esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dental Biomaterials for VPT
### Recent Advancements in the Vital Pulp Therapy (Table 2)
#### 4.2.1 Platelet-Rich Plasma/Fibrin, Chitosan, and Collagen  
Platelet-rich plasma (PRP) and platelet-rich fibrin (PRF), derived from a patient's autologous blood, are rich sources of bioactive molecules that stimulate tissue regeneration and dentin bridge formation. These preparations contain high concentrations of growth factors, including vascular endothelial growth factor (VEGF), transforming growth factor-beta (TGF-\(\beta\)), platelet-derived growth factor (PDGF), insulin-like growth factor (IGF), and epidermal growth factor (EGF) [67; 68]. These factors play crucial roles in cell recruitment, angiogenesis, odontoblastic differentiation, and modulation of the inflammatory response, making PRP/PRF highly promising adjuncts for direct pulp capping. Its anti-inflammatory properties can also reduce tissue inflammation and accelerate healing. However, further research is needed to optimize its clinical application and long-term effects [62; 63; 69].  
Chitosan-based scaffolds, particularly those functionalized with calcium silicate or silver-doped bioactive glass, offer dual benefits of bioactivity and antimicrobial action. They support odontogenic differentiation and have been shown to stimulate VEGF release, enhance cell proliferation, and promote dentin-pulp complex regeneration [15; 61]. Similarly, gelatin-based systems, such as chitosan-gelatin blends and gelatin methacryloyl (GelMA) hydrogels, enhance cell adhesion, mimic extracellular matrix structure, and release endogenous factors to promote angiogenesis and reparative dentinogenesis [60; 64].  
Collagen-based systems, often reinforced with chitosan or crosslinked for stability, further contribute to regenerative pulp therapy by mimicking natural extracellular matrix environments, promoting angiogenesis and odontoblast differentiation [59]. These systems support cell migration and the delivery of signaling molecules necessary for pulp repair and revascularization. Together, these advanced materials create a microenvironment favorable for healing, reduce the need for extensive pulp removal, and enable the development of targeted, biologically driven treatment strategies.
# Selecting a Proper Microsphere to Combine Optical Trapping with microsphere-Assisted Microscopy
## 2 The Selection of Microsphere Types
### Optical Trapping Simulations
However, the BTG sphere cannot be trapped, because its high refractive index (above 1.8) increases the scattering force, which always pushes the sphere away from the focused laser spot, resulting in a failure in optical trapping. Another impact factor in single-beam optical tweezers is the gravity of the microsphere. In normal optical tweezers with an inverted microscope, the gravitational force is in the opposite direction of the scattering force, which assists the gradient force to overcome the scattering force. However, in microsphere-assisted microscopy, the upright microscope is always utilized in imaging and the diameter of the used microsphere is normally larger than 5 um to get an enough FOV. When we use an upright microscope to combine the optical tweezers and microsphere-assisted microscopy, the  
Figure 2: Trapping landscapes. The maximum reverse axial force in the direction of beam propagation is shown, in terms of the trapping efficiency Q as a function of refractive index and microsphere diameter. The trapping efficiency Q can be converted to the maximum reverse axial force by multiplying by \(n_{m}P/c\), where \(n_{m}\) is the refractive index of the immersion medium, \(P\) is the beam power at the focus, and \(c\) is the light speed in free space. An objective with NA = 0.9 is used to focused the 1064 nm trapping beam, and the water is used as the immersion medium in simulations. The trapping landscape can be divided into two portions approximately. In the lower portion, we can see that microspheres with relatively low refractive index (\(n<1.8\)) can be trapped. In the upper portion, microspheres with high refractive index (\(n>1.8\)) are hard to be trapped with the used NA. relatively large gravity of the microsphere (if with a large diameter) will make the optical trapping difficult, because the gradient force is hard to overcome the scattering force and the gravitational force simultaneously. The optical trapping for a 25-um PS microsphere (density = 1.06 g/cm\({}^{3}\)) has been realized with an upright microscope [26], but few experiments were achieved for the BTG microsphere (density = 4.0 g/cm\({}^{3}\)) with a diameter larger than 10 um by a single-beam optical tweezers, to the best of our knowledge. Even with a higher NA objective, the optical trapping for the BTG sphere is still a tough topic and high-index microspheres are always trapped by counterpropagating optical tweezers in which the scattering forces are canceled [35]. This means that the BTG sphere may not be suitable when combining the single-beam optical tweezers with microsphere-assisted microscopy. For a single-beam optical tweezers in water, the SiO\({}_{2}\), PS, and MF microsphere can be optically trapped, and the BTG sphere is not the proper one for its high refractive index. Besides, the gravitational force of the sphere should be considered when selecting large spheres for optical trapping.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Design and Analysis
#### 2.3.2 Crossover Analysis  
The crossover is an important connecting unit in the Butler matrix. It is also a symmetrical four-port device and operates in such a state that when one of the ports is input, the diagonally opposite output port is at equal amplitude, and the remaining two ports are isolated. The crossover scattering parameters are  
\[[S]=\begin{bmatrix}0&1&0&1\\ 1&0&1&0\\ 0&1&0&1\\ 1&0&1&0\end{bmatrix} \tag{4}\]  
Figure 7: Simulation model of the coupler (**a**) without short-ended stubs; (**b**) with short-ended stubs.  
Two unloaded short-ended-stub hybrid couplers can be cascaded to create a Butler matrix crossover. Direct synthesis using a broadband coupler would significantly increase the overall size of the Butler matrix. The signal transmission path shows that when input port 1 is fed, output port 4 has equal amplitude output, and both output port 2 and input port 3 are isolated states modeled in Figure 8(a). The scattering parameter simulation results are shown in Figure 8(b); it can be seen that the isolation is more than 20 dB in the frequency range of 3.5-4.5 GHz, the insertion loss is less than 0.05 dB, and the curve shows symmetry centered at 4.0 GHz. The input VSWR is less than 1.2 in the frequency range of 3.8-4.2 GHz.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **CT** \\ **Procedure** \\ \end{tabular} } & \multirow{2}{*}{**Study**} & \multicolumn{4}{c}{**Age (Years, mSv)**} \\ \cline{3-6}  & & **<1** & **1 \(\leq\) to 5** & **5 \(\leq\) to \textless{} 10** & **10 \(\leq\) to \textless{} 15** \\ \hline \multirow{4}{*}{Head} & This study & 4.8 & 3.64 & 2.5 & 2.96 \\ \cline{2-6}  & USA (1) [26] & 4.01 & 3.17 & 2.43 & 2.58 \\ \cline{2-6}  & Malaysian [27] & 2.4 & 1.5 & 1.7 & 3.3 \\ \cline{2-6}  & USA (3) [28] & 1.9 & 2.4 & 3.1 & 3.5 \\ \hline \multirow{4}{*}{Chest} & This study & 1.93 & 1.51 & 1.91 & 2.05 \\ \cline{2-6}  & Malaysia [27] & 1 & 1.4 & 2.5 & 3.1 \\ \cline{2-6}  & USA (2) [29] & 2.9 & 3.3 & 3.7 & 5.1 \\ \cline{2-6}  & USA (3) [28] & 1.3 & 3.5 & 4 & 3.9 \\ \hline \multirow{4}{*}{Abdomen} & This study & 6.3 & 4.4 & 3.84 & 2.77 \\ \cline{2-6}  & USA [4,30] & 5.3 & 4.2 & 3.7 & 3.7 \\ \cline{2-6}  & Turkey [31] & 3.1 & 2.5 & 2.7 & 3.1 \\ \cline{2-6}  & USA (1) [26] & 3.67 & 3.33 & 3.61 & 8.06 \\ \hline \multirow{4}{*}{AP} & This study & 4.03 & 4.6 & 3.84 & 3.2 \\ \cline{2-6}  & USA (2) [29] & 4.2 & 4.6 & 5.8 & 8.9 \\ \cline{2-6}  & Belgium [32] & 4.56 & 5.04 & 5.06 & — \\ \cline{2-6}  & USA (3) [28] & 2.8 & 6.2 & 6.8 & 8.1 \\ \hline \multirow{4}{*}{CAP} & This study & 3.49 & 4.57 & 4.07 & 4.02 \\ \cline{2-6}  & USA (2) [29] & 4.4 & 4.4 & 4.7 & 8 \\ \cline{1-1} \cline{2-6}  & USA (3) [28] & 4.3 & 7.2 & 7.7 & 10.8 \\ \cline{1-1} \cline{2-6}  & Malaysia [27] & 1.8 & 2.9 & 5.8 & 11.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons between the calculated EDs for the pediatric CT procedures in this study and EDs found in the literature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **CT** \\ **Procedure** \\ \end{tabular} } & \multirow{2}{*}{**Study**} & \multicolumn{4}{c}{**Age (Years, CTDI\({}_{\textless{}}\))**} \\ \cline{3-6}  & & **<1** & **1 \(\leq\) to 5** & **5 \(\leq\) to \textless{} 10** & **10 \(\leq\) to \textless{} 15** \\ \hline \multirow{4}{*}{Head} & This study & 25 & 25 & 30 & 53 \\ \cline{2-6}  & USA (2) [29] & 23 & - & — & — \\ \cline{2-6}  & Europe [20] & 24 & 28 & 40 & 50 \\ \cline{2-6}  & Japan [33] & 30 & 40 & 55 & 60 \\ \cline{2-6}  & Germany [34] & 30 & 35 & 50 & 55 \\ \hline \multirow{4}{*}{Chest} & This study & 2.6 & 2.6 & 3.15 & 4.65 \\ \cline{2-6}  & USA (2) [29] & 1.6 & 2.4 & 2.9 & 7.2 \\ \cline{2-6}  & Europe [20] & 1.4 & 1.8 & 2.7 & 3.7 \\ \cline{2-6}  & Japan [33] & 3 & 4 & 6.5 & 6.5 \\ \cline{2-6}  & Germany [34] & - & 2.6 & 4 & 6.5 \\ \cline{2-6}  & France [35] & 2 & 2 & 2 & 4 \\ \hline \multirow{4}{*}{Abdomen} & This study & 4.2 & 4 & 4.2 & 3.65 \\ \cline{2-6}  & France [35] & 4 & 3 & 6 & 7 \\ \cline{1-1} \cline{2-6}  & International [36] & 5.2 & 7 & 7.8 & 9.8 \\ \cline{1-1} \cline{2-6}  & China [37] & 6.3 & 4.2 & 8.2 & 15.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons between the proposed DRLs for the CTDI\({}_{\textless{}}\) and the DRLs found in the literature.
## 4 Game Analysis
### Conditions for ESS
#### 4.2.3 Equilibrium Stability Analysis  
This section studies the stability of the equilibrium solutions. Among the existing Nash equilibrium solutions, \(E_{1}=(0,0,0,0)\), \(E_{2}=(1,0,0,0)\), \(E_{3}=(0,1,0,0)\), and \(E_{6}=(1,1,0,0)\) are desirable solutions. As a matter of fact, the proposed game is based on attack-defense strategies in which the defenders' strategies consist of investing in cybersecurity, i.e., playing 1 (\(S\) or \(T\)), in order to protect themselves effectively against cyberattacks. It is obvious that the ultimate and invariable condition that guarantees that the defenders will always be safe regardless of their choice of strategy is the absence of attacks, i.e., when the attacker plays \((0,0)\). We analyze the sign of the eigenvalues of the Jacobian matrices, i.e., \(J(E_{1}),\ldots,J(E_{12})\), obtained using the respective corresponding solutions, i.e., \(E_{1},\ldots,E_{12}\). An equilibrium solution \(E_{p}\) (with \(p=1,\ldots,12\)) is asymptotically stable if the eigenvalues obtained from \(J(E_{p})\) have all-negative real parts. Table 4 presents the results of the equilibrium stability analysis. The eigenvalues associated with each equilibrium solution, i.e., \(J(E_{p})\), are real; on the other hand, the sign of each eigenvalue depends on the Nash equilibrium. Thus, we have the following theorem. Theorem 4.1: _The proposed evolutionary game admits a unique ESS. Only \(E_{6}=(1,1,0,0)\) satisfies the conditions for asymptotic stability (\(\lambda_{q}<0\), where \(q=1,\ldots,4\))._  
Proof: We show that the eigenvalues of \(E_{6}\) are all negative. Then, we demonstrate that the other Nash equilibrium solutions have at least one positive eigenvalue. \(\bullet\)\(E_{6}\) _is asymptotically stable._  
The eigenvalues of \(E_{6}\) are \(\lambda_{1}=C_{12}-R_{11}\), \(\lambda_{2}=C_{20}-P_{20}+P_{21}-R_{20}\), \(\lambda_{3}=C_{14}P(A_{1}/T)-C_{30}\), and \(\lambda_{4}=(C_{13}+C_{21})P(A_{2}/S)-C_{32}\). First, we have \(\lambda_{1}<0\), because \(R_{11}>C_{12}\) (15). Then, we have \(\lambda_{2}<0\), because \(P_{20}+R_{20}>C_{20}+P_{21}\) (16). Next, we have \(\lambda_{3}<0\), because \(C_{30}>C_{14}P(A_{1}/T)\) (13). Finally, we have \(\lambda_{4}<0\), because \(C_{32}>(C_{13}+C_{21})P(A_{2}/S)\) (14). The eigenvalues \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), and \(\lambda_{4}\) are negative. Therefore, \(E_{6}\) is asymptotically stable.
## 4 Empirical Estimation Strategy
### Cointegration with ARDL Model  
[PERSON] and [PERSON] (1998) [66] developed the ARDL bounds testing approach to check the analysis of long-run and short-run relationships, which was further protracted by [PERSON] et al. (2001) [67], and [PERSON] et al. (2004) [68]. The cointegration testing approach is applicable regardless of the integration order with concerned variables, I(0) and or I(1), except for the occurrence of I(2). The long-run and short-run relations examined the ARDL representation of the unrestricted error correction model (UECM) of Equation (2), as depicted in Equation (5):  
\[\begin{array}{rl}\Delta\text{lnGDPPC}_{t}=\gamma_{0}&+\sum\limits_{i=1}^{P} \gamma_{1i}\Delta\text{lnGDPPC}_{t-i}+\sum\limits_{i=1}^{q^{1}}\gamma_{2i} \Delta\text{lnEAP}_{t-i}+\sum\limits_{i=1}^{q^{2}}\gamma_{3i}\Delta\text{lnEARP }_{t-i}\\ &+\sum\limits_{i=1}^{q^{3}}\gamma_{4i}\Delta\text{lnEAP}_{t-i}+\sum\limits_{i=1 }^{q^{4}}\gamma_{5i}\Delta\text{lnEN}_{t-i}+\sum\limits_{i=1}^{q^{5}}\gamma_{6 i}\Delta\text{lnPG}_{t-i}\\ &+\sum\limits_{i=1}^{q^{4}}\gamma_{7i}\Delta\text{lnRP}_{t-i}+\sum\limits_{i=1 }^{q^{7}}\gamma_{8i}\Delta\text{lnUP}_{t-i}+\Psi_{1}\text{lnGDPPC}_{t-1}+\Psi_ {2}\text{lnEAP}_{t-1}\\ &+\Psi_{3}\text{lnEAP}_{t-1}+\Psi_{4}\text{lnEAP}_{t-1}+\Psi_{5}\text{lnEN}_{t -1}+\Psi_{6}\text{lnPG}_{t-1}+\Psi_{7}\text{lnRP}_{t-1}\\ &+\Psi_{8}\text{lnUP}_{t-1}+\epsilon_{t}\end{array} \tag{5}\]  
where \(\Delta\) indicates the difference operator, \(\gamma_{0}\) represents the constant intercept, \(\Psi\) indicates the coefficients of long-run, while \(\gamma\) indicates the coefficients of short-run. The long-run co-movement among the variables of interest is ascertained on the basis of the estimated F-statistic. [PERSON] et al. (2001) constituted two values available for the test of cointegration: first, the critical values of lower bound, where the variables are integrated of order zero I(0), and secondly, the critical values of the upper bound; where the variables are integrated of order one I(1). The hypothesis of no presence of long-run association is excluded if the F-statistic estimation exceeds the critical values on the upper bound. Hence, we use the small sample critical values provided by [PERSON] (2005) [69]. Eventually, this empirical study investigates the long-run elasticity and short-run adjustment parameters in Equation (5).
# Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
## 5 Experimental Set-up
### Case Study 1--The River Paglia  
The first case study is based on the analysis of data selected from the hydrographic service of the Umbria Region, available online at [[https://www.regione.umbria.it/ambiente/servizio-idrografico](https://www.regione.umbria.it/ambiente/servizio-idrografico)]([https://www.regione.umbria.it/ambiente/servizio-idrografico](https://www.regione.umbria.it/ambiente/servizio-idrografico)), accessed on 11 November 2020, related to a single reach of the Paglia river during the flood event occurred in 2012.  
It is worth reporting that the river Paglia originates from an extinct volcano, i.e., the Amiata mountain, at 1738 meters above sea level. It flows from upstream to downstream towards the plain of the city of Orvieto and its course, which crosses the three Italian regions Toscana, Umbria and Lazio, the riverbed is mainly made up of gravelled and rocky materials with short grassy banks depending on the seasons. The hydrographic framework of this river has a basin of 1187 km\({}^{2}\) and an average flow rate of 11.3 m\({}^{3}\)/s. Its entire basin is generally characterised by low permeability materials, about 75%, and its slope is averaged at 4.8% up to its confluence with the canal Subbisone. This first stretch is followed by a steeper one, of 7% between this confluence and the Mount Rubiaglio, and by a final stretch sloping at about 3.3% (which is the one under investigation for this study).  
To evaluate the proposed optimisation procedure, we collected relevant data referring to a specific event taking place in the selected area of study Figure 3.  
During the night between the 11 th and 12 th of November 2012, pluviometers registered a massive rainfall leading to a peak discharge of 307 mm at the gauging station of the Orvieto Scalo area. This area corresponds to the cross-section labelled with '12,850', as shown in Figure 4.  
This event is of particular interest for our analysis as it produced a considerable rise in the hydrometric levels, thus generating deleterious overflows resulting in major damages in many villages and cities in the surrounding area. Note that the registered peak discharge, which we use as the upstream boundary condition for our study, was equal to \(Q=2200\) m\({}^{3}\)/s.  
Figure 3: Satellite (**a**) and DEM (**b**) representations of the simulated overflow event.  
All the performed simulations for this case study are based on the 1D steady condition for its fast computational time.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 3 Methodology  
As mentioned above, power forecasting is needed in multiple applications, such as obtaining the available flexible capacity for a customer, performing grid management tasks based on expected customer behavior, or improving the daily optimization of resources with an EMS. In this sense, a framework for forecasting electric power variables (generation/consumption) is proposed here. This framework has the objective of modeling the power values of a system for all timeslots in a given day.  
This forecasting could be achieved by developing a one-output model that gives a prediction for the selected time interval (or time step) with every execution or a multiple output model that gives predictions for all the intervals in a day with one execution of the model.  
Referring to time and performance criteria, the best approach would be to use multi-output models, which utilize a lesser amount of data for the training and testing processes (each dataset contains all the outputs for one day). In contrast, a one-output model is fed with every independent value for each interval of the available days, requiring more time for training and testing.  
Two main approaches are proposed here for modeling: rule-based baselines and artificial intelligence regression methods (which also consider the inclusion of baseline forecasts as additional inputs).
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 3 Materials and Methods  
### Pearson's Correlation  
Among the most widely used filtering methods for determining attribute relevance, we first encounter [PERSON]'s correlation, denoted by \(r_{xy}\) (see Equation (3)).  
\[r_{xy}=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-y)}{\sqrt{\sum_{i=1}^{n}(x_{i }-\bar{x})^{2}\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}} \tag{3}\]  
where \(x_{i}\) and \(y_{i}\) are the individual observations of the variables, \(\bar{x}\) and \(\bar{y}\) denote the means, and \(n\) denotes the total number of observations.  
This technique identifies attributes that exhibit a high linear correlation with the target variable, potentially suggesting attribute relevance. [PERSON]'s correlation values range between \(-1\) and \(1\). The closer the coefficient is to \(1\) or \(-1\), the stronger the linear correlation.  
A coefficient of 1 indicates a perfect positive correlation, meaning that as one variable increases, the other variable increases in constant proportion. Conversely, a coefficient of \(-1\) indicates a perfect negative correlation, meaning that as one variable increases, the other decreases in constant proportion. A coefficient of 0 indicates the absence of a linear relationship between the two variables, implying no linear correlation.  
It is important to note the limitations of [PERSON]'s correlation in detecting nonlinear relationships between variables, as well as its sensitivity to outliers, which can lead to biased or inaccurate interpretations in the attribute selection process.
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.6 Sixth Generation (6G)  
The Sixth Generation (6G) technology, which was envisioned in 2019-2020 to replace 5G technology, has a main goal of creating an \"Intelligent Internet of Everything\" and will guarantee better QoS requirements for high energy efficiency, high data rate, high reliability, extremely low latency, very broad frequency bands, and a highly intelligent Deep Learning (DL) and Machine Learning (ML) enabled network [44; 45]. 6G can be better described as a self-governing device that will provide a range of communication services and be able to mimic human intelligence and consciousness. Computation-Oriented Communications (COC), Contextually Agile eMBB Communications (CAeC), and Event-Defined uRLLC (EDuRLLC) are the three brand-new sophisticated use case applications suggested for the proposed 6G technology [46; 47; 48]. Due to the prospect that the coming technology will employ a much higher spectrum than previous generations, 6G is seen as a technology that will dramatically improve data transmission speed, up to 100-1000 times faster than 5G. Since the technology, which is predicted to be rolled out as early as 2028, is still at its early stage, research is still ongoing regarding how its visions will be fully accomplished [49].  
Figure 2 presents a pictorial representation of the mobile communication evolution showing the initial launch years and key characteristics. A comparison of all existing generations of mobile communication in terms of their respective features is presented in Table 3.  
Figure 2: Evolution of cellular communication from 1G to 5G.
# Involvement of _E. coli_ 6S RNA in Oxidative Stress Response
###### Abstract  
6S RNA, a small non-coding RNA present in almost all bacteria, inhibits transcription via direct binding to RNA polymerase holoenzymes. The mechanism of 6S RNA action was investigated to a large extent in _E. coli_, however, lack of 6S RNA (\(\Delta ssrS\)) was demonstrated to be unfavorable but not essential for cell survival under various growth conditions. In the present study, we revealed, for the first time, a lethal phenotype of the \(\Delta ssrS\) strain in the presence of high concentrations of H\({}_{2}\)O\({}_{2}\). This phenotype was rescued by complementation of the \(ssrS\) gene on a plasmid. We performed comparative qRT-PCR analyses on an enlarged set of mRNAs of genes associated with the oxidative stress response, allowing us to identify four genes known to be involved in this pathway (\(\approx\)_5_, _alpC_, _solA_ and _tpx_) that had decreased mRNA levels in the \(\Delta ssrS\) strain. Finally, we performed comparative proteomic analyses of the wild-type and \(\Delta ssrS\) strains, confirming that \(\Delta ssrS\) bacteria have reduced levels of the proteins AhpC and Tpx involved in H\({}_{2}\)O\({}_{2}\) reduction. Our findings substantiate the crucial role of the riboregulator 6S RNA for bacterial coping with extreme stresses.  
Keywords:small non-coding RNAs; 6S RNA; RNA polymerase; regulation of transcription; bacterial oxidative stress response +
Footnote †: journal: Molecular Sciences