esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards
#### 2.1.1. IEEE 802.11p (Wireless Access for the Vehicular Environment (WAVE))  
IEEE 802.11p is crucial for enabling cooperative systems and improving road safety. Beyond its role in road safety, IEEE 802.11p plays a fundamental role in the advancement of ITS by promoting vehicle-to-everything (V2X) communication. These standards form the backbone for emerging technologies like connected and autonomous vehicles, facilitating seamless data exchange not only between vehicles and infrastructure but also with pedestrians and other road users. Its deployment is essential in creating a responsive transportation network, contributing to the realization of safer, more efficient, and interconnected smart city mobility solutions [43].
# Large-Eddy Simulation of Droplet Deformation and Fragmentation Under Shock Wave Impact
## 4 Results and Discussion
### Droplet Breakup Dynamics
Furthermore, the interaction between shock front and water droplet induces a range of intricate flow phenomena within the airflow field, including the formation and development of important vortical structures. Figure 7 shows the vorticity field for \(M_{s}=1.47\) at the same time instants of the previous figure. As the flow field evolution at \(M_{s}=1.12\) results in being substantially similar, the higher Mach number case highlights the investigated effects more clearly. By analyzing the droplet morphology at early times (panel (a)), it is observed that a recirculation zone on the windward side begins to form on the droplet's surface. As time advances, the recirculation zone expands and slowly transitions from the center towards the equator, coinciding with the complete passage of the shock wave through the droplet (panel (b)). Importantly, the larger the windward recirculation zone, the greater the shearing force exerted by the airflow. The expanding windward recirculation zone leads to the formation of KH waves on the windward surface, similar to those described by [PERSON] and [PERSON] [46]. At \(t^{*}=0.14\), flow separation leads to the formation of the wake recirculation zone near the leeward stagnation point (panel (c)). As flow separation intensifies, the wake recirculation zone becomes more pronounced over time (panel (d)), following the shock-droplet interaction, eventually resulting in a flattened shape of the droplet on the leeward side (panel (e)). Basically, over time, the KH waves continue to grow, merge, and evolve into sheets or ligaments near the equator. The stretching of these liquid structures, along with the stripping of KH waves, contributes to the overall breakup process. The interaction between the sheets and wake recirculation zones on the leeward side of the droplet produces child droplets, primarily stripped from the radial edges of the parent droplet.
## 3 Design of Fractional Backstepping Sliding Mode Controller  
### Fractional Calculus Preliminaries  
In this section, fractional calculus is briefly introduced. \({}_{a}D_{t}^{a}\) is defined as the fundamental operator, where \(a\) and \(t\) are the bounds of the operation and \(\alpha\) is the order of fractional calculus. There are three definitions as follows:  
1. Grunwald-Letnikov definition  
\[{}_{a}D_{t}^{a}f(t)=\underset{h\to 0}{\lim}h^{-a}\underset{j=0}{\lim}(-1)^{j}( \begin{array}{cc}\alpha\\ j\end{array})(t-jh) \tag{12}\]
2. RL (Riemann-Liouville) definition  
\[{}_{a}D_{t}^{a}f(t)=\frac{d^{n}}{dt^{n}}[\frac{1}{\Gamma(n-\alpha)}\int\limits _{0}^{t}\frac{f(\tau)}{(t-\tau)^{\alpha-n+1}}d\tau]\quad n-1<\alpha<n \tag{13}\]
3. [PERSON] definition  
\[{}_{a}D_{t}^{a}f(t)=\frac{1}{\Gamma(n-\alpha)}\int_{a}^{t}\frac{f^{(n)}(\tau) }{(t-\tau)^{\alpha-n+1}}d\tau\quad n-1<\alpha<n \tag{14}\]  
For the purpose of simplifying the notation, in the following section, the fractional derivative of order \(\alpha\) is denoted as \(D^{\alpha}\) instead of \({}_{a}D_{t}^{\alpha}\).
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 2 Definitions and Basic Concepts  
### Fluid-Structure Interaction  
In the present review, Fluid-Structure Interaction in pipe systems is defined as the transfer of momentum and forces in both ways, between the pipe-wall and the contained fluid during unsteady flow [8]. Hence, FSI in pipe flow involves, at least, transient responses of two different physical systems. The interaction arises when the time scales of both system responses are shorter than the time scale of the overall transient event (i.e., time lag between the initial and the final steady state). If the disturbance source is shorter than both system responses, then fast fluid and solid transients simultaneously occur. If their interaction is strong enough, then the description of FSI might be worthwhile in water-hammer analyses and interaction mechanisms have to be taken into account.  
In a broad sense, Fluid-Structure Interaction embraces any form of energy transfer, one upon another, between the fluid and the structure. In common engineering problems, this transferred energy is typically kinetic and elastic or thermal. The former is termed mechanical Fluid-Structure Interaction and the latter thermal Fluid-Structure Interaction. Heat exchange effects in transient pipe flow are barely significant, processes are assumed adiabatic, and FSI analyses are mainly focused on the momentum exchange between the fluid and the pipe structure.  
Two different approaches may be followed to account for the momentum transfer into the structure [25]: considering that the structure moves as a rigid solid or by the propagation of a local excitation/deformation of the solid. In the first, no transient event is considered propagating throughout the solid, the structure element moves as a rigid body and its effect on the fluid is analysed. In the second, the modes of vibration of the structure element are excited and their respective transient states are taken into account and coupled with the fluid transient. The present review is focused only on the second form.  
FSI analyses may be classified according to the dimensions and the degrees-of-freedom with which the pipe system is allowed to move. Normally, in 1D water-hammer analysis, the classification criterion is based on the modes of vibration of the pipe, which is quite convenient for frequency-domain approaches. However, for time-domain analyses, a classification based on the pipe degrees-of-freedom is more physically intuitive. The latter is the classification criterion used herein.
# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
###### Abstract
In recent decades, several works of gait recognition have been described for different approaches such as soft biometrics, clinical analysis, and sports science, highlighting challenges and perspectives [29; 30; 31; 32]. Using unsupervised learning methods for recognizing crouch gait, few works have been reported. For instance, cluster analysis using gait kinematic data of the lower limbs, pelvis, and trunk of children with cerebral palsy was used to define four clusters [9]. In this case, hip and knee flexion were identified as the main clustering factors and a correlation between trunk and pelvic kinematics and the severity of flexion of these joints was found. In a similar work [33], a gait classification method for subjects with cerebral palsy was presented. Here, sagittal gait features were used as input in a K-means cluster analysis to classify five crouched groups, depending on severity. But these works, unlike ours, which proposes supervised learning, are based on unsupervised learning approaches to clustering. A more related work deals with the classifications of three hemiplegic patients and a healthy subject [34]. The categories for crouched patients are mild, moderate, and severe gait conditions, and they use time and frequency domains features. In the same way, an approach for a classification of crouch gait patterns was designed [35]. To classify the gait data into four classes: normal, crouch-2, crouch-3, and crouch-4, they use an artificial neural network and compare with k-means and k-nearest neighbor algorithms. However, the limitation of both works is the size of the dataset; using machine learning on small size datasets presents a problem, since the recognition capability of the algorithms depends on the size of the dataset [36]. In this work, we present a framework for crouch gait recognition. Specifically, multi-class recognition is performed using synthetic gait data in anatomical space using the joint angles of the 8 DoF multi-joint and multi-planar model presented in a previous work [37]. In particular, the following question is: is it possible to recognize multiclass crouch gait in anatomical space using gait synthetic data?. Through this paper, we demonstrate that the answer to this question is affirmative. Furthermore, the main contribution of this work is the synthetic joint angle generator for multiclass crouch gait recognition.
_Article_  
A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle  
[PERSON] 1,*, [PERSON] 1 and [PERSON] 2  
1 School of Highway, Chang'an University, Xi'an 710064, Shannxi, China; [EMAIL_ADDRESS]  
2 College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, Anhui, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 20 January 2020; Accepted: 10 February 2020; Published: 13 February 2020  
**Featured Application: This work can be used in autonomous driving control systems to alleviate traffic congestion.**  
**Abstract:** Although the difference between the velocity of two successive vehicles is considered in the full velocity difference model (FVDM), more status information from preceding vehicles affecting the behavior of car-following has not been effectively utilized. For improving the performance of the FVDM, an extended modified car-following model taking into account traffic density and the acceleration of a leading vehicle (DAVD, density and acceleration velocity difference model) is presented under the condition of vehicle-to-vehicle (V2V) communications. Stability in the developed model is derived through applying linear stability theory. The curves of neutral stability for the improved model indicate that when the driver pays more attention to the traffic status in front, the traffic flow stability region is larger. Numerical simulation illustrates that traffic flow disturbance could be suppressed by gaining more information on preceding vehicles.  
**Keywords:** velocity difference; car-following model; preceding vehicles; traffic density; linear stability theory
## 1 Introduction
Some researchers have already conducted gasification experiments using electric heating methods on biomass in fixed-bed systems [39; 40; 41]. Furthermore, many articles have been published regarding microwave pyrolysis on biomass, focusing on analyzing syngas and biochar properties [11; 42; 43; 44; 45; 46; 47; 48]. There have been notable advancements in the utilization of microwave heating compared to conventional heating methods, and significant strides have been made in the field of biomass pyrolysis. There remains a lack of research about the application of microwave technology in biomass gasification. In previous studies on the MG of biomass, the components of syngas and tar were mostly characterized, but few articles were reported about the properties of biochar. Less work compares EG and MG at the same time. In this study, syngas, tar, and biochar properties are characterized in detail through the experimental study of two different biomass samples under EG and MG. Compared with EG, MG has obvious advantages in producing syngas and microporous biochar. This study provides effective data support for the practical application of biomass gasification. In this study, electric and microwave heating gasification experiments with CO\({}_{2}\) are conducted on corn stalk and oak in a fixed-bed reactor. Gasification of biomass with CO\({}_{2}\) can effectively reduce the emission of CO\({}_{2}\). The effects of the gasification method and temperature on the properties of three-phase products are investigated. Gas chromatography (GC) is employed to detect the components and calorific value of the syngas. Gas chromatography-mass spectrometry (GC-MS) is employed to characterize the components of the tar. Fourier transform infrared spectrometer (FTIR) and X-ray photoelectron spectroscopy (XPS) are applied to study the evolution of the functional groups and the content of the elements on the surface of the biochar, respectively. The analysis of the surface morphology and pore structure of the biochar is conducted via Scanning Electron Microscopy coupled with Energy Dispersive Spectrometer (SEM-EDS) and a specific surface area analyzer based on the BET method. The results indicate that the microwave gasification could produce more syngas to compensate for the energy shortages. The biochar obtained from the microwave gasification is an attractive absorbent for CO\({}_{2}\) capture due to its microporous nature.
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 3 Propagation Characteristics of SH Wave in LiNbO\({}_{3}\)/Si
### Dispersion and Spurious Modes  
As shown in Figure 3a,b, the dispersive curves of the \(v_{\rm p,o}\)'s and \(k_{\rm eff}\)2's of the SH main mode, Rayleigh spurious mode, and S\({}_{0}\) Lamb mode spurious mode propagating in the LiNbO\({}_{3}\)/Si bonded structure with varied rotation angle of _YX_-LiNbO\({}_{3}\) are presented. Due to the sub-wavelength thick piezo-layer structure, the SH mode and Rayleigh mode show weak dispersive characteristics in the phase velocities. The S\({}_{0}\) Lamb wave, however, shows even stronger dispersion due to its plate-wave type whose characteristics are usually impacted a lot by the piezoelectric-layer thickness normalized to wavelength. Although varying LiNbO\({}_{3}\) thickness does not move the Rayleigh spurious mode away from the SH main mode in frequency, engineering the LiNbO\({}_{3}\) thickness does effectively keep the Lamb modes distant in frequency from the SH mode. Luckily, when the \(h_{\rm LiNbO3}\) < 0.5\(\lambda\), the closest Lamb mode S\({}_{0}\) mode would be 20% higher in frequency than the SH main mode, which makes a distance of at least 500 MHz if the SH passband is at 2.5 GHz.  
The dispersive characteristics in \(k_{\rm eff}\)2 is stronger than in \(v_{\rm p,o}\) for the SH mode, as shown in Figure 3b. In other words, the change of \(k_{\rm eff}\)2 for SH mode with \(h_{\rm LiNbO3}\) is more obvious than that of \(v_{\rm p,o}\). In order to achieve a high \(k_{\rm eff}\)2, the LiNbO\({}_{3}\) cannot be too thin or too thick and the design range is preferred to be >0.2\(\lambda\) and <0.5\(\lambda\). In addition, the Rayleigh and S\({}_{0}\) Lamb spurious modes can also be suppressed by choosing the LiNbO\({}_{3}\) thickness and cut-angle combination smartly. For the S\({}_{0}\) Lamb mode, the case is simpler since its \(k_{\rm eff}\)2 will lower at smaller LiNbO\({}_{3}\) thicknesses under different LiNbO\({}_{3}\) cut angle, and the design range of \(h_{\rm LiNbO3}\) < 0.5\(\lambda\) fortunately happens to be able to suppress the S\({}_{0}\) Lamb modes. As a contrary, the LiNbO\({}_{3}\) thickness and rotation angle have to be optimized together for the Rayleigh mode, and a slightly larger rotation angle is preferred for the 0.2\(\lambda\) < \(h_{\rm LiNbO3}\) < 0.5\(\lambda\) design range, such as 30\({}^{\circ}\)_YX_-LiNbO\({}_{3}\).  
Figure 3: Calculated dispersion curves of (**a**) open-surface phase velocities \(v_{\rm p,o}\) and (**b**) effective coupling coefficient \(k_{\rm eff}\)2 for the LiNbO\({}_{3}\)/Si with varied rotation angle of _YX_-LiNbO\({}_{3}\).
# A Low-Temperature and Low-Pressure Distillation Plant for Dairy Wastewater
## 3 Results and Discussion
### Concentrate
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Coding** & **Temperature (\({}^{\circ}\)C)** & **SC** & **pH** & **Conductivity** & **BODS5** & **COD** & **TN** & **TP** \\ \hline P1 & & \(2.91\pm 1.22\) & \(0.92\pm 0.09\) & \(3.33\pm 0.52\) & \(3.33\pm 0.22\) & \(3.33\pm 0.12\) & \(3.33\pm 1.10\) & \(3.24\pm 1.79\) \\ P2 & 60 & \(6.74\pm 1.06\) & \(0.86\pm 0.08\) & \(6.55\pm 0.64\) & \(5.37\pm 0.27\) & \(5.00\pm 0.09\) & \(10.05\pm 4.40\) & \(4.51\pm 2.65\) \\ P3 & & \(11.15\pm 1.59\) & \(0.94\pm 0.06\) & \(10.88\pm 1.01\) & \(7.62\pm 0.53\) & \(10.82\pm 0.33\) & \(8.23\pm 2.54\) & \(4.89\pm 2.76\) \\ \hline P4 & & \(3.26\pm 0.53\) & \(0.92\pm 0.06\) & \(4.00\pm 0.74\) & \(4.00\pm 0.23\) & \(4.00\pm 0.09\) & \(4.00\pm 1.34\) & \(4.28\pm 2.86\) \\ P5 & 53 & \(6.67\pm 0.77\) & \(0.94\pm 0.06\) & \(6.67\pm 1.00\) & \(10.89\pm 1.14\) & \(6.67\pm 0.09\) & \(6.67\pm 1.67\) & \(5.20\pm 3.37\) \\ P6 & & \(11.77\pm 1.96\) & \(0.93\pm 0.06\) & \(11.76\pm 1.52\) & \(11.52\pm 0.93\) & \(7.89\pm 0.11\) & \(11.76\pm 5.11\) & \(7.52\pm 3.67\) \\ \hline P7 & & \(3.47\pm 0.69\) & \(0.95\pm 0.07\) & \(4.65\pm 0.58\) & \(4.56\pm 0.34\) & \(4.65\pm 0.07\) & \(4.65\pm 1.06\) & \(4.46\pm 2.90\) \\ P8 & 46 & \(8.33\pm 1.50\) & \(0.95\pm 0.06\) & \(7.09\pm 0.65\) & \(6.94\pm 0.39\) & \efficiency characterizes the retaining capability (RC) of the plant for the considered pollutant in the raw whey referred to SC (this capability is hypothesized to be 1, or 100%, for SC). Table 6 reports the results of the linear model applied to conductivity, BOD5, COD, TN and TP versus SC% values. As previously stated, the pH does not vary as its slope is very small and near zero, therefore, the RC for pH is negligible. On the contrary, the conductivity shows the maximum concentration efficiency and intermediate values are related to BOD5, COD and TN, whereas TP shows a low value of RC. In addition, in order to assess the thermal damage corresponding to different processing conditions, an index of heat treatment associated with the percentage of non-denatured whey protein in the concentrate can be calculated by evaluating the protein loss of solubility: briefly, the denaturation varies from about 81% (trial P1) to 34% (trial P12) increasing as the time process increases and the treatment temperature decreases (energy of activation of roughly 38 kJ/mol).
# Correlation of Parental and Child Dental Plaque Levels: A Clinical Study
## 1 Introduction
The oral microbiota can be transmitted from parents to children, primarily through close contact and shared habits. Since bacteria from a parent's oral microbiome can be introduced to a child's mouth, this transfer often begins early in life through common interactions, such as kissing, sharing utensils, or cleaning a baby's pacifier with the mouth. Effective management of dental plaque through regular, thorough brushing, flossing, and professional dental cleanings is essential to preventing the onset and progression of these common yet preventable oral health conditions [15]. This clinical study sought to investigate the relationship between dental plaque levels observed in parents and those found in their children, focusing specifically on the potential familial patterns that may influence plaque formation and distribution. By examining how plaque levels in parents correlate with those in their offspring, the study aimed to shed light on the hereditary and environmental factors that contribute to oral health outcomes. Understanding these connections could provide valuable insights into the transmission of oral bacteria and the development of effective preventative strategies for managing dental plaque and associated diseases within families. Through this research, we hope to identify trends that may help inform future oral health education and intervention programs tailored to both parents and children, ultimately promoting better dental hygiene practices across generations. The specific objectives included:  
1. Mother-child vs. father-child correlation: This study sought to identify significant differences in plaque levels between mother-child and father-child pairs, enhancing understanding of the distinct roles each parent may play in their child's oral health. 2. Parental education's impact: The study also aimed to evaluate how parental education on oral hygiene practices influences children's habits and dental health, emphasizing the role of parents' knowledge and involvement.
# On Sharing an FIB Table in Named Data Networking
## 4 Performance Evaluation
### Performance Comparison with Other Schemes
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Shortest Path** & **Flooding** & **The Proposed** \\ \hline Number of FIB entries & 278,000 & 169,358 & 5700 \\ Number of total characters & 13,631,174 & 8,299,488 & 283,712 \\ Number of Bloom filters & - & - & 465 \\ \hline Memory using Equation (4) (MB) & 13.26 & 8.07 & 0.39 \\ Ratio & 1 & 0.61 & 0.03 \\ \hline Memory using Equation (5) (MB) & 4.51 & 2.75 & 0.21 \\ Ratio & 1 & 0.61 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Memory consumption of AS-1221. On the other hand, the _flooding_ scheme does not cause any overhead traffic for routing, since it updates the connectivity information when a corresponding _Data_ packet arrives after an _Interest_ packet is flooded. However, since a _Data_ packet has to be sent back as a response for every _Interest_ packet, a lot of bandwidth is wasted to deliver _Data_ packets for the flooded _Interest_ packets. Moreover, the size of a _Data_ packet, which carries an actual content, is usually much larger than the size of the packet carrying the connectivity information. Even though the _flooding_ scheme does not lead to any traffic for routing, a huge amount of network bandwidth is wasted to retrieve _Data_ packets for flooded _Interest_ packets. In the BFR [13], a Bloom filter is only used for propagating the connectivity information and the FIB is populated whenever a new _Interest_ is issued. As a result, the full FIB is built for all requested contents similar to _flooding_. However, our proposed work utilizes the Bloom filter for both sharing the connectivity information and building the forwarding engine. The proposed scheme builds the lookup architecture simply by combining the receiving S-BFs, and hence it is not necessary to store the full FIB table. While there is no protection for the extra traffic and the delivery towards the wrong origin due to false positives in the BFR, the proposed scheme adapts the auxiliary FIB in order to reduce the impact of the false positives. The BFR may have to search both a FIB and Bloom filters in the case of no matching prefix in the FIB.