esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Eligibility Criteria  
Question: Does the application of platelet-rich fibrin enhance periodontal regeneration when compared to a group where it is not applied?  
Participants: Patients with intrabony defects.  
Interventions: Surgical procedures involving a flap, with the addition of platelet-rich fibrin.  
Comparisons: Periodonal regeneration without the application of platelet-rich fibrin.  
Outcomes: Pre-operative and 6-month post-operative periodontal probing depth, clinical attachment level, and bone fill.  
Study design: Randomized controlled trials.  
The following criteria were required for studies to be included in this study: (1) studies with randomized controlled trials; (2) studies involving the use of platelet-rich fibrin in the management of periodontal intrabony defects; and (3) studies making a comparison between interventions and control groups. We excluded the following from our study:in vitro and animal studies, studies with a sample size of less than six, studies that involved additional procedures such as LASER application to enhance healing, literature reviews, case reports, case-control study designs, retrospective study designs, and studies published in languages other than English.
## 3 Communication Protocols in Home Automation System  
Smart home communication protocols are divided into wireless and wired groups based on the communication platform. Wired communication protocols are less diverse due to the need for peripheral hardware. One of the most significant developments in Europe in recent years in the field of smart home technology is the development of the KNX/EIB protocol for home automation [35]. KNX is an internationally recognized standard for smart home implementation and control [36]. The European Standard for Electronic Standardization (CENELEC) recently introduced the KNX standard in the EN 50,090 series, which became the international standard ISO/IEC 14,543 in July 2006 [37]. According to research, around 12 million KNX devices have been installed worldwide, more than 500,000 devices are compatible with this standard, and the organization controlling the features of this standard is formed by more than 150 members, including Siemens and Schneider. KNX enables connections using twisted pairs, power lines, and wireless and Ethernet links, and the range of sectors and applications that can benefit from this standardization is wide. There are also different types of wired protocols, such as Ethernet, X10, and UPB, which, in addition to increasing security, have disadvantages such as setup problems, incompatibility with products, and poor encryption. The emergence of the IoT has witnessed tremendous success in the application of wireless sensor networks and pervasive computing for various applications [38]. In general, wireless sensor networks have more advantages than traditional wired networks, including a high reliability, easy setup and installation, and increased productivity [39]. Also, recent electronics and wireless communication advances have made it possible to design and manufacture sensors with low power consumption, small size, reasonable price, and various applications. These small sensors can perform actions such as receiving multiple types of environmental information (based on the type of sensor), and processing and sending that information, causing people to replace traditional wired networks with wireless networks [40; 41]. Of course, with the expansion of smart homes in recent years, wireless network technology has also faced challenges that have delayed its widespread adoption [42]. As shown in Figure 4, when it comes to home automation systems [43], Bluetooth [44], Zig-Bee [45], Z-wave [46], and Wi-Fi [47] are among the most widely used protocols that users use to communicate. According to the statistical graph shown, these four protocols have the largest share in terms of application compared to other protocols. Also, according to the chart, the remaining 18% includes protocols such as LoRaWAN, 5G, 6 LoWPAN, etc., which will be explained below. These protocols are generally connected to devices or peer-to-peer networks. They can be used independently, share their sensor data, or, if necessary, store their data permanently on a local server save [48].
## 4 Technological Issues
### The Multifunction Network Analyser  
Each electrical cabin is equipped at least with one FRER Q96U4 that is a multifunction network analyser. This device allows the visualization, on a single unit, of all the main variables of an electric  
Figure 1: The MeshNetics sensor board.  
network including active and reactive energy counting. In addition, an RS485 interface is available for connectivity purposes. The serial interface allows to integrate the instrument in supervision and/or energy management systems. The used protocol is ModBus in ASCII or RTU mode. Up to \(99\) instruments can be connected on the same RS485 line, at a maximum distance of \(1200\) meters.  
The measurement of the main variables (currents and voltages) is performed through sampling which allows the correct computation of the True Root Mean Squared (TRMS) value even in presence of distorted waveforms, often encountered in modern electrical installation.  
In addition, the device computes and provides additional information useful to verify the good behavior of the system, to evaluate the energy withdrawing and to prevent from exceeding the contractual limits. These additional measures are:  
* the average current (_thermal current_) computed over a given time interval,
* the maximum value reached by the thermal current,
* the average active power computed over a given time interval,
* the maximum value reached by the average active power (_maximum demand_),
* the active energy,
* the reactive energy.
To sum up, there has already been much research on the topic of optimal dispatching and system allocation of RES-EV integrated systems [15; 16]. Nevertheless, EV's ability of across-time-and-space energy transmission (ATSET) in the context of multi-microgrid systems is not discussed in the literature. The relationship between system economic dispatching and capacity configuration is not considered, and cooperative optimization method considering both allocation and dispatching are not mentioned in the studies. On the one hand, an allocation without system dispatching would cause a redundant installation of RESs and increase the total cost of the system. On the other hand, EV dispatching without optimal allocation of the system would decrease the efficiency of their integration into the microgrid system. Therefore, a cooperative optimization that simultaneously considers allocation and dispatching is necessary for the system's economic operation. For the reference, an optimization method using both the configuration of RESs and EVs is not considered in most of the works. In order to fully exploit the potential of EV's mobile storage ability as well as reduce the redundant installation of RESs, a cooperative optimization considering EV's across-time-and-space energy transmission is presented in this paper. Both the installation capacity of RESs and the number of EV charging/discharging infrastructures (EVCDIs) are considered as allocation optimization variables. The main contributions of this paper are listed as follow:  
1. An integration mechanism of RESs and EVs is illustrated, and a mathematical model of the V2G-integrated regional multi-microgrid system is established;The concept of EV's ATSET is illustrated. Based on the ability of EV's ATSET, a cooperative optimization method for economic dispatching and system capacity allocation is proposed;
3. To fully exploiting the potential of the EV's mobile energy storage ability, both the installation capacity of RESs and the number of EVCDIs are optimized in the cooperative optimization process. The following manuscript is organized as follows. In Section 2, basic concept of cooperative optimization is illustrated. Mathematical model of a typical regional multi-microgrid system is constructed and elaborated in Section 3. In Section 4, objective functions and restrictions are described. The cooperative optimization model is demonstrated in Section 5 and case studies are performed in Section 6. Finally, in Section 7, conclusions are drawn.
## 2 Materials and Methods
### Chromatography  
#### 2.4.1 HPLC-DAD  
For the most promising experimental conditions in terms of TPC and TFC, the chemical species in the extracts were identified and quantified to understand the role of enzymatic treatment with respect to the preferential release of specific compounds. The presence of common phenolic acids, flavonoids, and sesquiterpenes in mullein extracts was evaluated using the following analytical standards: gallic acid, chlorogenic acid, vanillic acid, ferulic acid, \(p\)-coumaric acid, syringic acid, caffe acid, 3,5-dicaffeoylquinic acid, quercetin, rutin, apigenin, naringin, and verbascoside. (Sigma Aldrich, Milan, Italy).  
The preliminary identification and quantification of such chemical species present in the extracts was conducted using HPLC-DAD (Shimadzu Italia, Milan, Italy). A LiChro-CART 250-4 column, Purosphere STAR RP-18e (250 \(\times\) 4.6 mm, 5 \(\mu\)m), was used for HPLC-DAD analysis. The mobile phases were formic acid (0.2 vol% in water, (Phase A) and acetonitrile with 0.2 vol% formic acid (Phase B). The flow rate in the column was 1 mL/min. The starting gradient consisted of 90% phase A, maintained for 2 min and then decreased to 35% at 15 min. From 15 to 19 min phase A was reduced to 0% until 35 min; then the starting gradient at 90% of A was restored. Phenolic compounds were analyzed using a wavelength of 254 nm, while flavonoids were analyzed using a wavelength of 350 nm [29].
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 2 Operating Principle of Single-Phase ESs
### \(\delta\) Control of Single-Phase ES  
The scheme of the existing setup of the \(\delta\) control for a single-phase ES is depicted in Figure 2a. In the figure, a double loop control is arranged with a PR controller in the outer CL voltage loop and a P controller in the inner ES current loop. The purpose of the \(\delta\) control is to impose the phase lag of \(v_{S}\) with respect to \(v_{G}\). The calculation of \(\delta\) is based on the vector diagram of the circuit in Figure 1 and is executed for the ES to implement a certain compensation function.  
The details of the control scheme in Figure 2a are as follows. Signal \(v_{S\_ref}\) is the sinusoidal reference of voltage for \(v_{S}\). Its magnitude is fixed by the user while its phase angle lags that of \(v_{G}\) of \(\delta\). The error between \(v_{S}\) and \(v_{S\_ref}\) is fed into the PR controller to generate the reference signal \(i_{\_}{\textit{ref}}\) for the ES of current. In turn, the error between the actual ES current \(i_{\_}{L}\) and \(i_{\_}{\textit{ref}}\) is fed into the P controller to generate, via a limiter, the signal \(v_{\_}{compp}\) for the pulse width modulation (PWM) generator. The latter one delivers the four commands for the VSI switches. It is a matter of fact that the calculation of \(\delta\), schematized within the dashed line box in Figure 2a, depends on the compensation function to be achieved and, accordingly, determines the voltage reference signal As explained in Reference [2], \(\theta_{\_}0\) denotes the phase of the grid; \(\varphi_{\_}1\) is the impedance angle of line impedance; \(a\),\(b\) and \(m\) denotes the coefficients to calculate \(\varphi_{\_}4\); Besides, \(\theta\) and \(\varphi_{\_}3\) are the complementary results during the \(\delta\) calculation.  
Figure 1: The typical topology of a single-phase ES embedded in a power system. ES: electric springs; PCC: point of common coupling.  
Figure 2: \(Cont.\)Figure 2b shows the vector diagram of the power system in Figure 1 when ES operates in the so-called capacitive mode which occurs when the CL voltage is lower than its rated value. Details on the \(\delta\) calculation can be found in Reference [2]. The inductive mode can be explained in a similar way. It is worth to remark that the \(\delta\) control assumes that the length of transmission line is known in order to get acquainted of the line resistance and inductance since their values are necessary to calculate \(\delta\); the way to derive the line impedance can be found in Reference [1].
## 5 Dynamic Loading Mitigation of Offshore Platform Using TMD
### Dynamic Responses and Discussion
#### 5.2.4 The Influence of Key Design Parameters on the Applicability of Results  
The essential experimental results show that TMD has a significant effect in mitigating the dynamic response of the studied jacket platform in 105 m water depth under extreme aligned and misaligned typhoon wave loads. However, it is crucial to discuss the influence of key design parameters on the applicability of these results. The analysis assumes idealized linear TMD behavior, excluding secondary damping contributions from non-structural components. While this simplification is common in preliminary TMD studies, future work should incorporate these effects for higher-fidelity predictions.  
The performance of a TMD is inherently tied to the dynamic characteristics (natural frequencies, mode shapes, damping) of the primary structure it is controlling. Variations in platform geometry (e.g., leg spacing, bracing configuration, and deck mass distribution), water depth, and especially seabed stratigraphy (governing SSI) will alter these dynamic characteristics. For instance, a significant increase in water depth would likely lead to longer structural members, increased flexibility, lower natural frequencies, and larger environmental loads. This would require re-optimizing TMD parameters (especially the frequency ratio \(f_{opt}\)) to maintain effectiveness, although the fundamental energy dissipation mechanism of TMD remains valid [38]. Similarly, changes in soil conditions (e.g., transition from the layered clay/sand in Table 2 to extremely stiff or soft soils) profoundly affect soil-structure interaction (SSI), influencing the stiffness and damping of the entire system [39]. TMD design must account for site-specific SSI characteristics, as rigorously modeled using the PySimple1, TzSimple1, and QzSimple1 elements in this study.
## 2 Model Introduction and Verification
### Sediment Model  
This paper uses MIKE software's ST (sediment transport) module to calculate sediment transport. The open boundary of the sediment model is driven by the calculation result of the FVCOM hydrodynamic model, and the wind field data are reanalyzed by ECMWF. MIKE FVCOM and Mike FVCOM use unstructured grids and finite volume methods to solve discrete integrals so they can share the same grid system. The governing equation of the sediment model is as follows:  
\[\frac{\partial\overline{c}}{\partial t}+u\frac{\partial\overline{c}}{\partial x }+v\frac{\partial\overline{c}}{\partial y}=\frac{1}{h}\frac{\partial}{ \partial x}(hD_{x}\frac{\partial\overline{c}}{\partial x})+\frac{1}{h}\frac{ \partial}{\partial y}(hD_{y}\frac{\partial\overline{c}}{\partial y})+\frac{F_ {S}}{h} \tag{13}\]  
Equation (13) is the sediment transport formula of the model, where the average sediment concentration is: \(u\) and \(v\) are the average vertical velocities in the x and y directions, respectively: \(h\) is the water depth there; \(D_{x}\) and \(D_{y}\) are turbulent diffusion coefficients of sediment in two directions, respectively: \(F_{S}\) is a function term of sediment source.  
Sediment source function \(F_{S}\) is defined by the following formula:  
\[F_{s}=\begin{cases}E(\tau_{b}/\tau_{ce}-1)^{n}&\tau_{b}>\tau_{ce}\\ 0&\tau_{ce}\geq\tau_{b}\geq\tau_{cd}\\ \omega_{s}c_{b}p_{d}&\tau_{b}<\tau_{cd}\end{cases} \tag{14}\]  
where \(E\) the sediment scouring coefficient of the bottom of the river is bed; \(\omega_{s}\) is the settling velocity of sediment, which is related to the shape, size, and content of sediment particles. \(p_{d}\) is the possible settlement probability: \(\tau_{b}\) is the shear stress at the bottom of the bed: \(\tau_{ce}\) is the critical shear stress initiated by surface sediment: \(\tau_{cd}\) is the acute shear stress at which suspended sediment begins to settle.  
The range of the sediment transport model (Figure 1) selects Shantou Port, including the Rongjiang River and Hanjiang River and its surrounding waters. Based on the hydrodynamic model, rivers are added. The study area is narrowed. Thus, the running time of the model is shortened, and the accuracy of the key research area is improved.
## 3 Results  
### SH-SYSY and SK-N-MC Cells Are Resistant to Filovirus GP-Driven Lentiviral Transduction  
To identify potential cell-type differences in filovirus susceptibility, we transduced a panel of twelve cell lines with HIV-based particles coding for the reporter luciferase and pseudotyped with the EBOV strain Mayinga GP (EBOVpp), the MARV strain Musoke GP (MARVpp), the VSV-G Indiana strain G (VSV-Gpp), or lacking any viral GP (NoEnvpp). At 72 h post transduction, we measured the luciferase activity and normalized it for each cell line to the one obtained upon VSV-Gpp transduction (Figure 1A,B). Particles bearing no GP did not result in any reporter expression, while VSV-G particles lead to strong luciferase activity in all cell lines. Susceptibility to filovirus GP-driven transduction was heterogeneous with the endothelial cell lines HPMEC and EA.hy926 being much more susceptible than the fibroblasts and epithelial cells, and the lymphocytic cell line Jurkat being resistant, as reported before [16]. Remarkably, the adherent, neuroblastoma cell lines SK-N-MC and SH-SYSY [55,56] were also not susceptible to filovirus GP-mediated transduction. Due to this unexpected phenotype, we set out to investigate the determinants underlying the observed resistance.  
Figure 1: Unbiased cell screening for susceptibility to EBOV (EBOVpp) and MARV (MARVpp) GP-driven transduction. A panel of twelve cell lines were transduced with EBOV (**A**) or MARV (**B**) firefly luciferase pseudoparticles. Pseudoparticles with no envelope proteins (NoEnvpp) or VSV-G  
(VSV-Gpp) were used as negative and positive control, respectively. Seventy-two hours post transduction, cells were lysed and luciferase activity was measured. Data were normalized to VSV-Gpp activity as indicated in material and methods. Graphs plot the mean values of three independent experiments performed in triplicate (\(n=9\)) with error bars representing the standard deviation (SD). Individual values are represented as dots, squares or triangles.
## 2 Experimental Design and Methodology  
### Specimens  
CEM Type 1 (Union Corp., Seoul, Korea) based on ASTM C150-85 and silica fume (Elkem Silicones, Oslo, Norway) was used to prepare the samples used in this study. Silica powder (SAC Materials, pure silica S-SIL 30, Ulsan, Korea), a micro filler, was also applied. For the color development of the pigments, white Portland cement with a Hunter whiteness index of 90.5 was selected. Nanomaterials were used to improve the dense hydration structure of the test specimen because of concerns regarding the decrease in strength due to the addition of pigments. Table 1 lists the chemical composition of the binders and pigments used. A polycarbonate-type superplasticizer (flowmix 3000U, Dongnam Sdn. Bhd., Siheung-si, Gyeonggi-do, Korea) was also used. Water-soluble inorganic red and green pigments were added at 5% and 10% of cement mass, respectively; these quantities were determined using the EN 12878:2014 and ASTM C979 standards, which recommend that the pigment content should not exceed 10%. Red and green pigments have the second-highest demand following gray and white pigments, which have the highest demand, and they are used in various finishing materials. In addition, the red pigment is mainly composed of iron oxide and exhibits stable properties when incorporated with concrete. In addition, specimens were prepared without aggregates or fiber to investigate the influence of pigment addition on the improvement of hydration in HSCs for developing colored concretes.  
Table 2 lists the mix proportions of the HSCs manufactured and the notation used for each specimen. Dry mixing was performed for 5 min to mix the binders with each pigment, following which water and superplasticizer were added to the mixtures. All the materials were further mixed for 5 min to ensure thorough mixing. The fresh mixture was confirmed to have a table flow of 200 mm or higher in the non-beating state according to ASTM C4137. Finally, the fresh mixtures were poured into a mold with a volume of 50 mm \(\times\) 50 mm \(\times\) 50 mm. Specimens were prepared for compressive strength, XRD, and TG spectroscopy analyses. Samples for Commission Internationale de l' Eclairage (CIE) L**a***b*** (CIELAB) measurements were fabricated as panels with dimensions of 200 mm \(\times\) 200 mm \(\times\) 20 mm. Furthermore, \(\mathcal{O}\)100 mm \(\times\) 200 mm specimens were prepared for measuring the water-absorption rate according to age. The prepared specimens were sealed with a film and cured in the ambient atmosphere for 24 h. Subsequently, they were demolded and subjected to steam curing at 80 \({}^{\circ}\)C for 72 h. Finally, constant temperature and constant humidity curing were performed until the 28 th day. Figure 1 shows photographs of specimens manufactured via this process.
## 5 De Novo Synthesis of EGCG and Methods of Isolation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Therapeutic Group**} \\ \hline
**Drug Name** & & & & \\ _Route of_ & & & & & \\ _Administration_ & & & & & \\ \hline \multicolumn{5}{c}{**status**} \\ \hline fluxastatin & & & & [GTE] no significant change in plasma concentrations [91] \\ _iv_ & & & & concentrations [91] \\ rousvastatin & & [GTE] \(\uparrow\)AUC, & & & \\ _po_ & & [EGCG] \(\uparrow\)AUC [92] & & & \\ simvastatin & & [EGCG] \(\uparrow\)AUC, \(\uparrow\)t\({}_{1/2}\), \(\downarrow\)CL [93] & & & \\ _jags_ & & [GTE] \(\uparrow\)AUC, \(\downarrow\)t\({}_{1/2}\) [94] & & & \\ simvastatin & & & [GTE] \(\uparrow\)AUC, \(\uparrow\)C\({}_{\max}\) [95] & & \\ _po_ & & & & [GTE] \(\uparrow\)AUC, \(\uparrow\)C\({}_{\max}\) [80] & \\ \hline \multicolumn{5}{c}{**calcium channel blockers**} \\ \hline amlodypine & & [EGCG] \(\uparrow\)AUC, \(\uparrow\)C\({}_{\max}\), \(\uparrow\)t\({}_{1/2}\), \(\downarrow\)CL, \(\downarrow\)T\({}_{\max}\) [76] & & \\ dilitiazem & & & & \\ _po_ & & [EGCG] \(\uparrow\)AUC, \(\uparrow\)C\({}_{\max}\), \(\downarrow\)CL [78] & & \\ nicardipine & & [EGCG] no significant change in plasma concentrations [77] & & \\ nicardipine & & & & \\ _po_ & & [EGCG] \(\uparrow\)AUC, \(\uparrow\)C\({}_{\max}\) [77] & & \\ verapamil & & & & \\ _po_ & & & & \\ \hline \multicolumn{5}{c}{**\(\beta\)-blockers**} \\ \hline atenol & & [EGCG] no significant change in serum level and & & \\ _po_ & & & tissues distribution [96] & \\ nadolol & & [GTE] \(\uparrow\)AUC, & & \\ _igas_ & & [EGCG] \(\uparrow\)AUC [97] & & \\ nadolol & & & & \\ _po_ & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Changes in pharmacokinetic parameters of selected drugs after their co-administration with epigallocatechin-3-gallate [EGCG] or green tea extract [GTE]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{Fixation} & \\ \cline{3-5}
**Fresh tea** & & & (Panning or steaming) & & \\
**leaves** & Wilting & Bruising and partial oxidation & Fixation & & \\
**lev** & Wilting & Bruising and full oxidation & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c} Fixation \\ (Panning or steaming) \\ Fixation \\ (Panning or baking) \\ \end{tabular} (Panning or baking) & Shaping (rolling) & Drying & Green tea \\
**Shaping (rolling)** & Drying & Oolong tea \\
**Shaping (rolling)** & Drying & Black tea \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Processing conditions of main types of teas.