esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Kappa Statistic
Formulated on the kappa statistic values of different classifiers utilized to classify the datasets, Inception-V3 produces a reasonably better outcome compared to ResNet-50 and VGG-19. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **CNN** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{**70\%–30\% Distribution**} & \multicolumn{3}{c}{**10-Fold Cross-Validation**} \\ \cline{2-7}  & **Accuracy (\%)** & **RMSE (sd)** & **Weighted Kappa** & **Accuracy (\%)** & **RMSE (sd)** & **Weighted Kappa** \\ \hline \multirow{2}{*}{VGG-19} & 85.91 & 0.2439 & 0.8456 & 88.12 & 0.2218 & 0.8677 \\  & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.03) & (\(\pm\)0.04) \\ \hline \multirow{2}{*}{ResNet-50} & 93.92 & 0.1638 & 0.9257 & 97.89 & 0.1141 & 0.9754 \\  & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.05) & (\(\pm\)0.06) & (\(\pm\)0.04) \\ \hline \multirow{2}{*}{Inception-V3} & 96.84 & 0.1346 & 0.9549 & 99.21 & 0.1109 & 0.9786 \\  & (\(\pm\)0.05) & (\(\pm\)0.05) & (\(\pm\)0.04) & (\(\pm\)0.05) & (\(\pm\)0.05) & (\(\pm\)0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of CNN models using dataset 2 for 40 epochs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **CNN** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{**70\%–30\% Distribution**} & \multicolumn{3}{c}{**10-Fold Cross-Validation**} \\ \cline{2-7}  & **Accuracy (\%)** & **RMSE (sd)** & **Weighted Kappa** (sd)** & **Accuracy (\%)** & **RMSE (sd)** & **Weighted Kappa** (sd)** \\ \hline \multirow{2}{*}{VGG-19} & 88.76 & 0.2154 & 0.8741 & 89.34 & 0.2096 & 0.8799 \\  & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.03) & (\(\pm\)0.04) & (\(\pm\)0.04) \\ \hline \multirow{2}{*}{ResNet-50} & 95.64 & 0.1466 & 0.9429 & 96.98 & 0.1232 & 0.9663 \\  & (\(\pm\)0.05) & (\(\pm\)0.05) & (\(\pm\)0.05) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.05) \\ \hline \multirow{2}{*}{Inception-V3} & 96.48 & 0.1382 & 0.9513 & 99.36 & 0.1094 & 0.9801 \\  & (\(\pm\)0.03) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.04) & (\(\pm\)0.05) & (\(\pm\)0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of CNN models using dataset 3 for 40 epochs. Figure 8: Comparison among VGG-19, ResNet-50, and Inception V3 in terms of RMSE for (**a**) dataset 1, (**b**) dataset 2, and (**c**) dataset 3. Figure 7: Comparison among VGG-19, ResNet-50, and Inception V3 in terms of accuracy for (**a**) dataset 1, (**b**) dataset 2, and (**c**) dataset 3. The second part of this section explores the performance comparisons of the aforementioned classifiers developed on various statistical performance estimators including TP-Rate/Recall, FP-Rate, precision, and F-measure. TP-Rate/Recall, Precision, FP-Rate, and F-Measure values are evaluated from the confusion matrix of individual classifiers.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering  
[PERSON]  
1 Bell Honors School, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (F.G.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (Q.  
on a previous study introducing an ultra-intense laser-pulsed magnetic field and a static magnetic field in the laser field.  
However, in previous work, on the one hand, the authors studied the effect of this parameter on the nonlinear Thomson scattering only from a certain point of view, i.e., the electron radiation, without discovering the intrinsic connection between the individual radiation characteristics; on the other hand, they only modulated the parameters of the laser, i.e., without changing the applied magnetic field. Therefore, it is reasonable that the collimation of their modulated electron radiation is poor, and the peak power is not high enough. In this way, the idea of combining the analysis of the spatial radiation with the motion trajectories of nonlinear Thomson scattering becomes particularly important; through this method, we can not only study the radiated spatial radiation itself but also the process of its radiation. Previous studies have proven that the introduction of an applied magnetic field can substantially improve the nonlinear Thomson scattering radiation characteristics. On this basis, this paper mainly studies and analyzes the relationship between the electron motion trajectory and radiation generation, as well as the spatial radiation of the electron itself, by changing the strength and pulse length of the applied magnetic field. This paper explains, for the first time, the correspondence between the magnitude of electron radiation at each moment and the trajectory of the electron, thus explaining the process of electron radiation. Figure 1 shows the schematic diagram of this paper.  
In summary, this paper focuses on the motion trajectory and spatial radiation of electrons from a unique perspective, investigating, for the first time, the integrated effect of pulse length and the applied variable magnetic field on electron-excited radiation. On this basis, we select the optimal parameter combination of the composite field of the laser field and the magnetic field, thus helping us to guide the modulation of high-peak-power, high-collimation rays.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 6 Results and Discussion
### Statistical Evaluation of Reliability in Scalable Mini-Datacenter  
We have consolidated multiple experimental runs, as shown in Figure 15, and it is observed that the mean occupancy metrics increase as the gateway count rises from 25 to 100. This suggests that adding more gateways contributes to higher average occupancy performance, likely due to more efficient distribution of load or reduced congestion. Further, it is observed that the confidence intervals do not overlap between 25 and 50, while there is a slight overlap between 50 and 100, but still show an upward shift in performance.
## 5 Issues and Recommendations
### Lattice Mismatch between Substrate and YIG  
Even though high-quality YIG thin films can be epitaxially grown on lattice-matched gallium gadolinium garnet (Ga\({}_{3}\)Gd\({}_{5}\)O\({}_{12}\), GGG) substrates using physical vapor deposition techniques, it is necessary to deposit phase pure YIG films on Si substrates to integrate this material into silicon photonic and spintronic systems. However, developing phase-pure YIG on silicon has been difficult due to the lattice mismatch between the silicon substrate and YIG. First, the unit cell of YIG has a significantly greater lattice constant. The unit cell of YIG has a significantly greater lattice constant (12.376 A) than that of the lattice constant of silicon (5.65 A) [145]. Furthermore, a process growth YIG on quartz substrate may be challenging due to weak adhesion and lattice mismatch [146].  
Figure 9: Publishers of the top articles.  
However, through misfit dislocation and thermal expansion coefficient mismatch, strain relief enhances magnetic properties [126]. For example, YIG growth on a YAG substrate has a larger lattice mismatch than YIG growth on a GGG substrate. YIG growth on the YAG substrate had a shorter FMR linewidth than YIG films deposited on GGG under the same circumstances, suggesting that lattice mismatch improves the magnetic characteristics of the YIG films [126]. The stresses generated during film growth are inherent and frequently compressive [137].
## 3 Methodology  
Our proposed methodology (Figure 1) offers an approach for classifying single-carrier QAM modulations. This technique integrates two distinct algorithms: SN-SC and PSO-FCM. It focuses on the analysis of constellation shapes within the in-phase and quadrature (I-Q) diagrams associated with QAM modulations. To ensure a precise classification process, the methodology follows a systematic procedure. Initially, phase correction is applied to received signals. After that, the SN-SC algorithm quickly goes through the corrected constellation points to find and identify the cluster centers in each I-Q diagram. By utilizing the cluster centers obtained from the SN-SC block, the integration of PSO and FCM algorithms further refines the received signals, resulting in an accurate classification of the QAM modulation order.  
Figure 1: Block diagram of the subtractive-clustering-based PSO-FCM algorithm.
## 3 Results and Discussion  
### Characterization of Amendments  
#### 3.1.1 SEM and FTIR Analysis of Vermicompost  
SEM images vividly depict the intricately dispersed reticular architecture adorning the surface of vermicompost, presenting an exalted degree of porosity that affords a plethora of adsorption loci for the sequestration of heavy metals (Figure 1A). FTIR is an indispensable methodology for scrutinizing and evaluating of the molecular functional groups inherent in substances [17]. Via a discerning examination of the FTIR spectra of vermicompost pre- and postCu\({}^{2+}\) adsorption (Figure 1B), a discernible congruity in the peak positions of the infrared spectra manifests itself, with the principal absorption bands aligning within the spectral range of 3300 to 3600 cm\({}^{-1}\). Notwithstanding, nuanced differentials in peak intensity are observed. The resonances at 3431 and 3425 cm\({}^{-1}\) find attribution to the stretching vibrations of O-H in phenolic and alcoholic entities [18]. The zenith at 2928 cm\({}^{-1}\) is ascribed to the stretching vibration of C-H within aliphatic moieties. The features at 1647 and 1651 cm\({}^{-1}\) correlate with the stretching vibrations of C=C within aromatic compounds, serving as proxies for the aromatic compound content within vermicompost [19]. The apices at 1386 and 1412 cm\({}^{-1}\) correspond with the deformation vibrations of C-H within -CH\({}_{3}\) and -CH\({}_{2}\) of aliphatic compounds [20]. Furthermore, the asymmetrical stretching vibrations of C-O within polysaccharides correlate with the peaks at 1041 and 1038 cm\({}^{-1}\)[18]. In comparison to untreated vermicompost, a discernible attenuation in the intensity of the absorption band corresponding to the aromatic C=C bond is observed in vermicompost post-Cu\({}^{2+}\) adsorption, imputed to the interaction between the delocalized \(\pi\) electrons inherent in aromatic structures and Cu\({}^{2+}\)[21]. The absorption bands associated with O-H and C-O bonds also exhibit a diminution in intensity post-Cu\({}^{2+}\) adsorption, with the primary impetus for this transformation potentially residing in the occurrence of chelation between Cu\({}^{2+}\) and oxygen-bearing functional groups [22].
# Investigation on Key Parameters in the Fabrication of Stamps for Transfer Printing of Micro Devices
## 4 Conclusions  
Via experiments and analyses, we have determined parametric values and criteria that can serve as the guidance for future designs of stamps. For both top and bottom faces, the average error between the post's width and the mold's width can be controlled within 5%. Similarly, the standard deviation of the height of posts fabricated in the same batch can be controlled within 0.5 \(\upmu\)m. The average deviation value between the width of the post and designed mask pattern width stays relatively stable and basically independent of the size. For posts with sizes of 20 \(\upmu\)m to 180 \(\upmu\)m, the overall average deviation of the top face is positive with the value of 16.84 \(\upmu\)m, whereas that of the bottom face is negative with the value of \(-\)3.23 \(\upmu\)m. During the development process, the photoresist is etched in a semi-ellipsoidal shape. Through fitting, empirical equations regarding \(r\), \(c\), \(w\), and \(t\) are obtained. A linear relationship exists between \(r\) and \(c\), \(r\) and \(w\), as well as \(c\) and \(w\), respectively, and the longitudinal etching speed is 1.37 times greater than that in the lateral direction. Fitting results provide a reference for how to realize the fabrication of high-precision posts. Factors that affect the elastic modulus of the stamp are studied, with results showing that the width and the curing temperature play a decisive role in the elastic modulus of the stamp. For posts of the same height, as the width increases, the elastic modulus of the stamp exhibits a maximum value. Finally, for posts of the same size, as the temperature increases, the elastic modulus of the stamp will show two maximum values.  
Conceptualization, W.G.; methodology, C.S.; software, C.S., Y.G., and J.-E.H.; validation, C.S. and H.L.; formal analysis, W.G. and Y.L.; investigation, C.S. and W.G.; resources, Y.L., Y.-J.L., T.W., and Z.C.; data curation, C.S. and W.G.; writing--original draft preparation, C.S.; writing--review and editing, Y.L., T.-M.S., and W.G.; visualization, C.S. and W.G.; supervision, Y.L.; project administration, Z.C.; funding acquisition, Y.L., Y.-J.L., T.W., and Z.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Nos. 11604285, 51605404, 11904302, and 11674054); the Science and Technology Project of Fujian Province (Grant Nos. 2018H6022, 2019H2020013, and 2019H604); the Natural Science Foundation of Fujian Province (Grant No. 2018J01103); the Fundamental Research Funds for the Central Universities (Grant No. 20720190005); and the Strait Postdoctoral Foundation of Fujian Province. The authors declare no conflict of interest.
## 2 Background: Classification, Drainage Analysis and State Parameter Assessment
### Liquefaction Susceptibility Evaluation  
To assess liquefaction susceptibility, the state parameter (\(\psi\)) methodology introduced by [PERSON], [PERSON] and [PERSON] (2017) offers a suitable approach. In this context, a value of \(\psi>-0.05\) signifies a contractive behavior during shear, consequently suggesting the potential for flow-induced liquefaction, as described by Equation (5):  
\[\psi=\alpha\left(\frac{\mathrm{p}^{\prime}}{\mathrm{P}_{\mathrm{a}}}\right)^{ \beta}+\chi\mathrm{ln}\left(\frac{\mathrm{G}_{0}}{\mathrm{q}_{\mathrm{t}}}\right) \tag{5}\]  
where \(\mathrm{p}^{\prime}\) is the mean effective stress acting on the material and \(\mathrm{p}_{\mathrm{a}}\) is the atmospheric pressure. Also, \(\alpha=-0.520\), \(\beta=-0.07\) and \(\chi=0.180\) are (dimensionless) mean values obtained from tests in calibration chambers (2017). Equation (5) was first introduced to be used for granular soils, and therefore, materials with full drainage behavior.
## 4 Case Study
### Performance of Proposed Model
#### 4.3.5 Adhesive Area  
The total error of the adhesive area is 6.42% (accuracy 93.58%), which consists of the ribbon (8.66%) and dab (3.48%), and the error of each type is shown in Table 5. Figure 12 shows the ground true and prediction values of adhesive area for each type. Type 8 has the highest error at 10.37%, which is likely due to the small size of ribbons and dabs, which tend to have larger error values even if the difference value is similar to the other types.
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 6 Materials and Methods  
To examine the performance of the proposed solution, we evaluate the proposed lightweight blockchain solution in two parts. First, the solution has been tested with a real IoT device, which is the Z1 (as shown in Figure 6), powered by an MSP430F2617 low-power microcontroller with a strong 16-bit RISC CPU running at 16 MHz, built-in clock factory calibration, 8 KB RAM, and 92 KB Flash memory. The well-known CC2420 transceiver, which is IEEE 802.15.4-compatible and works at 2.4 GHz with an effective data rate of 250 Kbps, is also included. The Z1 hardware selection ensures optimum efficiency and robustness at the lowest possible enerav cost.  
Second, Cooja simulation has been used to test the multi-situation of the IoT network with the new solution. Contiki is a Linux-based operating system that focuses on low-power Internet of Things devices, while Cooja is a Contiki-based network emulator. Cooja can mimic both big and small networks of IoT motes. Its simulation depicts several types of sensor nodes (heterogeneous networks). It uses a few functions to operate and evaluate a Contiki system. For example, the simulator can tell the Contiki system how to handle an event or retrieve the whole memory of the Contiki system for examination.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 2 Materials
### Prediction Model Construction  
The impact signals collected multiple times from the high-impact recorder exhibit high dimensionality and non-linearity. Furthermore, the acquired experimental data are limited, resulting in a small sample problem. SVM is particularly effective in addressing challenges related to small samples, non-linearity, and high-dimensional pattern recognition. Therefore, this paper utilizes SVM to construct the fault prediction model. To enhance the performance of the SVM-based fault prediction model, the IPSO algorithm is employed for parameter selection of the SVM model. Figure 2 illustrates the flowchart for constructing the IPSO-optimized SVM fault prediction model, with the specific steps outlined as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Algorithm**} & **Optimal** & \multirow{2}{*}{**Worst Values**} & **Means** & **Variances** \\  & & **Values** & & & \\ \hline \multirow{2}{*}{Func1} & PSO & 3.417 & 45.10 & 15.52 & 107.2 \\  & IPSO & 2.889 & 27.75 & 10.40 & 40.00 \\ \hline \multirow{2}{*}{Func2} & PSO & 60.33 & 3103 & 681.6 & \(3.52\times 10^{5}\) \\  & IPSO & 14.61 & 164.2 & 16.64 & 849.9 \\ \hline \multirow{2}{*}{Func7} & PSO & 1.017 & 4.602 & 2.198 & 0.572 \\  & IPSO & 0.4364 & 1.011 & 0.6271 & 0.0213 \\ \hline \multirow{2}{*}{Func9} & PSO & 0.0901 & 0.1010 & 0.0960 & \(6.84\times 10^{-6}\) \\  & IPSO & 0.0890 & 0.0993 & 0.0946 & \(4.47\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization Results of Test Functions.  
Figure 2: Model Training Flowchart.  
Divide the collected data that caused damage to the high-impact recorder into training and testing datasets.
2. Initialize the particle positions and velocities for the IPSO algorithm, and group the particles based on the initial ratio.
3. Update the particle positions, which represent the parameters to be determined for the SVM. Train the SVM model using the parameters from each group with the training dataset.
4. Conduct 10 rounds of 5-fold cross-validation on the trained SVM model, using the average as the fitness value.
5. Compare the fitness value of the current iteration with that of the previous iteration. If the current fitness value is better, accept the new position; if the fitness value is worse but the particle belongs to the exploration group, accept the position with a certain probability according to the principle outlined in Equation (8).
6. If the maximum number of iterations has not been reached, some particles from the exploration group will join the exploitation group, and the process returns to Step 3.
7. If the maximum number of iterations has been reached, use the global best particle position as the output, which serve as the final parameters for the SVM model. The final SVM model is obtained by training with these parameters and the training dataset.
8. Evaluate various metrics of the SVM model using the testing dataset to draw experimental conclusions.
# An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning
## 2 Related Work  
Previously, [PERSON] presented the challenges of integrating farmers' experience and knowledge with state-of-the-art technology in traditional agriculture and proposed a model that utilizes cameras and various sensors to detect features of the farm environment to solve these challenges [3].  
Deep neural networks have also been used for plant cultivation. In 1999, [PERSON] et al. used a small neural network to detect plant growth [4]. In their experiments, the number of units of the network was only seven in the input layer, eight in the hidden layer, and five in the output layer.  
[PERSON] et al. employed a plant growth detection method based on leaf movements [5]. In their study, the changes in the movement of lettuce leaves over time were extracted as features. These features were analyzed using a growth prediction neural network model to predict the growth state.  
[PERSON] et al. proposed a region-based convolutional neural network (R-CNN) model for tomato cultivation that uses images to determine whether the fruit is ripe and automatically determines when to harvest in real time [6].  
In 2019, [PERSON] et al. used 571 tomato images to train and provide a CNN model to recognize the nutrient deficiency status of tomatoes [7]. The model was able to predict calcium (Ca), potassium (K), and nitrogen (N) deficiency states with accuracies as high as 91%.  
In 2021, [PERSON] proposed a model called YOLO-Tomato, a modification of YOLOv3 [8]. It allowed tomato detection in complex environments and achieved 99.5% AP in the best model. This was more accurate than the SOTA model at the time.  
In 2022, [PERSON] et al. proposed a real-time object detection framework Dense-YOLOv4 based on an improved version of the YOLOv4 algorithm introducing DenseNet [9]. In orchards, it is important to detect the growth stage of leaves based on their quantity, size, and color in order to increase the yield. By applying DenseNet-fused YOLOv4, we were able to detect growth stages with high accuracy. This framework can be extended to detect various crops, diseases, etc.
## 4 Experiment, Results & Discussion  
### Optimization of Benchmark Problems  
The performance of the proposed PSA is studied using 13 multimodal functions from the CEC2014 benchmark test suite, function 4-16. These functions are minimization problems that are shifted and rotated from their original basic functions. The shift and  
Figure 2: \(pend_{i}^{d}(t)\) Magnitude of Oscillation.  
rotation avoids the issue of origin bias. Multimodal functions have many local optima and one ultimate global optimum. Multimodal functions are suitable to observe whether an algorithm is able to perform and avoid premature convergence or not, as well as observing the algorithm's exploration and exploitation ability. Therefore, only the 14 multimodal functions are adopted here. The names and ideal fitness of the functions are listed in Table 1; other details of the 13 benchmark functions such as their mathematical functions can be found in [25]. In this work, the dimension of the functions is set to 10.