esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 RIS Implementation Structures
### Toward Liquid Crystal (LCs) Based RIS
In the future, investigations related to improving the reflection loss performance and frequency tunability are required, which can be done by a depth study of LCss's material properties. The main limitation of LCs is the time taken by their molecules to change their orientation. This time consumed by the LCs based reflector for achieving frequency tunability needs to be further investigated. The above-stated drawback of LCs based RIS can be overcome by using novel active RIS implementation, as discussed in the next section.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 4 Results
### Applications of Blockchain Technology to Fight against COVID-19
#### 4.4.2 Sharing Information with Healthcare Collaborators  
Sharing the patient data, treatment methods that were used, prevention methods that were used by doctors and healthcare personnel, and so on are important data that must be shared among healthcare collaborators. These data play a fundamental role in COVID-19 research as shared data from health organizations and hospitals can discover new treatment methods that help reduce the number of deaths. Therefore, international bodies and organizations use HIPAA controls to maintain the data's confidentiality, especially patient information [3]. However, there are obstacles in implementing the data-sharing system more broadly.  
Blockchain features such as decentralization and transparency can be used to improve the healthcare system. It can take advantage of decentralization to increase the security and privacy of patient data, increase data control, and get rid of traditional medical records, which can be stored data in a blockchain-based system, making sharing data between health organizations and hospitals much easier and faster. The transparency feature can also be used to prevent data fraud and protect patients' privacy from abuse or misuse [3].
## 2 Materials and Methods
### Approach for Estimations
#### 2.5.2 TEC Domain  
There are several approaches to validate models in TEC domains. The first one is to compare experimental and model variations in slant TEC for an individual satellite-to-receiver line of sight [31]. Another one is to compare absolute vertical TEC from the model with well-validated reference, for example, IGS final Global Ionospheric Map. Both approaches have advantages and drawbacks. In our case, we directly compared absolute vertical TEC from model \(I^{M}\) with TEC from Global ionosphere maps IGSG \(I^{SIM}\) (for \(N\) measurements). Four parameters were used as error proxies.  
* Mean TEC error (TEC bias) <\(\Delta I\)>, \[\langle\Delta I\rangle=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}I_{i}^{M}-I_{i}^{SIM} \Bigr{)};\] (2)
* Mean absolute TEC error (MAE) <\(1\,\Delta I\) >, \[\langle|\Delta I|\rangle=\frac{1}{N}\sum_{i=1}^{N}\Bigl{|}I_{i}^{M}-I_{i}^{SIM} \Bigr{|};\] (3)
* Mean percentage TEC error (MPE) <\(\Delta I\)/\(I\)>, \[\langle\Delta I/I\rangle=100\%\cdot\frac{1}{N}\sum_{i=1}^{N}\frac{\bigl{(}I_{i }^{M}-I_{i}^{SIM}\bigr{)}}{I_{i}^{SIM}};\] (4)
* Mean absolute percentage TEC error (MAPE) <\(1\,\Delta I\) | /\(I\)>, \[\langle|\Delta I|/I\rangle=100\%\cdot\frac{1}{N}\sum_{i=1}^{N}\frac{\bigl{|}I_ {i}^{M}-I_{i}^{SIM}\bigr{|}}{I_{i}^{SIM}}.\] (5)  
The better the model in the TEC domain, the smaller absolute values of errors (2-5).  
In the literature, different statistical parameters have been used as performance indicators (e.g., RMSE or MAE [32]) In this work, MAE has been chosen as a reference, also considering that in general, all the other parameters exhibited a similar behavior.
## 7 A Review of Policy in the Remote Sensing Literature  
While reviewing the above, it was noticed that most of the examples were published outside the peer reviewed literature. This raised our interest in the question as to whether, and to what extent, peer reviewed literature contributes to the debate on the policy support of remote sensing. We therefore executed a literature search for papers on \"_remote sensing OR earth observation AND policy_\" published from 1991 onwards [65], which retrieved 302 peer reviewed papers. The number of remote sensing papers that refer to policy increased from 1991 to 2007 by 15.3% annually, which is significantly higher (two sample t-test, t = 19.1, d.f. = 16, P < 0.001) than the 8.8% growth rate of the remote sensing literature as a whole (Figure 6).  
Figure 6: Trends in the total number of remote sensing papers (\(\Delta\)) and number of RS papers referring to policy (o) published between 1991 and 2007 as retrieved from ISI records.  
The trend displayed in Figure 6 suggests an increasing interest of the remote sensing community in the relation between EO and policy. However, although all papers associated policy to remote sensing, not a single of these 302 papers described the _actual support_ of EO to a specific policy. Instead, the majority of papers demonstrate or claim a _potential_ of EO to support environmental policy. Other papers relate EO to policy while considering policies to support the EO industry financially [66], policies for the acquisition, access, and distribution of remote sensing data [4], or policies as an explanatory variable in empirical research.
# Role of Nutribiotics in Skin Care
## 2 The Skin Microbiome: A Unique Environment
Thus, it has been observed that the microbiomes in neonates looks like maternal vaginal communities when delivered vaginally (_Lactobacillus_ and _Prerotella_ spp. ), or maternal skin communities if delivered by cesarean section (_Staphylococcus_, _Streptococcus_, _Corynebacterium_, and _Propioniibacterium_ spp.). _Staphylococcus_, _Corynebacterium_, and _Prerotella_ abound in premature infants, while _Brevundimonas_, _Flavobacterium_, and _Sphingobacterium_ predominate in full-term infants [41]. At birth, the pH of the skin is neutral, but, in the first hours of life, the development of the cutaneous acid mantle begins, favoring colonization by commensal organisms and inhibiting the growth of pathogens. Breast milk contains microbes, antimicrobial metabolites, IgA antibodies, and cytokines that facilitate the development of the microbiome and the neonatal immune response. The microbiome is influenced by close contacts, and it evolves throughout childhood; thus, _Firmicutes_ (_Staphylococcus_ and _Streptococcus_) predominate in the skin of babies, followed by _Actinobacteria_, _Proteobacteria_, and _Bacteroidetes_[49; 50]. Microbiota diversity increases at least during the first eight years of life, which appears to be related to a reduced dominance of Lactobacillus (especially of the genus _Streptococcus_) in the skin. In 14 year olds, there is greater interindividual variation in diversity than in  
Figure 2: Relative abundance of bacterial, fungal, and viral components of the microbial community in the different skin microenvironments: sebaceous (yellow), moist (blue), and dry (green). Toenail (black) does not match these major microenvironments (Adapted from [PERSON] and [PERSON], 2014) [39]. younger age groups; the number of _Staphylococcus_ or _Streptococcus_ species decreases, and the amount of _Actinobacteria_ and _Proteobacteria_ species increases [51]. Puberty is another stage of changes in the skin microbiota; thus, _Frimicutes_ (_Streptococcus_ spp. ), _Bacteroidetes_, and _Proteobacteria_ are abundant, while the fungal community becomes more diverse [41]. The hormonal stimulus that occurs in the post-pubertal stage entails the stimulation of the sebaceous glands, with an increase in sebum production, favoring the overgrowth and spread of lipophilic microorganisms, such as _Propionibacterium_ spp., _Corynebacterium_ spp., and _Malassezia_ spp. [41]. At the adult stage, _Corynebacterium_, _Propionibacterium_, _Streptococcus_, and _Staphylococcus_ predominate [49]; finally, in the elderly population, the number of _Frimicutes_, including _S. aureus_ and _Cutiobacterium_ species, decreases [52; 53], with the production of antimicrobial peptides also decreasing, thus increasing susceptibility to bacterial infections [49]. [PERSON] et al. [54] investigated the skin mycobiome, showing that Malassezia predominated on the scalp, trunk, and arm skin of adults (age 20-30), and children (age \(<\) 14) had more diverse fungal communities, for example, Eurotiomycetes, which includes common dermatophytes, with _M. globose_ being the most predominant in children.
## 3 Results and Discussions
### Total Phenolic Content Analysis  
The Folin-Ciocalteu reagent assay is directed to assess the total phenolic content [30], measured as mg GAE equivalents per mg of raw material, extracted by each solvent. Our results showed that the highest yield (_p_ < 0.05) of phenolics, in _M. nodiforum_, was obtained by ethanol 80% (_v/v_), while the n-hexane gave the lowest yield (_p_ < 0.05) (Figure 1), together with SFE, in relation to the yield (Figure 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solvent** & **Yield (g/100)** \\ \hline N-Hexane & 0.43 \(\pm\) 0.28 a \\ Ethanol & 18.39 \(\pm\) 1.69 b \\ Acetone & 12.30 \(\pm\) 2.45 c \\ Water & 33.32 \(\pm\) 8.37 d \\ SFE & 0.79 \(\pm\) 0.27 a \\ \hline \hline \end{tabular} Lowercase letters (a–d) indicate significant differences between different solvents (_p_ < 0.05).  
\end{table}
Table 1: Extract yield (g/100g) obtained, with different solvents, from dried leaves of _M. nodiforum_.  
[PERSON] et al. [45] showed 70% (\(\
u\)/\(\
u\)) ethanol extracts to have a higher content of phenolics when compared to 70% (\(\
u\)/\(\
u\)) acetone extracts in wheat bran. This study found ethanol to have lower toxicity, and additionally, extracts could be recovered by reduced pressure distillation [45]. However, it was also shown that when the concentration of ethanol was increased above 80% (\(\
u\)/\(\
u\)), the total phenolic content decreased due to lipid components also being extracted [45].
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Vertebrates  
The jumping mechanisms of vertebrates are classified into four groups: (i) bipedal jumping; (ii) jumping with rapid pulling of forelegs and extension of hindlegs and spines; (iii) jumping with energy stored in tendons of the hindlegs and spines; (iv) leaping, as shown in Figure 2.  
Kangaroos are the most famous bipedal jumping animals and they can jump at a speed of 70 km/h for two hours [6]. They use their long and heavy tail to control balance and propulsion, and exploit their long elastic tendons in legs to store energy that is then suddenly released to catapult themselves into the next jump.  
Instead, animals like cats, dogs, and snow leopards, perform jumping mainly through rapid pulling of the forelegs and extension of the spine and hind legs; in addition, the flexion of their feet, leg joints, and spines play an important role in absorbing the shock of impact in landing phase [6].Blackbuck uses elastic energy, stored in the long tendons of the leg and in the spine, to catapult it into the next jump [6].  
Leaping is the characteristic locomotion mode of several species of primates including forest-dwelling lemurs and bush babies (galagoes). Typically, leaping is performed by animals in trees to move from branch to branch or trunk to trunk. Leapers tend to have relatively long hind limbs and a long, flexible spine. Propulsion mainly originates from the extension of the hind legs and spine, while landing is realized by means of the forelimbs. In the leaping of lizards, tails have proved to play an important role in aerial righting after take-off.  
In this part, the jumping mechanisms of vertebrate animals, including frog, galago, kangaroo, human, and dog, are discussed and the research statuses of the corresponding inspired jumping robots are concluded.
## 3 Results and Discussion
### Various Flow Rate Patterns of Pure Liquid Controlled with Syringe Pump
Figure 4: Quantitative comparison of pressure and shear stress for both streams under the stepwise increase in flow rates. 1\(\times\) PBS as reference fluid was supplied at the flow rate of 1 mL/h. Flow rate of glycerin solution as test fluid (i.e., \(C_{glycerin}\) = 10%, 20%, and 30%) increased stepwise from 0.2 mL/h to 1.6 mL/h. (**A**) Temporal variation of \(\beta\) as well as \(Q_{l}\) with respect to \(C_{glycerin}\). (**B**) Variation of viscosity of test fluid with respect to \(Q_{l}\). (**C**) Variation of pressure \(P_{r}\) and \(P_{t}\) with respect to \(C_{glycerin}\) and \(Q_{t}\). (**D**) Variation of \(\tau_{r}\) and \(\tau_{t}\) with respect to \(C_{glycerin}\) and \(Q_{l}\). From the experimental results, the present method was effectively applied to monitor the flow of glycerin solution as a test fluid under constant flow rates as well as sinusoidal flow rates. As both streams in the coflowing channel had the same pressure and shear stress (i.e., \(P_{r}\approx P_{t}\), \(\tau_{r}\approx\tau_{t}\)), the pressure and shear stress of the test fluid were estimated in terms of the pressure and shear stress of the reference fluid. Thus, four properties of the test fluid (i.e., \(P_{r}\), \(\tau_{r}\), \(w_{p}\), and \(w_{\tau}\)) were calculated consistently by monitoring the interface between the two fluids under various flow rate conditions.
# Design Optimization of Reconfigurable Liquid Crystal Patch Antenna
## 3 Results and Discussion
### Analysis of Optimal Liquid crystal Position on the Patch Surface
At this time, the tan \(\delta\) of the liquid crystal is used as an estimated value of 0.01. As a result of confirming the change in the resonant frequency of the patch antenna while changing the permittivity from \(\epsilon_{\parallel}\) to \(\epsilon_{\perp}\), it can be seen that it decreases by about 40 MHz from 3.43 to 3.39 GHz, as shown in Figure 8a. In addition, the E-plane radiation patterns of the antenna appear similar to that of a typical patch antenna, as shown in Figure 8b. The peak gain is about 4.8 dBi, and the radiation efficiency is estimated to be about 73%.
## 3 Results and Discussion
### ZnO Nanoparticle Characterization Analysis
#### 3.3.3 SEM Analysis of ZnO-Nanoparticle-Treated Bacteria and Fungi  
The microbial cells treated with the tested substances dissolved in DMSO were photographed by SEM, which was because of the good solubility of ZnO nanoparticles in DMSO. The SEM images of the untreated cells and microbial cells treated with the LPME, 50 mg LP-ZnO NPs, augmentin, and voriconazole are shown in Figures 4 and 5. As can be seen, the treatment of _P. aeruginosa_ and _S. aureus_ with the LPME and augmentin resulted in minor alterations to the cell surface, with some cells adhering to one another, as seen in Figure 4a-d. The _P. aeruginosa_ cell membranes were damaged by the 50 mg LP-ZnO NPs, whereas the _S. aureus_ cells showed more severe damage, including pitted, distorted, shriveled, stuck together, and even broken cells. As shown in Figure 5a-g, after receiving 50 mg of the LP-ZnO NPs, the _A. terreus_ hyphae developed wrinkles, and the nanoparticles gathered around the vesicles. Similar alterations were observed in the _A. niger_ hyphae following treatment with 50 mg of LP-ZnO NPs, but in this case, the vesicles vanished. The _A. terreus_ and _A. niger_ hyphae did not exhibit any significant alterations in response to the LPME or voriconazole.
## 4 Experimental Detail
### Prediction during Navigation, and Navigation towards a Goal Location
#### 4.2.1 Finding the Direction of a Goal Location  
By using the mapping as mentioned above of the environment into a grid code, the agent can obtain both current and the goal grid code. But here, the agent does not know which direction to choose to move forward so that the goal grid code can be reached. The agent solves this problem while it finds the activation grid points, which are closer to the current activation site for each participated grid neuron. The direction in which the overall match between the activation of each grid neuron with their corresponding activations in the goal grid code will be chosen by the agent to move forward. If none of the directions gives the match higher than the threshold, then test for the next activation site, first change the lowest scale grid neuron.  
For all participated grid neurons in all directions, the direction in which the distance between the activation of each grid neuron founds closer to their corresponding activation goal grid code (as shown in Figure 17) will be the direction of movement for the agent. But in real scenarios, mostly the goal location will be hidden to the agent. Therefore, the agent needs to consider the walls, objects encountered in the path of the agent, which can change the direction of movement. Here, image depth is used to find out whether an object or the wall is there or not in the path if anything is encountered then the agent would change their direction to move forward and recalculate the direction.
## 4 Discussion
These qualities enable a faster glide path and reduce procedural complexity, though ProTaper Next is less conservative regarding dentin preservation. Due to its physical structure [38], including the relatively high stiffness of the F1 file [39], as well as its working mechanism, ProTaper Universal and MTwo have been shown to create significantly more dentinal cracks than the Reciproc system or hand NiTi files [37]. When evaluating centering ability across systems made from different materials, such as M-wire and Gold-wire, ProTaper Next with a K-file glide path exhibited significantly more canal transportation and removed the most dentin from canal walls in [PERSON] et al. 's study [40]. Conversely, a recent in vitro study by [PERSON] et al. found that ProTaper Next achieved the best apical termination position and produced the least canal aberration in curved canals compared to ProTaper Universal or WaveOne [41]. It is evident that the glide path technique impacts the centering and transportation abilities of rotary systems, though findings in the literature remain somewhat inconsistent. Another important aspect of this study is that it directly compared different movement types, unlike the majority of published articles that focus exclusively on the mechanical characteristics of each system. With respect to mechanical properties, M-wire Ni-Ti rotary instruments exhibit greater fatigue resistance and flexibility than conventionally processed Ni-Ti wires, which are already highly effective in treating teeth with complex root canal anatomy [7; 42; 43; 44; 45; 46]. Although both MTwo and Reciproc have S-shaped cross-sections, their intracanal efficacy is also influenced by their respective motion types [39; 47; 48]. Recent publications suggest that the cross-sectional design has the most significant impact on debris extrusion, followed by motion type. Moreover, multifile systems may help reduce the extent of root canal transportation [49]. This indicates that multiple factors must be considered in selecting the most conservative rotary system that preserves the original root canal anatomy [28]. Regarding study limitations, the relatively small sample size has already been noted. Additionally, this study did not include the most recent innovations and systems. This choice was intentional as the objective was to investigate widely accessible and commonly used techniques and systems, which continue to serve as the standard for general dentists worldwide. Unlike most available studies that rely on acrylic blocks or other synthetic materials [49; 50; 51], this study was based on natural teeth, providing a closer approximation of rotary file behavior in human dentin when using the same irrigation protocols as in vivo, thereby allowing for more accurate conclusions. However, the in vitro design itself presents certain limitations, as access and patient-specific variables may vary in each in vivo case.
## 2 Proposed Algorithm
### Interval Type-2 Fuzzy System
#### 2.1.1 Variables of the Type-2 Fuzzy System
In contrast to our previous fuzzy-based clustering algorithms, we propose that the inference process follows a Tagaki-Sugeno-Kang (TSK) model [24]. Consequently, the output variables, as in other TSK systems, are functions whose parameters are the input variables. Those functions are usually linear combinations or constants. The reason for using TSK instead of Mamdani [27] is because, in TSK systems, the consequent of each rule can have as many parameters per rule as input values. This assures more degrees of freedom in the design than a Mamdani one and it provides more flexibility in the design of the system [28]. Another important feature of TSK systems over Mamdani ones is that they can approximate any Mamdani system with an arbitrary level of precision [29]. Moreover, because TSK rule's consequent is formed by a combination of parameters, TSK systems are more suitable than Mamdani systems in applications that use adaptive or optimization techniques, since it is more efficient [30]. The output intervals in our proposal, noted as (\(o_{l}\), \(o_{h}\)), are formed by five output sets configured as constants as it is detailed in Table 1:
## 4 Discussion
### Methodological Considerations and Study Limitations  
When interpreting the present findings, it is essential to acknowledge the methodological limitations that may have influenced the outcomes. First, the small sample size substantially reduced the statistical power to detect subtle effects and limited the generalizability of the results to broader populations. This is particularly important given the interindividual variability often observed in clinical and metabolic parameters. Second, the quasi-experimental design, without random assignment, may have introduced a potential risk of selection bias. The participants were allocated based on practical considerations, including institutional constraints, individual availability, and ease of transport to the training facility. Although this allocation method was not random, there is a risk that unmeasured differences between the groups could have influenced the outcomes. While this approach boosted participant adherence, which is key in studies involving vulnerable populations, it may have compromised the internal validity more compared to that of a randomized controlled trial. Third, the duration of the intervention (8 weeks) may have been insufficient to elicit significant changes in specific metabolic and functional outcomes. Adaptations related to lipid metabolism, glycemic control, and oxidative stress often require longer training periods to present robust physiological effects. Fourth, there was no systematic monitoring of the participants' spontaneous physical activity outside the intervention sessions. This lack of control, especially in the CON group, introduces the possibility that untracked variations in daily activity may have influenced the metabolic outcomes, limiting the interpretation of intervention effects. Finally, we did not consistently track medication throughout this study. Changes in medication, such as the initiation, discontinuation, and dose adjustment of lipid-lowering or antidiabetic drugs, could have significantly influenced the biochemical markers measured. To strengthen the validity and applicability of future research, we recommend the inclusion of a larger randomized sample; extended intervention durations; and the implementation of objective, systematic methods to track dietary intake, physical activity (e.g., via accelerometry), and medication adherence. These methodological improvements would allow for clearer attribution of observed effects to the intervention and enhance confidence in the intervention's efficacy.