esa-sceva/satcom-chunk-collection huggingface dataset:
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 2 Categories of Precipitation Polymerization
### Controlled/\"Living\" Radical Precipitation Polymerization  
Living polymers with reactive end groups can be easily obtained via controlled/\"living\" radical polymerization (CRP), which can be further extended to produce block polymers and polymers with other more complicated architectures. [PERSON] et al. first introduced the CRP mechanism into traditional polymer bead-forming, and proposed a series of controlled/\"living\" radical precipitation polymerization (CRPP) approaches, including (normal and reversed) atom transfer radical precipitation polymerization (ATRPP), inititer-induced \"living\" radical precipitation polymerization (ILRPP), and RAFT precipitation polymerization (RAFTPP) [37]. Other new techniques have also been developed, such as nitroxide-mediated precipitation polymerizations (NMPP) [93]. Similar to the TRPV, all CRPPs should involve the typical particle nucleation and growth stages, while their particle growth mechanisms are quite different. The particle growth mechanism of TRPV could be defined as \"grafting to\" the mechanism according to the grafting concept, while the normal ATRPP and ILRPP systems were suitable for \"grafting from\" particle growth mechanism, in which the polymer particles grow by directly capturing monomers from the reaction solutions through the surface-initiated CRPs [94; 95]. In addition, a combined \"grafting from\" and \"grafting to\" particle growth mechanism was found to be present in the reverse ATRPP and RAFTPP systems, which could be attributed to the presence of both the \"living\" initiating or chain transfer groups on the polymer particles and traditional free radical initiators in the polymerization systems [96]. The [PERSON] group has presented detailed overviews of CRPP approaches and their advances in the preparation of advanced  
Figure 5: The synthetic scheme of the micro-sized monodisperse poly(methacrylic monomer-divinylbenzene) microspheres fabricated by solvothermal precipitation co-polymerization. Reprinted with permission from Ref. [92]. Copyright 2021 Elsevier B.V.  
functional polymers [36; 37; 94], and [PERSON] et al. recently published a review summarizing various CRPPs for preparing molecularly imprinted microspheres (MIMs) as well as comparing advantages and disadvantages of each technique [97]. In the following parts, we will summarize the related works in recent years.
## 4 Generation Scheme
### Direct Generation
#### 4.1.1 Direct Generation with Composed Subcarriers  
As expressed in (11) the waveforms of both the real and imaginary parts for \(s_{\text{A}}(t)\) are square waves, which are functions with \(g(t)\), \(\alpha_{i}(t)\), and \(\varphi_{i}(t)\) with \(i\in\{\text{I},\text{Q}\}\). Figure 3 shows the scheme of direct generation with composed subcarriers. According to the signal state and frequency-hopping pattern, the parameters of generating the subcarriers can be obtained. Then, the amplitudes \(\alpha_{i}(t)\) and the subcarriers are multiplied to obtain the in-phase part \(\text{I}(t)\) and quadrature part \(\text{Q}(t)\). Finally, \(\text{I}(t)\) and \(\text{Q}(t)\), which are modulated on the cosine-phase and the sine-phase radio-frequency carriers, are combined to obtain \(s_{RF}(t)\).
## 2 Materials and Methods
### Adsorption Isotherms  
To plot Langmuir and Freundlich equilibrium isotherms, adsorption tests were made with variable nickel concentrations from 5 to 200 mg L\({}^{-1}\) and 100 mg of adsorbent. The tests were carried out in the same manner at the same pH. The parameters were determined by the linearized equations.  
The Langmuir linear isotherm can be written as:  
\[\frac{1}{q_{e}}=\frac{1}{q_{max}}+\left(\frac{1}{K_{L}q_{max}}\right)*\frac{1}{C_{ e}} \tag{1}\]  
where \(q_{e}\) is the amount of ions adsorbed in the equilibrium, \(K_{L}\) is the Langmuir constant and \(C_{e}\) represents the ion concentration in solution at equilibrium. It is possible to find an estimation of \(K_{L}\) and \(q_{max}\) from the plotting of \(1/q_{e}\) vs. \(1/C_{e}\)[26].  
The Freundlich linearized equation is:  
\[\log q_{e}=\log K_{F}+\frac{1}{n}\log C_{e} \tag{2}\]  
where \(K_{F}\) is the model constant. By plotting \(\log q_{e}\) vs. \(\log C_{e}\) the \(n\) and \(K_{F}\) parameters can be defined. The \(n\) parameter indicates the magnitude of the adsorption driving force or the surface heterogeneity. The adsorption isotherm is linear when \(n=1\), favorable when \(n<1\), and unfavorable when \(n>1\).
# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
## 2 Materials and Methods
### EVs' Charging Scheduling Method
#### 2.2.3 PSO Initialization  
Suitable initialization for the PSO search vector was applied to assist the algorithm to converge fast and with a high rate of success [38]. An initial vector for the charging power of the \(i\)th EV is calculated in Equation (17):  
\[\mathrm{P_{initial}(i,t)=\overline{P}\,(i)+k\cdot\frac{\overline{EP}-EP(t)}{ max(\big{|}\ \overline{EP}-EP\big{|})},\ \ t_{Arr}(i)\,\leq\,t<\,t_{Dep}(i),\ \forall\,i\,\in\,EV_{CS}} \tag{17}\]  
Figure 5: Particle structure.  
where \(\overline{\text{P}}\)(i) is the constant power over the charging period required by the \(i\)th EV to reach SoC\({}_{\text{t}}\), EP is the electricity price, \(\overline{\text{EP}}\) is the mean value of the electricity price and k is a coefficient defined by the system operator.  
If the initial values of the charging power obtained in Equation (17) exceed the power limit of the charger at the \(t\)th time slot, the charging power is set to the power limit of the charger, and the process shown in Figure 6 takes place to adjust the initial values without violating the chargers' power limits. The dpower variable used in Figure 6 is the cumulative surplus of the power limit of the charger over the entire charging period.
## 2 Materials and Methods  
### Site Descriptions  
Data for this study were collected at ecosystem stations located in mountainous ecosystems, Bily Kriz and Stitna, in the Czech Republic. The stations host eddy covariance systems that measure ecosystem exchange of CO\({}_{2}\). Equipment deployed at the stations is part of the CzeCOS ([[http://www.czecos.cz/](http://www.czecos.cz/)]([http://www.czecos.cz/](http://www.czecos.cz/)), accessed on 19 December 2022, Figure 1) and ICOS ([[http://icos-cp.eu](http://icos-cp.eu)]([http://icos-cp.eu](http://icos-cp.eu)), accessed on 19 December 2022) infrastructures. The Bily Kriz site is located in the northeastern Czech Republic (49\({}^{\circ}\)30\({}^{\prime}\)07.474\" N, 18\({}^{\circ}\)32\({}^{\prime}\)12.777\" E) at 875 m a.s.l. The ecosystem consists of a coniferous evergreen Norway spruce (_Picea abies_ L. Karst.) forest. The forest is on a 13\({}^{\circ}\) slope oriented south-southwest. The data presented here have been collected at a 37-year-old stand 17 m tall in the year 2018. The Stitna site is located in the southeastern Czech Republic (49\({}^{\circ}\)02\({}^{\prime}\)09.510\" N, 17\({}^{\circ}\)58\({}^{\prime}\)11.640\" E) at 540 m a.s.l. The Stitna site has European beech (_Fagus sylatica_ L.) trees which were 116 years old, with an average height 31 m in 2017. Site is oriented west-southwest, and trees are spread on a hill with a 10\({}^{\circ}\) slope.  
Figure 1: Positions of Bily Kriz and Stitna experimental sites within the Czech Republic. Photographs show instrumentation used for measurements of canopy sun-induced fluorescence at both sites. The upper row photos show the layout of instruments at the Bily Kriz site; lower row photos display instrumentation in the beech forest at the Stitna site.
# Theory of Regions for Control Synthesis without Computing Reachability Graph
## 3 Design of PN Controller
### MTSI Conditions  
Up to here, the reachability conditions and the basic cycle equations are known. Furthermore, the initial marking of PN model is given. Through these data and our previous work, one can calculate the MTSI conditions.  
**Corollary 1**.: _If the control specification is expressed as GMEC \(\left(\stackrel{{\rightarrow}}{{w}},\ K\right)\), then the identification of the set of forbidden states \(\mathrm{MI}\) is possible without generating reachability graph \(G(N,M_{0})\) using the canonic markings method [25]._  
**Remark 2**.: _The firing sequences of transitions are generated from the set \(\tau_{cycle}\) and not from \(T\) since the cycle equations exist in the desired behavior of the controlled system and not in the whole graph \(G(N,M_{0})\)._  
Since the set \(\mathrm{MI}\) of forbidden markings is known, one can determine the set of forbidden sequences \(\sigma_{\mathrm{MI}}\). Moreover, the calculation of \(\sigma_{\mathrm{MI}}\) can be done in two ways:  
* Algebraic method: By using the state equation: \(\mathrm{MI}=\mathrm{M_{0}}+\mathrm{C.\sigma_{\mathrm{MI}}}\)If \(\det\left(C\right)\
eq 0\), one can calculate the forbidden sequence \(\sigma_{\text{MI}}\): \[\sigma_{\text{MI}}=(\text{MI}-\text{M}_{0}).C^{-1}\]
* Canonic markings method [24]: From the GMEC, one can synthesize the forbidden state that does not satisfy the control specification. However, by performing back chaining \(\text{CH}_{\text{b}}\), one can find the initial marking \(\text{M}_{0}\): \[\text{CH}_{\text{b}}=\sum_{\text{i}}{{}^{\left(\text{t}_{\text{i}}\right)}}p \mid p\in\text{t}_{\text{i}}^{\left(p\right)},\text{ M}(p)\leq\beta_{\text{i}}^{-}\] \(\beta_{\text{i}}^{-}\): Is the weight for each fired transition during the back chaining. Consequently, one can determine the MTSI condition: \[\text{M}_{0}(\text{P}_{\text{c}})+\text{C}.\overrightarrow{\sigma_{\text{ MI}_{\text{i}}}}<0\] (12)  
By summarizing, a new interpretation of the theory of regions is developed in this work to determine the linear system formed of Equations (10)-(12).
## 4 Discussion
Although ELISA analysis of cytokine expression further reinforces the anti-inflammatory effects of oxygen therapy, this was observed at higher concentrations (2.4%), which negatively correlates with the other positive effects on HGnF observed at lower concentrations (0.6% and 1.2%) of blue(r)m oxygen fluid. Pro-inflammatory cytokines such as IL-1\(\beta\) and TNF-\(\alpha\) strongly stimulate IL-6 expression in human gingival fibroblasts by activating NF-kB [44]. Furthermore, in gingival fibroblasts, MMP activity upregulates the expression of TIMP-1 and IL-6 to modulate extracellular matrix remodeling. These findings highlight the complex regulatory network controlling cytokine and protease responses in gingival fibroblasts, which may significantly influence periodontal inflammation and tissue remodeling. The data from this study emphasize several critical findings that are relevant to future research. Initially, topical oxygen fluid therapy, particularly at lower concentrations of 0.6% and 1.2%, increases the viability and proliferation of HGnFs while simultaneously reducing oxidative stress and inflammatory cytokine production [12]. In the context of periodontal wound healing, fibroblast function is essential for the deposition of extracellular matrix and the restoration of tissue integrity, which is why these cellular effects are desirable [45; 46]. Secondly, the anti-inflammatory properties of blue(r)m oxygen fluid may provide therapeutic benefits over traditional antiseptics, such as chlorhexidine, which, despite their antimicrobial effects, may impede healing through cytotoxicity and inflammation [10; 15]. The results of this study offer preliminary insights that may assist in the selection of therapeutic parameters for topical oxygen therapies, thereby bolstering their potential for promoting gingival tissue regeneration while minimizing cytotoxic effects. Several limitations of this in vitro study must be acknowledged. The experimental model, while highly controlled, fails to completely replicate the intricate microenvironment of periodontal wounds, where interactions among immune cells, bacteria and mechanical forces are crucial. The long-term effects of topical oxygen fluid exposure were not evaluated beyond 14 days. Further research utilizing three-dimensional tissue models, co-culture systems incorporating immune cells and in vivo assessments is essential to validate these findings. Increased MMP-8 expression following oxygen fluid exposure may facilitate wound remodeling; however, excessive matrix metalloproteinase activity is also associated with tissue breakdown. Consequently, the equilibrium between matrix deposition and degradation requires careful consideration for prospective therapeutic applications. The observed rise in reactive oxygen species at elevated oxygen levels highlights the necessity of optimizing the therapeutic window for oxygen fluid application.
## 2 Materials and Methods
### Stage 1. Input Data Management
#### 2.1.2 Load/Generation Profiles Database  
The second objective of the input data management process focuses on determining the connection with the database of the SCADA master system that contains the information regarding the power profiles transmitted through the electric substations and those injected by the classical power plants and wind farms. Table 4 includes, as an illustration, a matrix structure uploaded from the Load/Generation Profiles Database covering the following records:  
* _Bus_--the identification number of the bus from the topology of the electric network;
* _Bus Type_--allows the identification of the buses depending on the classification in PQ, PV, and slack-bus (\(S\)). The PQ (or load) bus has known the real active (\(P\)) and reactive power (\(Q\)). The PV (or generator) bus supposes the known real power \(P\) and the voltage magnitude \(V\). The slack (or reference) bus (\(S\)) ensures the balance of the powers (active and reactive) in the system.
* \(P1\), \(Q1\), \(P2\), \(Q2\),... are the transmitted or injected active and reactive powers recorded at hour \(h=1\), \(2\),..., \(T_{H}\), usually \(T_{H}\) = 24, depending on the bus type (load or generator).  
The data may change from one specific period to another, but this can be mitigated by a sampling step with a value as small as possible in the SCADA system. The methodology works in real-time with input data collected from the SCADA system at different periods in the function by the sampling step (1, 5, 10, 15, 30, or 60 min). Using the same sampling step with the lowest possible value for all RTUs represents a solution adopted at the level of the SCADA system, which includes the frequent changes in the wind generation. An optimal value can be identified, but it must be identical to the value used for each RTU. The issue should be solved in the design and implementation phases of the SCADA system, but also the modernization phase. Regardless of the set value of the sampling step, once the data from the electric substations and wind farms have been registered in the SCADA database, they are uploaded immediately by the OPF module (that integrates the proposed methodology), and the commands to the OLTCs are transmitted instantly based on the solution obtained through the fast optimization process presented in the following. All tasks of this process are performed within a few seconds depending on processing speed, data transmission speed, and the changing speed of the taps. The real-time implementation assumes they work only with real data, without forecasted data.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
###### Abstract  
The advancement of urbanization has led to stricter requirements for the prediction and control of excavation-induced deformations. To achieve this goal, this study proposes a novel method that integrates a spatiotemporal graph attention network (ST-GAT) with a proportional-integral-derivative (PID) controller to proactively control wall deflections caused by excavation. The ST-GAT model improves wall deflection prediction by capturing spatial relationships between monitoring points near steel struts and dynamically assigning weights based on their importance. The interpretability of the model is greatly improved by generating a feature attribution map across various input features and visualizing the weight distribution between nodes in the GAT network. A proactive control method of wall deflections is proposed by replacing current monitoring values in the PID control system with predicted values for multiple steel struts using the ST-GAT model. Compared to the standard PID method, this approach can control wall deflections before significant deformations occur. A real excavation project equipped with a servo support system is used to validate the effectiveness of the proposed method. The results show that the ST-GAT model significantly outperforms other models, and its performance improves when utilizing spatial relationships from more monitoring points. With a reasonable combination of PID hyperparameters, the proposed ST-GAT-based PID controller can control wall deflections close to a target value.  
graph attention network; deformation control; deep learning; braced excavation; spatiotemporal relationship +
Footnote †: journal: Applied Sciences  
1
# A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems
## 6 Conclusions  
In this paper we have proposed a low-complexity pilot pattern and a frequency domain coupling matrix estimation for ICI mitigation method in OFDM systems. Our proposed channel estimation method relies on two facts: (1) the ICI generation mechanism to obtain interference-free samples of the coupling matrix; and (2) the sparse band structure of the coupling matrix to save numerical operations. Then, a LDL based MMSE equalization is proposed to remove the ICI. Simulation results have revealed that our proposed method efficiently reduces the ICI and leads to a lower BER in the high SNR regime than other schemes of reduced complexity.  
Conceptualization, F.J.M.-V.; methodology, F.J.M.-V.; software, F.J.M.-V.; validation, F.J.M.-V.; formal analysis, F.J.M.-V.; investigation, F.J.M.-V.; resources, G.G.; data curation, G.G.; writing--original draft preparation, F.J.M.-V.; writing--review and editing, G.G.; visualization, G.G.; supervision, G.G.; project administration, G.G.; funding acquisition, G.G. Both authors have read and agreed to the published version of the manuscript.  
This work has been funded by the European Fund for Regional Development (FEDER), the Junta de Andalucia and the University of Malaga under the projects P18-RT-3175, P18-TP-3587, UMA-CEIATECH-06 and the postdoctoral grant Ref., DOC_00265 (\"seleccion de personal investigador doctor convocado mediante Resolucion de 21 de mayo de 2020\", PAIDI 2020).  
This work do not report any data.  
The authors declare no conflict of interest.
# Renewables and Advanced Storage in Power Systems: The Iberian Case
## 3 Power System Planning under Uncertainty:the Point Estimate Method
### Uncertainty Assessment Method  
Planning studies face the problem of uncertainty regarding assumptions and parameters. Additionally, the high computational requirements make it difficult to apply usual probabilistic techniques such as Monte Carlo sampling.  
However, there are methods that, with a moderate increase in computational time, lead to an assessment of the uncertainty of expected results. Point estimate analysis is a method to obtain the moments of an output variable \(z\) from those of an input variable \(x\), where \(z=h(x)\), and has been applied to probabilistic power flow (a few basic references are [31; 32]). The method may be briefly described as follows. It must be remarked that the method described here is the two-point estimate method, which is the most used method in power system analysis. Other variants might be more convenient for other applications.  
Let \(z=h(x)\), where \(x\) is a random variable, with mean \(\eta_{x}\) and standard deviation \(\sigma_{x}\). Then, the mean value of \(z\), \(\eta_{z}\), can be approximated by:  
\[E[z]=\eta_{z}\approx p_{1}h(x_{1})+p_{2}h(x_{2}) \tag{1}\]  
and the second-order moment by the formula:  
\[E\Big{[}z^{2}\Big{]}=m_{z,2}\approx p_{1}h^{2}(x_{1})+p_{2}h^{2}(x_{2}) \tag{2}\]  
where \(x_{1}\) and \(x_{2}\) are defined in Equation (4). The variance in the random variable \(z\), \(\sigma_{z}^{2}\), is the second order central moment, \(\mu_{z,2}\), which can be obtained as:  
\[\sigma_{z}^{2}=\mu_{z,2}=m_{z,2}-m_{z,1}^{2} \tag{3}\]  
where \(m_{z,1}\) is the mean value of \(z\) and \(m_{z,1}=\eta_{z}\). Parameters \(p_{1}\) and \(p_{2}\) are weight coefficients, where \(p_{1}+p_{2}=1\) and:  
\[x_{j}=\eta_{x}+\xi_{j}\sigma_{j}\qquad j=1,\;2 \tag{4}\]  
The parameters \(p_{1}\), \(p_{2}\), \(\xi_{1}\) and \(\xi_{2}\) can be obtained by solving the following set of equations:  
\[p_{1}+p_{2}=1 \tag{5}\]  
\[p_{1}\xi_{1}+p_{2}\xi_{2}=0 \tag{6}\]  
\[p_{1}\xi_{1}^{2}+p_{2}\xi_{2}^{2}=1 \tag{7}\]  
\[p_{1}\xi_{1}^{3}+p_{2}\xi_{2}^{3}=\lambda_{x,3} \tag{8}\]  
where \(\lambda_{x,3}=\frac{\mu_{x,3}}{\sigma_{x}^{3}}\) and \(\mu_{x,3}\) is the third-order central moment of the variable \(x\).  
The solution of this system is:  
\[\xi_{j}=\frac{\lambda_{x,3}}{2}+\left(-1\right)^{3-j}\sqrt{1+\left(\frac{ \lambda_{x,3}}{2}\right)^{2}} \tag{9}\]  
\[p_{j}=(-1)\frac{\xi_{3}-j}{\xi} \tag{10}\]  
\[\zeta=\xi_{1}-\xi_{2}=2\sqrt{1+\left(\frac{\lambda_{x,3}}{2}\right)^{2}} \tag{11}\]  
for \(j=\)1, 2.  
The method can be generalized to functions of several variables. Since the method is used here only for one variable, the interested reader may consult the provided bibliography.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 3 Materials and Methods
### Operational Objectives
#### 3.2.4 Technological Objectives  
The prototype includes basic drone status control technologies, such as power level, battery level, GPS, and WiFi antenna communication, as well as more advanced flight and communication features. Specifically, during drone operation, it can manage and monitor basic flight statuses (take-off, flight, landing) as well as execute predefined autonomous flight scenarios. Additionally, a companion computer will be available for integration on the UAV.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 5 Discussion  
### Overall System Performance  
The four-month experiment at Googong reservoir between 1 February 2024 and 30 May 2024 provides valuable insights into the overall performance of the proposed multi-sensor GNSS-IoT system. The results indicate sub-centimetre agreement between this system and reference data for 6 h averaged solutions, demonstrating the high precision of the system in tracking medium-term WSE variations. A key contributor to this strong performance is the inclusion and estimation of the \(datum_{offset}\) parameter, which effectively aligns system-derived WSE estimates with the reference benchmark. This suggests that the system solution is trustworthy and repeatable for WSE monitoring and useful for validating remotely sensed Earth observation data. In addition to its performance for 6 h averaged solutions, the system also demonstrates \"high\"-quality epoch solutions at the level of 2 cm, which is within the error budget estimation shown in Table 3. This indicates that the system can capture high-frequency, large-amplitude water surface changes with acceptable accuracy.  
However, it is important to note that this performance evaluation is based on the specific experiment location and period, as well as the short distance to the nearby base station. Further deployments of the system in different locations and aquatic environments are necessary to further validate its performance comprehensively.