esa-sceva/satcom-chunk-collection huggingface dataset:
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Hydroxyl Groups
#### 4.2.2 Phosphorylation  
Other functional groups, in addition to the hydroxyl functionalities of alginate, have been achieved with phosphorylation. These reactions generated alginate derivatives that possessed lower molecular weight than their origin and a decreased gelation ability.  
[PERSON] et al. utilized the heterogeneous urea/phosphate reaction to successfully creating the phosphorylation of alginate hydroxyl groups [83]. The functionalization of the alginate M unit preferentially occurred at the C3 equatorial carbon of the polysaccharide ring, while the reaction on the G unit is still unclear. Due to alginate chain degradation upon the modification, the hydrogel was synthesized by blending the phosphorylated product with native alginate. This study discusses the enhancement of the hydrogel due to the participation of phosphate ions in interchain crosslinking and their effect on the induction of HAP surface mineralization.  
Another study developed an injectable hydrogel from sodium alginate O-phosphorylation, which was achieved using a mixture of H\({}_{3}\)PO\({}_{4}\)/P\({}_{2}\)O\({}_{5}\)/Et\({}_{3}\)PO\({}_{4}\)/hexanol [84]. An internal gelation process produced the hydrogel of the resulting phosphorylated alginate calcium complex and sodium alginate. The suitability of the resulting injectable hydrogel for artificial soft tissue replacement was studied on MC3T3 cells.
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 1 Introduction
In order to optimize the performance of MR fluid dampers in these applications, it is of fundamental importance to quantitatively examine the response (output) of MR fluid dampers under various input pressure profiles. In some modern studies [2; 37; 38; 39], MR fluids and other particulate mixtures are modelled using coupled computational fluid dynamics and discrete phase modelling approaches. However, this method is very computationally intensive, and can only be applied in modelling MR fluids in microscale configurations. The macroscale behavior of MR fluid dampers has been experimentally investigated in various studies [40; 41; 42]. However, effective approaches in numerically modelling such systems at the device level remain underdeveloped. In this study, a macroscopic computational model was developed to simulate the response of MR fluid dampers under various input pressure profiles, including constant pressure, a sinusoidal pressure profile, and a seismic pressure based on the Northridge earthquake in 1994, utilizing the computational fluid dynamics and magnetohydrodynamics models. The effectiveness of damping was investigated by examining the static pressure at different locations along the annular orifice of the MR fluid damper under a range of applied external magnetic fields. This novel computational approach enabled the macroscale simulation of MR fluids by significantly reducing the computational resources required compared to other computational approaches, where only microscale simulations are possible. It can also significantly reduce the human and economic resources required in the optimization of MR fluid dampers when compared to experimental approaches.
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
###### Abstract  
Keyhole laser beam welding (LBW) of 304L stainless steel sheets with a gap in between was numerically simulated with a three-dimensional, transient, multi-physical model for laser material processing based on the finite volume method (FVM). First, the model's ability to reproduce experimental results on a relatively coarse computational mesh within reasonable computing time, so as to serve as process optimization tool, is presented. An example of process optimization is shown, wherein a given set of weld seam quality criteria is fulfilled by iteratively optimizing a secondary laser beam. The relatively coarse mesh, in combination with a good model calibration for the experimental conditions, allows for sufficiently fast simulations to use this approach for optimization tasks. Finally, using a finer spatial and temporal discretization, the dynamic processes in the vicinity of the keyhole leading to the formation of pores are investigated. The physical phenomena predicted by the simulation are coherent with experimental observations found in literature.  
laser beam welding; welding with gap; keyhole dynamics; pore formation; multiphysical simulation; CFD; FVM; process optimization; mesh refinement; 304L stainless steel +
Footnote †: journal: Applied Sciences
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Count of Cluster Formation Analysis  
The number of clusters plays a crucial role in shaping the functionality of VANETs. The results, shown in Table 9 and Figure 5c suggest that maintaining fair-flow connectivity  
Figure 5: Comparative analysis of the existing ACO and proposed DA-TRPED models. (**a**) Cluster head lifetime (CHL) analysis; (**b**) cluster member lifetime (CML) analysis; (**c**) cluster formation analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Vehicles** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 174 & 220 \\ \hline
40 & 164 & 214 \\ \hline
60 & 155 & 280 \\ \hline
80 & 176 & 187 \\ \hline
100 & 164 & 176 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative results of CML analysis for network performance evaluation.  
within the network supports both cluster heads (CHs) and cluster members (CMs) while simultaneously reducing the overall cluster count.  
In a recent study, ECBLTR, an enhanced cluster-based lifetime VANET protocol was investigated to evaluate the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end-to-end delay, and overhead transmission [42]. ECBLTR showed an efficient selection method of CHs through the fuzzy system and an increase in network lifetime compared with CBLTR. Our proposed DA-TRPED technique outperforms the traditional ACO method in terms of packet delivery ratio, dropout rate, latency, and cluster formation.  
A small opposite trend is observed when the count of vehicles becomes 60. This is due to the estimation of the dynamic parameter, which brings little network changes. This innovative approach effectively addresses severe congestion problems by optimizing route selection and cluster head assignment during peak hours. Additionally, it leads to a gradual reduction in aggregate distances traveled by vehicles under specific circumstances. However, some limitations are defined in the usage of network cycling and to prevent the search from falling into a local optimum point and global searching potentials.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 4 Experimental Setup  
This section provides a comprehensive description of the experimental setup implemented to perform OCC transmission. In this study, both Line-Of-Sight (Figure 15a) and Non-Line-Of-Sight (Figure 15b) scenarios are considered, with a direct and indirect light transmission from the transmitter to the receiver, respectively. While the camera only receives light directly emitted by the LED source in LOS transmission, NLOS transmission takes advantage of the light reflections in the surrounding environment to increase the light source area within an image [23].
# ARAAC: A Rational Allocation Approach in Cloud Data Center Networks
###### Abstract  
The expansion of telecommunication technologies touches almost all aspects life that we are living nowadays. Indeed, such technologies have emerged as a fourth essential utility alongside the traditional utilities of electricity, water, and gas. In this context, Cloud Data Center Networks (cloud-DCNs) have been proposed as a promising way to cope with such a high-tech era and with any expected trends in future computing networks. Resources of cloud-DCNs are leased to the interested users in the form of services, such services come in different models that vary between software, platform, and infrastructure. The leasing process of any service model starts with the users (i.e., service tenants). A tenant asks for the service resources, and the cloud-provider allocates the resources with a charge that follows a predefined cost policy. Cloud resources are limited, and those cloud providers have profit objectives to be satisfied. Thus, to comply with the aforementioned promise, the limited resources need to be carefully allocated. Existing allocation proposals in the literature dealt with this problem in varying ways. However, none proposes a win-win allocation model that satisfies both the providers and tenants. This work proposes A Rational Allocation Approach in Cloud Data Center Networks (ARAAC) that efficiently allocates the available cloud resources, in a way that allows for a win-win environment to satisfy both parties: the providers and tenants. To do so, ARAAC deploys the _Second Best-Price_ (SBP) mechanism along with a _behavioral-based reputation_ model. The reputation is built according to the tenants' utilization history throughout their previous service allocations. The reputation records along with the adoption of the SBP mechanism allows for a _locally free-equilibrium_ approach that allocates the available cloud-DCN resources in an _efficient_ and _fair_ manner. In ARAAC, through an auction scenario, tenants with positive reputation records are awarded by having the required resources _allocated_ at prices that are _lower_ than what they have offered. Compared to other benchmark models, simulation results show that ARAAC can efficiently _adapt the behavior_ of those _rational_ service-tenants to provide for better use of the cloud resources, with an increase in the providers' profits.  
cloud data center networks; service allocation; second-best price auctions; behavior-based reputation +
Footnote †: journal:  
_Future Internet_**2017**, 9, 50; doi:10.3390/f9030050
## 4 DSM, Orthomosaic and VR Scenario Building
### Photogrammetry Processing for UAV-Captured Pictures
Values for Tie and Key points were improved following the discussion on Agigsoft METASHAPE forum regarding alignment ([[https://www.agisoft.com/forum/index.php?topic=3559.0](https://www.agisoft.com/forum/index.php?topic=3559.0)]([https://www.agisoft.com/forum/index.php?topic=3559.0](https://www.agisoft.com/forum/index.php?topic=3559.0))), where the resolution of the camera is also considered for these settings; in addition, Tie Point accuracy has been changed and set to 0.5 pix. A new Sparse Cloud resulted from this processing (Figure 6a). The third step consisted of a Dense Point Cloud building (Figure 6b): in this case, it was generated using the aggressive depth filtering and medium quality settings, so the sparse point cloud generated in Step 2 was mandatory. In addition, we applied the \"Filter by Confidence\" tool to remove noisy points from the dense cloud, resulting from matching of Nadir and Oblique pictures. Especially, we removed some points in the range of 0-3 from the vertical cliff, and we removed all points which did not relate the final area for the model we wanted to obtain. The fourth and fifth steps were both based on processing on the Dense Cloud and on the production of the DSM, the Orthomosaic and the 3D Tiled Model, necessary to work with immersive Virtual Reality. DSM was generated from the dense point cloud using default settings, and successively, the Orthomosaic was produced using the corresponding DSM. Both have been built using the reference system WGS84/UTM35. Figure 6: Sparse (**a**) and dense (**b**) points clouds derived by photogrammetry processing. The final step consisted of building the 3D Tiled model, which includes both mesh and texture. We suggest using a Medium quality set-up with a tile size of 4096 \(\times\) 4096 pixels and to export it in Agisoft Tile Archive selecting OBJ file format. The areal coverage of our study area has an extent of about 520 \(\times\) 400 m; the surveys were carried out during the early autumn 2018 campaign, during which we collected a total of 5794 photos during a total flight time of 3.7 h. The SfM-derived models regard a high-quality DSM and Orthomosaic (Figure 7a,b), respectively characterized by a resolution of 3.7 and 0.9 cm/pixel, and the 3D Tiled model (Figure 7c) that is characterized by a texture resolution identical to the Orthomosaic. In regard to the processing time, as detailed in Table 3, all outputs were generated in 39.6 h, where the most time-consuming processing were the Alignment process and the 3D tiled model generation. Figure 7: Structure from Motion (SfM)-derived Digital Surface Model (DSM) (**a**) and Orthomosaic (**b**) of the study area, with corresponding resolution indicated in the lower center. Altitudes are shown as a color range (legend in figure), Ground Control Points (GCPs) are represented by blue dots; reference system: WGS84/UTM35. (**c**) The final three-dimensional (3D) Tiled model.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology  
### Structure of Fuzzy Wavelet Neural Networks  
Considering that the BP network falls easily into local extreme value and that the wavelet neural network has poor adaptability to uncertain input information, the wavelet neural network embedded with a fuzzy neural network was used to evaluate the phase shift in the time-series signal. The FWNN harnesses the time-frequency localization properties of wavelets in evaluating the phase shift in specific frequency-defined signals.  
The structure of the fuzzy wavelet neural network is shown in Figure 2. The network consists of four basic layers and a newly included fifth layer for EMT demodulation: the first layer is the input that is directly linked to the second layer, the membership function layer is also known as the fuzzification layer, the third layer is the inference layer, and the fourth layer is the defuzzification layer for the wavelet function, which conventionally produces the desired output. However, the desired output in EMT demodulation is a logical response with multiple output units. Therefore, an additional layer with a logical  
Figure 1: Typical backpropagation neural network.  
function, in this case, a sigmoid function, was introduced to produce a final output at the fifth layer.
# Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
## 2 Materials and Methods
### Mathematical Models
#### 2.1.3 Parameters estimation  
The quality of the proposed model \(f_{\theta}\) is measured with loss function \(\mathcal{L}_{\theta}(y,f(x|\theta))\). In the study, \(f_{\theta}\) is the deep neural network with unknown parameters of \(\theta\in\Theta\subset\mathbb{R}^{d_{\theta}}\). Since the model \(f_{\theta}\) is parametric, the unknown parameters are updated using the gradient of loss function: \(\
abla_{\theta}L_{\theta}\)[11]. In simple form, parameters \(\theta\) are updated iteratively:  
\[\theta_{t+1}=\theta_{t}-\eta\cdot\
abla_{\theta}\mathcal{L}_{\theta t} \tag{3}\]  
where \(\eta\) is learning rate or the size of the steps to reach minimum loss, \(t\)-iteration number [11].  
For the process to converge successfully, a proper learning rate has to be applied for avoiding being trapped in local minima [11]. Usually, there are multiple optimization techniques that can be employed to solve this problem. In this study, Adaptive Moment Estimation (from here after abbr. as Adam) optimization was used. [PERSON] computes learning rates for each parameter, using information on exponentially decaying averages of past gradients [11].  
Let gradient of loss function to the \(i\)th parameter \(\theta_{i}\) at iteration \(t\) be: \(g_{t,i}=\
abla_{\theta}\mathcal{L}_{\theta t,i}\). Then  
\[\begin{split}& m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}\\ & v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\end{split} \tag{4}\]  
where \(m_{t}\) and \(v_{t}\) are estimates of the first moment (the mean) and the second moment (the uncentered variance) of the gradients \(g_{t}\). \(\beta_{1}\) and \(\beta_{2}\) are hyper-parameters and usually values of 0.9 and 0.999, respectively, are used [11].  
As \(m_{t}\) and \(v_{t}\) are initialized as 0's vectors, they are biased towards zero, especially during the initial state. These biases are corrected by computing first and second moment estimates such that [11]:  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}},\quad\hat{v}_{t}=\frac{v_{t}}{1-\beta _{2}^{t}} \tag{5}\]  
The Adam update rule \(t\geq 1\):  
\[\theta_{t+1}=\theta_{t}-\frac{\eta}{\sqrt{\hat{v}_{t}}+\epsilon}\hat{m}_{t} \tag{6}\]  
Here, \(\epsilon\) is smoothing term (avoiding zero in denominator) usually set to \(10^{-8}\).
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
###### Abstract  
Ground-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfied. A ground-motion simulation software was developed to resolve these issues. The developed software employs machine learning methods to match the amplitude, spectrum, and duration features of the target region. Principal component analysis is employed to extract features from the actual ground-motion database to detect characteristic ground motions and predict the target acceleration amplitude, response spectrum, and duration, based on the response spectrum and duration prediction equations. The results show that the simulated ground motion can match the amplitude, spectrum, and duration characteristics well. Therefore, the simulated ground motion can provide more reasonable input for the structure. Moreover, the developed software provides visualization functions that enable the user to determine the target area and obtain the amplitude field intuitively.  
Keywords:reginal characteristic ground motion; ground motion; ground-motion field simulation; principal component analysis; genetic algorithms; MATLAB-based simulator +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Results for Time Domain
#### 3.2.3 Influence of Finite Porosity Layer Thickness  
Considering that the structure of the pipe in the model is an important factor affecting the propagation characteristics of the waveform, this study uses a liquid-filled pipe embedded in a finite porous medium model, and analyzes the influence of the thickness of the finite porosity layer on the time-domain waveforms.  
Figure 8: Group velocity dispersion curves of the first three orders of modes in the water-filled pipe embedded in (\(\beta\) = 0.1) (**a**) infinite porous medium and (**b**) finite porous medium.  
Figure 9: Time-domain waveforms with respect to time for various values of porosity in an infinite porous medium (**a**) at the liquid–pipe interface and (**b**) at the external interface of the finite porous medium. The thickness of finite porous medium is \(d\) = 0.05 m. Black, red, and blue lines represent the porosity values of \(\beta\) = 0.1, 0.2, and 0.3, respectively.  
The time-domain waveforms in the liquid-filled pipe embedded in a finite porous medium with the same porosity value of \(\beta\) = 0.1 and variable finite porosity layer thickness values of \(d\) = 0.025, 0.05, and 0.10 m are shown in Figure 10. The time-domain waveforms at the external interface of the finite porous medium are described, showing that the arrival time of the wave is almost independent of the thickness of the medium, and the displacement amplitude of the wave decreases with thickness [46].
## 4 Results
### Basin Modeling Results
#### 4.4.2 Mass of Hydrocarbon Generation  
The mass of hydrocarbon generation from the two types of source rocks (J\({}_{2}\)kz, J\({}_{1}\)y, T\({}_{3}\)_t_, and T\({}_{3}\)_h_) were calculated and compared. The results indicate the following. (1) The main stage of oil generation occurred in the interval from 23.3 Ma to 12 Ma, and the main gas generation occurred from 5.3 Ma to 3 Ma, accompanied by a small amount of gas formed by oil cracking (Figure 16A). (2) The Sag Belt was the main area of oil generation in the early stage of 145.6 Ma-12 Ma, while the contribution of source rocks in the Slope Belt and Anticlinal Belt increased during the late stage of 12.5 Ma-0 Ma (Figure 16B). The gas was generated mainly in the Sag Belt, with increasing contributions from the Slope and Anticline in the late stage (Figure 16B). (3) In the early stage, the T\({}_{3}\)_h_ lacustrine source rocks were the principal source of oil generation, while oil generation mainly occurred in the J\({}_{2}\)kz, J\({}_{1}\)y, and T\({}_{3}\)_t_ coal measure source rocks after 23.3 Ma (Figure 16C). The amounts of gas generated from the coal measure source rocks have increased greatly from 5.3 Ma to the present (Figure 16C). (4) The amount of oil and gas from dark mudstones was predominantly in the study area. The occurrences of petroleum in the basin are the result of generation from a combination of different source rocks since 23.3 Ma (Figure 16D).  
Figure 16: Hydrocarbon generation of two types of source rocks at different geological times in the northern Kuqa Foreland Basin, showing the evolution of oil and gas generation (**A**); hydrocarbon generation among different sub-structural belts (**B**); hydrocarbon generation from different types of source rocks (**C**); hydrocarbon generation from different lithologies (**D**). The histogram is formed by the calculated percentages of the total hydrocarbon generation in all geological time.  
Figure 15: Transformation ratios (TR) of the source rocks in the AA\({}^{\prime}\) section at present in the northern Kuqa Foreland Basin, showing a decreasing trend from the Sag Belt to the Central Anticline Belt.
## 4 Experimental Results
### Basic Performance Test
#### 4.1.2 Performance Test of Hybrid Controller with Initial Error
Figure 4: Circular trajectory tracking results of the mobile platform using the HILC controller with initial error. **(a1)** Path change tracking using HILC law (46). **(a2)** Final path tracking using HILC law (46). **(a3)** Average tracking error change using HILC law (46). **(b1)** Path change tracking using HILC law (47). **(b2)** Final path tracking using HILC law (47). **(b3)** Average tracking error change using HILC law (47).