esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### PID Controller Tuning Parameters  
The PID controller values, \(K_{p}\), \(K_{i}\) and \(K_{d}\), obtained by GA, PSO and CSA for the inputs of the quadcopter, denoted as roll (\(\phi\)), pitch (\(\theta\)), yaw (\(\psi\)) and thrust (\(z\)), along with the fitness values, are presented in Table 3.  
The fitness cost values obtained by the GA-based approach for the quadcopter inputs in Table 3 are encouraging for all inputs of the quadcopter, which confirm reasonable control for the movement of the quadcopter. Table 3 confirms that PSO reported low fitness costs for all inputs of the quadcopter. Obviously, the fitness cost reported for the input \(\phi\) is lower than the fitness cost reported for the other inputs of the quadcopter. However, the difference is relatively small compared to the inputs, \(\theta\) and \(\psi\), which are insignificant. Furthermore, PSO reported a fitness cost for the input \(\phi\) that is significantly less than the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{3}{c}{**GAs**} \\ \hline
**Parameter** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & **Fitness cost** \\ \hline Roll & 4.2936 & 9.999 & 0.2758 & 0.04247 \\ Pitch & 1.7989 & 10 & 0.20463 & 0.00993 \\ Yaw & 49.999 & 9.4626 & 0 & 0.1323 \\ Thrust & 50 & 50 & 43.0091 & 0.10289 \\ \hline \multicolumn{1}{c}{**Method**} & \multicolumn{3}{c}{PSO} \\ \hline
**Parameter** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & **Fitness cost** \\ \hline Roll & 4.3088 & 10 & 0.3254 & 0.0257 \\ Pitch & 2.0232 & 9.9998 & 0.1736 & 0.0405 \\ Yaw & 50 & 0.7083 & 0 & 0.0354 \\ Thrust & 50 & 32.798 & 37.814 & 0.1798 \\ \hline \multicolumn{1}{c}{**Method**} & \multicolumn{3}{c}{**CSA**} \\ \hline
**Parameter** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & **Fitness cost** \\ \hline Roll & 3.476 & 9.9357 & 0.2952 & 0.03737 \\ Pitch & 1.8020 & 9.9996 & 0.2037 & 0.00994 \\ Yaw & 49.796 & 49.941 & 0.0275 & 0.22835 \\ Thrust & 37.072 & 0.0011 & 20.8235 & 0.0450 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fitness and PID controller values for the quadcopter inputs as obtained by GAs, PSO and CSA.  
fitness cost reported for the input \(z\). PSO also reported a fitness cost for the input \(\theta\) that is comparable to the fitness cost reported for the input \(\psi\). In a nutshell, the low fitness values obtained by PSO in Table 3 underline the suitability of PSO in controlling the movement of the quadcopter through optimizing the PID controller parameters. The fitness values obtained by CSA are promising, where the values obtained for inputs roll and thrust are smaller than those obtained by GA. However, the fitness values reported by CSA for inputs pitch and yaw are larger than those reported by GA.
## 5 Experimentation and Analysis
### Analysis of Experimental Arithmetic
#### 5.4.4 Comparison of Four-Season Load Forecasting Models with Weather
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Spring**} & \multicolumn{3}{c}{**Summer**} & \multicolumn{3}{c}{**Autumnn**} & \multicolumn{3}{c}{**Winter**} \\ \cline{2-13}  & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline MVMO-LSTM & **0.2582** & **0.2013** & **0.988** & **0.2785** & **0.2409** & **0.993** & **0.3026** & **0.2914** & **0.990** & **0.4607** & **0.3977** & **0.983** \\ \hline LSTM & 1.1098 & 0.9024 & 0.794 & 1.2886 & 1.0724 & 0.883 & 0.5338 & 0.4738 & 0.775 & 2.5911 & 2.4433 & 0.637 \\ \hline FOA-LSTM & 0.8111 & 0.6895 & 0.833 & 1.5098 & 1.3224 & 0.883 & 0.6940 & 0.6007 & 0.757 & 2.3165 & 2.1342 & 0.701 \\ \hline GWO-LSTM & 0.2904 & 0.2250 & 0.979 & 1.0904 & 0.9146 & 0.943 & 0.3816 & 0.2546 & 0.979 & 0.7142 & 0.5931 & 0.956 \\ \hline SSA-LSTM & 0.3927 & 0.3159 & 0.971 & 0.5899 & 0.4781 & 0.980 & 0.3194 & 0.2934 & 0.985 & 0.7074 & 0.5911 & 0.958 \\ \hline DBO-LSTM & 0.3462 & 0.2738 & 0.975 & 0.5156 & 0.4382 & 0.989 & 0.3457 & 0.3323 & 0.981 & 0.6812 & 0.5638 & 0.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the performance comparison of the four-season combination prediction model. Figure 7: Comparison chart of the various models with weather. (d) The MVMO-LSTM model performs the best in the comparison of this dataset, with a smaller average error and more stable prediction capability. This highlights the significant advantage of the model in processing the current dataset, making it closer to actual historical data. Figure 8: Comparison of the forecast model errors for four seasons.
## 4 Experimental and Discussion
### Evaluations of the Algorithm Effectiveness  
In this paper, the performance of the proposed algorithms is defined as the degree of geometric feature restoration of the original meshes and the accuracy of numerical simulation. Accordingly, two kinds of measures are proposed to evaluate the effectiveness of the algorithms. Firstly, for the degree of geometric feature restoration of the original meshes, the use of visual renderings allows for the visual and intuitive assessment. In addition, volume and surface area are the basic geometric information of a model. We set the geometric feature parameter \(r\) as the ratio is a surface-area-to-volume to measure the geometric information of the triangular mesh. Volume, surface area, and their ratio \(r\) can be calculated by the following equations, where \(v_{i},v_{j},v_{k}\) are vertices on a triangle \(t_{ijk}\):  
\[\left\{\begin{array}{l}volume=\sum\frac{1}{6}(v_{i}\times v_{j})\cdot v_{k}, \\ surfacearea=\sum\frac{1}{2}(v_{j}-v_{i})\times(v_{k}-v_{i}),\\ r=\frac{surfacearea}{volume},\end{array}\right. \tag{6}\]  
Secondly, to demonstrate the effectiveness of numerical simulations, we used a geodesic distance based on the gradient calculation to simulate the thermal diffusion of isotropic simulation [8]. The accuracy of the computation of the scalar function determines the accuracy of the final numerical simulation, where the discrete Laplace operator is defined as a key factor, in the computation of the gradient of the scalar function. In particular, when calculating the gradient of thermal diffusion (see Figure 7), it can be specifically expressed as  
\[\Delta u_{i}=\frac{1}{2A_{i}}\sum(\cot\alpha_{ij}+\cot\beta_{ij})(u_{j}-u_{i}) \tag{7}\]  
Here, \(u\) is a scalar function defined on the triangle mesh, and \(A_{i}\) is one third of the area of all triangles incident on vertex \(v_{i}\). The sum is calculated by adding up all neighboring vertices \(v_{j}\in\Omega(v_{i})\), where \(\alpha_{ij},\beta_{ij}\) are the angles opposing to the corresponding edge \(e_{ij}\in\Omega(v_{i})\).  
Figure 7: The cot formula for a scalar function on the mesh.
# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 3 Method
### Algorithm Design  
#### 3.2.1 Label Classifier  
The label classifier (\(G_{y}\)) is a core component of the model, responsible for performing the main classification task. Once the model is trained, only the feature extractor (\(G_{f}\)) and label classifier (\(G_{y}\)) are used for predicting the image classification. The label classifier consists of a fully connected neural network that takes the feature (\(f\)) corresponding to the input data (\(x\)) and outputs the prediction of \(x\)'s category, represented as a C-dimensional vector in Figure 4. Since the label classifier is a supervised classification model, it is trained using labeled data from the source domain. The loss function used for the label classifier is the cross-entropy loss (\(L_{y}\)), which is represented as follows:  
\[L_{y}=-\frac{1}{n_{s}}\sum_{x_{i}\in D_{s}}\sum_{c=1}^{C}P_{x_{i}\in c}\log( \hat{y}) \tag{8}\]  
\(\hat{y}=G_{y}\big{(}G_{y}(x_{i})\big{)}\), where \(P_{x_{i}\in c}\) is the probability that \(x_{i}\) belongs to class \(c\). The label classifier serves two functions during the training process: (1) it performs supervised learning using labeled source domain data and target domain data with reliable pseudo-labels (represented by the solid line in Figure 4 for the calculation of \(L_{y}\)); (2) it generates pseudo-labels for the target domain data to guide the multi-class domain discriminator in aligning conditional distributions (represented by the dashed line in Figure 4 for the calculation of \(L_{l}\)).
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Direct Trust, Indirect Trust and Comprehensive Trust  
The direct trust value for every node denoted by T1 is computed using various availability metrics such as the node's packet forwarding history, packet delay, residual energy, reputation, etc. Next, the indirect trust value T2 is derived using the recommendations of the shared neighbors, as shown in Figure 3. The direct trust and indirect trust values are first calculated and combined with weighting variables to provide a comprehensive trust value, which is represented as a T-comp against each node. Due to the drop in their  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Sensor Node’s Initial Energy & 0.87 J \\ Area of Interest (The concrete simulation scene) & 500 m \(\times\) 500 m \\ Data Length & 200 bits \\ Threshold Comprehensive Trust Value [0, 1] & 0.78 \\ Malicious Nodes & Random \\ Probability of Clusters & 0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters of Efficient and Secure Fog Based Routing in IoT network.  
Figure 2: The mathematical implementation of Efficient and Secure Fog Based Routing in IoT network.  
earlier trust values as well as in the ratio of delivered data packets because of volatilization factors, the comprehensive trust values of hostile nodes executing black hole attacks are significantly lowered, as shown in Figure 3.  
Additionally, due to the hello flood attack, the volatilization factor causes the value of received data packets of faulty nodes to become zero, which pushes the node's overall comprehensive trust value below the threshold comprehensive trust values. Moreover, the comprehensive trust values of nodes launching selective forwarding and sinkhole attacks are also lowered than the threshold comprehensive trust value. As a result, the route would become safe and secure since all nodes whose comprehensive trust value appears below the threshold would be disqualified for data transfer.
## 3 Characterization
### Optical Property and Stability  
UV-vis diffuse reflectance spectroscopy (DRS) is commonly applied to investigate the optical properties and energy band features of BCPs. Carbon can expand the absorbance range to the visible region [74]. The BCPs showed a red-shift toward longer wavelengths when semiconductors were combined with BC to form the composites [40; 41]. However, the absorbance was relatively weak in the visible region because of the low content of BC [43]. The band gap energy of BCPs can be calculated by application of the Kubelka-Munk function [36; 80]. The BCPs displayed much different photocatalytic performance in terms of the difference in band gap energy values [41].  
Thermogravimetric analysis (TGA) is performed to assess the mass loss and relative stability of BC. [PERSON] et al. [81] applied TGA to analyze the thermal stability of sulfonated BC. The derivative thermogravimetric (DTG) peak at 600-700 \({}^{\circ}\)C diminished while the peak at 200-300 \({}^{\circ}\)C became predominant after the sulfonation process. At 1000 \({}^{\circ}\)C, the TGA spectra exhibited a more significant mass loss of sulfonated BC (20 wt.%), in comparison with raw BC (16 wt.%), indicating that sulfonation inevitably weakened the thermal stability of BC.
###### Abstract  
Additive manufacturing procedures are being increasingly developed, from prototyping to finished functional products. However, their rapid development also brings along the testing of properties with different manufacturing parameters. In selective laser sintering, the most influential manufacturing parameter is the energy density, which also consists, among other things, of the hatch distance. For better usage of the entire chamber and a reduction in the overall price of the finished product, in practice, the manufacturing of products at different heights (levels) of the working chamber with different orientations is inevitable. The study examines how hatch distance and product orientation impact the tensile strength and dimensional stability of polyamide products across two levels within the chamber. Upon analysis, it was observed that manufacturing products at different levels within the working chamber does not influence their dimensions. Achieving precise product dimensions comparable to those in the CAD model is possible. Furthermore, the same factors (orientation and hatch distance) and their combinations affect the length, thickness, and width of the product. Although all test specimens were tested, a tensile strength analysis of variance (ANOVA) of test specimens produced at the lower level of the chamber with a combination of hatch distance (ranging from 0.23 to 0.6 mm) and orientation (ranging from 0\({}^{\circ}\) to 60\({}^{\circ}\)) was not feasible in the design of the experiment. Despite this limitation, it was noted that both chamber levels had the potential to reach a maximum tensile strength of 47 N/mm\({}^{2}\). Nevertheless, the average tensile strength of PA12, obtained through combinations of input factors, stood at only 30 N/mm\({}^{2}\), which is quite a low value for polyamide made by selective laser sintering.  
dimensional stability; hatch distance; mathematical model; orientation; polyamide PA12; response surface; selective laser sintering--SLS; tensile strength; working chamber levels +
Footnote †: journal: Applied Sciences
## 4 Discussion  
### UV Measurements  
The effective irradiance at the cage (Distance = 0 mm), which is the treatment area in contact with the patient, was 0.36 \(\upmu\)W/cm\({}^{2}\) for the SteriPlas with the 55 L/min extraction system and 0.33 \(\upmu\)W/cm\({}^{2}\) for the SteriPlas with the 80 L/min extraction system. These values show that the air pump with a higher extraction flow rate lowers the UV energy incident upon the patient. Furthermore, the effective irradiance was calculated to provide a more accurate value for the maximum safe exposure limits; previously, Adtec had simply added together the UV power emitted at different wavelengths, which is a safe practice, erring on the side of caution, but does not show the maximum time a human may be exposed to the SteriPlas.  
The International Commission on Non-Ionizing Radiation Protection (ICNIRP) states that for the most sensitive, non-pathologic, skin photo-types, ultraviolet radiant exposure in the spectral region of 180 to 400 nm upon the unprotected skin should not exceed 30 J/m\({}^{2}\), effective spectrally weighted using the spectral weighting factors calculated from Equations (2)-(4) and shown in the work of [17].  
Figure 8: Ozone measurements: 80 L/min extraction system.  
Figure 7: Ozone measurements: 55 L/min extraction system.  
Now, 30 J/m\({}^{2}\) = 30 W/m\({}^{2}\) = 3000 \(\mu\)W/cm\({}^{2}\) if the exposure time is set to 1 s in a given 24 h period. Therefore, by simply dividing 3000 \(\mu\)W/cm\({}^{2}\) by the effective irradiances above, the maximum exposure time in a given 24 h period is obtained at the treatment area under the SteriPlas: maximum exposure time (SteriPlas 55 L/min extraction system) = 3000 \(\mu\)W/cm\({}^{2}\)/0.36 \(\mu\)W/cm\({}^{2}\) = 2 h, 18 min, and 53 s; maximum exposure time (SteriPlas 80 L/min extraction system) = 3000 \(\mu\)W/cm\({}^{2}\)/0.33 \(\mu\)W/cm\({}^{2}\) = 2 h, 31 min, and 30 s.
## 4 Results and Discussion
### Performance and Microstructure
The improved surface quality of the 57\({}^{\circ}\) SPIFed sample is related with finer grains (Figure 11c) of the material. The reason for this is that the local deformation is more uniform for SPIF process at higher forming angle, which leads to a smaller distortional energy difference across the grain boundaries and thus suppresses grain growth, improving the surface quality during forming. It has been shown [25] that equiaxial fine grains substantially increase the strength and the plasticity of the alloy. This explains the better performance of the sample SPIFed at higher angle (57\({}^{\circ}\)). However, lower forming angles generate larger grains during the forming process, which not only have adverse effects on surface quality but also reduce strength and elongation. In addition, precipitation strengthening is an important factor in improving the high strength of Al-Li alloys, and the T\({}_{1}\) phases is the most important strengthening precipitation phase in Al-Li alloys. The precipitation phase is the most important factor in precipitation strength. TEM observations were performed on the samples SPIFed at different angles along [110] zone axis, as shown in Figure 12. It can be found by SAED and TEM that the precipitated phase is T\({}_{1}\) phases and the presence of \(\theta^{\prime}\) phase is not observed. The quantity density of T\({}_{1}\) phases in aging state with different forming angles was statistically analyzed. The quantity density and distribution of T\({}_{1}\) phases precipitated in the forming angle 57\({}^{\circ}\) (267.3 \(\upmu\)m\({}^{-2}\)) is higher and more uniform than those at 51\({}^{\circ}\) (177.5 \(\upmu\)m\({}^{-2}\)) and 54\({}^{\circ}\) (233.1 \(\upmu\)m\({}^{-2}\)), which also contribute to its higher strength. It is widely reported in the literature that higher T\({}_{1}\) phases density is beneficial for strength improvement [24; 25; 26; 27; 28].
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 5 Conclusions and Future Work  
The novelty of this study was in its use of ultrasonic waves for wireless communication for healthcare systems. The experimental results for the three principal modulation schemes of OOK, BPSK, and BFSK are provided. The experiment was set up in both water and air. The binary data rate was set to 500 bps, and the carrier frequencies were 40 kHz for OOK and BPSK, and 470 kHz  
Figure 11: Bit error rate over different transmission ranges for the three modulation schemes in the experimental results.  
Figure 10: Workflow of signal processing.  
and 480 kHz for BFSK modulation. Although all three modulation schemes show potential for digital communication, BFSK had better performance in terms of both the BER and the shape of the modulation signal. OOK had a higher average voltage (0.3 V) than BFSK (0.18 V) and BPSK (0.15 V).  
This study is based on an analysis of the feasibility of using ultrasonic communication. It is difficult to detect the signal from the receiver. To detect the signal, we studied the physical characteristics of ultrasound, frequency selection; investigated commercial transducers and their properties for analysis, and designed the experiment. In a future study, we will attempt to use an amplifier, apply robust modulation, and enhance the demodulation algorithm to reduce the BER.  
[PERSON] designed and performed the experiments; [PERSON] suggested the idea of the work; all of authors commented and wrote the paper.  
This research was supported by the Jilin Scientific and Technological Development Program (20180520030 JH), China and the Mid-Career Researcher Program (2016R1A2B3009423) through NRF and the ITRC support program (No. IITP-2015-0-00390) supervised by the IITP, the grant from the MSIT (Ministry of Science and ICT), Korea.  
The authors declare no conflict of interest.
## 4 Numerical Simulation Results and Validation
### The Influence of Different Factors on TVMS and LTE
#### 4.2.2 The Influence of Modification Amount on TVMS and LTE  
In order to study the influence of tooth profile modification on the TVMS and LTE of internal helical gears, \(\Delta C_{\max}\) was increased from 0 \(\upmu\)m to 25 \(\upmu\)m, with 5 \(\upmu\)m as the interval. As shown in Figures 15 and 16, the TVMS gradually decreased with the increase in modification amount, but the LTE increased, demonstrating an inverse tendency between the two.  
Figure 14: The fluctuation value of the load transmission error of different loads.  
Figure 15: Time-varying mesh stiffness of different modification amounts.  
Figure 17 illustrates the variation trend in the LTE fluctuation value with the modification amount. It can be clearly seen that the amplitude fluctuation of LTE reached its minimum value when the tooth profile modification amount was 5 \(\upmu\)m. Therefore, it can be known that the appropriate modification effectively reduces the LTE fluctuation value and mitigates the vibration impact during meshing. However, excessive modification can lead to a decline in the TVMS and an increase in the LTE fluctuation values. Thus, a balanced consideration should be given when selecting the modification amount.
## 5 Conclusions  
In this study, we have demonstrated the effectiveness and efficiency of the DeepONet framework in modeling and predicting the nonlinear dynamics of fiber pulse propagation. Our results indicate that DeepONet can accurately predict pulse propagation dynamics, yielding outcomes that align closely with traditional numerical methods such as the Nonlinear Schrodinger Equation (NLSE).  
Accelerated by CUDA, DeepONet generates output pulses of any length and transmission distance in an average time of approximately 0.0014 s, with prediction errors remaining consistent regardless of propagation distance. This efficiency makes it highly suitable for quickly and accurately modeling long-distance fiber pulse propagation. Furthermore, by scaling up the model's size, its robustness is enhanced, suggesting significant potential for future applications in commercial production.  
Importantly, the generalization capability of DeepONet lies in its ability to simultaneously simulate the propagation dynamics of multiple fibers, a feature that other models struggle to achieve. We anticipate that DeepONet could become a standard method for laser dynamic simulation and modeling, delivering notable advancements in both efficiency and accuracy for practical industry applications. The adaptability and computational speed of DeepONet make it a valuable tool for advancing fiber optics research and development, offering a reliable and efficient alternative to traditional simulation methods.  
Conceptualization, investigation, and writing, Y.Z.; review and editing, S.K. and N.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data supporting the findings of this study can be publicly accessed on 12 August 2024 GitHub: [[https://github.com/usagimamahaha/fiber2.git](https://github.com/usagimamahaha/fiber2.git)]([https://github.com/usagimamahaha/fiber2.git](https://github.com/usagimamahaha/fiber2.git)). The dataset was obtained from fiber channel numerical simulations based on the Nonlinear Schrodinger Equation (NLSE), using the Python package PYNLO-V0.1.2. The authors declare no conflicts of interest.