esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 MATLAB Simulation and Experimental Data Analysis
### Experimental Data Analysis  
The experimental data came from the tests on a lake at the xiannjiang experimental field of the institute of acoustics, Chinese Academy of Sciences in December 2018. AUV-\(II\) sensed the position of MRS by using the ultra short baseline (USBL) system. The detailed descriptions of the docking test are given in reference [24]. The Experimental results are shown in Figure 12.  
The actual PNCM of AUV-\(II\) is unknown, and the PNCM is given based on experience. We compare the performance of TT-EKF algorithm and adaptive TT-EKF algorithm with this PNCM. The original Experimental data analysis results are shown in Figures 13 and 14. Due to the sensor precision of AUV and the lake experimental environment, the measurement noise of AUV forward speed and heading is small. We add the same noise as the simulation to the input vector of AUV-II. The experimental data analysis results are shown in Figures 15 and 16.  
Figure 11: The real trajectory and estimated trajectory of AUV and MRS.  
Figure 12: Trajectories of AUV-\(II\) and MRS recorded by IMU in AUV-\(II\) homing to MRS.  
The representation of each color line in Figures 13-16 is the same as the simulation. From the MATLAB simulation results and Experimental data analysis results, we can know that:  
1. As the PNCM is unknown, we assumed the PNCM is \(\mathbf{Q}_{1}\), which is inaccurate. The contrast between the red lines and the blue lines indicates that the inaccurate process noise statistics leads to the degradation of the state estimation performance of TT-EKF.
2. The contrast between the red lines and the green lines indicates that the state estimation performance of TT-EKF has been improved with the PNCM is estimated by VB algorithm.
3. The contrast between the green lines and the black lines indicates that the state estimation performance of TT-EKF has been further improved with the prediction error compensator based on neural network.  
Figure 14: Trajectories of AUV-\(II\) and Mobile docking station(M-DS) recorded by IMU in AUV-\(II\) homing to MRS.  
Figure 13: The positioning error of AUV and MRS, in which (**a**) illustrates the horizontal direction of the positioning error components of AUV in global frame; (**b**) illustrates the vertical direction of the positioning error components of AUV in global frame; (**c**) illustrates the horizontal direction of the positioning error components of MRS in global frame; (**d**) illustrates the vertical direction of the positioning error components of MRS in global frame.
# 5G Price Competition with Social Equilibrium Optimality for Social Networks
## 6 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Number of Users** & \(\varepsilon=10^{-3}\) & \(\varepsilon=10^{-4}\) & \(\varepsilon=10^{-5}\) & \(\varepsilon=10^{-6}\) & \(\varepsilon=10^{-7}\) \\ \hline
20 & 2022.4 & 3105.7 & 4209.9 & 5314.0 & 6209.9 \\
30 & 2068.8 & 3006.3 & 4068.8 & 5110.5 & 6110.5 \\
40 & 2407.0 & 3802.9 & 5011.2 & 6302.9 & 7532.0 \\
50 & 2786.8 & 4203.5 & 5807.7 & 7016.0 & 8203.5 \\ Number of iterations & 60 & 3145.8 & 4833.3 & 6312.5 & 8020.8 & 9520.8 \\
70 & 3525.6 & 5129.8 & 7025.6 & 8733.9 & 10,067.3 \\
80 & 3988.8 & 5509.6 & 7530.4 & 9509.6 & 10,926.3 \\
90 & 4243.6 & 6035.3 & 8056.1 & 10,076.9 & 11,931.1 \\
100 & 4435.9 & 6456.8 & 8435.9 & 10,540.1 & 12,519.3 \\ \hline \end{tabular}
\end{table} 
Table 1: Average time to convergence for different \(\varepsilon\). When \(\varepsilon R_{m}\) is larger than the gap between demand and supply, we define the number of iterations as convergence. In comparison, the smaller the parameter \(\varepsilon\), the larger the gap between supply and demand, so more iterations are required. For instance, if \(\varepsilon=10^{-3}\), the time to convergence is 2000–4000 in most cases, if \(\varepsilon=10^{-7}\), the time to convergence is 6000–12,000. \begin{table}
\begin{tabular}{c c c c c} \hline
**Number of Users** & **5 Suppliers** & **7 Suppliers** & **9 Suppliers** \\ \hline
20 & 6814.7 & 4199.3 & 3199.3 \\
30 & 3947.5 & 3493.0 & 3045.4 \\
40 & 3493.0 & 3793.7 & 3793.7 \\
50 & 3849.6 & 4150.3 & 4255.2 \\ Number of iterations & 60 & 4402.0 & 4604.8 & 4807.6 \\
70 & 4807.6 & 5101.3 & 5304.1 \\
80 & 5108.3 & 5437.0 & 5611.8 \\
90 & 5611.8 & 5723.7 & 6108.3 \\
100 & 6059.4 & 6213.2 & 6562.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Average time to convergence for different numbers of suppliers when \(\varepsilon=10^{-3}\). The chart shows that when the ratio of suppliers to users is too high or low, the algorithm will iterate many times to converge. For instance, nearly 7000 iterations are required for 9 suppliers and 20 users. When the number of users increases gradually, the number of users is approximately positively correlated with the average convergence time.
## 4 Results
### Statistics and Trends
#### 4.5.2 Overview of the Trends by Methods
Models trained on DS3 and tested on DS1 data achieved better results than those models tested on DS2 dataset for each of the data processing methods used.
# Effect of Ultrasonic Vibration on the Surface Adhesive Characteristic of Iced Aluminum Alloy Plate
## 3 Effect of PZT Patch Size on Shear Stress
### The Effect of Thickness  
To investigate the effect of the thickness of the PZT patch on the shear stress at the adhesive interface, the variations in maximum shear stress and deicing area ratio \(P\) were calculated by harmonic response analysis. The model size of the iced aluminum alloy plate was 60 mm \(\times\) 60 mm \(\times\) 2 mm, and the thickness of the ice was 2 mm. Based on the simulation results in Section 3.1, the PZT patch with a side length of 16 mm was selected as the research object, and the thickness was in the range of 1-3 mm. The driving voltage V\({}_{\text{P-P}}\) (peak to peak) was 400 V. The waveform of driving voltage was sinusoidal. The calculation results are shown in Figure 10.  
Figure 10: Maximum shear stress and the deicing area ratio changing with the thickness of the PZT patch.  
Figure 9: Variations in the maximum shear stress and the deicing area ratio with side length of the PZT patch.  
As shown in Figure 10, when the thickness of the PZT patch is 1 mm, the maximum shear stress and the deicing area ratio are both high, which are 7.17 \(\times\) 106 Pa and 0.62, respectively. The cloud image of the distributions of shear stress is shown in Figure 11.  
As shown in Figure 11, the higher deicing shear stresses mainly concentrate in the four corners of the iced aluminum alloy plate. This distribution characteristic can also reduce the adhesive strength of the ice near the corners. In contrast, according to Figures 8b and 10, as the thickness of the PZT patch is 2 mm, the deicing area ratio is slightly lower. However, the maximum shear stress is highest. They are 0.59 and 8.69 \(\times\) 10\({}^{6}\) Pa, respectively. In these two cases, the deicing effects are similar. As the thickness of the PZT patch continues to increase, the maximum shear stress and the deicing area ratio both decrease. Therefore, as the sizes of the iced aluminum alloy plate and the PZT patch are constant, and the thickness of the PZT patch is thin, the shear stress and the deicing area ratio are both high.  
The reason for the reductions in shear stress and the deicing area ratio is that, as the PZT patch is too thick, it leads to an increase in the stiffness of the iced aluminum alloy plate. Then, the bending deformation of the iced aluminum alloy plate caused by the inverse piezoelectric effect decreases, and the shear stress at the adhesive interface also decreases.
## 4 Results and Discussion
### Forecasting Results  
Accurate short-term forecasting of PV power output gives pertinent information for maintaining the desired power grid production delivery and storage [7, 53]. This section assesses the eight models (i.e., RNN, GRU, LSTM, BiLSTM, ConvLSTM, RBM, AE, and VAE) and compares their forecasting performance using PV power output collected from two different PV systems. Towards this ends, we first build each to capture the maximum variance in training data and then use them to forecast the future trend of PV power output. The training data in Data Set 1 consists of one-minute power data collected from 1 January 2017 to 29 June 2017. The training data in Data Set 2 is collected from 1 January 2018 to 19 October 2018. The hyper-parameters of the built deep learning methods based on training datasets are tabulated in Table 2. For all models, we used the cross-entropy as loss function and Rmsprop as an optimizer in training.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 2 Maritime Scenario and Problem Statement
### Problem Statement and Future Vision  
Besides the details provided in the two subsections above, there are several aspects that need to be considered when planning and using radio access networks and/or radio links over open seas and on moving ships. As already pointed out, different weather conditions--fog, waves, ice, etc.--can affect radio waves to a large extent, which makes the coverage conditions inconsistent [27]. The greater the involved distances, the greater the potential negative performance impact. Such changing channel conditions not only impact the signal but also the interference power strength variability, which can be especially challenging at low frequencies as they propagate larger distances. Moreover, on open seas and border areas, different legislation (e.g., frequency regulations on international waters) and frequency coordination agreements need to be taken into account.  
To address the technical aspects listed above, our 5G connectivity vision for the maritime ecosystem consists of three main components to provide seamless connectivity: short distances from land stations, and a combination of multi-hop and 5G non-terrestrial networks to cover mid-distances up to international waters. A graphical schematic of this vision is provided in Figure 4. Additionally, multi-hop [28], satellite connectivity and their combination [29] will improve not only the maritime scenario but also, e.g., aviation environments, where they will complement terrestrial 5G networks and bridge coverage gaps in rural and remote areas. Regarding other areas that can benefit from satellite connectivity, the 5G Automotive Association has partnered with the European Space Agency to investigate the use of satellites [30], recognizing the continuous and ubiquitous connectivity essential for many automotive use cases, albeit with a need to improve the technological maturity and cost-effectiveness of the solution.
## 2 System-Level Design Optimization
### Methodology and Framework  
When implementing system-level optimization methods, a consensus has emerged around five key steps, which ensure that the full potential of the approach is realized [19; 20; 21]. These seem to offer a well-rounded and systematic procedure that addresses the complexities inherent in optimizing drive systems. The multi-step methodology appears to provide a comprehensive framework for balancing various competing factors between machine and controller, such as efficiency and cost, within the design process. The steps are laid out with a clarity that offers a solid foundation for both further research and practical applications in the field.  
Step 1: Definition of the system's overall requirements and design objectives. The system requirements are determined, such as efficiency, torque ripple, average torque, weight, and cost. Constraints are defined, like maximum current, minimum efficiency, and search limits of each parameter for the optimization algorithm, primarily based on the application in which the implementation is carried out.  
Step 2: Definition of the drive system characteristics, including both the machine and the controller. In this step, the machine's topology and specifications are selected, alongside the controller, which includes power electronics such as the inverter and control algorithms, to ensure proper functionality. The selection process between the machine and controller can be a complex task, as these two components are highly interdependent and coupled, often requiring expertise and experience.  
In summary, the optimization problem is structured into the following three main branches: the machine level, control level, and the system level. At each level, the system-level optimization methodology conducts an optimization process aimed at maximizing the overall system performance. The first two levels, referred to as the component level, focus on refining their respective elements. While individual components may not be optimized independently, the methodology ensures the near-optimal performance of the overall system.
SSTs without a DC link are impossible to be interfaced with RESs and fuel cells [21]. However, SST with a DC link [22] offers advantages such as the availability of DC port, and much reduced size due to high frequency operation over [20; 21]. However, all aforementioned topologies involve a large number of converter valves. The isolated type topology, presented in this paper, involves the least possible number of controlled switches, completely isolates the grid from load, and provides independent control of the voltage, frequency, power flow and power factor. A very few instances (no instance, in fact) in the literature are reported where such a kind of topology can accomplish all the above-mentioned tasks. This justifies the applicability and validation of the proposed research. The suggested topology of SST also can maintain a clean voltage and current waveforms of utility supply irrespective of the type and nature of the load. This is what that has been stressed upon in this paper. The paper is constructed in the following way. Section 2 pinpoints the proposed SST circuit topology, specifications and complete control layout. In Section 3, detailed simulation results are presented to investigate the role of SST as a variable frequency drive, power factor improvement device and renewable interface with bidirectional power flow. An active power flow mechanism with balanced and unbalanced load is also simulated in this section. Conclusions are drawn in Section 4.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Acute Sinusitis, Rhinitis, Rhinosinusitis  
Bromelain also acts as a mucolytic agent, decreasing mucus production while promoting natural drainage, the formation of inflammatory prostaglandin and decreasing swelling in nasal cavities. Several trials have investigated the role of this supplement in sinusitis, rhinitis, rhinosinusitis and chronic rhinositis, although there is a lack of long-term data [105; 106]. [PERSON] et al. have treated 49 patients affected by sinusitis with either bromelain or a placebo. In association with the conventional treatment, the bromelain group showed general improvements in overall rating, breathing difficulties and nasal discomfort [107]. Similar results have been obtained by [PERSON] et al. who observed that bromelain administration improved nasal mucosal inflammation and breathing in people with acute sinusitis [108]. Finally, the administration of bromelain (average intake: six tablets = 3000 Federation Internationale Pharmaceuquite (FIP)) to 12 patients with chronic rhinosinusitis, both with and without nasal polyps, in a prospective, open-label, observational pilot study provided a significant improvement in total symptom scores (TSS), total rhinoscopy score (TRS) and quality of life (Sino-nasal Outcome Test 20) [98].
# Infrared Fault Classification Based on the Siamese Network
## 4 Experiments
### Various Attention Comparison Experiments  
The experimental results of the proposed network using different attention mechanisms are shown in Table 1.  
The squeeze-and-excitation (SE) strategy considers information about channels in terms of their duration, and is suitable for scenarios with a relatively high number of channels. This approach was adopted in the current study, achieving an accuracy of 74.3%. Efficient channel attention (ECA) requires only a few parameters and delivers significant performance gains. The training accuracy reached 75.6%. The convolutional block attention module (CBAM) pays attention to both the spatial and channel aspects of an image, enabling the model to achieve even higher accuracy. After training, the accuracy reached 76.3%. The proposed ACA attention system combines and improves upon the ECA theme; it selects diverse convolutional feature maps for adaptive learning, further enhancing the efficiency of the model and achieving the highest training accuracy, at 83.9%.
## 4 Monte Carlo Simulation
### MCS Implementation Notes
* Absorption and Scattering Event Routing: Model absorption and emission processes within the seawater environment using appropriate IOP coefficients and emission sources and accounting for wavelength-dependent absorption and emission if required. This helps to define the routing logic in the channel object. The channel class object handles this implementation item via its function _simulate_one_photon_travel_, called the ChannelCoefficient class object. * Sampling Strategy: To implement Equations (73)-(78), we need to develop an efficient sampling strategy for generating random positions, directions, and wavelengths of photons. This step is critical because it will ensure the fulfilment of the CLT that MCS relies on. Use stratified or necessary sampling to improve sampling efficiency, especially in regions with high photon density. * Statistical Convergence: Monitor statistical convergence of simulation results by tracking key observables (e.g., radiative flux, received optical intensity distribution) as a function of the number of simulated photons, using statistical tests to determine convergence criteria. * Output and Visualisation: For further analysis and visualisation, output simulation results (e.g., radiative flux profiles and optical intensity distributions) can be stored using a data management class object. Matplotlib Python libraries such as Matplotlib or Plotly can be used for plotting and visualisation. The usage of this library can be facilitated through data visualisation class objects. * Optimisation and Parallelization: Optimise the simulation code for computational efficiency, including algorithmic optimisations and parallelisation techniques (e.g., multiprocessing, parallel computing libraries) and distribute the computational workload across multiple processors or computing nodes to accelerate simulation run time. * Verification and Validation: Verifying the correctness of the MCS implementation by comparing it with analytical solutions, benchmark problems, or experimental data and validating the accuracy and reliability of simulation results against independent sources. * Documentation and Maintenance: Thoroughly document the simulation code, including comments, documentation strings, and descriptive variable names. Maintain the codebase regularly, incorporating bug fixes, performance improvements, and new features as needed. Following this approach, we can develop a robust and efficient Python implementation of MCS runs for solving the RTE, producing accurate and reliable results for various scenarios.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 3 Background
### Hidden Markov Model  
Hidden Markov model (HMM) is composed of a quintuple: \(\lambda=(X,Y,A,B,\pi)\), where \(X\) represents the set of hidden states; \(Y\) represents the set of observational states; \(A\) is a hidden state probability transfer matrix; \(B\) represents the probability transfer matrix of observation state; and \(\pi\) represents the probability in the initial time of each hidden state. The transfer between hidden states is a random process, and the transfer between observed states is also a random process, so the most obvious feature of HMM is a double random process.  
HMM is a probabilistic model that predicts the possibility of attack in the form of probability. For L-DDoS attacks with background traffic, HMM can remove noise and fully distinguish attack events in sequential events. Unlike traditional detection methods, which classify states into two categories-attack or no attack-HMM can define the number of states according to different degrees of attack, and finally detect L-DDoS attacks at different rates adaptively.  
\[\begin{array}{l}A=\big{[}a_{ij}\big{]}_{N\times N},a_{ij}=P\big{(}x_{t+1}=s _{j}\big{|}x_{t}=s_{i}\big{)},i\geq 1,j\leq N\\ B=\big{[}b_{ij}\big{]}_{N\times N},b_{ij}=P\big{(}y_{t}=o_{j}|x_{t}=s_{i}\big{)},1\leq i\leq N,1\leq j\leq M\\ \pi=(\pi_{1},\pi_{2},\cdots,\pi_{N}),\pi_{i}=P(x_{i}=s_{i}),1\leq i\leq N\end{array} \tag{5}\]  
The Equation (5) showed that at time \(t\), if the state in previous moment is \(s_{i}\), \(a_{ij}\) will be the probability for next moment when the state is \(s_{j}\); if the state is \(s_{i}\), \(b_{ij}\) will be the probability when the observed value is \(o_{j}\); \(\pi_{i}\) represents the probability when \(t=1\) and the state is \(s_{i}\). \(A\) and \(\pi\) determine the hidden Markov chain and generate an unobservable state sequence. \(B\) determines how to generate observations from the state and integrate with the state sequence to determine how to generate observation sequences.
# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 2 Materials and Methods
### Image Acquisition  
This paper deals with five types of concrete pathologies, cracking, steel corrosion spots in concrete, concrete splintering, efflorescence, and exposed steel bars in reinforced concrete. Determining the pathologies to be studied leads to choosing appropriate datasets that enable detection and classification for these pathologies.  
The CODEBRIM [17] dataset is a base annotated to enable the detection of pathologies in concrete structures. This dataset allows the detection of five pathologies, cracking, exposed steel bar, concrete fragmentation, efflorescence on the concrete surface, and corrosion spots on the concrete surface.  
The \"Concrete Crack Images for Classification\" dataset, Ozgenel [18; 19; 20], fits the case of cropped image analysis for a specific pathology, in this case, cracks in concrete. This dataset contains 20,000 images for each classification, for a total of 40,000 images of the types. The images are in 227 \(\times\) 227 pixel format with RGB channels.  
One of the concrete pathologies that cause a lot of concern is the presence of cracks or fissures. This pathology has its severity evaluated according to the thickness of its opening. Figure 2A shows a crack case, a pathology that is annotated in both [17; 18] datasets. Figure 2B shows a cropped image of a case of an exposed steel bar. This is a case of pathology in reinforced concrete, a scenario where you have concrete and steel as structural elements. Figure 2C shows part of an image of a fragmented concrete. The fragmentation can start in another pathology, such as cracking or splitting. It can be a consequence of the expansion of steel that suffers corrosion or even loss of the structure that has suffered some impact. Figure 2D shows an image cutout with a case of efflorescence, which is white spots on the surface of concrete produced by the accumulation of crystalline deposits. In the last case analyzed, Figure 2E, we present an example of a corrosion spot. Steel corrosion occurs in the case of reinforced concrete, concrete, and steel, collaborating to ensure the strength of the structure.  
Figure 1: Proposed method as diagram.