esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the RL  
#### 3.2.1 FT-IR Analysis of the RL  
FT-IR analyses were performed on the RL samples to examine potential chemical modifications induced by the DES during depolymerization. Figure 4 illustrates the FT-IR spectra of the KL feedstock and RL obtained from the DES treatment of KL. Notably, the peaks at 3388 cm\({}^{-1}\) correspond to the hydroxyl groups (O-H) of aliphatic and aromatic compounds. Compared to the untreated residue and original KL, the peak intensity appears diminished for the RL obtained from the DES treatment, indicating a reduction in hydroxyl groups following DES treatment [37]. The twin peaks at 2937 cm\({}^{-1}\) correspond to the C-H  
Figure 3: Effect of reaction (**A**) temperature, (**B**) time, and (**C**) DES-to-lignin ratio on the yield of depolymerized products. Reaction conditions for (**A**): 3.0 g of lignin, 8 h, DES-to-lignin ratio of 1:1, and 90 mL of ethanol; reaction conditions for (**B**): 3.0 g of lignin, DES-to-lignin ratio of 1:1, 190 \({}^{\circ}\)C, and 90 mL of ethanol; reaction conditions for (**C**): 3.0 g of lignin, 190 \({}^{\circ}\)C, 8 h, and 90 mL of ethanol.  
bonds of methyl and methylene groups [38]. The intensities of these peaks also decrease for the DES-treated RL, indicating a weakening of C-H bonds during DES processing. The carbonyl peak at 1670 cm\({}^{-1}\) corresponds to the stretching vibrations of the C=O functional group, which is associated with the lignin side chain conjugated with aromatic compounds [39]. The peaks at 1372 cm\({}^{-1}\) are attributed to phenolic hydroxyl groups [40]. Compared to the non-DES-treated RL, the peak at 1038 cm\({}^{-1}\) diminished for the DES-treated lignin, suggesting effective lignin depolymerization. Furthermore, the peaks at 1217 cm\({}^{-1}\) correspond to guaiacyl rings [41]. The reduction in the intensity of the guaiacyl peak for the DES-treated lignin sample indicates that lignin depolymerization decreases the guaiacyl unit concentration. Figure 5A,B illustrate the FT-IR spectra of the raw KL and RL obtained from the DES treatment of KL at different reaction temperatures and times. The most notable changes were observed in the RL obtained from KL depolymerization at 190 \({}^{\circ}\)C with a DES-to-lignin ratio of 1:1 for 8 h. Noticeably, the decrease in the 1038 cm\({}^{-1}\) peak for the regenerated lignin sample after the DES treatment in ethanol at 190 \({}^{\circ}\)C for 8 h suggests that ether bonds in the lignin structure have been cleaved or significantly altered. This could be due to the solubilizing action of the DES and ethanol, combined with the high temperature, which leads to the depolymerization or degradation of lignin.
## 5 Transmission Line Fault Classification  
Transmission line faults are categorized using the algorithm described in Figure 2. The peak magnitude of the FI is high in comparison to the pre-set threshold magnitude for one phase and below the threshold for two phases presenting the L-G fault. If the FI has higher magnitudes related to all phases compared to the threshold, then the LLL fault is present in the system. The severity of the LLL fault with and without ground involvement is the same; therefore, further identification is not required for protection purposes. If the FI has a higher magnitude related to the two phases than the threshold value and a lesser value than the threshold for one phase, in this case, faults with an LL nature are present where the ground may or may not be involved. These can be discriminated from each other with the help of the proposed GFI. The threshold value (GFTH) of \(4\times 10^{10}\) is selected for the discrimination of the LL faults with and without the connection of the ground. The values of the GFI for differentiation among the LL and LL-G faults using the GFI are illustrated in Figure 7. The GFI corresponding to the LL fault without involving the ground is higher compared to the GFTH, whereas it is below the GFTH for the LL faults involving the ground. Therefore, all types of faults were recognized effectively by the use of the proposed algorithm.  
Figure 6: Identification of the fault with an LLL-G nature: (**a**) signals of the current, (**b**) W-index, (**c**) S-index, and (**d**) FI.  
Figure 7: Differentiation among the LL and LL-G faults using the GFI.
## 3 Data Analysis
### Two-Factor ANOVA on Controller Situational Awareness Related Indicators
#### 3.2.1 Analysis of Controllers' Eye Movement Characteristics
Experienced controllers, after acquiring flight information, often rely on memory to extract and verify details, using enhanced working memory in air traffic management and adopting a holistic view of airspace resources to maintain safe separations. Thus,  
Figure 3: Comparison of two-factor ANOVA results for each eye movement parameter. the total fixation length and average fixation time metrics for experienced controllers were much lower than those of trainee controllers in both exhausted and non-fatigued conditions, as seen in Figure 3c,d. The length of a saccade indicates the velocity at which controllers acquire information. Figure 3e shows a reduction in the saccade length for tired controllers compared to those in a non-fatigued state. Furthermore, seasoned controllers had somewhat reduced saccade durations compared to novice controllers, suggesting a decrease in information-acquisition velocity under fatigue. The reduction in information-gathering speed for seasoned controllers was less significant than that for novice controllers. Average saccade counts may reflect the concentration levels of controllers throughout the management process. Figure 3f demonstrates that experienced controllers exhibited lower average saccade counts in exhausted circumstances compared to non-fatigued situations, but trainee controllers displayed greater average counts in fatigued states, leading to contrasting patterns. This suggests that seasoned controllers maintain a more calm control state despite tiredness, resulting in diminished attention and reduced average saccade counts. Trainee controllers, with less situational awareness, may often alter their fixation locations to obtain more information, even when exhausted. Effective situational awareness is essential for controllers to manage emergencies correctly, underscoring the psychological maturity of seasoned controllers in contrast to trainee controllers, who may find it simpler to improve alertness. The pupil is the tiny circular opening at the center of the iris, with its diameter principally regulated by the contraction and relaxation of the iris smooth muscle. The mean pupil diameter signifies the average size of a controller's pupils over time, showing the degree of stress encountered throughout the management process. Figure 3g illustrates a comparison of average pupil diameter measurements for controllers with differing qualifications across varied fatigue conditions. Both experienced and trainee controllers demonstrate a progressive reduction in average pupil diameter when fatigued. The decrease in pupil diameter indicates that tiredness results in lower tension levels, with trainee controllers exhibiting somewhat more stress than their experienced counterparts. In general, increased weariness is associated with reduced pupil diameters.
## 4 Results
### InSAR-Derived Ground Displacements
The DTs in the epicentral area to the northwest (the dotted area in Figure 4a-c) show an initial fast increase because of the coseismic fault slip, reaching median LoS displacements of approximately +12 cm and maximum values up to +17 cm (Figure 5a). A further increase in LoS displacements occurs in the postseismic phase, with median values of approximately +1.8 cm (Figure 5b) and maximum values up to +3.5 cm. The latter accumulates from approximately one to two months after the 20 May 2012 event. Figure 5: LoS DT displacement time series from CSK SAR data. **(a)** Co-seismic and post-seismic LoS displacement time-series for all the DTs falling in the dotted area in panels **(a–c)** of Figure 4. The dashed red line indicates the theoretical exponential uplift according to Equation (15). (\(d_{0}\) = 11, cm, \(a\) = 2.6 cm, \(b\) = 11d) **(b)** Detail of the postseismic displacement time series (the dashed black rectangle in panel **(a)**) scaled from 23 May 2012. **(c)** Coseismic and postseismic LoS displacement time series for all the DTs falling in the dashed area in panels **(a–c)** of Figure 4. **(d)** Detail of the postseismic displacement time series (the dashed black rectangle in panel **(c)**) scaled from 23 May 2012. In all the panels, the black curve defines the median trend of all DTs. The deviation from the median trend is represented by shades of blue (panels **(a,b)**) and orange (panels **(c,d)**) colored bands defining the quantiles calculated every 10%. The black vertical dashed lines in panel **(d)** identify the timing of panels **(a–c)** in Figure 4.  
This area is near the zone that experienced the maximum coseismic uplift (see Figure 3a), thus suggesting that the observed postseismic displacements have a tectonic origin. Analytical and numerical models from the literature confirm this hypothesis, as the observed post-seismic ground movements are given by fault afterslip and crustal poroelastic stress changes after the mainshock [26; 30; 36]. The DTs falling in the gridded area over the Sant'Agostino, San Carlo, and Mirabello villages in Figure 4a-c show a complex displacement trend (Figure 5c). The area is first affected by coseismic uplift, but with a lower amplitude than the epicentral area. Then, starting from 23 May 2012 (i.e., the first available SAR image after the 20 May event), we observed a further LoS displacement increase up to median values of +0.5 cm on 20 June 2012 (Figure 5d), followed by a progressive displacement decline until stabilizing at median values of approximately \(-0.7\) cm (maximum values up to \(-2\) cm) at approximately 4-5 months from the 20 May event, while the total median displacement decline, considering the early increase, was approximately \(-1.2\) cm.
# Virtual Zirconia Implants Replace Titanium Implants?
## 4 Osteointegration
A recent network meta-analysis [67] concluded that, regarding osseointegration in the early healing stages, SLAActive shows better results. However, over time, TiUnite is the best option among the four implants evaluated. Concerning stability, all implants are eligible with ISQ values greater than 60. Additionally, a previous systematic review evaluation of 27 randomized clinical trials including 38 different implant types concluded that relatively smooth (turned) surfaces were less prone to lose bone as a result of chronic infection (peri-implantitis) than implants with much rougher surfaces (titanium-plasma-sprayed) [68]. Considering the effect of nanostructure at the cellular level, a systematic review concluded that this type of structure has a remarkable advantage in osteoblast proliferation [69]. Specifically regarding zirconia implants, according to [PERSON] and colleagues [70], limited bone remodeling with machined-zirconia implants containing yttria over time can decrease the secondary stability of this type of implant compared with machined and resorbable blast media titanium ones. In the same way as happens in the titanium implants, the surface modification of the zirconia implants may dictate differences in their osseointegration. Preclinical studies have revealed bone apposition on zirconia implants with various surface modifications, including sandblasting, etching [71], sintering, and coating [72], and even subtle alterations in the surface may have an enormous benefit in the osseointegration. Owing to the increase in demand for zirconia implants, mainly in an esthetic-driven patient, clinicians and researchers question themselves about the efficiency of this type of implant and its non-inferiority in relation to conventional titanium implants. Mostly preclinical, but also some randomized clinical trials and systematic reviews have been published to answer these questions. Table 1 summarizes the results from existing systematic reviews analyzing the osseointegration of zirconia implants. A study with human biopsies from 22 failed zirconia implants (failed from trauma or extensive bone loss compatible with peri-implantitis) showed dense bone with stable laminar structure in tight contact with the implant surface and no gaps. The bone-implant area ranged from 58.1 to 93.7% [77].
## 4 Transformed Cross-Section Method  
The transformed cross-section method was used for the theoretical analysis. This model assumes a proportional increase in the timber cross-section to replace the reinforcement elements. This creates sections made from the same material. The work of [1,2,27], among others, describes the model's assumptions in detail. Its application to the assessment of the performance of full-size laminate and CFRP sheet-reinforced laminated veneer lumber beams was described in [27]; the behavior of LVL slabs reinforced with FRP sheets was described in [28]; and CFRP-LVL sandwich structures were described in [29].  
Based on the proportionality coefficient \(\eta\) obtained from Formula (5), the increase in area was estimated [27]:  
\[\eta=\frac{E_{FRP}}{E_{LVL}}, \tag{5}\]  
where \(E_{LVL}\) is the modulus of elasticity of laminated veneer lumber (Table 3), and \(E_{FRP}\) is the modulus of elasticity of composite material (Table 1). This factor is then multiplied by the horizontal dimension of the composite. In the case of the reinforcement bonded to the bottom, this is its width. For the composite placed on the sides it is the sheet thickness.  
Equation (6) determines the position of the center of gravity of the section:  
\[z_{c}=\frac{S_{y}}{\sum_{i=1}^{n}A_{i}}, \tag{6}\]  
where \(S_{y}\) is a static moment of the surface area concerning the assumed reference system; \(A_{i}\) is the surface area of the elementary figure. The initial position of the datum was such that the cross-section fell entirely within the first quadrant of the coordinate system, and its outline was adjacent to the assumed axes.  
Equation (7) was used to determine the moment of inertia of the section:  
\[I_{y}=\sum_{i=1}^{n}\Big{(}I_{yi}+A_{i}\times(z_{c}-z_{i})^{2}\Big{)}, \tag{7}\]  
where \(I_{yi}\) is the moment of inertia of the elementary field, and \(z_{i}\) is the distance from the center of gravity of the elementary field to the assumed datum system.  
Equation (8) was used to estimate the maximum value of the loading force:  
\[f_{m,0,edge}=\frac{M_{max}\times z}{I_{y}}\to M_{max}=\frac{f_{m,0,edge} \times I_{y}}{z} \tag{8}\]  
where \(f_{m,0,edge}\)--the bending strength of the non-strengthened laminated veneer lumber determined from experimental tests; \(z\)--distance from the center of gravity to the outermost compression fibers.  
The bending stiffness coefficient \(k\) was estimated as the quotient of the loading force and the deflection according to Formula (9) [27]:  
\[k=F/u \tag{9}\]  
where \(u\) is the deflection value at the center of the beam span; \(F\) is the value of the force loading.  
Figure 3 shows schematics of the transformed cross-sections and their designations.
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation of Processes Occurring in Emulsions during Storage  
A Turbiscan Lab Expert (Formulaction, L'Union, France) using multiple light scattering (MLS) was used to analyze destabilization processes occurring in the produced emulsion systems. The light source was a diode emitting radiation in the near-infrared range (\(\lambda\) = 880 nm). The device was equipped with two synchronized detectors receiving transmitted (T, or transmitted) and backscattered (BS, or backscattered) light through the sample. The identification and quantitative analysis of destabilization processes in emulsion systems is made possible by changes in the intensity of the T and BS signals, which depend on the size of the dispersed phase droplets and their volume contribution [21]. During the measurement, the samples were scanned with the moving optical head of the camera in their entire height (about 40-42 mm). The scans were performed for a period of 30 \(\pm\) 1 days, at intervals of several days. The measurement and storage temperature of the samples was 25 \({}^{\circ}\)C.  
TSI (Turbiscan stability index) was calculated according to the following formula [22]:  
\[\text{TSI}=\sum_{i}\frac{\Sigma_{h}|scan_{i}(h)-scan_{i-1}(h)|}{H}\]  
where  
\(scan_{i}\) (\(h\))--average BS value for each time (\(i\)) measurement,  
\(scan_{i-1}\) (\(h\))--average BS value for each time (\(i-1\)) measurement,  
\(H\)--the total height of the sample.  
TSI is a statistical parameter whose value is the sum of all processes occurring in the sample [23]. This parameter, therefore, makes it possible to assess the kinetics of destabilization changes occurring in the system. It takes values from 0 (for highly stable systems) to 100 (for extremely unstable systems) [24]. According to the information provided by the manufacturer of the Turbiscan device in the Application Note (2019) [25], dispersions can be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emulsion** & **Modified Fat Blend Symbol** & \
\begin{tabular}{c} **HPMC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Emulsifier** \\ **(\%)** \\ \end{tabular} \\ \hline E1 & T-1 & 0.6 & 7.2 \(\pm\) 0.3 \\ E2 & & 0.8 & \\ E3 & & 1.0 & \\ E4 & T-2 & 0.6 & 7.4 \(\pm\) 0.4 \\ E5 & & 0.8 & \\ E6 & & 1.0 & \\ E7 & T-3 & 0.6 & 7.4 \(\pm\) 0.3 \\ E8 & & 0.8 & \\ E9 & & 1.0 & \\ E10 & T-4 & 0.6 & 7.0 \(\pm\) 0.3 \\ E11 & & 0.8 & \\ E12 & & 1.0 & \\ E13 & T-5 & 0.6 & 7.2 \(\pm\) 0.3 \\ E14 & & 0.8 & \\ E15 & & 1.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emulsion composition.  
divided into 5 categories depending on the TSI value obtained. The category descriptions proposed by [PERSON] et al. (2020) [26] we used in the manuscript (Table 3).
# ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management
###### Abstract
That is, fog computing extends cloud computing into the edge for secure control and management of domain-specific hardware, storage and network functions within the domain and enables secure robust data processing applications across the domain. Similarly, the concept of MEC was proposed by the European Telecommunications Standard Institute (ETSI) and provides powerful cloud computing capabilities within a radio access network (RAN) [25]. In other words, MEC is considered as a paradigm close to mobile user devices in 5G network and provides powerful both computing capability and delay-sensitive services to mobile users quickly and effectively. Although many kinds of research for MEC are actively conducted, there are few studies on schemes related to mobility management for mobile users. To solve these problems, this paper proposes the MEC-based mobility management scheme that enables the mobility of mobile users efficiently by managing MECS as the concept of Zones. The proposed scheme arranges MECS in the concept of Zone so that mobile users can continue to receive content and use of server resources efficiently even when they move. In other words, through the exchange of information between orchestras managing access MECS in the concept of Zone, it is possible to support continuous content delivery and efficient use of server resources even when the mobile users move. The rest of the paper is organized as follows. Section 2 presents the related works. Section 3 presents the proposed Zone-based MEC scheme for user device mobility management and then, we present the performance evaluation results in Section 4. Finally, we conclude the paper in Section 5.
## 3 The Method of Dynamic Adjustment Learning Rate and Convergence Proof
### Dynamic Regulation Method Based on Temporal-Difference  
We believe that in the process of dynamic programming and approximation using reinforcement learning, the error of the neural network in different stages is distributed with a certain probability. Therefore, the sensitivity of the algorithm can be improved intuitively by dynamically adjusting the learning rate at different stages. According to this intuition, we add a mapping from TD error to the learning rate in the algorithm to dynamically adjust the learning rate to improve the performance of the learning algorithm. At the same time, due to the different advantages of different learning rates at different stages, this method can inherit the advantages of different learning rates at different stages and finally improve the performance of the algorithm. The TD reinforcement learning value function updating formula of the dynamically adjusting learning rate can be seen in Formula (2)  
\[\begin{split}&\delta=r-Q\left(a_{i}\right)\\ & Q_{i}\left(a_{i}\right)=Q_{i}\left(a_{i}\right)+mapping\left( \delta\right)\cdot\delta\end{split} \tag{2}\]  
The subscript \(i\) represents an agent's name, where \(a_{i}\) represents the action performed by the agent \(i\). The label \(Q_{i}\) represents the value function it maintains, where the output of \(mapping(\delta)\) is the learning rate in reinforcement learning. This mapping dynamically adjusts different learning rates through TD Loss at different stages. It can be seen from the Hysteretic Q-Learning (HQL) [23] algorithm that the performance of the algorithm can be optimized by reasonably adjusting the learning rate during the training process. The idea of this kind of regulation comes from fuzzy control theory [24]. Through mapping similarly to the membership function [25; 26], TD Loss is used to dynamically adjust the learning rate.  
Because the neural network is used to fit the Q function, the stability of network training needs to be considered. To maintain the stability of the neural network, the setting of the learning rate should not fluctuate too much. So this mapping is usually defined as a piecewise function. In the following we will prove the convergence of the dynamic learning rate algorithm. Then the setting process is given through experiments.