esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description
### DC-AC Inverter Model  
The DC power is transferred to the grid through the DC-AC inverter. As shown in Figure 3, the DC-AC inverter is composed of four power switches. \(S_{1}-S_{4}\) are all fully-controlled power switches, and \(C_{ac}\) and \(L_{ac}\) are the filter capacitance and inductance in the grid side, respectively, \(R_{L}\) is the load.  
Some ideal conditions are assumed to derive the dynamic model of the inverter.  
**Assumption 2**.: \(S_{1}\)_-\(S_{4}\) are all ideal switches with zero turn-on impedance, no dead time, and no capacitive or inductance effects. There is no parasitic resistance existing on inductance \(L_{ac}\) and capacitance \(C_{ac}\). There is only one group of switches on at any time, and the opening time and shutdown time for each switch can be ignored since it is small enough._  
According to Kirchoff's Current Law (KCL) and Kirchoff's Voltage Law (KVL), it can be obtained that:  
While \(S_{1}\), \(S_{4}\) are on:  
\[\left\{\begin{array}{c}-u_{dc}+L_{ac}\frac{di_{cc}}{dt}+u_{ac}=0\\ i_{ac}-\frac{1}{R_{L}}u_{ac}-C_{ac}\frac{du_{ac}}{dt}=0\end{array}\right. \tag{2}\]  
While \(S_{2}\), \(S_{3}\) are on:  
\[\left\{\begin{array}{c}L_{ac}\frac{di_{cc}}{dt}+u_{dc}+u_{ac}=0\\ -i_{ac}+C_{ac}\frac{du_{ac}}{dt}+\frac{1}{R_{L}}u_{ac}=0\end{array}\right. \tag{3}\]  
Assuming that \(D\) is the duty cycle of \(S_{1}\) and \(S_{4}\), then the duty cycle of \(S_{2}\) and \(S_{3}\) is one dimensional. According to the state space average model, the mathematical expression of the inverter can be expressed as:  
\[\left\{\begin{array}{c}L_{ac}\frac{di_{ac}}{dt}=(2D-1)u_{dc}-u_{ac}\\ C_{ac}\frac{du_{ac}}{dt}=i_{ac}-\frac{1}{R_{L}}u_{ac}\end{array}\right. \tag{4}\]  
Figure 3: DC-AC inverter model.  
Figure 2: Boost DC-DC chopper.  
Then, the derivative equation is obtained as:  
\[\frac{d^{2}u_{ac}}{dt^{2}}=-\frac{1}{R_{L}C_{ac}}\frac{du_{ac}}{dt}-\frac{1}{L_{ ac}C_{ac}}u_{ac}+\frac{2D-1}{L_{ac}C_{ac}}u_{dc} \tag{5}\]  
In practical applications, parameter variations and external disturbances always have an influence on the inverter. Considering the uncertainties in the inverter, Equation (5) can be rewritten as:  
\[\frac{d^{2}u_{ac}}{dt^{2}}=-(\frac{1}{R_{L}C_{ac}}+\Delta_{1})\frac{du_{ac}}{ dt}-(\frac{1}{L_{ac}C_{ac}}+\Delta_{2})u_{ac}+(\frac{2D-1}{L_{ac}C_{ac}}+ \Delta_{3})u_{dc}+d(t) \tag{6}\]  
where the parameter variations are \(\Delta_{1}\), \(\Delta_{2}\), and \(\Delta_{3}\), and external disturbances are \(d(t)\), which result from the instability of \(u_{dc}\). Let \(g(t)=-\Delta_{1}\frac{du_{ac}}{dt}-\Delta_{2}u_{ac}+\Delta_{3}u_{dc}+d(t)\), and the dynamic model of the inverter is obtained as Equation (7):  
\[\frac{d^{2}u_{ac}}{dt^{2}}=-\frac{1}{R_{L}C_{ac}}\frac{du_{ac}}{dt}-\frac{1}{ L_{ac}C_{ac}}u_{ac}+\frac{2D-1}{L_{ac}C_{ac}}u_{dc}+g(t) \tag{7}\]  
where \(u_{ac}\) and its derivative, as well as \(u_{dc}\), can be measured.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 5 Conclusions  
This paper considered the beam scheduling problem of radar networks for multiple smart-target tracking, where the TEC and dynamic states of each target evolve based on the tracking action, the non-linear filter, and dynamic model probability parameters. The trade-off between more observation of smart targets for better tracking performance and the maneuvering reaction of smart targets to elude themselves was solved by the RMABP model based on the Whittle index policy. Through numerical simulation results for both scalar and multi-dimensional states, this paper has demonstrated the better performance of the US-MP index policy by comparing it with the myopic baseline policies. For future research, our plan is to prompt the joint resource scheduling for tracking smart targets through the MP index policy.  
Figure 10: The AWRMSE in the constant ratio \(1/4\).  
Figure 9: The discounted cost per target in the constant ratio \(1/4\).  
**Author Contributions:** Conceptualization, Y.H., Z.W. and J.N.-M.; methodology, Y.H. and J.N.-M.; software, Y.H.; validation, Y.H. and J.N.-M.; formal analysis, Y.H. and Z.W.; investigation, Y.H., [PERSON] and [PERSON]-M.; resources, [PERSON] and M.Y.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], J.F. and [PERSON]-M.; visualization, Y.H.; supervision, [PERSON], [PERSON]-M. and Q.P.; project administration, Z.W., [PERSON]-M. and Q.P.; funding acquisition, Z.W., [PERSON]-M. and Q.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Part of this work is performed during [PERSON]'s research stay in the Department of Statistics at Carlos III University of Madrid, Spain. This work was in part supported by the National Natural Science Foundation of China (grant No. 62473317, U21B2008, 62233014). The work of [PERSON] was funded in part by Spain's State Research Agency (AEI) project PID2019-109196 GB-I00/AEI/10.13039/501100011033.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Methodology
The Circos diagram was constructed with the bibliometric study of the publications for the most relevant determining factors found in the literature (left-hand side of the semicircle): SA, PE, HM, WR, RM, PF, TG, SF, HR, and DM. Figure 2 shows that DM (red) and SF (light green) are the most mentioned determining factors in the research. The red (DM) and light green (SF) relationship between the contributing factor and all-time intervals can be seen. DM ribbons (red) are balanced, except in 1991-1993 and 1985-1987. SF ribbons (light green) are always presented as a significant factor through the years of research. PE (orange) also shows a prevalence in selected years but with fewer papers. The other seven contributing factors, HM (light red), HR (dark orange), PF (light orange), RM (yellow), and SA (yellow-green), are of equal importance, although some are less frequent. The right-hand side semicircle clearly shows that the earliest years, 1973-1981, are low in publications, and DM, WR, and SF were the first contributing factors that appeared. PF first appeared in 1985-1987; it was not a common contributing factor explored in the literature in the following years. This leads to the idea that DM and SF were the most critical determining factors regarding the flight characteristics evaluation process. The most \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **Description** & **Topic** & **Authors** \\ \hline SA & Situational awareness & The concept is described as _“the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future”_. & [25; 26] \\ \hline PE & Performance & The concept is described as a consequence of doing an inflight or preflight task. & [27] \\ \hline HM & Human–machine & The concept is described as systems that enable people to be effective in the complex aviation environment. & [28] \\ \hline WR & Weather & Weather planning and implications to the weather flight planning. & [29] \\ \hline RM & Risk management & The concept is described as the risk assessment for preflight and inflight planning. & [8] \\ \hline PF & Preflight & All the preflight tasks associated with preflight planning. & [28] \\ \hline TG & Training & The topic is described as pilot training and the ability to cope with an unexpected situation. & [3] \\ \hline SF & Safety & Described as all components involving flight safety. & [30] \\ \hline HR & Human error & Human error in performing tasks while on preflight or inflight. & [31] \\ \hline DM & Decision-making & The topic is described as pilot decision-making and the ability to cope with an unexpected situation. & [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acronym description according to literature. often returned contributing factors were decision-making (DM), safety (SF), performance (PE), weather (WR), training (TG), and situational awareness (SA), whereas preflight (PF), human error (HR), and risk management are the less returned factors, showing that these should be studied in-depth when investigating preflight planning.
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 2 Results and Discussion
### Antioxidant Activity of Ethanol Extracts In Vitro  
Seasonal variations in antioxidant activity of \(C\). _spongiosus_ extracts produced by MAE and UAE were evaluated by ORAC, FRAP, and DPPH assays (Figure 7). All extracts from  
Figure 6: Correlation loadings (**a**) and score plot (**b**) of the dominant volatile organic compounds (VOCs) of fresh and air-dried _Cladosteplus spongiosus_ samples obtained via hydrodistillation and analysed via gas chromatography-mass spectrometry (GC-MS).  
MAE showed higher activity. The ORAC results ranged from 54.9 \(\pm\) 1.5 to 69.8 \(\pm\) 1.9 uM TE for UAE extracts and from 55.1 \(\pm\) 1.1 to 74.2 \(\pm\) 1.0 uM TE for MAE extracts. The highest peroxyl radical inhibition activity was observed for the samples harvested in May. The FRAP results ranged from 157.4 \(\pm\) 3.9 to 238.5 \(\pm\) 21.4 uM TE for UAE extracts, and from 176.4 \(\pm\) 4.7 to 293.8 \(\pm\) 9.2 uM TE for MAE extracts. The highest reducing activity was also observed for the samples harvested in May. The scavenging ability of DPPH radical ranged from 18.2 \(\pm\) 0.7 to 27.3 \(\pm\) 0.7% for UAE extracts, and from 24.4 \(\pm\) 1.8 to 32.1 \(\pm\) 0.5% for MAE extracts. The highest inhibition was recorded for August samples. [PERSON] et al. [4] reported DPPH inhibition of _C. spongiosus_ macerated with hexane, ethyl acetate, and methanol for 48 h at 15.6 \(\pm\) 0.5%, 52.2 \(\pm\) 0.0%, and 41.5 \(\pm\) 0.1%, respectively, which is comparable to our results. Moreover, [PERSON] et al. [37] extracted _C. spongiosus f. verticilla-tum_ with methanol and ethanol solutions (100%, 80%, and 70%) for 120 min using UAE. The extracts showed antioxidant potential through 2,2-azino-bis(3-ethylbenzothazoline-6-sulfonate) (ABTS) radical scavenging and cupric ion reduction. [PERSON] et al. [6] showed that _C. spongiosus_ harvested from the Peniche coast, and extracted with dichloromethane, has the ability to scavenge peroxyl and DPPH radicals. The antioxidant activity results mentioned above cannot be compared with our results because the authors used a different methodology or expressed their results in different units [38].  
Figure 7: Seasonal variation of the oxygen radical absorbance capacity (ORAC) (**a**), ferric reducing/antioxidant power (FRAP) (**b**), and 2,2-diphenyl-1-picrylhydrazyl radical scavenging ability (DPPH) (**c**) results for _Cladosteplus spongiosus_ extracts obtained with ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE).
###### Abstract  
The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year's season. Artificial lighting plays an essential role in CPPSs as it promotes growth by providing optimal conditions for plant development. Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and profitability in closed plant production systems. However, no tools have been applied in the literature to contribute to energy savings in LED-type artificial radiation systems through the configuration of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI) methods integrated into the prediction crucial variables such as each input-variable light color or specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency (pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous operation light modes in artificial LED lighting systems.  
light wavelength; energy efficiency; features selection; machine learning +
Footnote †: journal: Applied Sciences
## 4 Simulation Results Analysis
### Performance Analysis of the EA-CA Model
#### 4.1.2 The Communication Radius of the Agent  
Figure 3 shows the evolutions of state using NA-AS strategy with different communication radius. It can be seen that the number of candidate agents is positively correlated with the size of communication radius, so the convergence speed of the system increases accordingly. The number of neighbor agents decreases when the communication radius is too small, which causes divergence.
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 3 Identification Strategy for Nonlinear Acoustic Damping
### Identification Algorithm  
The identification algorithm is summarized as follows:  
1. Obtain \(M_{m}^{\prime}\) and \(R_{m}(v)\) from the normal electroacoustic testing system and keep them unchanged during the identification process.
2. Initialize the parameter vector \([w_{p}]\) as \([w_{p}(0)]=[R_{e}(0)\ L_{e}(0)\ b_{0-4}(0)\ k_{0-4}(0)\ c_{0}(0)\ c_{1}(0)]\) and start the iteration from \(n=1\).
3. Select an appropriate step size vector \([\mu_{p}]\) and specify a convergence indicator \(\varepsilon\), which is defined as the relative deviation of the parameters of adjacent frames.
4. Solve \(u_{p}[n]\) using the measured current \(i_{m}[n]\) as well as the current parameter vector \([w_{p}[n+1]]\), and calculate the gradient of the instantaneous value of the error \(\frac{\partial\varphi[n]}{\partial w_{p}}\).
5. Update \(w_{p}[n+1]\) from Equation (21) and set the search iteration to \(n=n+1\).
6. Check for termination. If \(\varepsilon\) is smaller than a tiny tolerance, or the maximum number of iterations is reached, end the iteration process; otherwise, restart from step (4).
7. Calculate the nonlinear acoustic damping using Equation (23) with the identified values of \(c_{0}\) and \(c_{1}\).
## 3 Underlying Terminology and Concepts
### Spatiotemporal Sampling
The fulfilment of this requirement has some non-negligible costs, which are difficult to cover as part of a single mission or Agency's budget. At the present time, except for the Aerosol Robotic Network (AERONET) [52], there are no operational Land Cal/Val networks entirely fulfilling this need. The pragmatic approach to this problem consists in reinforcing international collaborations, leveraging on existing regional and continental infrastructures, with the objective to build a network-of-networks system. This approach is now being considered in the frame of the Copernicus Ground-Based Observations for Validation (GBOV) [53].
### Contributions of the Study  
This study makes several significant contributions to the field of propaganda detection and classification. _First_, it introduces an innovative methodological framework specifically designed for social media text corpora, utilizing fine-tuned RoBERTa models. By addressing the limitations of traditional methods such as long short-term memory (LSTM), CRF, \(n\)-gram, and TF-IDF, this study advances the state of the art in detecting nuanced propaganda techniques. Grounded in the diffusion of innovations theory, the research demonstrates how these advanced DL techniques offer relative advantages and compatibility with existing IS frameworks, promoting their broader adoption.  
_Second_, another critical contribution is the development of a comprehensive dataset that focuses on COVID-19-related discussions. This dataset, curated from the ProText corpus and social media platforms such as Facebook and Twitter, includes rigorous labeling of six distinct propaganda techniques and six COVID-19 themes. The meticulous annotation process establishes a robust foundation for ML-MC classification tasks. By adhering to the principles of trialability and observability emphasized in the diffusion of innovations theory, the dataset enables researchers and practitioners to evaluate and adopt the proposed methodologies effectively.  
_Third_, this study enhances the performance of DL models by optimizing network parameters through data augmentation and an analysis of information uncertainty. Theseoptimizations significantly improve the accuracy and robustness of multi-label classification systems, emphasizing the practical applicability of the research. The insights derived from these advancements hold particular relevance for real-world scenarios, especially for health practitioners and policymakers tasked with monitoring and mitigating the spread of misinformation during crises.  
_Lastly_, this research contributes strategically to the design and implementation of health information systems, particularly in the context of global health crises. By addressing the critical role of IS in combating propaganda and misinformation, this study highlights the importance of integrating advanced DL techniques into public health communication strategies. This alignment with the IS community's core values of innovation, rigor, and real-world impact ensures that the proposed methodologies have both academic and practical significance, fostering their widespread adoption and utility.
## 5 Discussion
### Relationship between Temperature and Deformation  
A steel truss bridge is a structural form that converts a solid mesh steel plate beam bridge into an open mesh according to certain rules, resulting in a light weight and strong crossing ability. Steel truss beam bridges have a large coefficient of temperature expansion and are sensitive to temperature changes. The geometric shape and internal force state of the bridge structure are closely related to temperature changes [27].  
The monthly average temperature from January 2020 to June 2022 is shown in Figure 12. The temperature during the period was 4.5-31.8 \({}^{\circ}\)C, showing a relatively obvious seasonality. In order to correlate the dimensions of deformation and temperature during the study period, the temperature data were interpolated, as shown in Figure 13. The interpolated temperature was plotted against the deformations of P1-P7, and it can be observed that only P1 and P2 showed a positive correlation with temperature.  
The Spearman correlation coefficient can not only respond to linear correlations but also detect nonlinear monotonic relationships [28]. Therefore, we calculated the Spearman correlation coefficients of temperature and the seven characteristic points, and the results are shown in Table 3. The calculated results were basically consistent with the comparison results shown in Figure 13, but the overall correlations were all weak. It must be considered that an influencing factor could be due to the temperature data being sourced from a meteorological station located 15.5 km away from the bridge, which may not accurately represent the temperature on the bridge itself, therefore having a certain impact on the results. Currently, we observed a clear positive temperature-deformation correlation for only a portion of the Wuhan Yangtze River Bridge. However, further quantification of this relationship will require the use of more precise temperature data in the future for verification.
## 3 Design and Analysis of the Sliding-Mode Controller  
The sliding-mode controller must be designed to ensure the following conditions:  
* Stable operation of the Zeta converter, which requires the stable relation between \(i_{L1}\) and \(i_{L2}\) currents given in Equation (20).
* Regulate the output current of the Zeta converter (\(i_{L2}\)) to provide the high-frequency components of the load current (\(i_{0}\)), and at the same time, regulate the \(C_{2}\) voltage to avoid a deep discharge or a dangerous overcharge.  
The first condition is ensured by introducing the first part of the switching function \(\Psi\) given in Equation (24), which corresponds to relation (20). The second condition is achieved by introducing the second part of \(\Psi\). The \(i_{R}\) value is provided by a high-pass filter, which is discussed in Sections 4.2 and 4.4; the deviation of the \(C_{2}\) voltage from the safe value \(v_{R}\) is also introduced in this second part, where the value of the \(k_{v}\) gain will be discussed in Section 3.4. Finally, the sliding surface \(\Phi\) forces the switching function to slide around zero to impose a null error; this is formalized in Equation (25).  
\[\Psi=\underbrace{[k_{c}\cdot i_{L2}-i_{L1}]}_{\text{First part}}+ \underbrace{[i_{R}+k_{v}\cdot(v_{R}-v_{C2})-i_{L2}]}_{\text{Second part}} \quad\text{where}\quad k_{c}=\frac{d}{d^{\prime}} \tag{24}\] \[\Phi=\{\Psi=0\} \tag{25}\]  
The feasibility of designing an SMC based on the previous surface must be confirmed using the transversality, reachability, and equivalent control analyses discussed in [33]; the following subsections report the results of those analyses.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 3 Results
### Evaluating the Results of Applying MLFB Algorithm  
Figure 3A illustrates the correlation between HRFPG derived from rPPGMLFB and HRCPG obtained via cPPG across four conditions (Resting, Stroop task, Post-workout, and Movement) from 10 subjects. The results demonstrated a strong correlation between the two approaches, with a correlation coefficient of 0.99, except for the condition involving movement, where the correlation coefficient was 0.95.  
Figure 3B shows the Bland-Altman plot depicting the difference between the HRs measured by the two methods as a function of the average HRFPG and HRCPG. The mean differences and 95% limits of agreement were as follows: Resting (\(-\)0.39 ppm, \(-\)4.39-4.09 ppm), Stroop task (\(-\)0.42 ppm, \(-\)3.55-3.09 ppm), Post-workout (\(-\)0.83 ppm, \(-\)3.12-1.72 ppm), and Movement (2.25 ppm, \(-\)7.03-10.53 ppm). All data except for the movement condition showed differences of <5. Furthermore, the results of applying various noise reduction algorithms to pulse signals extracted using the same method indicate that, in  
Figure 3: (**A**) Correlation between heart rate calculated using contact photoplethysmography (HRCPG) and heart rate extracted from video data using the machine learning-based filter bank algorithm (HRCPG). (**B**) Bland–Altman plot comparing the two methods.  
Figure 2: Example of computed remote photoplethysmography signals (rPPG) using the plane-to-orthogonal-skin (POS) algorithm in blue (rPPGPCO) and the proposed machine learning-based filter bank algorithm in pink (rPPGMLFB). Resting, Stroop task, and Movement represent the situation of the subject during video recording. The gray line plots contact photoplethysmography (cPPG) signals used as reference.  
most cases, the highest accuracy was achieved when the proposed algorithm was applied (compare the data in the columns in Table 2).
## 3 Mathematical Formulation of the Reactive Power Supply
### Cost of Reactive Power Generation of the Compensators  
Reactive power compensators of interest are SVC and TCSC, which are connected in series and parallel. In this section, these two devices are modeled and then, similar to previous sections, the cost of generating each reactive power unit is calculated. It should be pointed out that the optimal location and capacity of these two devices in the network is not known, and their generation costs and the total cost of reactive power supply should be minimized by selecting the proper location and capacitance.