esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Stationary Wind Fields
#### 5.1.2 Ergodic Stationary Wind Fields  
Based on the double-index frequency, \(\omega_{kl}\), the ergodic stationary wind field can be simulated. The time duration of generated samples adopts a period, i.e., 41,984 s. The other time and frequency parameters are the same as the last example. The first 1024 s of generated ergodic wind fluctuation samples at Points 1, 21, and 41 are displayed in Figure 5, which comparing the samples obtained by two phase schemes under the same random phase. Obviously, the influence of the phase on the simulated ergodic sample is similar to that of the non-ergodic stationary simulation. Therefore, using the classical phase formula to simulate the ergodic stationary wind field will still cause errors.  
Figure 4: Estimated correlation functions by nonergodic stationary wind fluctuation samples (1000 samples): (**a**) \(R_{21,21}(\tau)\); (**b**) \(R_{1,21}(\tau)\).  
Figure 5: Simulated ergodic stationary wind fluctuation samples (first 1024 s): (**a**) Point 1; (**b**) Point 21; (**c**) Point 41.  
Based on Equation (24), the temporal auto-/cross-correlation functions of the simulated ergodic samples can be estimated. Figure 6 shows the comparison of two typical correlation functions obtained by two phase schemes with the corresponding targets, where the targets are also calculated by Equation (22). The same phenomenon as the above non-ergodic simulation can be observed in this ergodic simulation. Thus, the classical phase formula will also lead to the incorrect simulation of ergodic wind fields.
# MOCA: A Network Intrusion Monitoring and Classification System
## 5 Measurement Results
### Multi-Class Classification Accuracy  
Decision Tree (DT), Random Forest (RF), and XGBoost are used as multi-class classifiers. This paper presents the measurement results from an XGBoost model because it has better performance than other models. Figure 2 shows the performance of the classifier trained by top-k selected features. The top-k features are selected by Algorithm 1. The classifier trained using top-5 and top-3 features from both datasets had a very high detection rate. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**CICIDS2017**} & \multicolumn{4}{c}{**CICDDOS2019**} \\ \hline
**Selected Features** & **Accuracy** & **Prec** & **Rec** & **F1-Score** & **Accuracy** & **Prec** & **Rec** & **F1-Score** \\ \hline Top-20 F w/CF & **1.000000** & 0.999952 & 0.999952 & 0.999952 & **1.000000** & 0.999987 & 0.999987 \\ Top-10 F w/CF & 0.996000 & 0.995979 & 0.995978 & 0.995978 & **1.000000** & **1.000000** & **1.000000** \\ Top-5 F w/ 3 CF & 0.989400 & 0.989414 & 0.989368 & 0.989361 & 0.999800 & 0.999796 & 0.999796 \\ Top-3 CF & 0.951900 & 0.951822 & 0.951863 & 0.951871 & 0.999100 & 0.999060 & 0.999055 & 0.999056 \\ \hline Top-20 BF & 0.999800 & 0.999846 & 0.999846 & 0.999846 & **1.000000** & 0.999974 & 0.999974 & 0.999974 \\ Top-10 BF & 0.991200 & 0.991227 & 0.991209 & 0.991211 & **1.000000** & 0.999883 & 0.999883 \\ Top-5 BF & 0.970/00 & 0.971417 & 0.970/745 & 0.970/795 & 0.986600 & 0.986546 & 0.986613 & 0.986345 \\ Top-3 BF & 0.915100 & 0.918395 & 0.915075 & 0.914359 & 0.982900 & 0.982693 & 0.982857 & 0.982448 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of binary classifier trained with different selected features. The classification results for 14 classes in the CICIDS2017 dataset are shown in Table 2. The last three entries in Table 2 are the three attacks involving multiple connections. The results clearly show that MOCA can detect all three attacks, which is better than previous work [2]. In [2], a Random Forest model was trained by ten basic features. We found that MOCA can detect the majority of imbalanced web attacks. On the contrary, the model in [2] could only detect a few attacks. The results shows that MOCA improves performance over other methods, especially for imbalanced attack classes. Most previous works [27] showed poor performance in detecting the web attacks in the CICIDS2017 dataset due to the imbalanced data based on the nature of different web attack behaviors. The SMOTE method [28] has been applied to address the issue of imbalance by oversampling the imbalanced samples in the training dataset. MOCA presents very high detection accuracy in all three web attack classes without using the SMOTE method. Table 3 shows the performance of MOCA in detecting 13 classes in the CICDDOS2019 dataset. Similarly, using three connection features to train the multi-class classifier can identify more attack classes than can a Random Forest model trained with all basic features.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 4 Experiment and Simulation Analysis
### Model Validation and Calibration
Therefore, this paper used the number of LoRa wireless transmission groups to analyze the relationship between the maximum delay and bottleneck energy of the system. As shown in Figure 7, when the number of packets was 0, there was no cellular mobile transmission mode in the network, and the whole network used LoRa to transmit data directly. Transmission towers No. 50 and No. 51, which are the farthest away from the substation supervisory control and data acquisition (SCADA) transmission system, have the longest transmission distance, so their delay was the maximum system delay. The No. 1 transmission tower nearest to the substation SCADA transmission system needs to transmit the most data, so the energy consumed is the bottleneck energy of the system. When the number of LoRa wireless transmission groups is greater than 1, a cellular transmission mode is added in the network, so the system delay is significantly reduced, but the bottleneck energy of the system is greatly increased. As the number of LoRa wireless packets continues to increase, the decline trend of the maximum delay and bottleneck energy gradually decreases, and it may reach a stable value in the future. According to the above analysis, when the system only uses the LoRa transmission mode, it can achieve the minimum system energy consumption; when using cellular mobile transmission and LoRa transmission, it can achieve the minimum system delay.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
###### Abstract  
This paper presents Z-OFDM, a high-performance solution for underwater acoustic communication. Traditional underwater orthogonal frequency division multiplexing (OFDM) systems suffer from spectrum leakage and distortion due to the narrowband nature of underwater acoustic signals and the picket fence effect of the fast Fourier transform (FFT). Z-OFDM addresses these issues by integrating zoom-fast Fourier transform (ZoomFFT) with OFDM and redesigning the modulator and demodulator to replace the conventional FFT. This integration enhances spectral resolution, resulting in higher channel capacity, improved Signal to Interference plus Noise Ratio (SINR), and reduced Bit Error Rate (BER). Computer simulations using underwater acoustic channels from Fuxian Lake and Wuyuan Bay demonstrate that the Z-OFDM system achieves a 6 dB gain compared to conventional OFDM systems at a BER of \(10^{-3}\). These results demonstrate the effectiveness of Z-OFDM in overcoming the limitations of traditional FFT-based OFDM systems in underwater environments.  
O 2024, 13, 3543. [[https://doi.org/10.3390/electronics13173543](https://doi.org/10.3390/electronics13173543)]([https://doi.org/10.3390/electronics13173543](https://doi.org/10.3390/electronics13173543))
# Surface Functionalization of Polyethylene by Silicon Nitride Laser Cladding
## 4 Discussion  
Surface analyses showed that laser cladding could be suitable for producing a Si\({}_{3}\)N\({}_{4}\) coating on the PE surface. Compared with prior studies using a Si\({}_{3}\)N\({}_{4}\) coating on a Ti-alloy [24], the ceramic powders did not undergo chemical decomposition to form free Si. This might be due to the presence of a \"soft\" polymeric matrix that melted at relatively low power densities.  
The presence of stoichiometric Si\({}_{3}\)N\({}_{4}\) particles resulted in an active antibacterial effect. Previous work showed how Si\({}_{3}\)N\({}_{4}\) in an aqueous environment generates ammonia that damages bacterial cells [25; 26; 27]. When compared to previous data, the antibacterial effect seems to act faster in the laser cladded coating, probably due to increased surface area.  
Biological assays confirmed that the Si\({}_{3}\)N\({}_{4}\) powders reduced the proliferation of bacteria on the PE coated surface as compared to bulk PE, especially after long-term exposure. Fluorescence microscopy analyses also confirmed the antibacterial effect of the Si\({}_{3}\)N\({}_{4}\) coating. Additionally, the N and Si species released from Si\({}_{3}\)N\({}_{4}\) enhanced cell proliferation and bone tissue formation [28; 29; 30].  
In summary, the Si\({}_{3}\)N\({}_{4}\) laser-cladded coating process was found to be capable of transferring the beneficial biological properties of bulk Si\({}_{3}\)N\({}_{4}\) to PE.  
Figure 10: Fluorescence micrographs of the investigated samples after exposure to SaOS-2 cells. [PERSON] and red stain marked osteoclast and osteepontin, respectively, while the blue stain located the cells’ nuclei.
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars
### Automatic Monitoring Process
#### 5.4.2 Displacement Numerical Processing  
The initial positioning distance of the monitoring point by the laser rangefinder was \(L_{1}\). The small rotation angle generated by tracking the displacement of the measuring point through the radar feedback input was \(\alpha\). After moving, the laser rangefinder can obtain the distance \(L_{2}\) from the new location of the measuring point. After the above parameters were obtained, they were remotely transmitted to the computer for data processing through the unlimited connection device set on site. The actual displacement of the monitoring point is calculated through the cosine theorem of the trigonometric function, as shown in Formula (1), where \(L_{3}\) is the displacement of the monitoring point. Figure 11 shows the above processing calculation.  
\[L_{3}=\sqrt{L_{1}{}^{2}+L_{2}{}^{2}-2\times L_{1}\times L_{2}\times\cos\alpha} \tag{1}\]  
The vertical displacement of the main tunnel vault \(V_{1}\), the ramp tunnel vault \(V_{2}\), the horizontal displacement of the main tunnel left arch waist \(H_{3}\), the horizontal displacement of the main tunnel right arch waist \(H_{4}\), the horizontal displacement of the ramp tunnel right arch waist \(H_{5}\), and the horizontal displacement of the ramp tunnel left arch waist \(H_{6}\) were calculated using Formula (1).
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 5 Hybrid BESSA Technique  
As mentioned in Section 1, the hybridization of optimization techniques ([PERSON] et al., 2018) is one of the most popular and efficient methods for improving upon them. In ([PERSON] et al., 2018), it is explained that two algorithms can be fused together and hybridized to evolve into a novel algorithm at a high level or at a low level with co-evolutionary methods or relays. The idea here is to combine the algorithmic strength of both the BES and SA, which includes the exploitation capability of SA and the exploration capability of the BES in a population-based Hybrid BESSA algorithm to arrive at different optimal parameter values required for the modeling of solar photovoltaic modules.  
The proposed Hybrid BESSA technique hybridized the local search Simulated Annealing (SA) Technique and the global search BES technique. The metaheuristic algorithm reaches a final stage depending upon its initial population.  
In the proposed BESSA technique, in the first step, the BES is implemented to obtain an initial solution, then SA is implemented to arrive at an ultimate solution. This process embodies the hybridization of a global search with local searches. In this process of hybridizing the BES and SA, the capability of social thinking, i.e., finding the best solution, is merged with the local search capability of SA. The task of the global search is conducted with the BES technique and the local search is conducted with the SA technique. Here, the BES technique uses a random selection process to choose the initial solutions. The improvement in the final solution results from the application of SA. Thus, this process leads to a better, more accurate, and finer search for final solutions.
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 4 Analysis and Prospects
### Measurement Technology of Charged Droplets  
Droplet charge to mass ratio is a term associated with aerial electrostatic spray technology, and the measurement results of charge to mass ratio provide a reference for the evaluation of electrostatic spray system performance. In the absence of interference (e.g., indoors), the droplet charge to mass ratio is positively correlated with the deposition effect [49]. However, when working in an outdoor environment, there is usually a big difference between the droplet charge to mass ratios at the nozzle end and the terminal target, which requires the terminal measuring device to evaluate the charge amount. However, due to the absence of electrical grounding, the device that collects charged droplets often results in an inconsistent result with the actual effect of settling on the target [50]. For the electrostatic spray technology, the electric field intensity varies with the plant form, liquid conductivity, and environmental factors. For example, the electric field near the tip or terminal part of the leaf is the strongest [51]. [PERSON] [52] reported that gaseous discharges between sharp leaf tips and incoming charged spray clouds had been shown to limit deposition.  
In many studies, aerial electrostatic spray had a better deposition effect compared with conventional electrostatic spray and non-electrostatic spray, but there are not a few reports that the effect of electrostatic spray is not satisfactory [1; 49; 53]. However, it remains to be further confirmed whether the difference in operation effect is caused by a sharp reduction in the charge to mass ratio of charge droplets, faulty experimental design scheme, or environmental influence. In recent years, more detection methods such as laser particle size analyzer have been continuously applied to the measurement of droplets effect in  
Figure 6: Key milestones and research groups working on aerial electrostatic spray technology.  
China [54, 55]. However, there are many problems such as high manufacturing cost, large measurement error, and complex measurement process. Therefore, it is necessary to develop a droplet characteristic detection technology suitable for aerial electrostatic spray systems to monitor the real state of charged droplets.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### Chernobyl (CIH Virus)  
The CIH, also known as the Chernobyl Virus, emerged in 1998 as the first known hardware-targeting attack. CIH has the capability to overwrite system drive data and even attempt to flash the BIOS, rendering machines non-functional. This malware primarily targets PCs with the intent of direct hardware damage. The capacity to flash the BIOS is one of the unique approaches to causing PDoS and makes this the first known PDoS malware.
## 2 The Principle of INS-Aided Acquisition  
### Characteristics of GPS L1 Frequency and BDS B1 Frequency Signals Modulation  
The C/A and P(Y) signals in the GPS L1 frequency can be written as [2]:  
\[S_{\text{L1}}(t)=A_{\text{C}}C(t)D(t)\sin(2\pi f_{\text{L1}}t+\phi_{\text{L1}} )+A_{\text{P}}Y(t)D(t)\cos(2\pi f_{\text{L1}}t+\phi_{\text{L1}}) \tag{1}\]  
where \(S_{\text{L1}}(t)\) is the signal at L1 frequency, \(A_{\text{C}}\) is the amplitude of the C/A code, \(C(t)\) represents the phase of the C/A code, \(f_{\text{L1}}\) is the L1 frequency, \(\phi_{\text{L1}}\) is the initial phase, \(A_{\text{P}}\) is the amplitude of the P(Y) code, \(Y(t)\) represents the phase of the P(Y) code, \(D(t)\) represents the data code.  
The in-phase component (I) and the quadrature component (Q) signals in the BDS B1 frequency are also in quadrant phase of each other and they can be described as follows [18]:  
\[S_{\text{BI}}(t)=A_{\text{BII}}C_{\text{BII}}(t)D_{\text{BII}}(t)\cos(2\pi f_{ \text{B1}}t+\varphi_{\text{BII}})+A_{\text{B1Q}}C_{\text{B1Q}}(t)D_{\text{B1Q }}(t)\sin(2\pi f_{\text{B1}}t+\varphi_{\text{B1Q}}) \tag{2}\]  
where \(S_{\text{B1}}(t)\) is the signal at B1 frequency, \(A_{\text{B1I}}\) is the amplitude of the I component, \(C_{\text{BII}}(t)\) represents the phase of the I component, \(f_{\text{B1}}\) is the B1 frequency, \(\varphi_{\text{BII}}\) is the initial phase, \(A_{\text{B1Q}}\) is the amplitude of the Q component,\(C_{\text{B1Q}}(t)\) represents the phase of the Q component,\(D_{\text{B1Q}}(t)\) represents the data code.  
The BDS B1 frequency signal and the GPS L1 frequency signal have many similarities in the center frequency, signal modulation and signal characteristics. A comparison of the characteristics of the BDS B1 frequency signal and GPS L1 frequency signal is shown in Table 1, so the INS-aided acquisition can be verified in GPS/BDS dual-mode software receiver.
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 2 Methods
### Data Analysis  
The experimental results (joint angle, GRF, and EMG signal) and modelling results (joint moment and TF joint contact force) were analyzed. The dynamic Plug-in Gait model in the Vicon system was used to generate required joint angles. Data processing for the recorded markers, EMGs, and GRFs were based on previous study [34]. Raw marker data and GRFs were stored in.C3D file formats; they need to be converted to OpenSim file formats (.trc and.mot files) by a Matlab programme with zero-lag second-order low pass Butterworth filter at the cutoff frequencies of 6 Hz and 8 Hz respectively. Considering the delay of 312 ms, preprocessed EMGs of each muscle were normalized to the peak values, which were obtained from a series of gait trials and exported to.mot files. The processed marker trajectories and GRFs were imported into OpenSim software to compute joint moments and JRF of the TF joint using ID, OS, and JRF analysis sequentially. The compressive TF contact force was computed as a component of the resultant force acting on the tibia and parallel to the long axis of the tibia [17]. A complete gait cycle (from the right heel strike to the next right heel strike) of three gait patterns (natural, toe-in, and toe-out) was analyzed to obtain lower limb joint angle, muscle EMG, joint moment, TF, and JRF. The stance phase (right heel strike to right toe off) was analyzed to produce the GRF curves.  
One-way repeated measures of ANOVA were performed for maximum joint angles, GRFs, JRFs, and joint moment to detect statistically significant differences among the gait patterns; [PERSON]'s test was used to determine statistically significant difference between the gaits. Variables with statistically  
Figure 2: Schematic diagram for foot progression angle in the toe-out and toe-in gait patterns: L represents the left foot position, R the right foot position, and the arrow represents the forward direction while walking.  
significant differences are denoted by * or # in the paper. The alpha level was set to 0.05; all statistical analysis was carried out using OriginPro 9. Maximum joint angles, GRFs, JRFs, and joint moment were presented in tables as mean \(\pm\) SD (standard deviation) of the 12 participants with 5 trials of each gait pattern. However, mean \(\pm\) SD of peak EMG/activation of 5 trials for each muscle of an individual participant were presented in Appendix A due to large differences among 12 participants.
## 2 Related Work  
A few desktop virtual globes are freely downloadable for scientists and the general public for Mars exploration, including Mars in Google Earth [22], Microsoft's WorldWide Telescope (WWT) [23], and NASA World Wind from NASA AMES Research Center [24]. [PERSON] _et al._[25] gave a review on the technical developments and standards of the virtual globes. These tools give excellent global views of Mars from orbit. They also provide the capability to visit a specific region by clicking any point in the globe.  
However, the user might not identify detailed features at centimeter levels when one goes to a small local region, because of the relative lower resolution base maps being applied in these tools. NASA World Wind displays MGS Mars Orbiter Camera (MOC) Narrow Angle (NA) images with a resolution from 1.5 to 12 m/pixel on the globe. The best available orbital data with Mars in Google Earth and WWT are HiRISE images, which can see down to about 0.25 m per pixel [26]. Both MOC NA and HiRISE data lack a complete coverage on Mars. The pixelated texture or a blank scene requires adding other data such as ground images into the virtual globes. None of the tools seems to integrate the ground data with the orbital data to generate a high-resolution basemap. Some of the tools just provide options to pop-up image mosaics taken by the MER rovers. Others provide functions for users to place customized 2D Web Mapping Service (WMS) image overlays, but the task is non-trivial for general users without programming, cartography, GIS, and image processing background.  
Figure 1: View of the Virtual Astronaut is shown. Icons in the upper left are for selecting the tools’ functions. Santa Maria Crater is located at the center of the view. The image data is a combination of orbital HiRISE (High Resolution Imaging Science Experiment) data from Mars Reconnaissance Orbiter (MRO) mission and ground-based Panoramic Camera (Pancam) and Navigation Camera (Navcam) images acquired by Opportunity. The Opportunity Rover is located in the red circle in the figure.  
A number of projects have come up with the VR applications for small areas of Mars. These VRs can be used interactively via the web with network improvements. Exploration guides to Gale Crater, Spirit's Journey, and Curiosity's Journey [27] were developed as part of NASA/JPL/CalTech's outreach program. These tools are good introductions to Mars and the rover missions for the public. For example, Spirit's Journey tells the user what the Spirit Rover saw using pop-ups of rover images, how it drove, and how far it went. However, these tools lack high-resolution surface textures and detailed data and information needed for more-complete scientific studies.  
The above-mentioned VR systems are high-quality visualization tools for planetary image data sets. These tools tend to either focus on orbiter or ground-based data, but do not combine both types of data in the surface texture. This research will build a virtual environment by integrating the real orbital and landed mission data to provide users the context from various perspectives, including a rover's.