esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## 6 Conclusions  
This paper proposes an adaptive PI + VPI harmonic current compensation strategy for the complex working conditions, which can maintain the optimal operation of the APF when the grid-side voltage is unbalanced, distorted and frequency deviation occurs of a weak grid. The analysis shows that the SRF-PLL with adaptive harmonic suppression characteristics can improve the estimation accuracy of phase and frequency. The ANF-PLL adaptive parameters proposed in this paper are fewer, only one, and the simplified gradient algorithm used in the adaptive law has better convergence characteristics. The simulation results show that ANF-PLL can maintain high phase and frequency tracking performance under the conditions of grid-side voltage unbalance, distortion and frequency deviation. Before and after the frequency deviate of +4 Hz, the phase and frequency estimation errors can be maintained below 0.0005 rad and 0.2 Hz.  
The adaptive PI + VPI harmonic current compensation strategy applies the grid-side frequency and phase estimation information output by ANF-PLL to the real-time VPI resonant current control link, can compensate for the 19 th harmonics generated by the three-phase nonlinear load and show good harmonic current tracking performance under distortion conditions. However, when the specific harmonic content is high, an additional VPI regulator can be added to extend the APF harmonic current compensation strategy.  
Compared with the fixed VPI/SRF-PLL strategy and the adaptive VPI/NF-PLL strategy, the experimental results show that the adaptive VPI/ANF-PLL harmonic compensation strategy proposed in this paper is better than the above two schemes. When the frequency deviation occurs in the frequency range of \(\pm\)4 Hz, the APF system can maintain the THD of the compensated network side at approximately 3.65%, which is much higher than the requirements of the IEEE standard, and has a good dynamic transition process. When the load changes, after approximately 2.5 fundamental cycles, a new steady-state compensation is achieved.  
Methodology, L.Z., Z.Z. and W.H.; validation, L.Z. and W.H.; writing--original draft preparation, L.Z. and W.H.; writing--review and editing, J.Q. and W.H.; supervision, Z.Z.; project administration, L.Z. and W.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 51467017, in part by the Inner Mongolia Natural Science Foundation of China under Grant 2018 MS05026 and Inner Mongolia Autonomous Region Talent Development Project Foundation of China under Grant RC201701.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the reviewers and editors for their reviews of this research.  
The authors declare no conflict of interest.
## 4 Experimental Evaluation
### Sleep Quality Forecasting  
To evaluate the effectiveness of our system in forecasting the individual's sleep quality in the following night based on past smartphone data, we used a leave-one-person-out cross-validation approach, i.e., the data of one person were used for testing, while the data of the remaining people were used for training, and testing was iteratively executed with all subjects. The code to run the experiments was implemented in Python, using its machine learning libraries.  
As explained before, we treat sleep quality forecasting as a regression problem, where the target value (i.e., the quality of sleep of the next night) is represented by a real number ranging from 1 (very bad sleep quality) to 4 (very good sleep quality). We performed the experiments with two well-known algorithms: the random forest regressor and the random tree regressor. We chose these algorithms because they achieved good performance in similar tasks even when the dimension of the available training set was relatively small [33].  
We measured the effectiveness of prediction using the Pearson correlation coefficient \(r\)[34]. The results, shown in Table 1, indicate that the random tree regressor and the random forest regressor have similar performance on this task. Indeed, the former achieves an \(r\) value of 0.33, while the latter achieves \(r=0.22\). However, the logic behind the predictions is more understandable when using the random tree because it consists of a series of binary decisions based on input features. Instead, the logic behind the predictions in a random forest is more complex to interpret because it consists of the aggregation of predictions from several trees. Hence, in order to provide the LLM with clear and simple information about the model prediction to generate suggestions and explanations, for the rest of our experiments, we used the random tree regressor to implement the sleep quality forecasting module.  
The achieved results indicate that there is a statistical correlation between the forecast of our system and the actual sleep quality perceived by the individual. In particular, a correlation value \(r\) between 0.2 and 0.5 indicates a moderate correlation between the predicted score and the ground truth. We point out that, in our experiments, the models were trained with data acquired from a relatively small number of individuals. We expect to obtain higher correlation scores using a larger training set.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 5 Conclusions  
In this study, an AC/DC LED converter has been proposed to remove the flicker of a T8 LED tube. The proposed LED converter uses an output ripple eliminator and a SSBB converter topology for the input voltage 100-240 VAC. A 10 W prototype was designed to verify the high performance. The calculated percent was 1.9% at all input conditions. The power efficiency is lower than that of a conventional converter by 2.7% at 100-240 VAC, but it is still high (>87% and even 89% at 220 VAC). The experimental results represented the LED output current regulation as less than 0.92% at 100-240 VAC and the LED converter has a high power factor (>0.84) and low total harmonic distortion (<14.3%). Moreover, the harmonic current of the LED converter reaches the IEC 61000-3-2 Class D standard at 100 VAC and 240 VAC input voltages for high-quality LED converters.  
Figure 11: Total harmonic distortion (THD) according to the input voltage.  
Figure 12: Harmonic current at 100 VAC 50/60 Hz.  
**Author Contributions:** For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used Conceptualization, S.K. and H.J.; methodology S.K. and H.J.; software, S.K.; validation, S.K. and H.J.; formal analysis S.K. and H.J.; investigation, S.K. and H.J.; resources, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K. and H.J.; visualization, S.K. and H.J.; supervision, H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Project Overview
In this study, multiple measurement points were established along the underground section of the Taizhou S1 Line, with a tunnel centerline distance of 30 m chosen. This distance is based on the typical spacing found in existing suburban rail dual-track tunnels, ensuring the representativeness of the research findings. The measurement points were evenly distributed along the outer side of the tunnel, covering varying distances and directions to comprehensively capture the characteristics of vibration propagation. The specific locations of the measurement points are shown in Figure 1, which includes vibration acceleration measurements in the x, y, and z directions. High-precision accelerometers and a data acquisition system were employed to ensure data accuracy. All equipment underwent rigorous calibration before use to guarantee the reliability of the measurements. During the measurement process, clear weather conditions were selected to minimize the influence of weather fluctuations and nearby structures on vibration propagation, thereby ensuring the authenticity of the data. Figure 1 illustrates the spatial relationship between the tunnel layout and the ground measurement points. Figure 2 provides a detailed description of the tunnel's structural design, including its depth, diameter, and the layout of the track slab. It also explains how these design parameters influence the propagation path and attenuation characteristics of the vibration waves. Together, Figures 1 and 2 offer a visual understanding of the geographical and structural features of the study area, facilitating a deeper comprehension of the physical mechanisms underlying vibration propagation.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Search Strategy  
The search strategy composes of a search pattern, which describes the repositories used in this review. In addition, we considered three viewpoints for research questions analysis: population, intervention, and outcome. Finally, the search strings were built using Boolean ANDs and ORs, in a defined time range.  
The _Search Pattern_ used combinations of key terms in digital libraries such as Scopus, Science Direct, ACM Digital Library, and IEEE Xplore. Journals or conference proceedings have been included to select the main studies of this work.  
The _Population_ defined for this study are: seismological data centers, seismic-volcanic monitoring network organizations, and the scientific community. These groups are focused on the diffusion of early warnings of seismic and volcanic events.  
The _Outcome_ has been defined through two main factors, which could support the availability of information in the SDCs. The first factor refers to designing a mechanism to retrieve seismic data, and the second factor considers a validation method to add redundant networks.  
Procedures considered in _Intervention_ help to identify algorithms applied in data acquisition and seismic transmission networks. Likewise, it includes seismic data formats, protocols, and software solutions for data storage and processing.  
It is essential to mention that a recommended way to create the search string is to structure them in terms of population, intervention, comparison, and outcome [21]. The selected search period is from the years 2000 to 2021 because, since 2000, most SDCs had already migrated their analog to digital systems, including transmission media and systems for the acquisition and processing of seismic data. The expressions were constructed using logical operators for searches (AND and OR), shown in Table 1.  
The most important terms were \"data acquisition and processing systems\" for seismic-volcanic monitoring phenomena, \"algorithms and protocols\" related to the availability of information, \"seismic networks\" description, and \"seismic data standards\" for data transmission. The search results allow the selection of publications related to this study.
## 3 Description of Methods and Tools
### Computational Mechanics Tools  
Within the classical finite element analysis of large-scale models, the need for reduced order models (ROM) has been studied. The classical tools are focused on linear static and dynamic problems, known as static condensation or Gyan reduction [8]. Modern approaches deal with nonlinear problems. Provided that an accurate model exists, for example, a finite element model of a high order, the reduction can be based on engineering arguments and techniques, such as hierarchical modeling, macro-modeling, and domain decomposition, or on mathematical tools, such as singular value decomposition (SVD) [9]. Conversely, data-driven methods produce a reduced order model based on the input/output data that may be collected from an experiment or measurements on a real structure. Data-driven methods using data analytics or artificial intelligence are very popular within the DT community. In both cases, the goal of model reduction is to provide a reliable substitute for the full-scale model, by making the difference between the full-order model and reduced-order model small under some appropriate norm.  
Within the context of DT, the existence of ROM allows for quick, possibly real-time implementation. The additional step of extracting local information at areas of interest from the predictions of the reduced model is also of interest. Provided that a warning has been sent by the DT model, one wants to examine a component in more detail, for example by calculating stresses and stresses, to use results from a fracture and fatigue analysis and proceed with the estimation of appeared damages. A schematic representation of the concept is shown in Figure 6.  
Figure 5: Schematic representation of a DT based on a numerical or experimental model of a semi-rigid steel structure’s joint and neural networks.
## 1 Introduction
Polystyrene (PS) is a thermoplastic material that is suitable for versatile applications in the engineering and coatings fields [21]. However, the thermal stability of PS is a key factor in determining its suitability for a particular application. Layered silicates possess relatively high thermal stabilities. Therefore, the formation of PS and MMT nanocomposites could compensate for the relatively low stability of PS such that the resulting material could have more extensive applications in high-temperature processes. Several methods have been applied for the preparation of polymer-clay nanocomposites, such as bulk polymerization, solution polymerization, emulsion polymerization and the melt-intercalation method [22; 23; 24; 25]. Among these methods, emulsion polymerization was considered the most promising technique for the fabrication of monodispersed polymer nanoparticles with controlled molecular weights, faster reaction rates, and improved environmental friendliness because water is used as the reaction solvent. Moreover, emulsion polymerization facilitates the preparation of exfoliated structures in polymer-MMT composites. [PERSON] et al. prepared mono-disperse silica-polymer core-shell microspheres via emulsion polymerization [26]. In addition, [PERSON] et al. synthesized exfoliated PS-MMT nanocomposites by emulsion polymerization using zwitterions as the clay modifier, and these nanocomposites showed improvements in thermal stability compared with intercalated composites and the neat polymer [27]. [PERSON] et al. observed that ultrasound or ultrasonic irradiation processes assisted emulsion polymerization and could promote the formation of exfoliated layers during nanocomposite synthesis [28]. In addition, the results of [PERSON] et al. indicated that PS-clay nanocomposites synthesized via emulsion polymerization had better layer dispersion than those synthesized by suspension polymerization [29]. Therefore, emulsion polymerization is an efficient technique for preparing polymer-MMT nanocomposite particles with exfoliated structures and monodispersity. In this paper, we report the effect of anionic surfactant modifiers on MMT and discuss a possible mechanism of the effect of the anionic surfactant in the intercalated MMT. The PS-OMMT nanocomposite particles were prepared via emulsion polymerization, and the effects of OMMT on the morphology, structure, and thermal properties of the polystyrene matrix were investigated. In addition, the tribological properties of PS-OMMT nanocomposite particles as additives in polyalphaolefin (PAO) were investigated. The results showed that these nanocomposite particles were effective friction reduction and antiwear materials and could have applications in oil and gas drilling engineering to improve drilling fluid lubrication. Further work in this direction is currently in progress.
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
###### Abstract  
The gas-curtain launch is designed to address the shortcomings of conventional underwater launchers, such as poor dependability and low muzzle velocity. In this paper, the influence of jet structures on the propulsion performance and internal flow field of an underwater gas-curtain launcher is investigated. To conduct the experiment on a small-aperture underwater launcher, three projectiles with different jet structures were designed. The experimental results show that a projectile with a central nozzle is more conducive to gas-curtain formation than one with four sidewall grooves. Additionally, the central nozzle can reduce launch resistance and improve propulsion performance more effectively. Furthermore, increasing the diameter of the central nozzle aids in gas-curtain formation and propulsion performance. Following the experiment, a numerical model of the internal flow field for gas-curtain launch is built in order to develop numerical simulations under three jet structures. The calculation results show that the three gas-curtain projectiles can likewise acquire good propulsion performance. Different jet structures have significant impacts on the launching resistance of a gas-curtain launcher, thereby affecting its propulsion performance. The launch resistance is lower when the central nozzle jet structure is utilized; however, the muzzle velocity is also lower because more gas is consumed for drag reduction and the projectile force area is smaller. This study reveals the effect of jet structure on the propulsion performance and flow field evolution of a gas-curtain launcher.  
gas-curtain launch; jet structure; drag reduction; propulsion performance +
Footnote †: journal: Applied  
Accepted: 24 May 2022  
Published: 27 May 2022
## 5 Conclusions  
In this paper, the wheel/rail contact characteristics in the fixed frog area are comprehensively analyzed, the optimization models of wing rail lifting values and nose rail height values in the fixed frog area are established, and the specific optimization design process based on particle swarm optimization algorithm is proposed. The main conclusions are as follows:  
1. If the wheelset is close to the nose rail with a large lateral displacement, there is a risk that the wheel will climb onto the nose rail. In order to reduce the vertical impact of wheelsets passing through the fixed frog area and improve the safety and stability of vehicles passing through the turnout center, the geometric parameters of the fixed frog should be set reasonably.
2. After wing rail lifting values optimization, the maximum vertical displacement of the wheelset is reduced by 34.6%. On the stock rail side, the maximum wheel/rail lateral force is reduced by 12.8%, and the maximum wheel/rail vertical force is reduced by 10%. On the frog side, the maximum wheel/rail lateral force is reduced by 38%, and the maximum wheel/rail lateral force is reduced by 12.5%.
3. After nose rail height values optimization, the maximum vertical displacement of the wheelset is reduced by 19.5%. On the stock rail side, the maximum wheel/rail lateral force is reduced by 15.4%, and the maximum wheel/rail vertical force is reduced by 3.5%. On the frog side, the maximum wheel/rail lateral force is reduced by 18.5%, and the maximum wheel/rail lateral force is reduced by 9.4%.  
Figure 17: On the frog side: (**a**) Wheel/rail lateral force; (**b**) Wheel/rail vertical force.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection
### Mixed Gibbs Sampling with Multiple Restarts
Accordingly, [23] proposed the following:  
\[\theta_{s}(\mathbf{x})=\lceil\max(c_{\min},10\log_{2}\mathcal{M}\exp(\phi( \mathbf{x})))\rceil, \tag{15}\]  
where \(c_{min}\) is the minimum number of iterations following a stalling event and \(\mathcal{M}\) is the constellation size, as defined in Section 2 (and here made equal for all dimensions). Likewise, a stopping criteria for the maximum number of repetitions \(R\) can be defined. In a similar fashion to the previous case (see [23] for further insight), the algorithm stops if the number of restarts performed so far is greater than the following integer threshold:  
\[P=\lfloor\max(0,0.5\log_{2}\mathcal{M}\phi(\mathbf{x}))\rfloor+1. \tag{16}\]  
The usage of both MGS and the stopping criteria in (15) and (16) will provide close to optimal solutions, while yielding limited complexity, as will be verified in the next subsection.
## 4 Conclusions  
In this study, a joint modulation format identification and OSNR--monitoring algorithm is proposed and experimentally demonstrated using the KNN algorithm for IMDD OFDM systems. A modified AH of received signal is employed to serve as the classification feature to simplify the computation. According to the experimental results, five common QAM modulation formats can be identified with a 100% accurate estimation at the RoP of \(-11\) dBm. At the same time, system OSNR monitoring also can be achieved with an average prediction MSE of 0.69 dB\({}^{2}\), which is similar to that using artificial neural network. Robustness and computational complexity of the proposed scheme are also experimentally assessed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Multiplications** & **MFI** & **OSNR Monitoring** \\ \hline \multirow{3}{*}{ANN} & \(\text{C}_{\text{train}}=\text{N}_{\text{sp}}(n\text{n}_{\text{n}_{\text{ mid}}}+n_{\text{n}_{\text{mid}}}\text{n}_{\text{o}})\) & \(\text{C}_{\text{train}}=100\times(101\times 4+40\times 5)=\) & \(\text{C}_{\text{train}}=100\times(101\times 40+40)=\) \\  & \(\text{C}_{\text{predict}}=\text{n}_{\text{mid}}+n_{\text{mid}}\text{n}_{ \text{o}}\text{n}_{\text{o}}\text{n}_{\text{o}}\) & \(\text{C}_{\text{test}}=101\times 40+40\times 5=4240\) & \(\text{C}_{\text{test}}=101\times 40+40=4080\) \\  & \(\text{C}_{\text{ANN}}=\text{C}_{\text{train}}+\text{C}_{\text{predict}}\) & \(\text{C}_{\text{ANN}}=428,240\) & \(\text{C}_{\text{ANN}}=412,080\) \\ \multirow{3}{*}{KNN} & \(\text{C}_{\text{KNN}}=\text{C}_{\text{train}}+\text{C}_{\text{predict}}=\) & \(\text{C}_{\text{KNN}}=2\times 30\times 101=6060\) & \(\text{C}_{\text{KNN}}=2\times 30\times 101=6060\) \\  & \(2\text{C}_{\text{train}}=2\text{N}_{\text{TSB}}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The complexity comparison between KNN and ANN.  
Figure 10: The identification accuracy of (**a**) ANN algorithm and (**b**) KNN algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MSE** & **4-QAM** & **16-QAM** & **32-QAM** & **64-QAM** & **128-QAM** \\ \hline KNN(dB\({}^{2}\)) & 0.66 & 1.3 & 0.77 & 0.3 & 0.42 \\ ANN(dB\({}^{2}\)) & 0.87 & 0.65 & 0.69 & 0.84 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MSE of KNN and ANN algorithm.  
**Author Contributions:** [PERSON] put forward the research and innovation points of the article and revised the paper. [PERSON] carried out experimental verification and prepared the paper. [PERSON], B.C., [PERSON], Y.S., J.C., J.Z. and M.W. help provide funding and experimental environments.  
**Funding:** This research was funded by National Natural Science Foundation of China (Project No. 61601279, 61420106011, 61601277, 61635006); Shanghai Science and Technology Development Funds (Project No. 17010500400, 16511104100).  
**Conflicts of Interest:** There is no conflict of interest.
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements  
It should be noted that a few of the mentioned network requirements below are simultaneously under the scope of research on 5G and beyond 5G, such as network slicing [51] and edge computing [52]. However, research on these network features is still in its primitive state, and there is much work to do. Consequently, it is highly likely that proper, efficient, and wide-scale network utilization of these network features will only be available by the time beyond 5G networks are in use.