esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
## 4 Optimization Process  
### Process Integration and Design Optimization (PIDO)  
The optimization procedure was implemented in the commercial Process Integration and Design Optimization (PIDO) platform Optimus [12; 13]. The software provides a wrapper where different simulation tools (Commercial Off-The-Shelf or specifically created) can be implemented, connected and investigated, using a suite of design and optimization methods. To achieve this automatic analysis of the model behavior, the simulation requires an a priori study to determine its parameters (input variables not under user control), design variables (number, type, range), outputs, constraints (limitations to the design space) and objective(s).  
Depending on the structure of the problem and its behavior, different analysis methodologies can be implemented. The first step typically involves one or more Design of Experiments (DOE) to gain knowledge on the design variable to outputs relationship.  
A set of experiments, decided using only the range of the design variables (as for the factorial-class methods, or Latin Hypercube methodology just to name some of the most commonly used) is performed and the values of the outputs are studied either directly or involving more elaborate data post-processing as correlation studies, clustering, meta-modeling.  
Figure 9: Cross section of the model adopted for (**a**) SC and the (**b**) RC, respectively.  
The acquired insight on the problem responses can then be re-used to determine the best algorithm to assess the design that better respond to a specific need (single objective optimization) or satisfies multiple criteria at the same time (multi-objective optimization).  
The same general approach can be extended to include uncertainties, both in the variables and the analyses, and perform uncertainties propagation and robust optimization.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 2 Sizing Ratio in GCPV Systems  
As we mentioned previously, one of the most common expressions utilized to refer to the relationship between the peak power of the energy-caption subsystem and the nominal power of the energy-processing subsystem is the PV-to-inverter sizing ratio [10; 11; 12], noted as \(R_{S}\). This concept is formulated in Equation (1), where \(P_{PVG(STC)}\) (in W\({}_{\text{P}}\)) is the peak power of the PVG, specified in STC, and \(P_{INV}\) (in W) is the rated power of the central inverter.  
\[R_{S}=\frac{P_{PVG(STC)}}{P_{INV}} \tag{1}\]  
Moreover, in the specialized literature, a wide variety of terms can be found to denote this concept, such as inverter-to-PV array size ratio (SF) [13; 14], inverter-to-PV array de-rating factor (k) [15; 16], inverter-to-PV power ratio (r) [17], inverter power ratio (IPR) [18] or power ratio (PR) [19; 20], inverter sizing factor (ISF) [21; 22], array-to-inverter power sizing ratio [23], and inverter sizing ratio (ISR) [24; 25; 26]. Works presented in [8; 9] synthesize the state of the art of the optimum sizing of GCPV systems and summarize the key aspects of the developed research on this topic. [PERSON] et al. include economic considerations in the optimum inverter sizing of GCPV systems in [10].  
Regardless of how the optimum value of the sizing ratio (\(R_{S\;OPT}\)) has been determined, it is defined as the value of \(R_{S}\) that maximizes the energy efficiency of the GCPV system during a period (\(\eta_{E}\)) and fulfills the relationship shown by Equation (2). In this expression, \(E_{AC}\) represents the energy delivered to the grid by the central inverter and \(E_{DC}\) stands for the available energy at the input of the inverter, with \(P_{AC}\) and \(P_{DC}\) being the corresponding power magnitudes.  
\[\eta_{E}(R_{S\;OPT})=max\bigg{(}\frac{E_{AC}}{E_{DC}}\bigg{)}=max\bigg{(}\frac {\int_{0}^{T}P_{AC}(t)\;dt}{\int_{0}^{T}P_{DC}(t)\;dt}\bigg{)} \tag{2}\]  
The dependence of \(R_{S\;OPT}\) with time is shown in Equation (2). On one hand, \(R_{S\;OPT}\) depends on the interval of time \(T\) considered in the calculation (hour, day, month, year...); on the other hand, it is related to the time-dependent factors involved in the determination of \(P_{DC}(t)\) and \(P_{AC}(t)\), such as irradiance \(G\) and ambient temperature \(T_{A}\).  
As can be inferred, deriving the \(R_{S\;OPT}\) value is not a trivial task since the produced energy depends on the power processing features of all elements involved in the power conversion chain of the GCPV system. Some of these parameters include irradiance and temperature in the considered location, PVG operating temperature and installation mounting type, solar panels' material, tilt and orientation used for PV panels, and inverter electrical characteristics, among others. The next subsections are devoted to introducing a simulation procedure for the estimation of the \(R_{S\;OPT}\) value and describing the numerical models utilized for the characterization of GCPV system elements.
# Ozonized Water Administration in Peri-Implant Mucositis Sites: A Randomized Clinical Trial
## 2 Materials and Methods
### Randomized Clinical Trial
#### 2.2.3 Interventions and Outcomes  
At the first appointment (T0), patients were asked to sign the informed consent to participate to the study. After that, an instructed operator assessed the following periodontal clinical indices on each peri-implant site by means of a probe (UNC probe 15; Hu-Friedy, Chicago, IL, USA): Probing Pocket Depth (PPD) (measured on six sites per element), Bleeding on Probing (BOP), Bleeding Score (BS), and Plaque Index (PI) [15]. Then, a professional supragingival and subgingival oral hygiene was conducted using a piezoelectric instrument (Multipiezo, Merton S.p.a, Carasco, Italy) and Gracey curettes (Hu-Friedy, Chicago, IL, USA), followed by supragingival and subgingival application of a decontaminating glycine powder (Glycine Powder, Merton S.p.a., Carasco, Italy). At this stage, patients were randomly divided into two groups according to the treatment of the peri-implant mucositis sites: patients assigned to Group 1 (trial group) received an administration of ozonized water with the Aquolab(r) professional device with an angulation of 45 degrees of the nozzle and for 60 s time per site; conversely, patients assigned to Group 2 (control group) received the administration of pure water by means of the same device. Participants were instructed to a proper oral hygiene and underwent a 2-month follow up with appointments after 1 month (T1) and 2 months (T2) from baseline. At each appointment, the periodontal assessment with the collection of clinical indices, the decontaminating procedure with glycine powder, and the application of ozonized water or pure water were performed. The timing considered was chosen with the aim of focusing on the short-term efficacy of ozone applications for the domiciliary maintenance of peri-implant mucositis sites, and also on the basis of a previous report [22].  
The protocol of the study is shown in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Appointment** & **Procedures** \\ \hline \multirow{7}{*}{Baseline (T0)} & \(\bullet\) Signature to the informed consent for the study. \\  & \(\bullet\) Assessment of periodontal clinical indexes. \\  & \(\bullet\) Professional supragingival and subgingival oral hygiene. \\  & \(\bullet\) Supragingival and subgingival decontamination of glycine powders. \\  & _Trial Group_: administration of ozonized water on peri-implant mucositis sites. \\  & _Placebo Group_: administration of pure water on peri-implant mucositis sites. \\ \hline \multirow{7}{*}{After 1 month (T1)} & \(\bullet\) Assessment of periodontal clinical indexes. \\  & \(\bullet\) Supragingival and subgingival decontamination of glycine powders. \\ \cline{1-1}  & _Trial Group_: administration of ozonized water on peri-implant mucositis sites. \\ \cline{1-1}  & \(\bullet\) mucositis sites. \\ \cline{1-1}  & _Placebo Group_: administration of water only on peri-implant mucositis sites. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Protocol adopted for the study.
## 4 Cases of Study and Validation
### Visual Analysis of Spatial Patterns  
While absolute LST values were used for statistical validation and to confirm the overall reliability of the models, the visual comparisons between predicted and reference LST values (Figures 3-6) serve a different purpose. Their aim is to qualitatively assess spatial distribution patterns and local variations, rather than to perform direct temperature-by-temperature comparisons.  
The maps show predicted LST values across the road network, rendered in relation to the synthetic ASTER-derived temperature baseline. It is important to note that these temperatures are relative values, not absolute, which may explain some discrepancies in road coloration--for example, roads near green spaces or undeveloped land often appear cooler than surrounding urban fabric.  
In some instances, isolated anomalies (e.g., an unexpectedly warm or cool segment) can be attributed to local conditions not fully captured by the models, such as microtopography, shadowing effects, or unique land use patterns. These deviations highlight both the strengths and the limitations of the linear modeling approach, which, despite its simplicity, successfully reflects broader thermal patterns but may miss hyperlocal variations.  
Figure 4: Modeled nighttime Land Surface Temperature (LST) distribution in the city of Teruel (SCS 2): (a) estimated LST at 50 m road segment resolution; (b) spatially interpolated LST surface for continuous visualization.  
Figure 5: Modeled nighttime Land Surface Temperature (LST) distribution in the city of Guadalajara (SCS 3): (a) estimated LST at 50 m road segment resolution; (b) spatially interpolated LST surface for continuous visualization.  
Overall, the case studies confirm that the proposed methodology offers a scalable and interpretable tool for UHI modeling across diverse urban and climatic contexts, with error margins appropriate for planning applications and spatial analysis.
# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 3 Results
### Prediction Methodologies
#### 3.2.1 Works Based on Traditional (2D) Methodologies
An analysis was performed on thirty-two linear measurements on the cephalograms superimposition to assess the differences in the soft tissue profile between the post treatment results and the predicted result. It showed differences on 16 linear measurements with the most prediction errors on the upper and lower lip having a mean difference relatively small, with a greatest mean difference of 2 mm in the vertical position of stomion inferius. The authors concluded that CASSOS 2001 produced a clinically meaningful forecast of soft-tissue profile changes following bimaxillary surgery [17]. The same CASSOS software was evaluated by [PERSON] et al. for 33 patients affected by class III skeletal deformities and underwent maxillary advancement (17 patients) or bimaxillary surgery (16 patients) [18]. The post-operative cephalograms were used to determine surgical bony movements to produce the CASSOS profile simulation. Linear differences between the predicted profile and the real outcome were measured at 12 soft tissue landmarks. The authors' conclusion was that the profile predictions obtained with CASSOS could be considered useful for both type of surgeries, even if substantial variations were found. As previously [17], the most inaccuracies were found in the lip region.
# A Novel Procedure of Enhancing the Throughput of a Mode-2 Sidelink Based on Partial Sensing
## 3 How to Combine All the Partial Sensing Results
P-UE#2 and P-UE#1 exit the RSU coverage from an initial state wherein the three P-UEs have been aligned, as shown at the top in Figure 7. As P-UE#2, whose partial sensing window has a time slot of 40-80 ms, exits the coverage, the partial sensing window of P-UE#3 occupies this time slot. Similarly, as P-UE#1, which occupied 0-40 ms, exits, P-UE#3 takes this time slot for partial sensing. Through the abovementioned procedure, each P-UE can acquire the sensing results for the time slot corresponding to its own window duration of 40 ms, and for the entire observation period of 200 ms. The only possible conflict included in the proposed procedure is that, as briefly mentioned in Figure 6, each P-UE selects its resources based on the sensing results obtained approximately 400 ms before the actual transmission. The key part of the proposed technique is that the total throughput of each P-UE is expected to increase almost to that of full sensing, which is five-fold larger than that of the conventional partial sensing method.
## 4 Conclusions  
The proton beam characterization included energy spread, transverse beam size, and transverse beam emittance. This study initially focused on the energy spread, which directly influences the depth-dose distribution in terms of the intensity and depth position of R\({}_{\rm BP}\), R\({}_{80}\), as well as the distal fall-off length. To investigate this effect, the energy spread of the proton beam was examined for proton energies of 70 MeV, 100 MeV, 150 MeV, and 220 MeV. The depth-dose distribution was simulated using GEANT4-based Monte Carlo simulations. The proton beam energy and energy spread were varied over the ranges of 70 MeV to 220 MeV and 0% to 5%, respectively.  
Two key effects of the energy spread on the depth-dose distribution were observed. First, the dose intensity decreases as the energy spread increases. Second, the shape of the dose profile changes, with the Bragg peak shifting to a shallower depth as the energy spread increases. The R\({}_{\rm BP}\), R\({}_{80}\), and distal fall-off were analyzed, revealing that the energy spread has a stronger influence on the Bragg peak position and distal fall-off than on R\({}_{80}\). This confirms why R\({}_{80}\) is widely used to define the depth position.  
The distal fall-off length from the simulation was utilized to match the measured W\({}_{80-20}\) length in order to determine the energy spread corresponding to each energy. Matching results indicated that the optimal energy spreads for 70 MeV, 100 MeV, 150 MeV, and 220 MeV are 1.5%, 1.25%, 1%, and 0.5%, respectively. These optimized energy spreads provide close agreement between the simulated and measured Bragg peak and R\({}_{80}\) positions, with a relative error of less than 1%.  
Conceptualization, P.T. and S.W.; Methodology, P.T., E.K., K.T. and P.K.; Formal analysis, E.K.; Writing--original draft, P.T. and E.K.; Writing--review & editing, P.T., E.K. and N.C.; Visualization, E.K. and K.T.; Project administration, W.P.; Funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Thailand Science Research and Innovation (TSRI), and National Science, Research and Innovation Fund (NSRF) grant number 4161655.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank [PERSON] and [PERSON] for their invaluable assistance in facilitating the beam measurement at the King Chulalongkorn Memorial Hospital. Their critical insights into the impact of proton beam parameters on treatment planning systems also offered valuable perspectives from a medical standpoint.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
M. Monte Carlo.  
I.M.
## 1 Introduction
### Procedure for Estimating Inelastic Spectral Displacement and Inelastic Mode Vector  
For estimating the peak inter-story drift ratio of the steel frame with steel hysteretic dampers by using Equation (1), the inelastic spectral displacement \(Sd^{I}\) and the inelastic mode vector \(\phi_{i}^{I}\) can be estimated by the following steps (as shown in Figure 3):  
1. Define the period \(T_{f}\) for the first mode of the bare frame, as obtained from EVA of the bare frame.
2. By using the NSPA results of the bare frame, in which a lateral load pattern is based on the first mode vector, define the stiffness reduction factor to consider the post-elastic behavior of the bare frame.
3. Define the mode vector \({}_{s}\phi_{1,i}^{E}\) by performing EVA of the steel frame with dampers. Subsequently, by using Equations (4), (5), and (14)-(16) with \({}_{s}\phi_{1,i}^{E}\), determine the effective mass \({}_{s}\overline{M_{1}}\), the effective height \({}_{s}\overline{H_{1}}\), and the skeleton curves of the inelastic springs equivalent to the dampers.
4. By using Equations (9)-(11) with \({}_{s}\phi_{1,i}^{E}\) obtained from Step (3) and the stiffness reduction factor obtained from Step (2), determine the skeleton curve of the inelastic spring equivalent to the bare frame.  
Figure 3: Procedure for estimating the inelastic spectral displacement \(Sd^{I}\) and the inelastic mode vector \(\phi_{i}^{I}\).  
(5) Generate the equivalent inelastic SDOF system with multi-springs using the effective mass \({}_{s}\overline{M_{1}}\), effective height \({}_{s}\overline{H_{1}}\), and inelastic springs with the skeleton curves, as obtained from Steps (3) and (4). Subsequently, perform NTHA using the equivalent inelastic SDOF system with multi-springs to evaluate the peak drift ratio, as follows: \[\theta_{\text{SDOF}}^{l}=\frac{Sd^{l}}{s\overline{H_{1}}}\] (17) where \(Sd^{l}\) denotes the inelastic spectral displacement of the steel frame with dampers for the first mode that is obtained from NTHA using an equivalent inelastic SDOF system with multi-springs.
* Obtain the shear force versus the drift curve for each story by performing NSPA of the steel frame with dampers, in which the lateral load pattern is based on the first mode vector.
* Define the step number \(N\) at which the response corresponds to \(\theta_{\text{SDOF}}^{l}\) in the roof drift ratio versus the step number curve obtained from the NSPA result in Step (6).
* Determine the first inelastic mode vector \(\phi_{i}^{l}\) using the pattern of story drifts of the shear force versus the drift curve defined in Step (6) at the \(N\)th step defined in Step (7).
## 2 System Model and Problem Formulation
### Max-Min Optimization Problem Formulation  
The goal is to assess the system's potential transmission capability with fairness consideration for the cognitive SWIPT-based relay network. The primary network's transmission should be guaranteed first. To this end, we propose joint power control and resource allocation optimization, aiming at maximizing the minimum transmission rate of the cognitive relay system. The optimization problem is formulated as  
\[\begin{split}\text{OP1}:&\quad\max\min(R_{1},R_{2} )\\ s.t.&\quad\text{C1}:(3a),(3b)\\ &\quad\text{C2}:\text{Equation }(15)\\ &\quad\text{C3}:\text{Equation }(9)\\ &\quad\text{C4}:\text{Equation }(17)\\ &\quad\text{C5}:t\in(0,1)\\ &\quad\text{C6}:\rho\in(0,1)\\ &\quad\text{C7}:\alpha\in(0,1)\end{split} \tag{20}\]  
where \(P=\{P_{1},P_{2},P_{R}\}\), \(\text{C}_{1}\) and \(\text{C}_{2}\) are, respectively, the transmission power limits of the source nodes and relay; \(\text{C}_{3}\) and \(\text{C}_{4}\) are the transmission rate region limits of the MA phase and the BC phase, respectively; and C5, C6 and C7 are the range of the time allocation parameter, the range of the power splitting parameter and the range of the power control parameter, respectively.  
Since multiple variables are coupled in conditions C2\(\sim\)C4, OP1 is non-convex. Analysis reveals that when \(P_{1}\) and \(P_{2}\) are fixed, OP1 degenerates to a joint optimization problem determined by \(t\) and \(\rho\); when \(t\) and \(\rho\) are fixed, OP1 degenerates to an optimization problem determined by \(\alpha\). Thus, the original problem can be decoupled into two parts deriving the optimal power control and power allocation parameters when \(t\) and \(\rho\) are fixed and deriving the time allocation and power splitting parameters with joint resource allocation when the transmission power is fixed. Based on the analysis, we develop a sub-optimal algorithm to solve this complex problem.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## Appendix A Solving (26)  
As we mentioned before, the optimal value \(p^{*}\) can be obtained by simply taking the derivative of \(P_{d}\) and setting it to be zero. The detailed procedure is described as follows. By using the chain rule and the fundamental theorem of calculus to find the derivative of \(\frac{\partial P_{d}}{\partial p}\), we rewrite (26) as  
\[\frac{\partial P_{d}}{\partial\Lambda}\frac{\partial\Lambda}{\partial p}=0, \tag{27}\]  
where \(\Lambda=\left[Q^{-1}(\alpha)\sigma_{0}+\sqrt{D}(\mu_{0}-\mu_{1})\right]/ \sigma_{1}\triangleq A/\sigma_{1}\). With some mathematical manipulations, it can be shown that  
\[\frac{\partial P_{d}}{\partial\Lambda}=-\frac{1}{2\pi}e^{-\frac{\Lambda^{2}}{ 2}},\frac{\partial\Lambda}{\partial p}=\frac{\frac{\partial A}{\partial p} \sigma_{1}-A\frac{\partial\sigma_{1}}{\partial p}}{\sigma_{1}^{2}}. \tag{28}\]  
Defining \(\Psi(x)=\frac{d}{dx}\ln\Gamma(x)=\frac{\Gamma^{\prime}(x)}{\Gamma(x)}\) which is known as the \(\Psi\)-function [18], we have  
\[\frac{\partial A}{\partial p}=Q^{-1}(\alpha)\frac{d\sigma_{0}}{dp }+\sqrt{D}\left(\frac{d\mu_{0}}{dp}-\frac{d\mu_{1}}{dp}\right), \tag{29}\] \[\frac{d\mu_{0}}{dp}=\frac{2^{p/2-1}}{\sqrt{\pi}}\Gamma\left( \frac{p+1}{2}\right)\left[\ln 2+\Psi\left(\frac{p+1}{2}\right)\right],\] (30) \[\frac{d\sigma_{0}}{dp}= \frac{2^{p/2-1}}{B\sqrt[4]{\pi}}\left[B^{2}\ln 2+\Psi\left(p+\frac{1} {2}\right)\Gamma\left(p+\frac{1}{2}\right)\right]-\frac{1}{\sqrt{\pi}}\Psi \left(\frac{p+1}{2}\right)\Gamma^{2}\left(\frac{p+1}{2}\right),\] (31) \[\frac{d\mu_{1}}{dp}= \frac{2^{p/2-1}(1+\gamma)^{p/2}}{\sqrt{\pi}}\Gamma\left(\frac{p+ 1}{2}\right)\times\left[\Psi\left(\frac{p+1}{2}\right)+\ln(2+2\gamma)\right], \tag{32}\]\[\frac{d\sigma_{1}}{dp}= \frac{2^{p/2-1}(1+\gamma)^{p/2}}{B\sqrt[p]{\pi}}\bigg{[}B^{2}\ln(2+2 \gamma)+\Psi\left(p+\frac{1}{2}\right) \tag{10}\] \[\times\Gamma\left(p+\frac{1}{2}\right)-\frac{1}{\sqrt{\pi}}\Psi \left(\frac{p+1}{2}\right)\Gamma^{2}\left(\frac{p+1}{2}\right)\bigg{]},\]  
where \(B=\sqrt{\Gamma\left(p+\frac{1}{2}\right)-\frac{1}{\sqrt{\pi}}\Gamma\left( \frac{p+1}{2}\right)}\). By substituting (11)-(10) into (12), the solution of (26) can be numerically found. With other fixed parameters, \(P_{d}\) is a function of the single variable \(p\). Thus, we first select an guess interval for \(p\), then apply efficient numerical tools (e.g., Newton method [16]) to obtain the approximate roots of (12). We also assume that the error produced due to computing process can be ignored, i.e., the numerical result is acceptable for all cases.