esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Theory
### Wavelet Analysis Features  
The second feature extraction technique is wavelet transform [27], classified as time-frequency analysis features. These are highly effective in EEG data analysis, as they break down signals into various frequency components while preserving temporal information. In this study, we used the discrete wavelet transform (DWT) to derive features from our EEG dataset. Our approach emphasizes calculating the mean absolute value of wavelet coefficients at each decomposition level, which reflects the average magnitude of these coefficients and serves as an indicator of the signal's energy, as shown in Equation (10):  
\[\mu_{t,c}^{(j)}=\frac{1}{N_{j}}\ \ \sum_{k=1}^{N_{j}}{\left|c_{t,c}^{(j)}[k]\right|} \tag{10}\]  
where the following apply:  
* \(\mu_{t,c}^{(j)}\) is the mean absolute value at level \(j\) for channel \(c\) in trial \(t\);
* \(N_{j}\) is the total number of coefficients at level \(j\);
* \(\left|c_{t,c}^{(j)}[k]\right|\) denotes the absolute value of the \(k-th\) coefficient at level \(j\).
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment
### Data Sets  
In the prediction of the trajectory of ocean floating objects, the trajectory of ocean currents is an important component of the prediction, and for some small and low-wind resistance objects, we approximate their drift trajectory as the trajectory of ocean currents in the current sea area when the uncertain factors of the objects themselves are unknown. Therefore, we decided to first use a model to predict the trajectory of ocean currents in a specific sea area. In order to demonstrate the model's generalization ability, we fine-tuned the model to predict the drift trajectories of relevant AUVs. The ocean current data is from the ship-mounted ADCP dataset of scientific investigation over the South China Sea [40], while the AUV data is from the BCO-DMO website [41].  
For the ocean current data, since the daily variations are small and the number of data points per day is inconsistent in the dataset, we applied the median aggregation method during data preprocessing to merge multiple data points from the same day into a single data point. Using the median allows us to capture the central position of the ocean current trajectories for a given day, minimizing the impact of outliers. This approach helps eliminate the interference of anomalous ocean current data, provides a more accurate depiction of the central position and primary flow direction of the currents, and facilitates the identification of underlying periodic patterns in the data.  
The ocean current data consists of ten features: date, time, longitude, latitude, observation layer, east component, north component, flow speed, flow direction, and probe temperature. We conducted a correlation analysis between each column of the dataset and the target variable to select the most relevant features. The columns were sorted in descending order based on the absolute values of their correlation coefficients. We then chose the top six variables with the highest correlation and used them for training.  
During data preprocessing, we employed linear interpolation to fill in missing values, removed outliers, and standardized the data by subtracting the mean and dividing by the standard deviation to achieve a mean of 0 and a standard deviation of 1. We also examined the periodicity of the data and tested for autocorrelation periods. A similar approach was applied to the processing of AUV data.
# Experimental Study on Grouting Visualization of Cover Layer Based on Transparent Soil
###### Abstract  
Grouting, as a widely applicable and versatile foundation treatment technology, plays a crucial role in addressing seepage control problems in cover layers due to its flexibility and convenience. The effectiveness of grouting largely depends on slurry diffusion; however, due to the opaque nature of geotechnical media, the diffusion mechanism of slurry in the cover layers remains insufficiently understood. To investigate this, a visual grouting model device was designed and fabricated, and grouting tests were conducted using transparent soil materials to simulate the cover layers. The slurry diffusion patterns and the velocity field within the transparent soil were analyzed. The results show that, based on refractive-index matching, fused quartz sand of specific gradation and white mineral oil were selected as simulation materials for the cover layers. A stable slurry suitable for transparent grouting was also chosen to satisfy visualization requirements. The transparent soil grouting model, integrated with a Digital Image Correlation (DIC) monitoring system, has the advantages of demonstrating simple operation, real-time monitoring, and high precision. These tests verify the feasibility of visualizing slurry diffusion in cover layers. Furthermore, step-pressure grouting tests preliminarily reveal the dynamic mechanism of slurry diffusion. The results suggest that, in the cover layer, the cover layer in this grouting test is mainly splitting grouting, accompanied by compaction grouting. These methods offer new insights and methods for model testing of cover layer grouting mechanisms.  
cover layers grouting; transparent soil test grouting visualization; digital image correlation technology +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of ECAP on Erosion-Corrosion at Different Test Durations  
The weight loss per unit area due to E-C of copper with respect to ECAP passes at different test durations is shown in Figure 6. The tests were conducted at durations of 12 h, 24 h, and 48 h at constant conditions: Velocity of 3.8 m/s, impacting angle 45\({}^{\circ}\), and sand slurry in water 20 wt.%. The results revealed that the experimental time has a substantial effect on E-C of copper at different ECAP passes. For annealed copper samples (pass 0), the weight losses were \(\sim\)3.8 \(\times\) 10\({}^{-6}\) m/mm\({}^{2}\), 6.7 \(\times\) 10\({}^{-6}\) g/mm\({}^{2}\), and 25.5 \(\times\) 10\({}^{-6}\) g/mm\({}^{2}\) at durations of 12, 24, and 48 h, respectively.  
Figure 5: Hardness measurement along cross section versus ECAP passes at different E-C velocities (\(t\) = 48 h, impacting angle = 45\({}^{\circ}\), 3.5% NaCl, and sand concentration = 20 wt%).  
Figure 6: Variation of weight loss with ECAP passes at different times. (\(v\) = 3.8 m/s, impacting angle = 45\({}^{\circ}\), 3.5% NaCl, and sand concentration = 20 wt%).  
Figure 6 shows no significant difference in the weight loss for 12 and 24 h tests while a noticeable difference was observed for the 48 h test (16.8 \(\times\) 10\({}^{-6}\) m/mm\({}^{2}\)). This corresponds to a 34% rise in resistance to E-C after four ECAP passes. The hardness of the annealed copper specimens before ECAP was 51 HV. After first, second, third, and fourth ECAP passes, the hardness increased to 110 HV, 130 HV, 131 HV, and 152 HV, respectively. The ECAP introduces surface compressive stresses that increases the incubation period of the material. The incubation period is the time needed for the initiation of new grains and generation of high-angle boundaries so that the new grains appear and can be observed [40]. The increase in hardness increases the resistance to solid particles penetration. Therefore, a lower E-C rate is obtained. Additionally, the materials with high work hardening capacity and low stacking energy were found to be useful for E-C resistance. Low stacking fault energy implies a wide separation of dislocations to cross-slipping. This difficulty interprets the relatively lower recovery rates, which results in lower erosion rates [41; 42; 43].
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 5 Case Studies
### Scenario 2
Figure 13 shows the distribution of ModCods within the service area for VCM. Availability can be increased up to 99.9%, meeting 100% SAP compliance by using four different ModCods in the service area (64 APSK 11/15, 16 APSK 32/45-L, 32 APSK 11/15, and 16 APSK 3/5). For CCM, all evaluated ModCods have an SAP below 98% for a link availability of 99.9% except 16 APSK 3/5, which is 100% SAP compliant (Figure 9). In a VCM scheme for 99.9%, there are geographical coordinates where a ModCod 64 APSK 11/15 can be used, although there are also coordinates that use 16 APSK 3/5 (Figure 10). Comparing CCM and VCM for scenario 2, a satellite capacity of 51.54 Gbps using CCM and 78.16 Gbps using VCM are achieved. Figure 12: Service Area Percentage for different ModCods (CCM). Scenario 2 (87 beams and a user \(G\)/\(T\) of 17 dB/K). Figure 13: VCM map (DVB-S2X), scenario 2, 87 beams, and a user \(G\)/\(T\) of 17 dB/K (availability 99.9%).
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 3 State-of-the-Art Review
### Attacks and Defense Strategies  
In IoT applications such as smart campus, attacks need to be anticipated since this environment is serving the campus community, depending on a wide range of technologies and types of equipment. This normally includes several unsecured devices, systems and applications that communicate information via insecure media and use weak protocols such as HTTP, FTP, telnet [62]. Attacks are activities taken to harm a system or disturb ordinary tasks by exploiting vulnerabilities using different techniques and tools. Attackers launch attacks to accomplish objectives either for individual realization or rewards [45]. An attack could be presented in numerous structures, including network attacks to monitor unencrypted traffic in pursuit of sensitive data; passive attacks, for example, monitoring unprotected network communications to decrypt weakly encrypted traffic and getting authentication data; close-in attacks; exploitation by the users of the system [45]. The attackers can make use of these weaknesses to gain access to the systems, swipe sensitive data, and acquire confidential information for later manipulation [63]. Malicious entities can also harm the devices and stop the functionality of the services [62]. In this document, attacks will be classified into five distinct categories, cf. Table 3.  
The attacks presented in Table 3 could be performed in LPWANs. Regarding this defined set, some mitigation and defense strategies are presented focusing on the previously described attacks. Some of the countermeasures require short modifications on the firmware or the way some technologies, for example, LoRaWAN, transceivers are integrated into an IoT device. Others require modifications to the standard to mitigate the attack vector at the beginning of the problem.
## 2 Previous Work
Linear prediction (LP) algorithms are considered as an important member of statistics and mathematics and, in this case, the spectrum prediction and future values are predicted as a linear function of previous samples. LPs are used in digital signal processing (DSP) due to their remarkable simplicity. They have been implemented for the prediction of the spectrum in the time domain. Some common models for linear prediction models include the auto-regressive (AR) model, the mobile average (MA) model, the model of auto-regressive moving average (ARMA) and the auto-regressive integrated moving average model (ARIMA) [13; 14]. The Bayesian interference (BIF) is another approach that been a classic prediction method in CRN. It has been implemented as a part of the Markov system. The scheme can be defined as a derived probability distribution of the system. It is calculated as the posterior probability distribution according to the Bayes rule, from the prior probability distribution and the likelihood function. The problem is to specify a prior probability distribution because it has a significant influence on the complexity of the subsequent computation [13]. A machine learning framework is proposed for primary user emulation attack detection in cognitive radio networks in [15]. It is based on classification models that exploit features extracted using the proposed pattern-described link signature method (PDLS) from the channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM)-based transceivers [15]. The particle swarm optimization (PSO) algorithm has been applied to the hyper-parameter optimization of the SVM to establish a prediction model for the heat exchanger. A support vector regression (SVR) model is also used to achieve the online prediction of thermal resistance denoising for low-temperature superheaters, showing a prediction accuracy above 98% without adding expensive devices to the system by predicting the results with the SVR with 80% of the samples used for training and 20% for testing [16]. In general, machine learning techniques, if trained correctly, can be a good approach to minimize the delay of the detection and easily use complex models. They use features such as energy statistics, the probability vector of occupancy over time as an input to the model, and propose a classification problem to find the optimal decision if there is a PU, an attacker, or noise [17].
## 1 Introduction
### Related Works and Motivation
As it is crucially important for the theoretical research and application of multiple HAP networks, we will provide a theoretical analysis of diversity receiving utilizing V-MIMO techniques in the multiple HAP networks within this paper. The simulation will further prove that the proposed model can improve the quality of communication and reduce the fading margin.
## 5 Discussion
### Measurement of 28 GHz Band
The dielectric elastomer developed by [PERSON] and [PERSON] uses this system to disperse the SWCNTs more evenly, resulting in a uniform thickness. It has become possible to show growth. In addition to the above, it is considered that a higher radio wave absorption effect can be obtained by realizing a CNT spray paint specializing in the radio wave absorption effect by adjusting the CNT dispersion conditions, the types of binders, the compounding ratios, etc. Furthermore, in order to reduce the cost, it is considered that the shielding characteristics can be controlled by adding another carbon-based material or changing the coating thicknesses. As another improvement method, a method of devising a three-dimensional structure, such as laminating the same components as the sample material, can be considered. Alternatively, a porous material can be used as the base material to be applied, and an effervescent material can be added to the binder of the CNT spray paint. Then, it seems that a three-dimensional structure will be possible without stacking, and it will be possible to improve the radio wave absorption effect more simply. Currently, it is being verified that the reflected wave can be reduced and the radio wave absorption effect can be improved by combining the CNT spray coating film and the space in the radio wave propagation path. As a trial test, in order to simply verify the idea above, we created a sample using our CNT spray method and a highly foamable polypropylene with a thickness of 3 mm. The coating thickness at the measurement point was about 18 \(\upmu\)m. Figure 21 shows a screen copy during measurement. Even with a small amount of coating, the amount of radio wave absorption was improved by about 2.5 dB.
## 5 Conclusions  
This paper presents a new security communication model using Whirlpool instead of SHA-1 to protect communication of PCs and field devices in industrial control networks. Through establishing secure channels between the ends of communication, this model can provide integrity and authentication with a higher level of security and considerable efficiency, basically meeting the requirement of security and real-time modeling in industrial control networks. In the future this security profile can be included into OPC UA specification. With the increment of terminal calculation ability in the future, symmetric encryption algorithms with a higher efficiency can be integrated to this security profile to implement confidentiality, integrity and authentication of messages completely transmitted in networks.
## 3 Artificial Potential Field Method  
### Traditional Artificial Potential Field Method  
The traditional artificial potential field method assumes that there is a virtual repulsion field around the obstacles, and there is also a virtual attraction field around the target point. The robot is affected by the attraction field to approach the target point, then avoids the obstacles under the repulsion field, and finally reaches the target point without any collisions.  
The functions of the attraction field \(U_{att}(q)\), repulsive field \(U_{rep}(q)\) and resultant field \(U(q)\) are denoted as follows:  
\[U(q) =U_{att}(q)+U_{rep}(q) \tag{10}\] \[U_{att}(q) =\frac{1}{2}k_{att}\left(q-q_{g}\right)^{2}\] (11) \[U_{rep}(q) =\left\{\begin{array}{c}0,\ \ \ \rho(q)\ \geq\ \rho_{0}\\ \frac{1}{2}k_{rep}\Big{(}\frac{1}{\rho(q)}-\frac{1}{\rho_{0}}\Big{)}^{2},\ \rho(q)<\rho_{0}\end{array}\right.\] (12) \[\rho(q) =||q-q_{0}|| \tag{13}\]  
where \(k_{att}\) and \(k_{rep}\) are the attraction coefficient and repulsive coefficient, respectively, \(q\) is the current position of the robot, \(q_{o}\) is the position of the obstacle, \(q_{g}\) is the position of the target point, and \(\rho_{0}\) is the influence distance of the obstacle. When the distance between the obstacle and the robot is greater than \(\rho_{0}\), the robot cannot be affected.  
By deriving the distance from the above Equations (10)-(12), we can get the attractive force \(F_{att}(q)\), repulsion force \(F_{rep}(q)\) and resultant force \(F(q)\). Their functions are represented as follows:  
\[F(q) =F_{att}(q)+F_{rep}(q) \tag{14}\] \[F_{att}(q) =-\bigtriangledown U_{att}(q)=-k_{att}(q-q_{g})\] (15) \[F_{rep}(q) =\left\{\begin{array}{c}0,\ \rho(q)\ \geq\ \rho_{0}\\ k_{rep}\Big{(}\frac{1}{\rho_{0}}-\frac{1}{\rho(q)}\Big{)}(\frac{q-q_{o}}{\rho ^{3}(q)}),\rho(q)<\rho_{0}\end{array}\right. \tag{16}\]
# Compact Bistatic Iterative Passive Radar Based on Terrestrial Digital Video Broadcasting Signals
## 3 Proposal Description
### Calibration Process
Interestingly, the impact on the error on each coordinate is being balanced between \(\Delta_{0}\) and \(\Delta_{z}\). This is, when the effect of \(\Delta_{0}\) is positive, the one in \(\Delta_{z}\) is negative, so they can be compensated each other for some values. Looking at the results, the impact on coordinates \(T_{x},T_{y}\) and \(T_{z}\) is \(0.13137\Delta_{0}-0.07291\Delta_{z}\), \(-0.026269\Delta_{0}+0.014579\Delta_{z}\) and \(0.65646\Delta_{0}-0.3643\Delta_{z}\), respectively. Finally, if those error are smaller than 1 m, what is easy to obtain, the error in the final estimation is negligible. Figure 4: Error on the three coordinates for different \(\Delta_{0}\) when \(\Delta_{z}=0\). \(\Delta_{0}\) in meters. After this initial calibration, the system is ready for operation, although a few more steps are needed to complete its setup. The first step is to move in the \(z\) axis the position of each antenna on the \(z\) axis so that each one has a different height \(Rz_{i}\) (as explained earlier). This step will prevent the occurrence of a rank-deficient matrix in the estimation process. After these easy and simple steps, the receiver is initialized and ready for estimation.
## 2 Related Work
### Graph Neural Network Methods
#### 2.2.3 Hypergraph Methods  
In the hypergraph domain, methods like Hypergraph Convolutional Networks (HyperGCNs) [61], Hypergraph Networks with Hyperedge Neurons (HNHNs) [62], and Hypergraph Neural Networks (HGNNs) [63] rely on hypergraph Laplacian operators based on GCN. The HyperGCN method treats the learning problem as an approximation of graph learning by pairing the vertices connected by hyperedges to approximate the hyperedges in the hypergraph. Other hypergraph models, such as the Universal Graph Neural Network (UniGNN) [64], extend several GNN models to hypergraphs and propose a unified framework for explaining message-passing methods in Graph and Hypergraph Neural Networks. AllSet [65] is also the latest unified framework for Hypergraph Neural Networks, implementing Hypergraph Neural Network layers as a combination of two multi-set functions: AllDeepSets and AllSetTransformer. However, all of these hypergraph models ignore the heterogeneous semantic information in hypergraphs, which is one of the reasons for our research on GEVEHGAN.  
Although existing Graph Neural Network methods have made progress in handling heterogeneous information in LBSNs, they still face challenges in integrating spatiotemporal trajectory information and addressing high-dimensional noise. Therefore, this paper combines the Lite-GRU module to learn user mobility trajectory information, effectively capturing the dynamic changes in check-in trajectories in LBSNs, making it particularly suitable for modeling spatiotemporal features. Additionally, the VAE module is introduced to leverage the dimensionality reduction capabilities of Variational Autoencoders, effectively reducing high-dimensional noise in LBSN data, thereby improving the usability and robustness of the data.
# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 3 Results and Discussions
### Interpretation of Modeling Results of Dried Alfalfa
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Plant Type**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Criteria**} \\ \cline{3-6}  & & **RMSE** & **MAPE** & **MAE** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{Dried} & Extra Trees & **0.0171** & **0.0969** & **0.0099** & **0.9853** \\  & CatBoost & 0.0174 & 0.1068 & 0.0105 & 0.9838 \\  & Gradient Boosting & 0.0265 & 0.1936 & 0.0178 & 0.9624 \\  & Random Forest & 0.0306 & 0.2163 & 0.0191 & 0.9499 \\  & Extreme Gradient Boosting & 0.0223 & 0.1224 & 0.0124 & 0.9736 \\ \hline \multirow{4}{*}{Green} & Extra Trees & 0.0707 & 0.1604 & **0.0340** & 0.9472 \\  & CatBoost & 0.0850 & 0.1806 & 0.0387 & 0.9239 \\ \cline{1-1}  & Gradient Boosting & 0.1194 & **0.1447** & 0.0621 & 0.8497 \\ \cline{1-1}  & Random Forest & **0.0616** & 0.2135 & 0.0363 & **0.9600** \\ \cline{1-1}  & Extreme Gradient Boosting & 0.1026 & 0.1750 & 0.0542 & 0.8889 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the machine learning models. models obtained similar results. The differences between the models were negligible. Therefore, it can be said that all the models achieved successful results in the MAE metric. We can also make this comment for the RMSE and R\({}^{2}\). In the RMSE and R\({}^{2}\) metrics, the model established with the ET method was more successful with small differences compared to the models established with other methods. Although the model established with the ET method was slightly more successful than the models established with other methods, the models established with the ET, CB, and XGB methods had similar values. It can be said that these models were more successful compared to the models established with the RF and GB methods in the MAPE metric.