esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
## 3 Results
### Absorbance Measurements for Controlled Washing  
The UV-VIS spectroscopy test was used to investigate the possible leakage of PEG 2000 through the absorption curves realized in the baths from the controlled washing of the PCFs after cutting (Figure 14).  
Figure 13: Index of the respective functional groups of the PCFs. (**a**) PCF_50/50_120; (**b**) PCF_50/50_130; (**c**) PCF_50/50_140; (**d**) PCF_50/50_150.  
Initially, the dissolutions of the base materials (CA, DMF and PEG) were subjected to UV-VIS spectroscopy to verify their presence and respective influences in the controlled washing baths. In the UV domain, CA dissolved in DMF showed a low absorbance in the UV range of around 276 nm [31]. DMF diluted in dH\({}_{2}\)O, on the other hand, showed a high absorbance in the range of 235 nm [32]. Finally, as the literature shows, PEG 2000 dissolved in dH\({}_{2}\)O exhibited a maximum absorbance in the range of 203 nm [33].  
The results presented in Figure 14 showed that the PCF baths presented absorbances of 0.81 a.u., 0.75 a.u. and 0.78 a.u., with maximum peaks around 211 nm for the cuts at RT, 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively.  
When comparing with PEG 2000, it was observed that all baths, in addition to exhibiting absorbances higher than the PEG 2000 value, showed a slight shift of the curves to the right. A possible initial explanation for the higher absorbance of the baths in the UV domain could be the absorption of water molecules trapped in the PEG polymeric matrix, which absorbs in this UV range [33]. Another explanation involves the influence of DMF: during the production of PCFs, DMF may not have completely evaporated, remaining retained in the PCFs [12; 31]. During controlled washing, residual DMF may have been  
Figure 14: UV-VIS spectra of the respective controlled washings of the PCFs. (**a**) RT cut; (**b**) cut 50 \({}^{\circ}\)C; (**c**) cut 100 \({}^{\circ}\)C.  
released along with PEG, resulting in curves with higher absorbance and a shift to the right. Furthermore, there may be an affinity between the polymeric complexes of PEG and CA [31], as in index calculations, where PEG can stick to the protective sheath of CA. Thus, CA and PEG particles can be removed simultaneously, causing the curve to shift, as the CA curve is more to the right.  
Therefore, it can be concluded that even though the absorbance values are very similar for the three types of cuts, the 50 \({}^{\circ}\)C cut was the one that showed the lowest presence of residual materials in the baths since its absorbance value was the lowest among the three. Finally, knowing that the DMF and PEG curves are in the same UV domain, it is not possible to prove with certainty that there is only leakage from PEG.
# A Survey on Mobility Support in Wireless Body Area Networks
###### Abstract  
Wireless Body Area Networks (WBANs) have attracted research interests from the community, as more promising healthcare applications have a tendency to employ them as underlying network technology. While taking design issues, such as small size hardware as well as low power computing, into account, a lot of research has been proposed to accomplish the given tasks in WBAN. However, since most of the existing works are basically developed by assuming all nodes in the static state, these schemes therefore cannot be applied in real scenarios where network topology between sensor nodes changes frequently and unexpectedly according to human moving behavior. However, as far as the authors know, there is no survey paper to focus on research challenges for mobility support in WBAN yet. To address this deficiency, in this paper, we present the state-of-the-art approaches and discuss the important features of related to mobility in WBAN. We give an overview of mobility model and categorize the models as individual and group. Furthermore, an overview of networking techniques in the recent literature and summary are compiled for comparison in several aspects. The article also suggests potential directions for future research in the field.  
wireless body area network; mobility model; posture modeling; communication protocols; energy efficiency +
Footnote †: journal: Sensors
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 4 The Methodology
### Algorithm Intuition
#### 4.1.1 Lower and Raise Wavefronts  
Following the basic ideas of Dynamic Brushfire algorithms, CODB employs two kinds of wavefront, lower and raise wavefronts, to incrementally update DMs. CODB keeps a priority queue (denoted as _OPEN_) to sort the cells to be explored by the wavefronts. A cell's priority is determined by its \(dist_{s}\) value and is dequeued with increasing priority values. When a cell is popped from _OPEN_, the distance change on it will be propagated to its adjacent cells and any inconsistent cells (i.e., cells which are affected by the wavefront) are again put on _OPEN_, so as to prepare for the next round of propagation.  
Figure 3: The flowchart describing the process of building a DM, building a DM-based subgoal graph, engaging a high-level path planning task, and executing Learning Real Time A\({}^{\star}\) (LRTA\({}^{\star}\)) to fulfill a low-level navigation task.  
As shown in Figure 4a, when an obstacle cell (red dot denoted as \(s\)) is newly inserted into the center of the grids, it firstly sets \(dist_{s}\) as 0 and \(obst_{s}\) as its own coordinates; then, a so called \"lower wavefront\" is launched to propagate a distance reduction to its adjacent cells (as shown in Figure 4b). This propagation emanates from \(s\), the source of the change, and terminates when the distance change no longer affects anymore cells (as shown in Figure 4c,d, the wavefront encounters cells which keeps equal clearance to other obstacle cells and thus failed to continue the propagation.). Finally, new boundaries of the updated DM are reached and the distance values of all affected cells are updated (As shown in Figure 4e).  
On the other hand, when an obstacle cell is removed (e.g., as Figure 5a shows, we again remove the center obstacle cell \(s\) from the map), all the cells whose \(obst\) and \(dist\) value are computed based on \(s\) become invalid; therefore, a so called \"raise wavefront\" is launched to reset these invalid cells, declaring that they can be then updated by other lower wavefronts (as shown in Figure 5b). The \"raise wavefront\" terminates at the boundaries between \(s\) and other obstacle cells (as shown in Figure 5c), then lower wavefronts on the other sides of the boundaries are permitted to continue their propagation until the invalid region is again submerged (as shown in Figure 5d) and Figure 5e).
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 2 System Overview and Mathematical Modelling
### Dynamics  
The force of gravity acting at the CG of the rotorcraft, \(F_{g}=mg\), does not act along the z-axis of the body-fixed reference frame and has to be transformed from the Earth-fixed reference frame \(F_{E}=\{RO,x,y,z\}\) to the body-fixed reference frame using \(\mathbf{R}\) in Equation (2). The total sum of forces acting on the rotorcraft within the body-fixed reference frame \(F_{B}=\{R_{B}O_{B},x_{B},y_{B},z_{B}\}\) may thus be expressed in the Newton-Euler rigid-body equations of motions for a rotorcraft with mass \(m\) and the moment of inertia \(\mathbf{I}\) as follows:  
\[m\frac{d\mathbf{v}^{B}}{dt}+m(\omega^{B}\times\mathbf{v}^{B})=\mathbf{F} \tag{6}\]  
\[\mathbf{I}\frac{d\omega^{B}}{dt}+(\omega^{B}\times\mathbf{I}\omega^{B})= \mathbf{M} \tag{7}\]  
The sum of all external forces and moments that act on the rotorcraft is combined in the triple \(\mathbf{F}=[X\;Y\;Z]\) and \(\mathbf{M}=[L\;M\;N]\), respectively. In the following, we discuss the contributions of the different rotorcraft subsystems to the force and the moment vectors. The contributing subsystems are the main rotor, tail rotor, fuselage, horizontal and vertical stabilisers. The force of gravity is transformed into the body-fixed reference frame using \(\mathbf{R}^{T}\). These forces are illustrated in Figure 1.
## 2 Materials and Methods
### NSGA-II for Multi-Objective Optimization Calibrating Well Allocations  
Pareto optimality, i.e., the trade-off solutions, is the non-domination where no solution can be improved with respect to any objective without worsening at least another objective [4; 5; 8]. Multi-objective evolutionary algorithm searches the trade-offs that would give a vector of decision variables for all the objective functions under any given constraints (Equation (3)).  
\[f(X)=[f_{1}(X),\ f_{2}(X),\ \cdots,f_{n}(X)]^{T} \tag{3}\]  
In Equation (3), \(f(X)\) is an objective function with the vector (\(X\)) of decision variables, e.g., well allocation in this study, and the subscript \(n\) is the number of objective functions. NSGA-II algorithm consists of competing individuals with a genetic algorithm, non-dominated sorting, and crowding distance [5; 7; 8; 9; 10]. The genetic algorithm-based operations, e.g., crossover and mutation, form the offspring and then the individuals in the mixture of parent and offspring population are competing with non-dominated sorting and the crowding distance. The non-dominated sorting strategy of NSGA-II proceeds by comparing each individual one by one, and when a non-dominated front is found, it is temporarily stored, and then the next non-dominated front is searched. This non-dominated sorting continues until all individuals are ranked [1; 5] (Figure 2). The solutions can evolve to the direction of the real Pareto optimization set, as a larger probability is assigned to the individuals in the lowest dominant level in order to select the superior individuals into the next generation of the population.  
To maintain the solution diversity, the crowding distance calculates the congestion degree of each individual and other individuals [7; 8; 9] (Figure 2). Equation (4) defines the \(k\)-th individual's crowding distance (\(\delta_{k}\)).  
\[\delta_{k}=\frac{1}{n}\sum_{i=1}^{n}\frac{f_{i}(k+1)-f_{i}(k-1)}{f_{i}^{max}-f _{i}^{min}} \tag{4}\]  
In Equation (4), \(f_{i}(k+1)\) and \(f_{i}(k-1)\) indicate \(i\)-th objective function values for \((k+1)\)-th and \((k-1)\)-th individual, respectively. \(f_{i}^{max}\) is the maximum value, and \(f_{i}^{min}\) is the minimum for \(i\)-th objective function. By selecting the individuals with lower crowding distance value in the same dominant level, the evenness and diversity of the evolutional population can be guaranteed [9; 10]. Pareto optimality can be obtained by  
Figure 2: A conceptual diagram to explain non-dominated relationship and crowding distance. When assuming a minimizing problem with two objective functions, the open circles have lower rank than the blue circles. As searching for the lowest rank, NSGA-II operation to reach Pareto optimality with non-domination.  
reaching convergence conditions, or by repeating the process up to the maximum number of evolutions, and, thus, multi-objective optimization is costly because of enormous computational simulations.
# Modeling of Water Losses in Hydraulic Tunnels under Pressure Based on Stepwise Regression Method
## 4 Case Study
### Dataset  
During the exploitation of the tunnel, for the period from 1983 to 2017, 79 measurement sessions of total water loss were carried out. In this paper, data for the period from 2005 (year of the last tunnel reparation and injection) to 2017 were analyzed--measuring sessions of total water loss from 50 to 79, which generated 180 data measurement records. For the considered input and output variables, the following symbols were introduced: \(H_{r}\)--water level in the upper reservoir, \(H_{p3}\) and \(H_{p4}\)--water levels in piezometers P\({}_{3}\) and P\({}_{4}\), respectively, \(T_{c}\) --concrete temperature on the extrados of the concrete lining, \(T_{w}\) --temperature of water, \(H_{st}\)--average water level in the surge tank for one measurement step, \(cr_{3}\) and \(cr_{4}\)--width of crack openings at the measuring points MOP-3 and MOP-4, respectively, and \(q\)--total water losses from the tunnel. In addition to these variables, variable \(t\), which represents the elapsed time in months from the date of the last tunnel reparation and injection performed in 2005, is introduced. The variable \(t\) considers the impact of rheology on the aging and  
Figure 4: Water level change in surge tank during the measurement of total water losses (75 th session).  
deterioration of concrete as well as possible changes in geology that can lead to irreversible processes and have a lasting effect on total water losses in the tunnel.  
The elements of descriptive statistics of measured values are given in Table 1.
###### Abstract  
Steel reinforced elastomeric isolators are currently the most used bearings for seismic isolation purposes. The steel reinforcements are cut to the desired shape, sandblasted, cleaned with acid, and coated with bonding compound during the manufacturing process. Then the elastomer and steel layers are stacked in a mold and subjected to vulcanization so that they are glued together and constitute a single body. Good adhesion between the layers is very important for the correct functioning of the device. Adhesion conditions become critical when the isolators are subjected to tensile stresses, which arise under direct tensile actions or large shear strains. To analyze the influence of changes in the manufacturing process on the isolator adhesive behavior, the authors performed tensile tests on square-shaped small-scale specimens rather than expensive shear tests on full-scale isolators. Hence, the adhesion behavior between elastomer and steel layers was investigated through the tensile tests discussed herein. Among the influencing parameters that were considered, it was found that an increase in vulcanization time does not improve the adhesion, but it may actually worsen the capacity of the isolator in terms of strength. Moreover, it was found that using elastomer without an oily component improves the adhesion between the layers and increases the isolator's dissipative capacity.  
elastomeric isolators; adhesion; vulcanization time; corundum sandblasting; adhesive rubber; natural rubber; synthetic rubber; dissipative compound; non-dissipative compound +
Footnote †: journal: Applied Sciences
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 2 Histogram-Based Image Enhancement Arts
### Contrast-Limited Adaptive Histogram Equalization (CLAHE)  
The aim of CLAHE is to overcome the noise amplification problem of the histogram equalization scheme by applying the threshold on the slope of the function of input image intensity values to the resultant image intensities. Perhaps CLAHE is more appropriate for very low-contrast images. It uses a contrast-limited transformation function on the adjacent neighborhood pixels to boost the contrast. Contrast enhancement is directly associated with the height of the histogram, and therefore both limiting the slope and clipping the histogram height operate as the same function to control the enhancement rate.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 3 Results and Discussion
### Preliminary Analysis of Nozzle Flow Field
Therefore, the axial vapor volume fraction distribution at \(d/2\) can be used as the basis for judging the cavitation effect. In order to easily distinguish the internal and external flow fields to explore the cavitation effect of the angular cavitation nozzle, set the outlet position of the cylindrical section at 0 mm on the axial distance of the velocity distribution and the vapor volume fraction distribution. Then, the contraction section is between \(-\)33.5 mm and \(-\)7.5 mm, the cylindrical section is between \(-\)7.5 mm and 0 mm, the expansion section is between 0 mm and 10 mm, and 10 mm to 30 mm is the area of the external flow field. It can be seen from the axial velocity distribution of the angular cavitation nozzle (see Figure 14) that when the axial distance is less than \(-\)7.5 mm, the axial velocity of the jet increases with the extension of the axial distance. This is due to the convergence effect of the contraction section, which can enhance the flow velocity. When the axial distance is between \(-\)7.5 mm and 10 mm, the jet axial velocity is kept at about 200 m/s due to the constraint of the nozzle cylinder section. The length of the constant velocity core extends to the expansion section. When the axial distance is greater than 10 mm, the axial velocity of the jet decreases with the extension of the axial distance. At this time, the jet is in the outflow field, and the jet velocity is constantly attenuated due to doping with the surrounding liquid. It can be seen from Figure 15 that when the axial distance is less than \(-\)7.5 mm, the steam volume fraction at \(d/2\) is 0, indicating that there is no cavitation in the contraction section. When the axial distance is between \(-\)7.5 mm and 0 mm, the steam volume fraction at \(d/2\) rises suddenly and decreases slowly with the increase in the axial distance. It indicates that the nozzle has cavitation in the cylindrical section, but the cavitation degree is too low, and the steam volume fraction is only about 0.1. [PERSON] et al. [17] compared the experimental and CFD-predicted results, thinking vapor is generated at the sharp entrance of the nozzle orifice, as expected. With the extension of the cylindrical section, the cavitation clouds decrease due to the weakening of the boundary layer. When the axial distance is within the range of 0 mm to 12 mm, the steam volume fraction at \(d/2\) rises suddenly again and rises with the increase in the axial distance. As shown in Figure 12, the expansion section is conducive to the generation of cavitation clouds [11]. When the axial distance is more than 12 mm, the vapor volume fraction at \(d/2\) decreases in the external flow field with the increase in the axial distance.
## 3 Proposed Protocol
### Authentication Mechanisms  
Authentication is a crucial aspect of any key management framework as it ensures that only authorized nodes have access to the network. In UWSNs, authentication mechanisms are used to verify the identity of the sender and receiver of a message. The following authentication mechanisms can be used in our proposed key management framework:
## 4 Case Study Investigation
### Asphalt Material Production Monitoring System  
In the asphalt material production monitoring system, firstly, the quantity ratio of raw materials (such as base asphalt, stabilizers, and modifiers) was monitored, and the quantity was adjusted after an imbalance was found to ensure the quality of asphalt required for the K63 to K67 tenders. Secondly, by monitoring the temperature of the aggregate conveying equipment and the mineral powder input equipment, the production quality of the modified asphalt was ensured. During this period, the system sounded alarms and prompted staff to take cooling measures for the equipment. Finally, the energy consumption monitoring module monitored and collected aggregated data. The energy consumption and carbon emissions in the production of raw materials and slag cement production were calculated, and related graphs were drawn (as shown in Figures 11 and 12).
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization  
Clustering process optimization in WSNs comprises optimizing the processes of CH selection, data aggregation, cluster formation, and data communication. As shown in Figure 5, each component has some issues that need to be addressed. For instance, it is crucial to determine the optimal number of cluster formations, cluster density, and balance among the clusters during CH selection. In the cases of data aggregation and communication, both are closely related and must be maximized by determining the appropriate cluster size and degree of inter-cluster communication.  
Figure 5: Clustering optimization methods in a WSN.
## 4 Discussion
### Limitations  
Our study, however, presented some limitations; first, 41 subjects (more than half of SZ) were of Paranoid type, suggesting that it is unclear whether the observed results were of all SZ patients or were influenced by the large paranoid-type data (57%). Second, patients with schizophrenia may be using a variety of antipsychotic medications, which may influence their cognitive functions. A previous study revealed that SZ patients showed synchronized brain activity and decreased integration function across brain networks after undergoing six-week treatment with second-generation antipsychotic medications [118]. These antipsychotic medications may also affect their brain activities. For example, olan-zapine treatment increased default mode network (DMN) connectivity in patients with SZ [119]. Hence, attention should be paid while interpreting our results. Third, although we did not achieve statistical significance in variability, it is possible that a better-designed study could provide more definite results. Fourth, the lack of neuropsychological tests for SZ and HC may also limit the study since the data used in this study were obtained online (COBRE), resulting in no correlation analysis between altered rsFC and neuropsychological evaluation in both groups. Thus, intergroup difference in neuropsychological evaluation also remains unknown. Fifth, the authors of this work acknowledge that the sliding window technique has a number of drawbacks [25; 120; 121], such as that multiple parameters must be specified including window function, length, and step size, but due to a shortage of ground truth in resting-state fMRI data, the ideal settings are still uncertain. We confirm that our work, just like the vast majority of resting-state-dFC studies have employed this technique, mainly due to its simplicity [71]. Lastly, our biggest challenge was to obtain the dataset, and the main reason was the COVID-19 pandemic, which limited us only to use publicly available dataset.
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 2 Materials and Methods
### Synthesis of Gold Nanospheres (AuNS)  
Aqueous dispersions of AuNS were prepared by a modified Turkevich method [73]. Briefly, 2 mL of 1 mM gold(III) chloride dihydrate solution in ultrapure water were brought to boiling point on a hotplate under stirring, then 200 \(\mu\)L of trisodium citrate dihydrate solution (1% _w/v_) were quickly added and the mixture was vigorously stirred until it turned from yellow to deep red. The obtained colloidal dispersion was washed with ultrapure water through two centrifugation steps (15 min at 9766\(\times\)_g_ RCF, RT), in order to remove the excess of reactants. The average core diameter size (D) of citrate-capped AuNS was estimated by UV-VIS spectra, according to Equation (1):  
\[\lambda_{\text{max}}=515.04+0.3647\text{ D}, \tag{1}\]  
where \(\lambda_{\text{max}}\) is the maximum absorption peak [74]. Therefore, the actual concentration of AuNS dispersion was calculated according to Equation (2):  
\[\varepsilon=\text{AD}^{\gamma}, \tag{2}\]  
where \(\varepsilon\) (M\({}^{-1}\) cm\({}^{-1}\)) is the extinction coefficient, D is the core diameter of the nanoparticles previously calculated, \(\gamma\) and A (in mol\({}^{-1}\) dm\({}^{-3}\) cm\({}^{-1}\)) are experimental constants (\(\gamma=3.30\), A = \(4.7\times 10^{4}\) or \(\gamma=1.47\), A = \(1.6\cdot 10^{8}\) for D \(\leq\) 85 nm or D \(>\) 85 nm, respectively) [75]. Assuming the size monodispersity of spherical nanoparticles' dispersion and uniform face-centered cubic (fcc) structure, the average number of gold atoms (N) for each nanosphere was calculated By Equation (3):  
\[\text{N}=(\pi\rho\text{D}^{3})/(6\text{M}), \tag{3}\]where \(\varrho\) is the density for fcc gold (19.3 g/cm\({}^{3}\)) and M its atomic weight (197 g/mol) [76].