esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Multicast Routing
#### 2.1.3 Multicast Distribution Mechanisms  
There are two types of distribution mechanisms, tree and mesh. With a tree structure, each node is associated with one parent that forwards packets. In a mesh, there might be alternative paths and a node can have multiple parents. A relaying node forwards a packet once regardless of who it was received from. A mesh can either be guaranteed or probabilistic. In a guaranteed mesh, a node has multiple nodes that forward packets to it. In a probabilistic mesh, the number of parents depends on the location of the receivers. All of the mesh protocols surveyed are probabilistic. Mesh structures are more robust. However they may consume more resources and the resource reservation mechanism becomes more complex.
## 3 Results and Discussion
### Microstructure and Mineral Composition
After 40 drying\(-\)wetting cycles, the contents of feldspar and calcite decreased by 17.08%  
Figure 8: SEM images of samples under different drying\(-\)wetting cycles and pH solutions: (**a**) \(n=0\), pH = 7; (**b**) \(n=40\), pH = 7; (**c**) \(n=0\), pH = 5; (**d**) \(n=40\), pH = 5.  
and 29.11%, respectively. On the contrary, the content of quartz increased by 9.51%. Thus, the variation in the mineral contents tended to be obvious as the drying\(-\)wetting cycles and acid concentration increased. In the acidic solutions, the cations (K\({}^{+}\) and Na\({}^{+}\)) in the feldspar underwent a substitution reaction with H\({}^{+}\), and they were separated from the rock matrix and became free. Similarly, calcite also reacted with H\({}^{+}\). These chemical reactions caused the reduction in the minerals [22]. Quartz is relatively stable and hardly reacts in acidic solutions. However, the relative content of quartz inevitably increases with the decline of other minerals. Figure 9: XRD patterns and relative mineral contents of samples under different drying\(-\)wetting cycles and pH solutions: (**a**) pH = 7; (**b**) pH = 5; (**c**) pH = 3. In summary, the changes in the microstructure and mineral composition of red sandstone under acidic drying\(-\)wetting cycles were more obvious than those under neutral drying\(-\)wetting cycles. The gap between them became greater with increasing cycles and acid concentration, which is the cumulative result of drying\(-\)wetting damage and acid corrosion damage. Specifically, drying\(-\)wetting cycles focus on mechanical action, and acid corrosion focuses on chemical action. The two actions overlap and promote each other. When the red sandstone was treated with drying\(-\)wetting cycles in acidic solutions, water with hydrogen ions slowly infiltrated the interior along the primary fractures from the rock surface, and the cementation dissolved, resulting in the weakening of cohesive force between mineral particles. The water filled the open pores and evaporated constantly. This repeated action of drying and wetting led to the extension of primary fractures and the generation of new fractures. Meanwhile, hydrogen ions reacted with the feldspar and calcite in the rock, and more pores and fractures were formed. This acid corrosion further weakened the cohesive force between mineral particles, which provided better conditions for later drying\(-\)wetting cycles. Therefore, the coupling effect aggravated the deterioration of the microstructure and mineral composition of red sandstone and was much sharper than a single effect. The above micro-mechanism provides a favorable explanation for the deterioration of the physical properties, mechanical properties, and failure mode of red sandstone under acidic drying\(-\)wetting cycles.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 3 Clinical Applications
### Bone Regeneration for Dental Implants and Mandibular Defects
An alternative to nHA for bone regeneration, with a well-established history in endodontics, is the use of mineral trioxide aggregate (MTA)--a silicate-based inorganic material containing tricalcium silicate, dicalcium silicate, calcium sulfate dihydrate, and bismuth oxide [69; 70]. While many scaffolds are osteoconductive (used as the framework for bone growth on the surface), the unique composition of MTA enables this material to also possess osteoinductive properties (the recruitment and stimulation of immature cells into preosteoblasts). It is hypothesized that MTA functions through an increase in pH, and in calcium and hydroxide ions, which induce mineralization [69]. Despite its history of use, some limitations, such as a long settling time, weak handling properties, and discoloration over time, remain, though they may be overcome through the incorporation of nanostructures [69]. Nanoparticles have an increased surface area and can thus release more calcium and hydroxide ions and set more quickly. Nanomodified MTA (nMTA) is a novel concept explored by [PERSON] et al. nMTA contains additional compounds, such as gypsum, strontium carbonate, zeolite, and di-sodium hydrogen phosphates of sizes 40 to 100 nm [69]. In an in vivo white rabbit study, they showed that nMTA was superior in comparison to the standard MTA in various aspects, including reduced inflammatory reaction, reduced immunogenicity, increased calcium and hydroxide ion release, and increased bone regeneration [69]. Additionally, in a prior study, they also showed that nMTA set significantly more quickly than MTA as well as having a greater push-out bond strength--that is, the force required to displace the material post-setting [71]. While these studies show evidence for a promising alternative to nHA for bone regeneration, further studies are needed to reinforce the evidence found by [PERSON] et al., as they are the only group exploring this material to date. Recent studies used graphene to modify the surface of dental implants, facilitate the development of platforms for therapeutics delivery, and to generate synthetic membranes used in bone regeneration. Graphene is a carbon-based nanomaterial consisting of a single layer of carbon atoms arranged in hexagonal lattices, thus providing exceptional mechanical strength, thermal stability, and electrical conductivity. Graphene is a versatile nanomaterial that can be incorporated into different scaffolds, used to develop nanocomposites, and functionalized with a variety of bioactive molecules [19]. In fact, implant surfaces coated with graphene can be functionalized with antibacterial substances that may help to prevent implant-associated infections [19]. Enrichment of collagen membranes with graphene provided the membrane with advantages such as higher stiffness, lower deformability, increased roughness, and decreased hydration [19].
# Effect of Characteristics of Ground Motion on Seismically Induced Sliding Surface of Slopes
## 4 Results
### Effect of Intensity of Ground Motion  
In this section, the effects of the input ground motion intensity and frequency characteristics are evaluated. The baseline case (Case 09) was used in all simulations. Figure 8 shows the failure surfaces developed subjected to Whittier Narrows, Northridge, Ofunato, and Kobe motions scaled to four levels of PGAs. Figure 8 shows that the failure surfaces determined from dynamic analyses are different from those calculated from pseudo-static analyses. It can also be observed that the shapes of failure surface also depend on the type of input ground motion and its intensity. For the Whittier Narrows motion, the failure surfaces derived from the dynamic analyses display limited change in the shape with a variation in the intensity, whereas those produced from pseudo-static analyses widen with an increase in the intensity of input ground motion. The failure surfaces calculated from the dynamic analyses for PGA = 0.25 g and 0.5 g are wider compared to the pseudo-static analysis-based surfaces. However, they are almost identical for PGA = 0.75 g and 1.0 g. Similar trends are revealed for the Northridge motion. The discrepancy between the surfaces  
Figure 6: Effect of equivalent horizontal load on the sliding surface for \(c^{\prime}\) = 6 kPa and \(\phi^{\prime}\) = 30\({}^{\circ}\).  
generated from the dynamic and pseudo-static analyses increase with an increase in the \(T_{m}\) of the input ground motions. They show distinct deviations for both Ofunato and Kobe motions. For the Ofunato motion, the failure surfaces determined from dynamic analyses are relatively wider compared to those produced by the Whittier Narrows and Northridge motions up to PGA = 0.75 g, whereas the surface for PGA = 1.0 g is displayed to become significantly wider. It is possible that the longer \(T_{m}\) produces longer wavelengths and, therefore, generates wider failure surface. For the Kobe motion, which has the longest \(T_{m}\), the widest failure surfaces are generated. Almost all planar surfaces are produced by Kobe motions. It is demonstrated that the pseudo-static analysis fails to capture the change in the failure surfaces produced by different motions. The close comparisons demonstrate that the failure surfaces are influenced by both the \(T_{m}\) and intensity of the input ground motion. It is not possible to identify and quantify the influence of the frequency characteristics and intensity of the input motions based on the limited number of motions and slope characterizations utilized in this study. However, based on the observations, it is revealed that the failure surface needs to be characterized from a dynamic analysis rather than a pseudo-static method.
### Approaches to Minimize Alveolar Bone Resorption
#### 1.2.1 Alveolar Ridge Preservation  
Alveolar ridge preservation (ARP) techniques refer to any procedure performed after tooth extraction to minimize the external resorption of the alveolar bone ridge and maximize bone formation within the alveolus, within the limits of the alveolar space [4; 12]. Most ARP procedures focus on ensuring minimally invasive dental extractions, preserving the integrity of the alveolar bone walls, and filling the socket with biomaterials, such as bone grafts, to maintain a favorable alveolar ridge for future dental implant placement [9; 13].  
Bone grafts have been used for decades to reconstruct and regenerate bone defects caused by traumatic extractions and periodontal or endodontic lesions [14]. Bone grafts can be classified as allografts, xenografts, alloplasts, or autogenous [9; 15]. These grafts can be osteoinductive--promoting the recruitment of bone-forming cells, such as undifferentiated cells and pre-osteoblasts, and stimulating new bone formation through these cells; osteoconductive--where the graft provides a physical scaffold for bone regeneration; or osteogenic--where the cells within the graft promote new bone formation, even in the absence of mesenchymal stem cells [5; 16; 17]. Autogenous grafts are harvested and implanted in the same individual [18]. Autogenous bone is considered the gold standard due to its ideal properties of being osteoconductive, osteoinductive, and osteogenic [14; 19]. Due to histocompatibility, these grafts reduce the risk of immune reactions, infections, and potential graft rejection [20; 21]. However, the availability of autogenous bone grafts is limited, and they exhibit a high resorption rate when subjected to high loads and soft tissue stresses. Furthermore, their harvest requires an additional surgical site, increasing morbidity at the donor area [19; 20; 22].
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 1 Introduction
Particularly, and in contrast to glass optical fiber models, the strong diffusion of PMMA and the core-cladding surface roughness of POFs induce power transfer between neighboring mode groups or mode coupling, that can be modeled following different approaches. In the case of ray-tracing, this effect is described by a random change of the propagation direction that occurs after propagation up to a certain length. Another approach to describe mode coupling is the coupling-matrix that accounts for power transfer between all (and not only neighboring) modes, treating the fiber core-cladding roughness as a grating [19]. On the other hand, the most direct way to describe mode coupling is provided by the power flow equation introduced by [PERSON] to describe light transmission in multimode fibers [20], whose solution can be found using different strategies [21; 22; 23; 24]. In this equation, the changes in the propagating power distribution are described by a diffusion function that quantifies mode coupling and a differential attenuation function that accounts for the different losses depending on the propagation angle. This equation can be solved analytically only in some particular cases [25; 26]. A more general approach can be obtained using numerical methods [27; 28] that in most instances assume simplifying hypotheses or approximations over the mode coupling and mode-dependent attenuation. Following this path, a model for light propagation in large-core POFs has been devised that accurately describes the high multimode characteristics of these fibers and also the effects of other elementary components found in POF systems, such as connectors and other components causing localized disturbances [29; 30; 31; 32]. Along the way, we had to overcome many challenges to ensure the accuracy of the models and now the modeling approach is being adapted by a commercial software vendor [33]. Although we have experimented with both MC SI-POF and PF GI-POF, our modeling efforts in these areas are still rudimentary. Therefore, we will focus on the system-level modeling of large-core SI-POF in this review article. The article is organized as follows: first, we will discuss the generic simulation challenges related to large-core POF modeling and the associated components; second, we will briefly describe the modeling techniques behind the various large-core SI-POF and components that constitute the POF system; then, we will describe our system-level modeling, leading up to commercial software package implementation; this will be followed by a system-level example to demonstrate the utility of the modeling technique; and finally, we will mention some future directions and conclude the paper.
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 2 Non-Mechanical Beam Steering with PGs
### PG Fundamentals from a Beam Steering Perspective
Thus far, we have focused the discussion on polymerized PGs. The PG alignment can also be realized in the liquid crystal phase [32; 33]. When placed between transparent electrodes, such PGs can be electrically driven to a near homeotropic state, thus erasing the PG and transmitting incident light of either polarization handedness. These \"active\" PGs provide \(3^{N}\) potential steering angles, thereby enabling more steering angles with fewer stages [12]. For infrared applications, the increased layers of transparent electrodes mitigate the efficiency advantage of using fewer stages, however. It is also not practical to completely erase the grating due to surface anchoring of the LC molecules, reducing the efficiency of this state. Lastly, it is our experience that active PGs are susceptible to thermal drifts to efficiency, UV degradation, and alignment degradation over their lifetime that passive polymer PGs are immune to.
## 2 Materials and Methods
### Proposed Aggressiveness Detection Models
#### 2.1.2 Lexicon Approach  
The second approach implemented used a mixture of the emotions analysis using Lexicons to form the features vector and ML classifiers. The Lexicon used is the one proposed in [33], which consists of an affective Lexicon in Spanish based on an enriched Lexicon, which represents the emotion intensity of each word, as shown in the example in Table 4. This Lexicon considered so-called emotional words.  
Initially, a preprocessing of the text is done, filtering special characters (stress marks, punctuation, signs, etc.), as well as eliminating stopwords and the lemmatization of each word depending on the definitive values of defined hyperparameters. Tokenization is done using space (https://spacyio/models/es, accessed on 5 November 2021) in Spanish. After preprocessing, the analysis is made with the Lexicons, to form the features vector of each phrase that is comprised by 10 columns, detailed below.  
Each one of the first 8 columns represents the sum of intensities of the phrase's words, that appear in the Lexicon which represents the corresponding affective class. The affective classes proposed by [34] are considered (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust).  
Column 9 represents the division between the number of bad words (BW) found in the phrase and the number of words this has.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Word** & **Intensity** \\ \hline \hline fuss & 88 \\ angry & 75 \\ fury & 75 \\ inspire & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Extract from the Intensity Lexicon for the effective anger class.  
Figure 2: Training and testing process.  
Finally, column 10 represents the number of words in the phrase (NW).  
Table 5 shows an example of the features vector obtained for the offensive phrase \"_Oyyyyy few coluiao insoportable chucha nota esta cagao miedo\"_ (_Oi, ugly unbearable fucker, fuck, look they're fucking scared_) to exemplify the process. The angry column has a value of 56, as in the affective class Lexicon, the word \"_nota\"_ (look) has an intensity of 10 and the word \"_miedo\"_ (scared) an intensity of 46, therefore, on adding these two intensities, the total is 56. The process was done in the same way for the other columns of the affective class. In the column that represents the result of the division between the number of bad words and the number of words in the phrase, the value is 0.333, since there are 3 bad words found in the defined Lexicon: \"_foo_\" (ugly), \"_culiao_\" (fucker), \"_clucha\"_ (fuck), and a total of 9 words in the phrase.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Data Filtering  
For the tested 5B and ZDM lidar units, valid 10-min periods were obtained by filtering datasets as follows:  
* Cup wind speed \(\in[4,16]\) m\(\cdot\)s\({}^{-1}\): corresponding to the range of wind speeds for which the cup anemometer is calibrated, in a wind tunnel;
* Inflow angle (measured by sonic) \(\in[-2^{\circ},+2^{\circ}]\): to limit the contamination of \(V_{LOS}\) by the vertical wind speed;
* wind direction \(\theta\) measured by sonic anemometer \(\in\theta_{los}\pm 40^{\circ}\): except for the 1 st step of the LOS direction evaluation process (see Section 5.4), the direction sector is restricted due to the asymmetric geometry of the employed sonic anemometer that can cause flow distortion. Additionally, this corresponds well to normal operational conditions of nacelle lidars since the wind direction relative to the turbine's yaw position is usually \(<<40^{\circ}\);
* **5B lidar**: carrier-to-noise ratio \(>\)\(-18\) dB and LOS availability \(>\)95%. LOS availability is the ratio between successful and total attempts to measure \(V_{LOS}\). These two filters ensure the quality and quantity of data measured by the lidar for each 10-min period;
* **ZDM lidar**: LOS availability \(\gtrsim\)75%. The LOS availability is obtained simultaneously to the averaging of high resolution (\(\sim\)50 Hz) \(V_{LOS}\) measurements contained in a specified azimuth sector, that we chose to be \([179^{\circ},181^{\circ}]\) (i.e., the bottom of the ZDM scanning trajectory). Details on  
Figure 5: Calibration measurement setup at DTU Wind Energy’s test site, Hovsøer, Denmark.  
Figure 6: Schematic of the beam positions of the 5B (**green**) and ZDM (**red**) lidars at DTU Wind Energy test site, Hovsøer.  
the employed averaging process can be found in [5]. Note that the lidar was stable enough to ensure the beam was not hitting the cup anemometer.  
The filters are only given as an example of those that can reasonably be applied for the LOS velocity calibration. If different lidar systems, units and measurement setups are employed they should be adapted.
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
###### Abstract  
Unmanned underwater vehicles (UUV) face maneuverability and rapidity challenges when they are applied for detecting and repairing submarine oil and gas pipelines, and fiber cables near the seabed. This research establishes numerical models of the bare UUV and self-propelled UUV near the seabed using the computational fluid dynamics (CFD) method. The effect of dimensionless distance \(Hd\) and \(Re_{L}\) on the hydrodynamic performance of the vehicle and the interaction between the hull and the propeller is investigated. The range of \(Hd\) is 1.5\(D\)-10\(D\), and the \(Re_{L}\) is 9.97 \(\times\) 10\({}^{5}\)-7.98 \(\times\) 10\({}^{6}\). Findings indicate that: (1) There is an obvious strong coupling between the hydrodynamic performance of the bare UUV and \(Hd\). With the increase of \(Hd\), the hydrodynamic performance such as C\({}_{d}\), the absolute value of \(C_{l}\) and \(m_{y}\) decreases continuously and finally tends to be stable. The absolute values of C\({}_{d}\) and C\({}_{l}\) increase with the increase of \(Re_{L}\). The change trend of \(m_{y}\) is opposite to that of C\({}_{l}\). (2) The variation trend of hydrodynamic performance of the self-propelled UUV with \(Hd\) is consistent with those of the bare UUV. Additionally, it increases to some extent, respectively, compared with the bare UUV. (3) The self-propelled characteristics such as \(t\), \(\eta_{H}\), \(w\) and \(\eta_{i}\) are weakly related to \(Hd\). The \(t\) and \(\eta_{i}\) increase with the increasing of \(Re_{L}\), while \(\eta_{H}\) and \(w\) decrease with the increasing of \(Re_{L}\).  
hydrodynamic performance; CFD; self-propulsion; seabed; UUV +
Footnote †: journal: Applied Sciences
# The Impact of the S-Adenosylmethionine Analogue Sinefungin on Viral Life Cycles
## 2 Materials and Methods
### The Cytotoxicity Assay  
The cytotoxicity of sinefungin was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT, Sigma-Aldrich, St. Louis, MO, USA) assay. The VERO-76 cells were seeded at a density of 2 \(\times\) 10\({}^{4}\) cells/well into 96-well plates and incubated at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). After 24 h, the cells were treated with increasing concentrations (12.5-200 ug/mL) of sinefungin. Following 24 h of exposure, the cells were washed once with phosphate-buffered saline 1\(\times\) (PBS 1\(\times\), Gibco; Thermo Fisher Scientific, Waltham, MA, USA), and 100 uL of fresh DMEM containing 0.3 mg/mL of MTT reagent was added to each well. The plates were incubated for 3 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\); then, the MTT solution was removed, and formazan crystals were solubilized with 100 uL of 100% DMSO. The optical density was measured at 570 nm with a reference wavelength of 630 nm using a TECAN Infinite 200 PRO (TECAN, Mannedorf, Switzerland). Cell viability was expressed as a percentage compared to the untreated controls (CTRL\(-\)), while the positive control consisted of cells treated with 100% DMSO (CTRL\(+\)).
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 4 Results  
As described in Section 2.3, two experimental setups were conducted to evaluate the suitability of DiagElect. The first setup involves assessing the SDMD as an accurate voltage and current measurement device. The second setup focuses on evaluating DiagElect as an electrical measurement network that can be applied to various PQ analyses, including identifying microgrid nodes that inject harmonics, as proposed in this study.