esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Gas Relay Misoperation Under Inrush Current  
By adjusting the magnitude of closing angle and remanent magnetization, the excitation inrush waveforms under different operating conditions are obtained, and they are used as the excitation for the finite element simulation of the converter transformer to obtain the insulating oil flow rate through the gas relay under different operating conditions. The inrush current amplitude and maximum oil flow through the gas relay under different working conditions are shown in Table 4.  
By outputting the oil flow waveform simulation results under various working conditions to the insulating oil inlet of the gas relay finite element model, the relationship between the oil flow velocity and the deflection angle of the baffle can be shown in Figure 17.  
From the finite element simulation experimental results, with the increase in the amplitude of inrush current generated inside the converter transformer, the deflection angle of the baffle plate shows an increasing trend. This converter transformer generated an inrush current amplitude and gas relay baffle deflection angle mathematical fitting, obtaining the following expression:  
\[\theta=2.97626-2.29671\mathrm{i}+0.82904\mathrm{i}^{2} \tag{15}\]  
where \(\theta\) is the deflection angle of the gas relay baffle, and \(\mathrm{i}\) is the first wave peak value of the inrush current.  
Through simulation experiments and calculations for other different working conditions, the deviation of the results of the deflection angle of the gas relay baffle is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Closing Angle** & **Residual Magnetization** & **Inrush Current** & **Oil Flow Velocity** \\ \hline \(0^{\circ}\) & 0 pu & 3.08 kA & 0.172 m/s \\ \(0^{\circ}\) & 0.2 pu & 4.09 kA & 0.232 m/s \\ \(0^{\circ}\) & 0.4 pu & 5.10 kA & 0.420 m/s \\ \(0^{\circ}\) & 0.6 pu & 6.04 kA & 0.560 m/s \\ \(0^{\circ}\) & 0.8 pu & 6.89 kA & 0.619 m/s \\ \(30^{\circ}\) & 0 pu & 2.49 kA & 0.120 m/s \\ \(30^{\circ}\) & 0.2 pu & 3.50 kA & 0.251 m/s \\ \(30^{\circ}\) & 0.4 pu & 4.51 kA & 0.314 m/s \\ \(30^{\circ}\) & 0.6 pu & 5.45 kA & 0.528 m/s \\ \(30^{\circ}\) & 0.8 pu & 6.33 kA & 0.572 m/s \\ \(60^{\circ}\) & 0 pu & 0.79 kA & 0.054 m/s \\ \(60^{\circ}\) & 0.2 pu & 1.81 kA & 0.089 m/s \\ \(60^{\circ}\) & 0.4 pu & 2.81 kA & 0.132 m/s \\ \(60^{\circ}\) & 0.6 pu & 3.78 kA & 0.254 m/s \\ \(60^{\circ}\) & 0.8 pu & 4.63 kA & 0.342 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inrush current amplitude and oil flow rate under different operating conditions.  
From the results, it can be seen that the deflection angle of the gas relay baffle can be predicted by the obtained inrush current amplitude under different working conditions, and the deviation generated by the fitting results is within 3\({}^{\circ}\). As a result, considering the fitting error, the deflection angle of 22\({}^{\circ}\) is added to the fitting formula, and the risk of tripping of the gas relay is solved when the magnitude of the inrush current generated by the converter transformer reaches 6.37 kA.
# Role of Functional Monomers upon the Properties of Bisphenol A Molecularly Imprinted Silica Films
## 2 Experimental Section  
### Materials  
The functional organosilane monomers N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (DAMO-T, 98%), (3-Mercaptopropyl) trimethoxysilane (MPTES, 98 %), tetraethoxysilane (TEOS, 98 %), and the template Bisphenol A (BPA, 99%) were all purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). Ammonium hydroxide (NH\({}_{4}\)OH, 25 %) as catalyst and other solvents like acetic acid (AcA), obtained from Chimopar (Bucharest, Romania) were used as received. Ethanol (EtOH, 99.6%) was supplied by Fisher Scientific (Schwerte, Germany). The organic solvents used were analytical grade. Distilled water obtained from our laboratory purification system was used to prepare all aqueous solutions. The glass supports were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA).
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Navigation
#### 3.4.3 Guidance
\[Q_{smooth}=Q_{acc}+Q_{proj} \tag{15}\]The first term assigns an additional cost to commands that cause a linear or steering acceleration above predefined limits, as stated in Equation (16), while the second term, shown in Equation (18), rewards commands that, when maintained past their time horizon, for up to twice as long as originally intended, will not cause a collision with a tunnel wall. \[Q_{acc}=\sum_{i=2}^{n}R_{\delta\omega}\cdot f(\omega_{i}-\omega_{i-1},\Delta \omega_{m},\ \Delta\omega_{M})+R_{\delta\omega}\cdot f(v_{i}-v_{i-1},\Delta\omega_{m},\ \Delta\omega_{M}) \tag{16}\]  
\[f(x,\ x_{\mathit{nint}},\ x_{MAX})= \begin{cases}x-x_{MAX}&if\ x_{MAX}<x\\ 0&if\ x_{min}<x<x_{MAX}\\ x_{min}-\mathrm{x}&if\ x<x_{min}\end{cases}  \tag{17}\]  
\[Q_{proj}=\sum_{j=1}^{m}R_{p}\frac{\Delta\left[x_{j},y_{j},\theta_{j}|v_{n},\ \omega_{n}\right]\cdot\textit{ crash}(x_{j},y_{j})}{\Delta\left[x_{j},y_{j},\theta_{j}|v_{n},\ \omega_{n}\right]} \tag{18}\]  
\[\textit{crash}(x,y)= \begin{cases}1&if\ \textit{position}\ (x,y)\ \textit{is}\ \textit{in}\ \textit{ collision}\\ 0&if\ \textit{position}\ (x,y)\ \textit{is}\ \textit{not}\ \textit{in}\ \textit{ collision}\end{cases}  \tag{19}\]  
where \(R_{\delta\omega}\), \(R_{\delta\varphi}\), and \(R_{p}\) are the cost weights, selected for proper behavior; \(\Delta\omega_{m}\), \(\Delta\omega_{M}\), \(\Delta\omega_{m}\) and \(\Delta v_{M}\) are the parameters for the minimum and maximum steering acceleration and linear acceleration, respectively; \(\Delta\left[x_{j},y_{j},\theta_{j}|v_{n},\ \omega_{n}\right]\) are the displacement caused by the kinematic model of the machine, at time step \(j\) when the last optimization command of the previous process is applied. The outcome of the former process is a command vector for every time step in the selected timespan (\(\overset{\rightarrow}{u}=\left[u_{t_{0}},\ldots,u_{t_{f}}\right]\)), in which each element (\(u_{t_{i}}=\left[u_{\
u},u_{\omega},t_{i}\right]\)) represents a speed and steering command pair, alongside the timestamp on which this command is to be executed.
# Advancements, Dynamics, and Future Directions in Rural Environmental Governance Research in China
## 3 Data Sources and Research Methodology  
### Data Collection  
Data collection serves as the bedrock of literature analysis. Academic journal publications are a mirror reflecting the forefront of developments in academic research. Consequently, access to an extensive reservoir of literature becomes imperative as a fundamental prerequisite for conducting comprehensive literature reviews [26]. For this paper, the English-language literature data have been drawn from the Web of Science (WOS) platform, developed by Clarivate Analytics. Concurrently, the Chinese-language literature data have been curated from CNKI (China National Knowledge Infrastructure), presently recognized as China's largest academic journal database [27]. This selection ensures the representativeness and authoritative nature of our literature sources.  
The retrieval formula for Chinese literature data was formulated as follows: Topic = \"Rural Environmental Management\". English literature data were retrieved from the Science Citation Index Expanded (SCI-EXPANDED) database within the Web of Science core collection, utilizing \"Rural environmental management\" and \"China\" as the subject search terms. The search spanned from 1 January 2002 to 31 December 2022. After a rigorous process of repeated screening and examination, irrelevant data, including conference summaries, newspapers, books, duplicate publications, and publications lacking authorship attribution, were systematically excluded. Subsequently, after rigorous querying and filtering procedures, a total of 2689 Chinese articles and 681 English articles were successfully identified. The cumulative body of literature, eligible for quantitative analysis, amounted to 3370 articles. VOSviewer1.6.18 and BibliometrixR4.3.1 analyses were subsequently conducted to facilitate the attainment of a comprehensive understanding of the breadth and depth of the literature.
# A Multiprotocol Wireless Sensor Network for High Performance Sport Applications
## 2 Materials and Methods  
Some WSNs for particular sporting applications require, for example, a large number of sensors placed in a specific position and often located at a great distance from each other. In these cases, the design of a WSN must consider the attenuation of the transmitted signal; this is caused not just by the distance between the nodes themselves but also by the presence of obstacles between them as, among others, the bodies of athletes and the sport equipment. In fact, even the structure and the materials used for their manufacture can represent a shield for radio transmission (for example, both the hull and the shaft of a kayak paddle are made of carbon fiber). Ditto, the presence in the nearby area of other radio devices that work in the same frequency band of smartphones, Wi-Fi or Bluetooth networks, etc. They introduce electromagnetic noise and, consequently, a significant reduction in the real range of transmission of the nodes.  
For example, a WSN suitable for yacht racing could be composed of nodes mounted in very distant places [30]. Furthermore, the transmission system of the WSN could interfere with other on-board radio systems. Therefore, the only solution could be to install more nodes at a smaller distance between them: Some of them could, in fact, not collect data from sensors at all and work as simple repeaters of the signal coming from other nodes.  
The architecture of the proposed WSN is depicted in Figure 1. The Master Node can be connected with the User Terminal (i.e., tablet, smartphone, PC, etc.) by a Wi-Fi link over a dedicated WLAN and a webpage for realtime feedback of training or download of the whole session data. The master node can communicate with up to eight first level peripheral nodes via 2.4 GHz transceiver ISM band modules (based on the Nordic Semiconductor chip nRF24L01+) which guarantee a real life range in excess of 100 m and an acceptable power consumption. Furthermore, each of the eight first level peripheral nodes can connect to up to eight second level nodes if an increase of sensor nodes or a wider range is required.  
The system was originally designed for application in elite flatwater kayaking [31]. For this reason, it will be called e-kayak in the following. The main technical specifications of this project are as follows:  
* Measurement of boat displacement and velocity by means of high frequency GPS (sampling frequency \(\geq\) 10 Hz);
* Measurement of triaxial acceleration and triaxial angular velocity (sampling frequency \(\geq\) 50 Hz);
* Measurement of force on the paddle and footrest of each athlete (sampling frequency \(\geq\) 50 Hz);
* Real-time visualization of speed and stroke frequency on athlete and coach terminals;
* Wireless data download and system diagnostics from any platform and OS (Windows, MAC iOS, Android);
* Battery time for all the master and slave nodes \(\geq\)2 h to acquire a whole training session without the need to recharge.
###### Abstract  
As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems.  
vehicle trajectory data; traffic parameter extraction; signal control optimization; path identification; data analysis +
Footnote †: journal: Applied sciences  
Article
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 3 Materials and Methods
### Selection  
The selection operation is the last step of the differential evolution algorithm, which brings the optimal solution generated by the search process into the next iteration. In the selection operation, the original solution vector and the solution vector after mutation and crossover operations are usually compared using the fitness value, and the solution vector with better fitness is selected as a member of the next generation's population. Generally speaking, the selection operation can use the roulette wheel selection method (RWS) [48] or the competition selection method (CS) [49]. In the algorithm of this paper, the actual distortion produced after encoding the 360-degree panoramic video using the quantization parameter vector generated by the algorithm is used as the fitness value. By comparing the fitness value of the original quantization parameter vector with the quantization parameter vector after mutation and crossover, we retain the vectors with smaller distortion and discard the vectors with larger distortion. The selection operation is shown in (4).  
\[\left\{\begin{aligned} & X_{i,t+1}=Y_{i,t}&& ifD(X_{i,t})>D(\,Y_{i,t})\text{ and }R(\,Y_{i,t})\leq R_{T}\\ & X_{i,t+1}=X_{i,t}&& otherwise\end{aligned}\right. \tag{4}\]  
where R\({}_{T}\) is the target bitrate, i represents the index of the current individual in the population, t represents the number of iterations, X\({}_{i,t}\) is the original quantization parameter vector, and Y\({}_{i,t}\) is the value after mutation and crossover operations. If the actual bitrate of the 360-degree panoramic video encoded using Y\({}_{i,t}\) as the quantization parameter meets the target bitrate and the distortion value is smaller than X\({}_{i,t}\), then Y\({}_{i,t}\) will be retained. Otherwise, keep X\({}_{i,t}\).  
After repeating mutation, crossover, and selection operations several times, if the current number of iterations is greater than the maximum number of iterations set in the initialization step, the algorithm terminates and the current optimal quantization parameter vector is obtained.
## Materials and Methods
### Model Calibration and Validation
The same threshold was selected in the low-flow study in a heterogeneous catchment in Slovenia [81]. For the HBV-Land catchment model, based on the calibration period data from 1 October 1981 to 31 December 1991, the calculated low-flow threshold Q\({}_{70}\) is 0.39 mm. Usually, the coefficient of efficiency (\(\mathrm{R_{eff}}\)) is used in the HBV model. \(\mathrm{R_{eff}}\) is the Nash-Sutcliffe efficiency coefficient (NSE) of a whole calculated period and can range from \(-\infty\) to 1. When NSE = 1, then we get a \"perfect-fit value\" and it means that \(Q_{sim}(\mathrm{t})=Q_{obs}(\mathrm{t})\). When NSE = 0, the simulation is as good or as poor as the constant-value prediction. On the other hand, NSE < 0 means an inferior fit. As can be seen from the equations in Table 3, all discharges are time-dependent. The NSE coefficient is sensitive to extreme values. Model relevance can be objectively accepted or rejected based on the NSE greater than the threshold selected by the user. The acceptable values depend on the use of the model and the practice of modeling. Results of calculations presented in this paper showed a good approximation of the perfect-fit values, i.e., NSE = 1. Moreover, the parameter's relative deviation (%) was calculated by comparing the simulated parameter (\(\mathrm{P_{sim}}\)) to perfect-fit parameter (\(\mathrm{P_{PF}}\)) to standardize the various metrics and allow for a comparison of the parameters (Equation (2)). \[\mathrm{Parameter\ relative\ deviation}\ (\%)=(\mathrm{P_{PF}}-\mathrm{P_{sim}})/ \mathrm{P_{PF}}\times 100 \tag{2}\]
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0
### Enabling Precision Agriculture in Extremely Remote Locations  
Smart agricultural systems are commonly established and operated in geographically remote regions. With the evolution of 5G technology, it is possible to provide satisfactory remote coverage and connectivity services by integrating terrestrial and satellite systems [116]. However, the emergence of Agriculture 5.0 presents new challenges due to its ultra-precise service requirements. Consequently, it is expected that 5G networks will face difficulties meeting these demands, especially in remote and isolated areas [47].  
In the four-dimensional unified communication environment of 6G-IoT, however, novel technologies are anticipated to provide effective and dependable solutions for precision agriculture in extremely remote areas. In space, the advent of 6G VLEO satellite mega constellations will provide dense satellite swarms that will be able to cover even the most remote areas and provide low signal path loss and reduced communication latency [55]. Specifically, it is projected that 6G global coverage will be greater than 607 \(\times\) 103 km\({}^{2}\), offering a peak throughput of up to 18 Gbps/km\({}^{2}\). In terrestrial networks, RIS technology could be used to enhance wireless backbone networks, which are typically used to connect remote agricultural areas, given that it can extend the range of wireless connections and conserve energy. Moreover, due to the fact that RIS can be combined with wireless power transfer systems or SWIPT systems, it can also be very useful for charging IoT devices in Agriculture 5.0 remote monitoring scenarios [83].  
In the case of aerial networking, UAVs could be used as relays or even mobile wireless access points when part of the terrestrial network has been compromised or obliterated as a result of a natural disaster. UAVs are well-suited for this application due to their ease of shifting position and establishing a line-of-sight channel with ground devices, as well as their controllability [56]. Due to their low operating altitude, they are classified as a low amplitude platform (LAP), a category that will be supported by 6G standards. In addition, they may be combined with satellite systems to create a hybrid non-terrestrial network with enhanced capabilities, such as wider coverage, higher bandwidth, and more resilient communication connections [70].
## 3 Ambiguity Suppression Method  
In order to solve the problem of poor image quality caused by strong ambiguous signals, we developed an ambiguity suppression method aimed at ambiguous signals with odd ambiguous number. The general flowchart is given in Figure 2.  
Figure 2: Figure which shows the detailed steps of the proposed range ambiguity suppression method.  
From previous analysis results we know that the ambiguous signals with odd \(n\) show contrary modulation rate to the main signals, whereas even \(n\) show the same rate. Therefore, we can get the image result of ambiguous area with odd \(n\) through range compression with contrary modulation rate to main signals and simple azimuth compression. Actually, SAR imaging can be regarded as a process to gather the scattered signal power again. Through ambiguous area imaging, we can get the focused ambiguous signal power rather than that spreads out in echo data. Then the focused ambiguous area can be detected and obtained by means of CFAR. Next, the ambiguity suppression is applied to the detected ambiguous area by simply reducing the amplitude, with no destruction to the phase information. Finally, echo data with suppressed ambiguous signals are obtained by anti-compression in range and azimuth direction. The image after ambiguity suppression are provided with Chirp-Scaling algorithm. The details of each step are as follows.
# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 6 Conclusions  
This paper presented a TCN-based model based on a casual convolution architecture with a Q-learning algorithm to efficiently learn the correlations between meteorological features and wind power generation. The experimental results show that the Q-learning algorithm efficiently helps data engineers in determining appropriate parameters for temporal convolutional networks. Additionally, the TCN has a great capability for feature extraction of long-term sequence data and retains higher prediction accuracy than GRU and LSTM. Overall, the proposed TCN-based approach outperformed GRU, LSTM, and RNN in our experiments for wind power forecasting.  
Conceptualization, P.W.; methodology H.-C.L. and K.-M.C.; resources, P.W.; formal analysis, W.-H.L. and Z.-Y.Y.; data curation, W.-H.L. and Z.-Y.Y.; writing--original draft, W.-H.L.; writing--review and editing, P.W. and K.-M.C.; software, H.-C.L.; validation Y.-H.L., H.-C.L. and W.-H.L.; visualization, H.-C.L., Z.-Y.Y. and Y.-H.L.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan under grant no. MOST 110-2410-H-168-003 and the green energy technology research center on the featured areas research center program within the framework of the higher education sprout project from Ministry of Education (MOE) of Taiwan under Grant No. MOE 2000-109 CC5-001.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare