esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ozone Dose** & \begin{tabular}{c} **F\({}_{\text{max}}\)** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **dL at F\({}_{\text{max}}\)** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **E\({}_{\text{mod}}\)** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **W to F\({}_{\text{max}}\)** \\ **(Nmm)** \\ \end{tabular} \\ \hline
0 ppm 0 min & 3.06 \(\pm\) 0.28 a & 0.76 \(\pm\) 0.16 a & 0.0009 \(\pm\) 0.0002 a & 1.13 \(\pm\) 0.27 a \\ \hline
5 ppm 1 min & 2.99 \(\pm\) 0.36 a & 0.74 \(\pm\) 0.32 a & 0.0010 \(\pm\) 0.0003 a & 1.02 \(\pm\) 0.48 a \\ \hline
5 ppm 5 min & 2.91 \(\pm\) 0.27 a & 0.68 \(\pm\) 0.20 a & 0.0010 \(\pm\) 0.0003 a & 0.95 \(\pm\) 0.24 a \\ \hline
5 ppm 10 min & 2.94 \(\pm\) 0.33 a & 0.69 \(\pm\) 0.16 a & 0.0010 \(\pm\) 0.0002 a & 1.01 \(\pm\) 0.23 a \\ \hline
10 ppm 1 min & 2.83 \(\pm\) 0.44 a & 0.62 \(\pm\) 0.12 a & 0.0011 \(\pm\) 0.0002 a & 0.87 \(\pm\) 0.21 a \\ \hline
10 ppm 5 min & 3.04 \(\pm\) 0.46 a & 0.78 \(\pm\) 0.19 a & 0.0009 \(\pm\) 0.0002 a & 1.09 \(\pm\) 0.25 a \\ \hline
10 ppm 10 min & 3.04 \(\pm\) 0.32 a & 0.75 \(\pm\) 0.27 a & 0.0011 \(\pm\) 0.0003 a & 1.03 \(\pm\) 0.25 a \\ \hline \end{tabular} Note: Significant differences between values of the selected mechanical parameters of leaves from alligator plants in indentation test are marked with different letters, at significance level defined as \(\alpha\) = 0.05.  
\end{table} 
Table 1: Value of destructive force F\({}_{\text{max}}\) (N), deformation dL at F\({}_{\text{max}}\) (mm), elastic modulus E\({}_{\text{mod}}\) (MPa) and work performed to achieve maximum destructive force W to F\({}_{\text{max}}\) (Nmm) in leaves of _Kalanchoe dagremontiana_ in process of puncture test performed with indenter, diameter of \(\varphi\) = 4 mm (n = 30; \(\pm\)SD). the case of ozone-treated fruit, whereas on the opposite side there was a decrease in F\({}_{\text{max}}\) values corresponding to exposure to gaseous ozone [51]. Similar relationships were observed between the dose of ozone and mean deformation value, elastic modulus and work performed to achieve maximum destructive force in the leaves of _Kalanchoe dagrementinua_ in a process of indentation (Table 1). Alligator plants are used in the processing industry where isolates, such as juice, are extracted [52]. The amount of juice (isolate) obtained depends on the parameters of mechanical resistance. Plants with lower parameters related to destructive force F\({}_{\text{max}}\) are more effectively used in the process of acquiring juice by pressing. It should be noted that proposed solutions have a large commercialization potential. This is related to the great interest in some of the biological properties of the _Kalanchoe dagrementinana_, as well as the availability of industrial solutions that allow the ozonation process to be applied on a large scale [53]. The proposed technological solutions are protected by a patent application [no P.441824].
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Asphalt  
Two types of asphalt binders were used in this research project, namely base asphalt with 80/100 penetration grade (simply referred to as 90 A) and SBS-modified asphalt (simply referred to as SBS MA). They were obtained from Inner Mongolia Xindalu Asphalt Co., Ltd. (Chifeng, Inner Mongolia, China). Technical information of these two asphalt binders are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Asphalt** & **Technical Parameters** & **Unit** & **Test Results** & **Method** \\ \hline \multirow{4}{*}{90 A} & 25 \({}^{\circ}\)C Penetration & 0.1 mm & 84.6 & ASTM D5 [17] \\  & Softening point & \({}^{\circ}\)C & 47.8 & ASTM D36 [18] \\  & 10 \({}^{\circ}\)C ductility & cm & \textgreater{}100 & ASTM D113 [19] \\  & 60 \({}^{\circ}\)C viscosity & Pa\(\cdot\)s & 206 & ASTM D4402 [20] \\ \hline \multirow{4}{*}{SBS MA} & 25 \({}^{\circ}\)C Penetration & 0.1 mm & 62.3 & ASTM D5 [17] \\  & Softening point & \({}^{\circ}\)C & 57.5 & ASTM D36 [18] \\ \cline{1-1}  & 5 \({}^{\circ}\)C ductility & cm & 55.8 & ASTM D113 [19] \\ \cline{1-1}  & 135 \({}^{\circ}\)C viscosity & Pa\(\cdot\)s & 1.337 & ASTM D4402 [20] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of 90 A and SBS MA.
###### Abstract  
Error estimation and adaptive applications help to control the discretization errors in finite element analysis. The study implements the radial point interpolation (RPI)-based error-recovery approaches in finite element analysis. The displacement/pressure-based mixed approach is used in finite element formulation. The RPI approach considers the radial basis functions (RBF) and polynomials basis functions together to interpolate the finite element solutions, i.e., displacement over influence zones to recover the solution errors. The energy norm is used to represent global and local errors. The reliability and effectiveness of RPI-based error-recovery approaches are assessed by adaptive analysis of incompressibility elastic problems including the problem with singularity. The quadrilateral meshes are used for discretization of problem domains. For adaptive improvement of mesh, the square of error equally distributed technique is employed. The computational outcome for solution errors, i.e., error distribution and convergence rate, are obtained for RPI technique-based error-recovery approach employing different radial basis functions (multi quadratic, thin-plate splint), RBF shape parameters, different shapes of influence zones (circular, rectangular) and conventional patches. The error convergence in the original FEM solution, in FEM solution considering influence-zone-based RPI recovery with MQ RBF, conventional patch-based RPI recovery with MQ RBF and conventional patch LS-based error recovery are found as (0.97772, 2.03291, 1.97929 and 1.6740), respectively, for four-node quadrilateral discretization of problem, while for nine-node quadrilateral discretization, the error convergence is (1.99607, 3.53087, 4.26621 and 2.54955), respectively. The study concludes that the adaptive analysis, using error-recovery estimates-based RPI approach, provides results with excellent accuracy and reliability.  
error estimates; effectivity; incompressibility; recovery technique; radial point interpolation; radial basis function +
Footnote †: journal: Applied Sciences
# The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis
## 4 Results and Discussion
### Forecasting Workforce Shifts in the Emerging Market Landscape
#### 4.5.1 Preparing and Sensitizing Professionals for I5.0  
The interviews indicate that, while some healthcare professionals are aware of and preparing for I5.0, there is a considerable journey ahead. Comprehensive preparation, including not only technical training but also awareness of the possibilities offered by human-technology collaboration, is fundamental for a successful transition. The responses can be summarized in the following topics:  
* Challenges and resistance: Many professionals emphasized the overall lack of readiness among their colleagues for the paradigm shift, with the fear of job loss due to automation being a prominent concern (E1, E2). Additionally, the lack of digital health literacy (E3) and resistance to changing established routines (E5) were identified as significant barriers. Readiness for I5.0 was also perceived to vary considerably depending on the sector and region (E6), with some professionals questioning if they will ever feel fully prepared for such a change (E7).
* Paths for awareness and preparation: The interviews suggest that addressing these challenges requires a multifaceted strategy for raising awareness and preparing healthcare professionals. Education and training in the areas at the intersection of digital technology and healthcare were highlighted as crucial (E10), as well as the implementation of training programs by employers (E6). Additionally, sensitizing professionals to the advantages of new technological tools (E1), demonstrating how technology can improve healthcare delivery (E2), and creating new job opportunities (E6, E11) are essential steps to take. [PERSON] et al. also mentioned this topic in their studies [32].
* The role of education and innovation: The paradigm shift requires substantial investments in education and effective engagement of professionals who understand both technology and patient care (E8, E9). It is emphasized that, in addition to technical preparation, cultivating an open mindset to harness the opportunities presented by emerging technologies, such as \"reducing the burden on healthcare systems\" (E3), is crucial.
## 4 Discussion
### Effect of the Generated (Reactive) CAP Species on the Biofilms
#### 4.2.4 Penetration of CAP Species into the Biofilms and Their Effect on the Biofilm Structure  
As mentioned in Section 3.2.4, all examined combinations of plasma characteristics were able to cause cell death/damage into the deeper layers of the model biofilms. Therefore, no correlation was observed between the individual plasma characteristics and the ability of the generated CAP species to penetrate into the biofilm matrix. For the _Listeria monocytogenes_ biofilms, some CAP treatment conditions did not only result in inactivation of the biofilm-associated cells, but also in an etching effect, i.e., relatively large holes were created within the biofilm structure. Previous studies indeed indicated that some CAP species are able to interact with the EPS matrix [14; 23; 41; 64]. The research of [PERSON] et al. [41] reported that the presence of NO within the gas phase can result in a disruption of the biofilm matrix with dead and living cells being dispersed into the environment. However, in the research of [PERSON] and [PERSON] [64], etching of the biofilm resulted in a decreased biomass following CAP treatment. As this was not de case for the model biofilms used within the presented research [65], the holes within the _Listeria monocytogenes_ biofilms cannot be the result of some parts of the biofilms being dispersed, but rather of their contraction. Nevertheless, more research will be required to examine this phenomenon in detail.
## Appendix A.2.1 Architecture
### Physical-Layer Cell Identifiers  
Similarly to GSM, UMTS provides two physical parameters to locally identify a cell, namely:  
* The UTRA absolute radio frequency channel number (UARFCN), which is the radio carrier identifier, as in GSM; it is equal to five times the carrier frequency in MHz and ranges from 0 to 16,383 (14 bits);
* The primary scrambling code (PSC), which is the first part of the synchronization channel (SCH), a downlink signal used for cell search; it ranges from 0 to 511 (9 bits), and it allows one to identify the transmission in each cell.  
If the network is properly configured, it is impossible to detect the same pair (UARFCN, PSC) in the same area.
## 4 Results and Discussion of the Initial Cooling Channel
### Steady-State Numerical Simulation Results with Phase-Change Cooling
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Inlet Flow Rate (m/s)** & **Outlet Cross** & **Middle** & **Middle** & **Inlet Cross** & **Average Inlet and** \\
**Rate (m/s)** & **Section (Pa)** & **Section 1 (Pa)** & **Section 2 (Pa)** & **Section 3 (Pa)** & **Section (Pa)** & **Outlet Pressure** \\  & & & & & **Drop (Pa)** \\ \hline
0.5 & 0 & 58.62 & 1268.14 & 3685.09 & 6412.09 & 6412.09 \\
0.2 & 0 & 25.29 & 905.848 & 2431.23 & 4350.70 & 4350.70 \\
0.1 & 0 & 20.02 & 270.048 & 1638.14 & 3381.95 & 3381.95 \\
0.05 & 0 & 10.64 & 300.368 &
# Pathway to Carbon Neutrality in the Cement Industry: CO2
## 4 Co\({}_{2}\) Capture of RAs  
### Mixed Recycled Aggregates  
The XRD diffractograms of three mixed recycled aggregates collected directly in recycling plants (MRA-RP1, MRA-RP2 and MRA-RP3) are presented in Figure 1. The main minerals detected are quartz, muscovite, microcline, albite and calcite. No portlandite was detected under the detection limits of XRD analysis, which indicates a low carbonation potential of these MRAs.  
MRA-RP1, MRA-RP2 and MRA-RP3 were subjected to forced carbonation for 3, 5, 12 and 24 h (Figure 2). MRA-RP1 and MRA-RP2 both registered the highest CO2 content at 5 h, while for MRA-RP3 the maximum CO2 content was reached at 12 h of carbonation. It was found that after the maximum CO2 was achieved, the CO2 uptake decreased. This phenomenon could be related to the acid attack, which promotes the dissolution of part of the calcium carbonate with CO2 liberation. In addition, it is well known that the formation of calcium carbonates at the surface restricts the diffusion of CO2 into the sample, which limits the CO2 absorption after the initial CO2 uptake. [PERSON] et al. [68] also reported that the rate of CO2 uptake by RAs decreased with the carbonation time.  
The CO2 uptake by MRA-RP1 was 0.7% after 5 h, which corresponds to 7.0 kg of CO2 captured per tonne of aggregate. A similar value was obtained for MRA-RP3 (0.6%) and slightly higher for MRA-RP2 (1.8%). Due to the insignificant amount of portlandite, it is likely that other calcium-hydrated products were carbonated. MRAs are composed of several materials, but only part of the Rc fraction contributes to carbonation (between 11 and 16% of Rc, which corresponds to the cement paste). Just considering the cement paste, MRA-RP1, MRA-RP2 and MRA-RP3 can capture, on average, 12.6%, 22.5% and 12.3% of  
Figure 1: XRD pattern of noncarbonated MRA-RP1, MRA-RP2 and MRA-RP3. Notation: M—Muscovite; E—Ettrringite; G—Gypsum; K—Kaolinite; Fk—Microline; Fa—Albite; Q—Quartz; C—Calcite.  
Figure 2: Evolution of CO2 content in the samples MRA-RP1, MRA-RP2 and MRA-RP3 with increasing forced carbonation time.  
CO\({}_{2}\), respectively. This corresponds to 126 kg, 225 kg and 123 kg of CO\({}_{2}\) per 1 tonne of cement paste presented in these aggregates, respectively.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Proposed Method for Placing Multiple Markers
### Scoring Step
#### 2.2.3 MPS Score  
The MPS score, which indicates the suitability of a specific \(\mathbf{f}_{j,i}\) as a position for placing a marker, is expressed as Equation (6) using a weighted mean of both Equations (3) and (5) derived from each hypothesis.  
\[\text{MPS}\big{(}\mathbf{f}_{j,i}\big{)}=(1-w)\cdot\text{CSA}\big{(}\mathbf{f} _{j,i}\big{)}+w\cdot\text{normalize}\big{(}\text{DoI}\big{(}\mathbf{f}_{j,i}\big{)} \big{)} \tag{6}\]  
where MPS denotes a function that calculates the MPS score at \(\mathbf{f}_{j,i}\), and \(w\) denotes the weight used in the weighted mean.  
Multiple markers are placed at \(\mathbf{f}^{\prime}\)s with the highest MPS score in each area. Figure 10 shows the MPS scores calculated according to the number of segments \(k\) and weight \(w\) on a specific virtual large object, the car model.  
In Figure 10, green color indicates a high MPS score, and white color indicates a low MPS score. When no weights are considered (\(w=0\), second column in Figure 10a-c), it is shown that the closer to the CoA of each segmented area, the higher the MPS score, and the further away from the CoA, the lower the MPS score. On the other hand, when only the weights are considered (\(w=1\), fourth column in Figure 10a-c), the featured positions of each segmented region have higher MPS scores regardless of the CoA, and lower MPS scores otherwise. It is also shown that the number of segments (\(k\)) affects the MPS score because the segmented areas and CoAs are changed by \(k\).  
Figure 9: Example of DoI computation.
## 5 Discussion
### Efficiency Analysis  
In addition to the communication rounds necessary for convergence, which were discussed previously, we analyze the model's efficiency, considering factors such as the number of parameters and computational overhead. Since FedRecI2C does not modify the structures of client/item models, the aggregation method or the network topology, it does not introduce additional storage space overhead to the original FRSs. FedRecI2C incorporates communication-constrained clients, which are typically overlooked in other FRSs, into the training process. This incorporation leads to additional computational overhead. However, the learning process is decomposed into multiple small-scale learning processes. These processes are executed on numerous distributed devices that directly utilize local computational resources. This approach ensures the utilization of distributed computational and energy resources and enables parallel and efficient computing. As a result, the additional computational overhead imposed by FedRecI2C is mitigated. Additionally, as mentioned before, the I2C period parameter \(p(p>1)\) offers resource-constrained clients more time to retrieve model parameters from the server and perform local updates, which implies reduced-frequency resource usage. Finally, the application of the early-stop strategy in I2 CG also helps to reduce the additional computational overhead.
## 4 Effect of Oil-Water Infiltration on Rock Mechanical Properties
### AE Characteristics of Damaged Rock Samples with Different Liquid Contents
After 8 h of oil-water soaking, the AF values mostly range from 0 to 120 kHz, and the RA values from 0 to 4 ms/v. Compared to the dry rock samples, the distribution range and density of the RA-AF parameters decrease. The data points in both regions begin to shift toward the origin, indicating that the failure mode of the rock samples shifts from a mixed mode of tensile and shear failure to tensile failure. After 16 h of oil-water soaking, the AF values mostly range from 0 to 120 kHz, and the RA values from 0 to 4 ms/v. The density of the RA-AF parameters further decreases, and the failure mode of the rock sample is tensile failure. After 24 h of oil-water soaking, the AF values mostly range from 0 to 60 kHz, and the RA values from 0 to 4 ms/v. The data points near the AF axis show a decreased density, while those in the 2-4 ms/v section of the RA axis still have many data points. The rock RA-AF parameters show a typical \"low AF, high RA\" characteristic, indicating that the macroscopic failure mode of the rock sample is shear failure. Throughout the entire evolution process, as the soaking time increases, the data density in the shear crack region and tensile crack region gradually decreases. This is because the dry rock has a relatively complete internal structure and higher cohesion,  
Figure 13: RA–AF images of rock samples with different soaking times: (a) 0 h, (b) 8 h, (c) 16 h, (d) 24 h.  
leading to a large number of AE events during loading, which results in high RA and AF data density and a broad distribution range. However, as the soaking time increases, the number of various types of cracks inside the specimen decreases, and their distribution range becomes more concentrated towards the horizontal axis. This reduces the AE signals from the specimen, which in turn promotes the formation and expansion of shear cracks. Generally, as the soaking time increases, the rock failure mode transitions along the path of \"tensile failure \(\rightarrow\) tensile-shear failure \(\rightarrow\) shear failure\". However, in this experiment, the rocks had already undergone unloading and reloading damage before the oil-water soaking, so the failure mode transition path for the rock samples was \"tensile dominated tensile-shear failure \(\rightarrow\) shear failure\". Due to the prominent plastic characteristics of the damaged rock after oil-water soaking and the development of original cracks and voids, shear slip failure is more likely to occur.
###### Abstract  
Wind farm layout optimization is essential to maximize the energy production of renewable energy systems. A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to the wake effect without limitation on the number of turbines, the shape, or the extension of the concession area. The main engine of the algorithm is a gradient-descent method in which throughout the optimization process, new turbines are progressively and randomly included within the concession area and quickly expand outward, looking for areas with less perturbation, in turn, pushing the previous ones. When the optimization process ends, to avoid local maxima, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a map of potential for the entire area is created, and a greedy algorithm places new turbines to complete the layout with the final number of turbines. The overall process is completed in 25 s. To drastically speed up the search process and the creation of the potential map, a simplification has been validated and added: for turbines affected by multiple wakes, the resulting power has been calculated by using a linear aggregation of power deficits, instead of the usual linear (or quadratic) aggregation of speed deficits. Owing to this type of aggregation, an analogy is established between power deficit and repulsive non-isotropic electrostatic potential energy, which allows using the properties of conservative fields. Thanks to this, the process is 20 times faster than any other layout optimization algorithm found in the revised literature. Irregular concession areas are easily treated using Stokes' theorem to detect outer points.  
layout optimization; offshore wind farm; gradient descent; greedy repositioning; power deficit; deficit aggregation; computation time