esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Routing Protocol: HPA-SR
### The Proposed Clustering Protocol (ASCs): The Underlying Structure  
#### 4.2.1 The Basic Concepts of the ASCS  
As shown in Figure 2, the proposed clustering protocol [33], called advanced speed and cosine similarity-based clustering (ASCS) protocol as underlying structure, can improve the route connectivity and reduce the control overhead in VANETs. The proposed clustering protocol consider node speed and direction as cosine similarity and cosine distance to form the clusters. In addition, the ASCS clustering protocol elects gateway nodes to support the communication between CHs when the next CHs is out of the transmission coverages. Figure 3 demonstrates the flowchart of the proposed ASCS clustering protocol. The proposed ASCS clustering protocol consists of two sub-subsections, which can be summarized in the following subsection.  
Figure 1: The basic concepts of the proposed routing protocol: HPA-SR.  
Figure 3: The flowchart of the proposed clustering protocol: ASCS.  
Figure 2: The basic concepts of the ASCS clustering protocol.
## 3 Experiments and Result Analysis
### Analysis of Experimental Results
#### 3.2.2 Model Generalizability Test  
To verify the applicability of the model, it was applied to other regions and different types of AUVs, yielding similarly significant trajectory prediction results, as shown in Figure 16. By following the same experimental procedure, applying the model to predict AUV trajectories in other marine areas still yielded high prediction accuracy. As shown in Table 2, the RMSE for latitude, longitude, and depth are 4.88 \(\times 10^{-5}\), 9.25 \(\times 10^{-5}\), and 0.13915, respectively. This set of experiments demonstrates the applicability of the proposed AWOA-DBiLSTM model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Metric** & **Lat** & **Lon** & **Depth** \\ \hline \multirow{3}{*}{BiLSTM} & MSE & 8.18 \(\times\) 10\({}^{-6}\) & 3.44 \(\times\) 10\({}^{-6}\) & 0.22721 \\  & RMSE & 0.00286 & 0.00185 & 0.47666 \\  & MAE & 0.00228 & 0.00167 & 0.39565 \\ \hline \multirow{3}{*}{DBiLSTM} & MSE & 3.32 \(\times\) 10\({}^{-6}\) & 6.49 \(\times\) 10\({}^{-7}\) & 0.04509 \\  & RMSE & 0.00182 & 0.00081 & 0.21234 \\  & MAE & 0.00151 & 0.00065 & 0.15015 \\ \hline \multirow{3}{*}{AWOA-DBiLSTM} & MSE & 6.94 \(\times\) 10\({}^{-8}\) & 1.39 \(\times\) 10\({}^{-7}\) & 0.00159 \\  & RMSE & 0.00026 & 0.00037 & 0.03992 \\ \cline{1-1}  & MAE & 0.00021 & 0.00027 & 0.01974 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction results of different models.  
Figure 16: AWOA-DBiLSTM model transfer predicts AUV trajectory: (**a**) represents the prediction of latitude by AWOA-DBiLSTM. (**b**) represents the prediction of longitude by AWOA-DBiLSTM. (**c**) represents the prediction of depth by AWOA-DBiLSTM.
## 3 Experiments and Results Analysis
### Comparative Analysis of Experimental Results
#### 3.3.3 Discussion  
After VMD decomposition of the two datasets, the predictive results of all of the VMD-based models in this research became stable, which infers the importance of smoothing and stabilizing the load data for accurate prediction. Moreover, the combination of TCN and LSTM is helpful to fully extract the long-term temporal dependencies and local dependencies of the load data. Due to stacking multiple single modules, there is a lot of irrelevant feature information that is extracted from the load data. Then, SAM is used to dynamically adjust the correlation weights of different important features to obtain the key formation. Therefore, the novel architecture of the proposed model is very beneficial for feature extraction from large-scale load data with nonlinear patterns and dynamics. It should be pointed out that the computational cost of the proposed model increases greatly compared to other single models, due to its complicated structure. However, the computational cost problem can easily be overcome with the rapid development of computing power. In addition, all of the models were trained and tested several times in this study to obtain a series of stable results. Figure 14 shows a boxplot of the MAPE for all of the models based on the ISO-NE and GEFCom2012 datasets. One can see that the proposed model not only achieves the lowest MAPE value among all of the models for the two datasets, but it also achieved the smallest variation range in its MAPE value. This indicates that the novel model proposed in this study has excellent generalization and reliable prediction accuracy.
# Secure Convolution Neural Network Inference Based on Homomorphic Encryption
## 3 System Model, Encryption Scheme and Design Goals
### Design Goals  
A privacy-preserving neural network inference scheme built using the encryption techniques described in Section 3.4 should obtain the following security and performance guarantees:  
* Correctness: after the user correctly implements our proposed scheme, the prediction result should be correct. The correctness of homomorphic encryption schemes can be understood as follows: encrypting a plaintext, evaluating the ciphertext, and then decrypting the result is equivalent to evaluating the plaintext with the same operation to obtain the same result [4];
* Privacy: after the cloud service provider obtains the ciphertext provided by the user, it cannot obtain any valid information from the ciphertext, and can only perform certain operations on the ciphertext to achieve the user's expected goal;
* Verifiability: after the user obtains the ciphertext returned by the cloud service provider, there is a way to verify the correctness of the result.
* Efficient: including channel transmission time and inference time, the total time should be much shorter than the user's own local inference time.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Discussion and Analysis Based on Performance Metrics
#### 5.2.5 Analysis Based on the Accuracy  
In this part, the values of accuracy are discussed. Table 19 shows the details of the papers' accuracy. In this table, papers [3, 13, 16, 19, 22, 23, 25, 26] used a CNN-based model; papers [34, 36, 45, 50, 52, 53, 54, 56, 58, 61, 62, 63, 64] used an LSTM based model; papers [67, 68, 74] used a DNN-based model; papers [28, 29] used an RNN-based model; and papers [87, 89, 92] used other deep learning methods.  
It could be found that paper [36] performed best, which used an LSTM model, and the paper's accuracy was in the range of 90-100%. Papers [52, 61, 68] also had great performances, but papers [56, 89] did not have high accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MSE Range** & **Reference No.** \\ \hline MSE & [22, 24, 30, 32, 33, 35, 38, 39, 43, 59, 70, 72, 81] \\ \hline \textless{}0.01 & [59] \\ 0–0.01 & [33, 40, 42, 81] \\ 0.01–0.1 & [30, 32, 39, 70] \\ 0.1–1 & [22, 43, 72] \\ 1–10 & [41] \\ 10–100 & [38] \\ \textgreater{}100 & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 18: Analysis based on Mean Square Error (MSE).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Accuracy Range** & **Reference No.** \\ \hline Accuracy & [3, 13, 16, 19, 22, 23, 25, 26, 28, 29, 34, 36, 45, 50, 52, 53, 54, 56, 58, 61, 62, 63, 64, 50, 52, 54, 56, 58, 61, 62, 63, 64, 57, 68, 74, 87, 89, 92] \\ \hline
0–50\% & [56, 89] \\
50–60\% & [19, 25, 34, 53, 58, 74] \\
60–70\% & [16, 23, 28, 29, 45, 50, 54, 67, 87] \\
70–80\% & [3, 13, 22, 62, 63, 64, 69, 22] \\
80–90\% & [52, 61, 68] \\
90–100\% & [36] \\ \hline \hline \end{tabular}
\end{table}
Table 19: Analysis based on the accuracy.  
Among the papers that had an accuracy measure metric, 23% of the CNN papers were in the range of 50-60%, 23% of the CNN papers were in the range of 60-70%, and 54% of the CNN papers were in the range of 70-80%; 8% of the LSTM papers were in the ranges of 0-50% and 90-100% each; 15% of the LSTM papers were in the range of 80-90%; 23% of the LSTM papers were in the ranges of 50-60%, 60-70,% and 70-80% each; and 33% of the DNN papers were in the ranges of 50-60%, 60-70%, and 80-90% each. All the RNN papers were in the ranges of 60-70%, and 33% of other deep learning methods were in the ranges of 40-50%, 60-70%, and 70-80% each.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 7 Following Stages of Ultrasound Research
### Harmonization of Processing Conditions  
Today, thousands of publications are dealing with ultrasound for food pasteurization. The number of microorganisms, enzymes, products, processing conditions, and quality analysis is vast. Most of the publications display encouraging results in microbial and enzyme inactivation but also in preserving the quality of the product. However, in many of the publications, some processing conditions are lacking. As a new area of research, some parameters that need to be reported are sometimes missing from the experimental details. Until today there is no general rule of what needs to be reported while working with ultrasound, making it challenging to compare experiments [23; 38].  
It is also known that each country has unique standards regarding food safety and regulations, but also, the meaning of some words differs from country to country. A Global Harmonization Initiative started a few years ago in different areas of food science. A specific working group is devoted to novel food preservation technologies. Even though various regulations worldwide can affect the development of novel technologies, this working group will provide, exchange and recap knowledge with the scientific community to align to international regulations and ensure the technology can be validated and approved in the future [108]. The development of previous emerging technologies and currently commercial applications, such as high hydrostatic pressure or microwave, set the pathway to be followed. It has been a joint effort between academia, industry, and government, with many years of research behind it. Ultrasound pasteurization is not the newest emerging technology; it has at least a few decades of research. At this point, it is necessary to explore the next stages of research and the pathway to follow for further validation and approval.
## 2 Experimental Design and Methodology  
### Laser Cutting Process  
The material of interest for this investigation is polymethyl methacrylate (PMMA), which is a transparent thermoplastic with a high molecular weight and excellent optical properties. The PMMA utilized in this study was in the form of sheets with a thickness of 5 mm supplied by Perspex (Brenderslev, Denmark). The properties of the PAMM are listed in Table 1. The main objective of this research was to analyze the kerr properties of the 5 mm PMMA sheets during the CO\({}_{2}\) laser cutting process. The study primarily focused on examining the width at the top and bottom of the kerr (mm), the ratio of the top to bottom, the cut kerr angle, and the potential correlations between these parameters and the process variables of laser power, cutting speed, and focal plane position.  
During the experiment, a Carbon Dioxide (CO\({}_{2}\)) laser machine operating at a wavelength of 1060 nm was employed to cut the PMMA plastic material. The laser power was  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Molecular Formula & (C\({}_{5}\)O\({}_{2}\)H\({}_{8}\))n \\ Molecular Weight (g/mol) & -100,000–500,000 \\ Melting Point (\({}^{\circ}\)C) & 105–160 \\ Glass Transition Temperature (Tg) (\({}^{\circ}\)C) & 105–165 \\ Density (g/cm\({}^{3}\)) & 1.17–1.20 \\ Refractive Index (at 589 nm) & 1.49–1.50 \\ Thermal Conductivity (W/(m\(\cdot\)K)) & 0.17–0.19 \\ Specific Heat Capacity (J/(g\(\cdot\)K)) & 1.4–1.9 \\ Coefficient of Thermal Expansion (\({}^{\circ}\)C) & 70–90 \(\times\) 10\({}^{-6}\) \\ Tensile Strength (MPa) & 50–100 \\ Young’s Modulus (GPa) & 2.7–3.3 \\ Elongation at Break (\%) & 2–6 \\ Transparency & Excellent \\ \hline \hline \end{tabular}
\end{table}
Table 1: PMMA thermal and physical properties. Extracted with permission from [34], Elsevier, 2023.  
applied on the surface of the sheets, and the machine used a continuous CO\({}_{2}\) laser to examine the thermophysical properties of the PMMA sheet's surface. A continuous laser beam was selected to ensure a stable and uniform energy delivery, which was essential for assessing the cut quality reliably. The laser beam was focused through a lens with a specific focal length, and the laser cutter used had a nominal output laser power of 60 W, which was verified using a laser power probe for the laser cutting machine. Compressed air was supplied to the cutting zone via a 1 mm diameter nozzle, and the distance between the nozzle and the object was set to 4 mm (Figure 1). Each consecutive cut was 50 mm in length, spaced 20 mm apart.  
Several methods exist for determining the focal position. In this study, a method involving an acrylic sheet placed at an 80-degree angle with respect to the horizontal plane was employed. By horizontally traversing the sheet across the vertical beam, the imprint of the beam on the sheet provided the location of the focal point (Figure 2) [35].
## 3 Receiver Integration for WDM with 10-Gb/s Throughput
The polarization-dependent loss is 0.2 dB or less at the center wavelength of the grid, which is the same as that of the MM-AWG itself. The coupling loss between the input fiber and the PD is estimated at 1 dB, the same as in the fiber-coupled measurement; therefore, there is almost no degradation due to optical coupling by mechanical alignment. As shown in Figure 5d, we also measured the bit-error-rate (BER) characteristics when all the channels were driven simultaneously. The experimental setup is shown on the left. The signal from the pulse-pattern generator (PPG) was a 1.25-Gb/s non-return-to-zero (NRZ) 27-1 pseudo-random bit sequence (PRBS). The WDM signal generated from 10-Gb/s transmitters and a coupler was transmitted in a single-mode fiber with back-to-back connection and fed into the photoreceiver while input power was changed by the valuable optical attenuator (VOA). The BER of the photoreceiver was measured by clock-and-data recovery (CDR) followed by an error detector (ED). We observed error-free transmission with the minimum receiver sensitivity from \(-24\) to \(-20\) dBm at a rate of \(10^{-11}\). Since the sensitivity tends to degrade in edge channels such as channel 1 and 8, the layout of transmission lines and electrical components on the circuit board should be optimized for further reduction of the sensitivity deviation.
## 3 Results and Discussion
### Image Segmentation Results
#### 3.3.4 Three-Dimensional Pore Reconstruction and Micro-Pore Structure Analysis
The pore radius distributions of the real and synthetic 3D pores are shown in Figure 11. The pore radius distribution of real 3D pores starts at smaller radii, reaching a peak probability distribution as the radius increases, and then gradually declines. The highest probability distribution for real 3D pores occurs at a pore radius of 9.342494 \(\upmu\)m, indicating that pores within this radius range are more common. In contrast, the synthetic 3D pores generated by the segmentation model reach their peak probability distribution at a slightly higher radius of 10.353425 \(\upmu\)m. This suggests that the segmentation model might be more sensitive or biased towards identifying medium-radius pores. As the pore radius  
Figure 10: (**a**) Throat length distribution of real and synthetic 3D pores; (**b**) coordination number distribution of real and synthetic 3D pores. further increases, the probability distribution of synthetic 3D pores declines more rapidly, particularly in the identification of larger radius pores, where the probability distribution is lower than that of real 3D pores. This implies that the segmentation model's accuracy diminishes in simulating large-radius pore structures. Overall, while the segmentation model exhibits trends similar to real 3D pores in certain aspects of the pore radius distribution, the rapid decline in probability distribution for larger radius pores indicates that further optimization and adjustment may be necessary to improve simulation in this area. As illustrated in Figure (b)b, the analysis of throat shape factor distribution in real and synthetic 3D pores reveals a high degree of overall consistency. Starting from the low-value region of the throat shape factor, the probability distributions of both the pore structures gradually increase, indicating a high occurrence frequency in the low to medium shape factor range. As the throat shape factor increases, the probability distributions of both pore structures peak and then begin to decline. This trend reflects that in the medium shape factor region, both real and synthetic pores tend to exhibit common throat morphologies. Although the peak probability distribution of the synthetic pores is slightly lower than that of the real pores, the overall peak regions are similar, demonstrating the high accuracy of synthetic pores in simulating the shape factors of real throats. In the high shape factor region, the probability distributions of both pore structures rapidly decrease, but the decline is more gradual for real pores, suggesting a certain proportion of throats with complex shapes in the real pores. Despite the steeper decline in the synthetic pores' distribution, the overall fit between the shape factor distributions of synthetic and real pores is high, indicating good performance of the segmentation model in simulating the shape factors of real pores. Overall, the shape factor distribution of the synthetic 3D pores shows a good fit with that of real 3D pores, especially in the medium shape factor range.
## 2 Materials and Methods
### Methods
#### 2.2.3 Statistical Analyses  
1. Pearson correlation analysis was used to determine the relationship between forest carbon allocation to leaves and the GPP at different timescales [19]. Statistical significance was set at \(p<0.05\). In the northern hemisphere, spring includes March, April, and May; summer includes June, July, and August; autumn includes September, October, and November; and winter includes December, January, and February. However, owing to the special drought conditions in Inner Mongolia, most areas did not turn green in March, and the forests entered the withering and yellowing stages in November. Therefore, this study only examined the correlation between carbon allocation and the GPP from April to October for the corresponding month.
2. Random forest (RF) This study used the random forest (RF) method to analyze the importance of six factors on carbon allocation to leaves in different time periods before the occurrence of the growth peak from 2001 to 2021. The RF method is both a supervised machine learning algorithm and an ensemble algorithm that builds and combines multiple decision trees to create a \"forest\" to obtain more accurate and stable results than that obtained using a single tree [20]. RF extracts observation samples from the training set, replaces them with bootstrap samples, randomly selects a feature subset in the tree model to form a collection of tree models, and combines the results by voting (classification) or averaging (regression) [21]. Therefore, RF can reduce the overfitting of individual trees and correlations between trees. In this study, RF was used for the regression, and it was implemented using the \"randomForest\" package [22] in R.
3. Structural equation model To further analyze the direct and indirect effects of environmental factors on forest carbon allocation to leaves at different time scales, the \"lavaan\" package in R version 4.1.3 software was used to construct the structural equation model (SEM) [23; 24]. The SEM adopts the maximum likelihood estimation method. The chi-square (\(\chi^{2}\)) test was used to evaluate the fitness of the model, that is, whether \(p>0.05\), the standardized root-mean-square residual (RMSEA) \(<0.05\), and the relative fit index (SRMR) \(<0.05\), and goodness-of-fit index (GFI) \(>0.95\), to evaluate the degree of model fit [25].
## 2 Problem Formulation of Topology Optimization of a PMPIC  
### Basic Configuration of a PMPIC  
A schematic diagram of a PMPIC is shown in Figure 1. A thin rectangular plate is used as the thin plate, which can be decomposed into periodic unit cells. In each unit cell, a hole is cut off and then filled by a piezoelectric patch that supports one or multiple small square masses. The piezoelectric patch and the masses form a local resonator. The top and bottom electrodes of the piezoelectric patch are connected to an interface circuit for electric power conversion. Under excitations, transverse vibrations of the PMP will propagate along the plane of the thin plate. Due to the existence of local resonators, vibrations within specific frequency bands cannot propagate through the periodic cells, leading to locally resonant bandgaps. In this case, vibration energies will be localized in some unit cells, which then can be converted into electricity through the piezoelectric patch.  
According to the bandgap theory, the band structure characteristics of the PMPIC are strongly dependent on the topology of the unit cell. Key components of each unit cell include the thin plate, the piezoelectric patch, the masses, and the interface circuit. Thus we need to optimize the topology of the unit cell to meet the band requirements of the PMPIC in practice, including material type, size, and shape, etc.  
Figure 1: Schematic diagram of a PMPIC.
## 4 Simulations and Results
### Security Features of Proposed System
#### 4.6.2 Privacy Preservation  
In our proposed system, crypto ID is used for communication between the vehicles. The real identification number of the vehicle is not used as its identity, because it leads to a privacy leakage problem. Hence, the privacy of the proposed system is preserved using the crypto IDs. All the transactions in VN are in encrypted form, which ensures data privacy. Therefore, our proposed system preserves the privacy of both data and user's identity.