esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Dental extraction can initiate a sequence of biological events that may lead to an insufficient bone volume, which can compromise implant placement. To minimize bone loss, alveolar ridge preservation procedures were developed. However, smoking has been shown to adversely affect the outcomes of the procedures. Hence, this clinical study aimed to assess the ability of platelet-rich fibrin to maintain ridge volume and its ability to generate vital bone in smokers. Methods: Eighteen current heavy smokers with a total of forty upper molars indicated for extraction and implant placement were randomly allocated to four different groups: an advanced platelet-rich fibrin group (A-PRF) (n = 10), a factor-enriched bone graft matrix covered by A-PRF group (A/S-PRF) (n = 10); a freeze-dried bone allograft covered by a crosslinked collagen membrane group (FDBA/CM) (n = 10), which served as a positive control; and a negative-control resolvable collagen plug group (RCP) (n = 10). Two consecutive high-resolution CBCT images were taken for each augmented socket to evaluate the bone volume, one at baseline and the other after six months, and four different measurements (vertical height, horizontal 1 mm H1, horizontal 3 mm, and horizontal 5 mm) were taken for each image. To evaluate the bone vitality, three bone samples were harvested for each group and were analyzed histologically using H and E staining. The results were analyzed using SPSS version 26.0 through the Wilcoxon sign rank test and Kruskal-Wallis test. Results: In terms of preserving bone volume, A/S-PRF showed no significant reduction in bone after six months, while A-PRF showed a significant loss according to two measurements (vertical and horizontal 5 mm), and the last two groups showed a significant loss in all four measurements. For vital bone formation, three groups (A-PRF, A/S-PRF, and RCP) showed the generation of only vital bone, while the fourth group (FDBA/CM) showed the generation of a mixture of vital and non-vital bone. Conclusions: Based on the outcomes of this study, PRF was able to minimize bone loss following dental extraction and generate vital bone in smokers.  
platelet-rich fibrin; bone regeneration; cigarette smoking; alveolar-ridge preservation; dentistry; implant; oral surgery +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### In Vitro CT Samples Preparation  
The CT phantom was formulated from the agarose gel to determine the possibility of using AuNPs for CT imagery. The CT phantoms were prepared from agarose gel XP, via the dissolution of 1 g of agarose gel XP in 100 mL distilled water at 80 \({}^{\circ}\)C. Then, 1 mg of AuNPs (powder) was dispersed in 700 \(\mu\)L of distilled water and then 300 \(\mu\)L of agarose XP hot solution was added to the mixture in 2 mL vials. The same procedure has been followed for the other concentrations. Five gel phantoms containing different amounts (1, 2, 3, 4, and 5 mg/mL) of NPs were prepared plus a blank (control) agarose gel phantom. The samples were kept cool until they solidified. A Toshiba Aquilion LB (Model TSX-210A) CT scanner was used for the CT imaging. The brightness of the images denotes the intensity of the signal. The criteria for CT imaging include 80 kV X-ray voltage, and 100 mA anode current through the head window (W: 120 Hounsfield units (HU), L: 40 HU)). To determine HU values, the images were evaluated using the program MICRODICOM based on signal strength (Equation (3)). The image matrix, slice thickness and slice distance and field of view (FOV) were set at 320 mm, 3 mm, and 640/640 mm, respectively.  
\[\text{HU = 1000}\times((\upmu\text{X - }\upmu\text{water})\ /\upmu\text{water}) \tag{3}\]  
where \(\text{HU = Hounsfield unit, X = tissue and = linear attenuation coefficient.
## 6 Results and Discussion
### Exergoeconomic and Environmental Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Component** & \(\dot{\mathbf{C}}_{\mathbf{D}}\) **(S/h)** & \(\dot{\mathbf{Z}}\) **(S/h)** & \(\dot{\mathbf{C}}_{\mathbf{D}}+\dot{\mathbf{Z}}\) **(S/h)** & \(f\) **(\%)** \\ \hline Compressor & 830.8 & 500.2 & 1331 & 37.58 \\ Combustion chamber & 11,758 & 27.69 & 11,785.69 & 0.23 \\ Gas turbine & 1175 & 275.3 & 1450.3 & 18.98 \\ Steam generator & 2174 & 240 & 2414 & 9.94 \\ Steam turbine & 1641 & 340.6 & 1981.6 & 17.19 \\ Condenser & 916.5 & 121 & 1037.5 & 11.66 \\ Pump & 18.52 & 4.38 & 22.9 & 19.15 \\ Total system & 18,513.82 & 1509.17 & 20,022.99 & 7.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Exergoeconomic analysis of Config. 1. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Component** & \(\dot{\mathbf{C}}_{\mathbf{D}}\) **(S/h)** & \(\dot{\mathbf{Z}}\) **(S/h)** & \(\dot{\mathbf{C}}_{\mathbf{D}}+\dot{\mathbf{Z}}\) **(S/h)** & \(f\) **(\%)** \\ \hline Compressor & 854.1 & 500.2 & 1354.3 & 36.93 \\ Regenerator & 760.6 & 112.3 & 872.9 & 12.86 \\ Combustion chamber & 6974 & 27.69 & 7001.69 & 0.395 \\ Gas turbine & 1144 & 236.4 & 1380.4 & 18.71 \\ Steam generator & 822.3 & 137.6 & 1019.9 & 13.49 \\ Steam turbine & 622.1 & 163.6 & 785.7 & 20.82 \\ Condenser & 372.3 & 46.51 & 418.81 & 11.11 \\ Pump & 8.15 & 2.36 & 10.49 & 22.32 \\ Total system & 11,557.55 & 1226.66 & 12,784.21 & 9.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Exergoeconomic analysis of Config. 2.
## 2 Materials and Methods
### Data Processing
#### 2.6.3 Apparent-Resistivity \(\rho_{a}\) Map  
The \(\rho_{a}\) map (Figure 5), which is obtained from our algorithm code, can be seen as a matrix of values of \(N\) rows and \(M\) columns (Equation (6)) where each column represents a 1-h time window, and each row represents each frequency/period point (\(N\) = 'Number of point periods' and \(M\) = 'Number of partial windows'). Each column corresponds to the apparent resistivity-fitted curve (Figure 3); the only difference is the color assigned to each resistivity value (Figure 5).  
\[\begin{array}{cccc}\rho_{a_{(0,0)}}&\rho_{a_{(0,1)}}&\cdots&\rho_{a_{(0,M-1)} }\\ \rho_{a_{(1,0)}}&\rho_{a_{(1,1)}}&\cdots&\rho_{a_{(1,M-1)}}\\ &\vdots&\\ \rho_{a_{(N-10)}}&\rho_{a_{(N-11)}}&\cdots&\rho_{a_{(N-1,M-1)}}\\ \end{array} \tag{6}\]  
Figure 4: Distribution of the first six frequencies chosen to represent the apparent resistivity curve.  
Figure 5: Anomaly registered before the 6.0 Mw Mesetas earthquake calculating the apparent resistivity from USME HQE-HFN channels. (Format of digitized data on the time axis: year-month-day hour).  
As a second option, moving on to the interpretation of the \(\rho_{a}\) map, this can also be seen as a depth profile. Each period in each column means a different and greater depth; let us remember the relation of Equation (1), which tells us that the longer the period, the greater the _skin depth_ (keeping in mind that this parameter also depends on the resistivity of the medium).
## 3 Topology Optimization Using the Cellular Automata Method
The idea of Cellular Automata is to model a complex system by its discrete idealization and to mimic system performance based on local interactions governed by simple local update rules. The application of CA requires decomposition of the design domain into a lattice of cells, which can have influence only on the neighboring cells. The examples of plane neighborhoods for rectangular cells are presented in Figure 4.  
Having selected a neighborhood, the problem can now be formulated as a set of local minimizations posed for each element:  
\[\text{min}\ U(d_{i})\ =\ d_{i}^{p}\mathbf{u}_{i}^{T}\mathbf{k}_{i}\mathbf{u}_{i} \tag{3}\]  
In order to solve the problem, a heuristic, local CA update rule is here proposed, utilizing the information gathered from a selected neighborhood. New values of design variables can be calculated based on the Gauss-Seidel iteration mode, i.e., cell updates its state based on updated values found for cell neighbors in the current and previous iteration, or on the Jacobi iteration mode, i.e., cell updates its  
Figure 4: Neighborhoods for plane problems: (**a**) empty; (**b**) von Neumann; (**c**) Moore; (**d**) MvonN; (**e**) extended Moore. state based on the states of the surrounding cells obtained in the previous iteration only. This scheme was chosen for this paper. In what follows:  
\[d_{i}^{(t+1)}\ =\ d_{i}^{(t)}+\Delta d_{i}^{(t)}, \tag{4}\]  
where \(t\) is the current iteration and:  
\[\Delta d_{i}^{(t)}\ =\ \frac{1}{M+1}\Bigg{(}\alpha_{i}+\sum_{k\ =\ 1}^{M}\alpha_{ ik}\Bigg{)}m. \tag{5}\]  
The rule is illustrated in Figure 5:  
In Equation (5) \(m\) denotes move limit (e.g., \(m\) = 0.2), coefficients, \(\alpha_{i}\) (for central cell \(i\)) and \(\alpha_{ik}\) (for \(M\) neighboring cells) are calculated based on the concept preliminary reported in [33]--see Equation (6):  
\[\alpha_{i}\ =\ \left\{ \begin{array}{cc}-C_{\alpha}\ \ \mbox{if}\ \ U_{i}<U_{1}\\ f(U_{i})\ \ \mbox{if}\ \ U_{1}\ \leq U_{i}\leq U_{2}\\ C_{\alpha}\ \ \mbox{if}\ \ U_{i}>U_{2}\,\end{array} \right.\ \ \ \ \ \alpha_{ik}\ =\ \left\{ \begin{array}{cc}-C_{\alpha}\ \ \mbox{if}\ \ U_{ik}<U_{1}\\ f(U_{ik})\ \ \mbox{if}\ \ U_{1}\ \leq U_{ik}\leq U_{2}\\ C_{\alpha}\ \ \mbox{if}\ \ U_{ik}>U_{2}\,\end{array} \right. \tag{6}\]  
where \(\ U_{i}\,\ U_{ik}\) are the values of compliances of the central and neighboring cells, \(U_{1}\) and \(U_{2}\) are the threshold values of compliances which separate cells of the lowest and the largest compliances, and can be selected for each example. For the remaining cells, the linear function was built to fulfill the assumptions \(f(U_{1})\ =\ -C_{\alpha}\ \ \mbox{and}\ f(U_{2})\ =\ C_{\alpha}\)  
\[f(U)\ =\ \frac{2C_{\alpha}}{U_{2}-U_{1}}U-C_{\alpha}\frac{U_{2}+U_{1}}{U_{ 2}-U_{1}}. \tag{7}\]
## 6 Current Challenges and Future Prospects
### Present Obstacles Faced by AM Technologies
#### 6.2.3 Additively Manufactured Electronics  
Additively Manufactured Electronics (AME) is a developing field that blends 3D printing techniques with the making of electronics, allowing for the direct construction of working electronic devices and circuits through additive approaches [153; 154]. By using conductive and non-conductive materials in the 3D printing process, AME makes it possible to create detailed 3D shapes while also adding electronic parts, connections [155], and circuits [156] at the same time. Benefits from this innovative method include customized designs, less material waste, and expedited production. Numerous industries, including consumer electronics, the automotive industry, healthcare, and aerospace, can benefit from it. As AME technology gets better, it could completely change how electronic devices are designed and made, leading to a future with flexible, connected, and intelligent electronics [157].  
AME's widespread use is held back by several major challenges. Ensuring printed electronics are conductive, reliable, and high-performing, along with high-resolution printing of detailed parts and connections, is crucial. Additionally, it is important to mix different materials, make large-scale production more affordable, and meet standards and certification requirements [158; 159; 160; 42; 158]. Solving these problems is key to fully realizing AME's capabilities and smoothly incorporating it into the making of electronics [161].  
The future potential of Additive Manufacturing Electronics in electronics manufacturing is vast. As technology progresses, increased adoption of AME is expected across industries such as the automotive industry, defense, and consumer electronics [162]. AME speeds up time-to-market, lowers waste, and increases design flexibility by enabling quick prototyping and tailored manufacturing [163]. Flexible electronics for curved surfaces and multifunctional devices with enhanced performance are made possible by integration with 3D-printed structures. New advancements in the manufacturing and utilization of electronic devices will be fueled by AME applications that will be expanded by improvements in printable materials, high-detail printing capabilities, and production capacity [46].
## 8 Conclusions and Future Works  
The core contributions of this study encompass a comprehensive analysis of recent implementations of DP and HE to handle privacy concerns within the context of FL. While FL is commonly perceived as a means of safeguarding privacy, our analysis has brought to light significant vulnerabilities present in various works. We delve into the spectrum of privacy attacks, illuminating their real-world relevance and implications for distributed learning. Furthermore, we offer nuanced insights into DP's deployment settings, HE's potential for safeguarding sensitive data, and the intersection of HE and DP techniques. Our work significantly augments the understanding of privacy strategies in FL and lays the groundwork for future advancements in this evolving landscape.  
Regarding DP, the main challenge is striking a balance between privacy and accuracy. Addressing this challenge entails further research into devising more resilient mechanisms that introduce minimal noise while offering heightened privacy assurances. Furthermore, alternative relaxations of DP specifically designed for the FL environment or enhancing DP through auxiliary methods like anonymization, subsampling, or cryptography could offer novel avenues of investigation.  
Concerning HE, the central challenge centers on mitigating the trade-off between privacy and computational complexity. Attacking this challenge requires a concerted effort to accelerate HE primitives while identifying algorithmic approaches to reduce the complexity of certain operations, such as division. By improving the efficiency of HE, we can simultaneously uphold privacy principles and mitigate computational overhead.  
Furthermore, the combination of HE and DP is also an interesting direction. However, this amalgamation is far from straightforward, necessitating a careful equilibrium between computational complexity, model precision, and privacy considerations. As suggested in the work of [PERSON] et al. [74], combining these two techniques has the potential to safeguard raw data across all participants in the FL process, thereby showcasing a direction for future exploration.  
Conceptualization, R.A., S.B. ([PERSON]) and S.B. ([PERSON]): methodology, R.A. and S.B. ([PERSON]); validation, R.A., S.B. ([PERSON]), S.B. ([PERSON]) and T.L.V.; investigation, R.A.; writing--original draft preparation, R.A., S.B. ([PERSON]) and S.B. ([PERSON]); writing--review and editing, R.A. and T.L.V.; supervision, S.B. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by ROC team attached to CEDRIC Lab, Cnam Paris.  
Not Applicable.
## 5 Results and Discussions
### System 2: The IEEE 69-Bus
#### 5.2.1 Case 1: APL Minimization  
The minimization of APL (OF\({}_{1}\)) only, is considered in this case. Hence, the weighting factors in Equation (18) are taken as \(\omega_{1}=1,\omega_{2}=\omega_{3}=0\). By applying QODELFA for three different values of power factor: Unity, 0.95 lag, and 0.82 lag, the obtained results are presented in Table 6.  
As it can be noted for cases 1.1 and 1.2, the active power loss is decreased from the base-case value (225 kW) to 69.426 kW with unity power factor DGs and to 20.716 with 0.95 lag power factor DGs by using the QODELFA. As demonstrated in Table 6 for each of cases 1.1 and 1.2, the values of APL obtained by applying QODELFA are better than those obtained by applying MINLP [13], QOSIMBO-Q [28], KHA [31], and SFSA [32]. The LR percentage is well increased to reach 98.09% when the power factor is set to 0.82 lag (Case 1.3). This value is higher than that from the improved analytical method (IA) [39]. The APL obtained by applying QODELFA in all cases is obviously better than those from several algorithms in the literature, where the small SD of results is small enough to verify its robustness.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 1 Introduction
* A simulation-based experimental study is conducted in an NS-3-based NDN simulator (ndnSIM) [11] to check the performance of the proposed scheme with relevant and state-of-the-art scheme [10]. The remainder of the paper is organized as follows. Section 2 describes the applications of VANETs; Section 3 presents the background related to PIT management in NDN-based VANETs. Section 4 presents the context-aware PIT management mechanism for NDN-based VANETs; Section 5 presents the performance analysis; and finally, Section 6 concludes our work.
###### Abstract  
In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier's removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.  
Bi-Directional Gated Recurrent Unit; Minimum Redundancy Maximum Relevance; Particle Swarm Optimization Algorithm; Recursive Feature Elimination; retail sales forecasting 2024  
4
## Materials and Methods
### Batch Adsorption Experiments  
The adsorption experiments were performed using nZVZ as the adsorbent and MG as the adsorbate. The experiment was carried out using 100 mL conical flask with 50 mL solution of known initial pH and initial MG concentration by adding 30 mg of the adsorbent. Then, the conical flask was mounted on a temperature controlled water bath shaker and agitated at 200 rpm for the desired temperature (20, 25 or 30 \({}^{\circ}\)C) and contact time (60, 90 or 120 min). After the experiment, the suspension was centrifuged at 4500 rpm for 5 min, and the concentration of the supernatant was measured at the maximum absorbance wavelength (616 nm). The removal efficiency of MG and the amount of adsorption at equilibrium (\(q_{e}\), mg/g) was calculated by using the following equation:  
\[Y=\frac{(c_{0}-c_{t})}{c_{t}}\times 100\% \tag{1}\]  
where \(Y\) is the removal efficiency of MG, \(c_{0}\) is the initial concentration of MG (mg/L), and \(c_{t}\) is the MG concentration at any time \(t\) (mg/L).  
\[q_{e}=\frac{(c_{0}-c_{t})}{m}\times v \tag{2}\]  
where \(q_{e}\) is the amount of adsorption at equilibrium (mg/g), \(v\) and \(m\) represent the solution volume (L) and the mass of adsorbent used (g), respectively.  
Responses surface methodology (RSM) is a collection of mathematical and statistical methods, which can help better understand the interactive effect of the process parameters and provide a smaller number of experimental runs [20]. In this study, the optimal conditions for maximum removal efficiency of MG by nZVZ were identified by the 3-level 4-factor Box-Behnken design (BBD) under RSM. The four important independent variables, viz. initial pH (A), temperature (B), contact time (C) and initial concentration (D), were selected to design experiments, whereas the removal efficiency (\(\gamma\)) was considered as the dependent variable. This design included 29 experiments with 5 central points. The levels of factors were coded as \(-1\) (low), 0 (middle) and 1 (high) (Table 1) [31]. The relationship between the response and the independent variables can be represented by the following quadratic equation:  
\[\begin{split} Y=\beta_{0}+\beta_{1}\text{A}+\beta_{2}\text{B}+ \beta_{3}\text{C}+\beta_{4}\text{D}+\beta_{5}\text{AB}+\beta_{6}\text{AC}+ \beta_{7}\text{AD}+\beta_{8}\text{BC}+\beta_{9}\text{BD}+\\ \beta_{10}\text{CD}+\beta_{11}\text{A}^{2}+\beta_{12}\text{B}^{2} +\beta_{13}\text{C}^{2}+\beta_{14}\text{D}^{2}\end{split} \tag{3}\]  
where \(Y\) is the predicted removal efficiency of MG, \(\beta_{0}\) is the intercept of quadratic model, \(\beta_{1}\)-\(\beta_{14}\) are the estimated coefficients, and A, B, C and D are the independent variables.
## 4 Discussion
### Total SFA  
In nearly all recovered studies (92-98%), SFA intake is higher than the 8%E recommendation [17]. The few exceptions were studies in the Gambia [43] (2-3 years, local study in rural population), Bangladesh [60] (2-5 years, local study in rural population) and South Africa [44] (3-7 years, local study in informal settlement of urban area), three studies also showing a very low total fat intake (12.9 to 23.8%E). This suggests that the 8%E limit for total SFA intake may not be compatible with the achievement of total fat recommendation of at least 35%E in younger children or at least 25%E in older children. In our review, the observed total SFA intake is higher than the 10%E recommendation [21] in 69-73% of studies, notably in all European countries for which we found data, for all ages considered. These findings are in line with earlier reviews, which show mean intakes of SFA higher than 10%E for \(<\)10 year old children in Europe using National Survey data [14], in 2-10 year old children worldwide [15] and in young and school-aged children of Eastern Mediterranean countries [16]. The range of SFA intake varied from 3.4-18%E in our review, a narrower range than what was found in adults, where global saturated fat consumption ranged from 2.3-27.5%E [115].  
In children, some benefits of reducing intake of SFA have been demonstrated in the context of cardiovascular health. A systematic review and meta-analysis concluded that reducing SFA intake in children aged 2-19 years significantly decreased total and LDL-cholesterol, as well as diastolic blood pressure, with no evidence of adverse effects on growth or development [116]. However, the wide age range of participants of the studies included in the review of [PERSON] et al., as well as the fact that five of the eight studies included subjects with hyperlipidemia, make it difficult to translate these findings to healthy toddlers and young children. Although cardiometabolic disease typically presents in later life, atherosclerotic plaques have been shown to appear in childhood [117] and are positively associated with dyslipidemia [118]. Elevated LDL-cholesterol in childhood has been associated with increased CVD risk factors in adulthood [118], however, there is no evidence of an effect of reducing SFA in the younger age group on later CVD risk.  
The association between total SFA intake and health is increasingly questioned and a beneficial effect of dairy SFA on the most prevalent chronic diseases in adults, such as diabetes, has been suggested [111; 119]. Recently, the SACN updated their recommendations for SFA intake in the UK and concluded that the maximum limit of 10%E for SFA intake does not apply before 2 years and applies, in full, from five years [24]. Thus, younger children may safely consume a higher proportion of their dietary fat as SFA, as a transition from full breast milk feeding to an adult diet. Then, from about 5 years of age, it may be important to keep SFA intake as low as possible to promote lifelong cardiometabolic health.