure 3: Diffusion mass transfer coefficients (D\({}_{\text{eff}}\)) in different treatments from Crank and Dincer models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Investigated** & **Products** & **System** & **Drying Condition** & **Calculation** & **D\_ **to (m\({}^{2}\)/s)** & **h\_m (m/s)** \\ \hline
[21] & Apple & HA, IR & \( \begin{array}{c}\text{T}\to 90\text{--}150\,^{\circ}\text{C}\\ \text{P}\to 0\text{--}2\text{--}0.499\,\text{W/cm}^{2}\end{array} \) & Crank, 1975 & \(3.49\times 10^{-7}\text{--}5.10\times 10^{-7}\) & – \\
[25] & Tomato & IR & \( \begin{array}{c}\text{T}\to 60\text{--}5\,^{\circ}\text{C}\\ \text{P}\to\text{constant}\end{array} \) & Crank, 1975 & \(1.90\times 10^{-9}\text{--}4.46\times 10^{-9}\) & – \\
[45] & Pears & HA & \( \begin{array}{c}\text{Diner}\to 60,70\,^{\circ}\text{C}\\ \text{P}\to 2002\end{array} \) & \( \begin{array}{c}\text{Bloom}\text{miver}\text{--}11.85\times 10^{-10}\end{array} \) & \(5.37\times 10^{-8}\text{--}8.38\times 10^{-8}\) \\
[31] & Apple & HA & \( \begin{array}{c}\text{T}\to 50\text{--}70\,^{\circ}\text{C}\\ \text{P}\to 40\,^{\circ}\text{C}\end{array} \) & Crank, 1975 & \(1.28\times 10^{-9}\text{--}6.75\times 10^{-9}\) & – \\
[24] & Corn & HA, IR & \( \begin{array}{c}\text{T}\to 40\text{--}60\,^{\circ}\text{C}\\ \text{P}\to 100\,^{\circ}\text{C}\end{array} \) & Crank, 1975 & \(0.61\times 10^{-8}\text{--}5.55\times 10^{-8}\) & – \\
[46] & Apple & HA & \( \begin{array}{c}\text{T}\to 30\text{--}5\,^{\circ}\text{C}\\ \text{P}\to 50\,^{\circ}\text{C}\end{array} \) & Crank, 1975 & \(5.59\times 10^{-12}\text{--}2.55\times 10^{-11}\) & \\
[34] & Kiwi & HA & \( \begin{array}{c}\text{T}\to 50\text{--}80\,^{\circ}\text{C}\\ \text{P}\to 50\,^{\circ}\text{C}\end{array} \) & Cinner and Dost, 1995 & \(0.16\times 10^{-8}\text{--}1.48\times 10^{-8}\) & \(1.93\times 10^{-7}\text{--}4.95\times 10^{-7}\) \\
[35] & Sweet potato & HA, IR &
## 5 Experimental Results and Comparison
### Comparison

Table 3: Processing speed for different input image resolutions. for implementation, markedly fewer than all other designs except [4]. Although the design in [4] utilizes a minimal amount of LUT resources, its throughput is limited to only 1,920,000 pixels per second. Additionally, we utilized Xilinx's power estimation tool to analyze the power consumption of the ELUM algorithm during operation. As shown in Table 2, the power consumption of the algorithm proposed in this paper is notably lower compared to other implementations with a similar performance.
## 3 Experiment and Results Discussion
### Effect of the Influence of Environmental Factors on UV-Vis Spectroscopy
#### 3.1.4 Analysis of the Influence of Multiple Environmental Factors on UV-Vis Spectroscopy  
Sections 3.1.1-3.1.3 provide a detailed analysis of the influence of individual environmental factors on UV-Vis absorption spectroscopy. However, in practical scenarios, the influence on detection is not due to a single environmental factor but is rather the result of the combined influence of multiple factors. Here, the combined influence of these environmental factors on the UV-Vis spectroscopy of both standard solutions and water samples, as discussed in Sections 3.1.1-3.1.3 is summarized in Table 3.  
The results from the experiments on the three environmental factors discussed in Sections 3.1.1-3.1.3 led to the following conclusions: water samples with varying pH levels induce red or blue shifts in the peaks and valleys of their UV-Vis spectra, accompanied by upward or downward shifts in absorbance. Temperature variations in water samples result in similar upward or downward shifts in the peaks and valleys of UV-Vis spectra. Additionally, changes in conductivity can cause shifts in these peaks and valleys. The influence of these environmental factors on the overall spectrum is intricate, and the absorbance at each wavelength cannot be fitted using a single rule or simple algorithm. Consequently, to enhance the accuracy of COD detection based on UV-Vis spectroscopy, it is essential to employ effective compensation methods to reduce the influence caused by these environmental factors.  
\begin{table}
\begin{tabular}{c c c} \hline
**Environmental Factors** & **Influence Mode** & **Influence Level** \\ \hline pH & Red or blue shift, accompanied by upshift or downshift & The influence is significant, and the relationship is complex when the pH of the solution is high. \\ \hline Temperature & Upshift or downshift & There is a certain influence, overall nonlinearity, and complex relationships. \\ \hline Conductivity & Upshift or downshift & The influence is significant, and the relationship is complex. \\ \hline \end{tabular}
\end{table}
Table 3: Analysis of the influence of environmental factors on UV-Vis spectroscopy.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 2 Basic Theory
### Data Fusion Technology Based on Weighted Least Squares
At this time, the corresponding fusion result has the highest accuracy, and its root mean square error (RMSE) is:  
\[\sigma_{\min}=\frac{1}{\sqrt{\sum\limits_{j=1}^{N}\frac{1}{{\sigma_{j}}^{2}}}} \quad j=1,2\cdots,N \tag{26}\]  
Equation (26) indicates that the variance after fusion is lower than that of any single hydrophone. The WLS data fusion method can effectively get rid of the elements with inaccurate range estimation results and improve the estimated accuracy significantly.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Machine Learning Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{10}{c}{**Accuracy Percentage (\%)**} & \multicolumn{10}{c}{**E-Tongue**} \\ \hline  & \multicolumn{10}{c}{**Plum**} & \multicolumn{2}{c}{**Strawberry**} & \multicolumn{2}{c}{**Apple**} & \multicolumn{2}{c}{**Cape Gooseberry**} & \multicolumn{2}{c}{**Plum**} & \multicolumn{2}{c}{**Strawberry**} & \multicolumn{2}{c}{**Apple**} & \multicolumn{2}{c}{**Cape Gooseberry**} \\ \hline  & **Am** & **Fu** & **Ac** & **Br** & **Am** & **Fu** & **Br** & **Cu** & **Da** & **Pr** & **Am** & **Fu** & **Ac** & **Br** & **Am** & **Fu** & **Br** & **Cu** & **Da** & **Pr** \\ \hline Decision Trees & 77.1 & 85.7 & 88.6 & 85.7 & 71.4 & 82.9 & 80.0 & 80.0 & 71.4 & 82.9 & 96.0 & 64.0 & 96.0 & 92.0 & 82.0 & 82.0 & 90.0 & 96.0 & 80.0 & 96.0 \\ \hline LDA & 82.9 & 71.4 & 71.4 & 85.7 & 77.1 & 82.9 & 65.7 & 80.0 & 82.9 & 60.0 & 92.0 & 92.0 & 98.0 & 92.0 & 96.0 & 96.0 & 98.0 & 90.0 & 96.0 & 92.0 \\ \hline Naive Bayes & 91.4 & 88.6 & 77.1 & 97.1 & 85.7 & 97.1 & 91.4 & 88.6 & 94.3 & 82.9 & 98.0 & 80.0 & 94.0 & 92.0 & 80.0 & 84.0 & 94.0 & 98.0 & 92.0 & 100 \\ \hline SVM (quadratic) & 97.1 & 80.0 & 71.1 & 91.4 & 91.4 & 88.6 & 88.6 & 88.6 & 97.1 & 85.7 & 96.0 & 84.0 & 96.0 & 92.0 & 84.0 & 90.0 & 94.0 & 94.0 & 94.0 & 96.0 & 98.0 \\ \hline SVM (cubic) & 97.1 & 85.7 & 74.3 & 94.3 & 94.3 & 88.6 & 88.6 & 88.6 & 94.3 & 88.6 & 96.0 & 86.0 & 94.0 & 94.0 & 94.0 & 88.0 & 90.0 & 96.0 & 98.0 & 100 \\ \hline K-NN (fine) & 97.1 & 94.3 & 82.9 & 100 & 88.6 & 97.1 & 82.9 & 94.3 & 80.0 & 80.0 & 92.0 & 90.0 & 96.0 & 90.0 & 94.0 & 92.0 & 96.0 & 96.0 & 96.0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification of pesticide concentrations in fruits through data acquired using E-tongue and E-nose.